pipeline: ipa: raspberrypi: Refactor and move the Raspberry Pi code

Split the Raspberry Pi pipeline handler and IPA source code into common
and VC4/BCM2835 specific file structures.

For the pipeline handler, the common code files now live in
src/libcamera/pipeline/rpi/common/
and the VC4-specific files in src/libcamera/pipeline/rpi/vc4/.

For the IPA, the common code files now live in
src/ipa/rpi/{cam_helper,controller}/
and the vc4 specific files in src/ipa/rpi/vc4/. With this change, the
camera tuning files are now installed under share/libcamera/ipa/rpi/vc4/.

To build the pipeline and IPA, the meson configuration options have now
changed from "raspberrypi" to "rpi/vc4":

meson setup build -Dipas=rpi/vc4 -Dpipelines=rpi/vc4

Signed-off-by: Naushir Patuck <naush@raspberrypi.com>
Reviewed-by: Jacopo Mondi <jacopo.mondi@ideasonboard.com>
Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
This commit is contained in:
Naushir Patuck
2023-05-03 13:20:27 +01:00
committed by Laurent Pinchart
parent 46aefed208
commit 726e9274ea
121 changed files with 172 additions and 109 deletions
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.. SPDX-License-Identifier: BSD-2-Clause
# _libcamera_ for the Raspberry Pi
Raspberry Pi provides a fully featured pipeline handler and control algorithms
(IPAs, or "Image Processing Algorithms") to work with _libcamera_. Support is
included for all existing Raspberry Pi camera modules.
_libcamera_ for the Raspberry Pi allows users to:
1. Use their existing Raspberry Pi cameras.
1. Change the tuning of the image processing for their Raspberry Pi cameras.
1. Alter or amend the control algorithms (such as AGC/AEC, AWB or any others)
that control the sensor and ISP.
1. Implement their own custom control algorithms.
1. Supply new tunings and/or algorithms for completely new sensors.
## How to install and run _libcamera_ on the Raspberry Pi
Please follow the instructions [here](https://www.raspberrypi.com/documentation/accessories/camera.html).
## Documentation
Full documentation for the _Raspberry Pi Camera Algorithm and Tuning Guide_ can
be found [here](https://datasheets.raspberrypi.com/camera/raspberry-pi-camera-guide.pdf).
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* cam_helper.cpp - helper information for different sensors
*/
#include <linux/videodev2.h>
#include <limits>
#include <map>
#include <string.h>
#include "libcamera/internal/v4l2_videodevice.h"
#include "cam_helper.h"
#include "md_parser.h"
using namespace RPiController;
using namespace libcamera;
using libcamera::utils::Duration;
using namespace std::literals::chrono_literals;
namespace libcamera {
LOG_DECLARE_CATEGORY(IPARPI)
}
namespace {
std::map<std::string, CamHelperCreateFunc> &camHelpers()
{
static std::map<std::string, CamHelperCreateFunc> helpers;
return helpers;
}
} /* namespace */
CamHelper *CamHelper::create(std::string const &camName)
{
/*
* CamHelpers get registered by static RegisterCamHelper
* initialisers.
*/
for (auto &p : camHelpers()) {
if (camName.find(p.first) != std::string::npos)
return p.second();
}
return nullptr;
}
CamHelper::CamHelper(std::unique_ptr<MdParser> parser, unsigned int frameIntegrationDiff)
: parser_(std::move(parser)), frameIntegrationDiff_(frameIntegrationDiff)
{
}
CamHelper::~CamHelper()
{
}
void CamHelper::prepare(Span<const uint8_t> buffer,
Metadata &metadata)
{
parseEmbeddedData(buffer, metadata);
}
void CamHelper::process([[maybe_unused]] StatisticsPtr &stats,
[[maybe_unused]] Metadata &metadata)
{
}
uint32_t CamHelper::exposureLines(const Duration exposure, const Duration lineLength) const
{
return exposure / lineLength;
}
Duration CamHelper::exposure(uint32_t exposureLines, const Duration lineLength) const
{
return exposureLines * lineLength;
}
std::pair<uint32_t, uint32_t> CamHelper::getBlanking(Duration &exposure,
Duration minFrameDuration,
Duration maxFrameDuration) const
{
uint32_t frameLengthMin, frameLengthMax, vblank, hblank;
Duration lineLength = mode_.minLineLength;
/*
* minFrameDuration and maxFrameDuration are clamped by the caller
* based on the limits for the active sensor mode.
*
* frameLengthMax gets calculated on the smallest line length as we do
* not want to extend that unless absolutely necessary.
*/
frameLengthMin = minFrameDuration / mode_.minLineLength;
frameLengthMax = maxFrameDuration / mode_.minLineLength;
/*
* Watch out for (exposureLines + frameIntegrationDiff_) overflowing a
* uint32_t in the std::clamp() below when the exposure time is
* extremely (extremely!) long - as happens when the IPA calculates the
* maximum possible exposure time.
*/
uint32_t exposureLines = std::min(CamHelper::exposureLines(exposure, lineLength),
std::numeric_limits<uint32_t>::max() - frameIntegrationDiff_);
uint32_t frameLengthLines = std::clamp(exposureLines + frameIntegrationDiff_,
frameLengthMin, frameLengthMax);
/*
* If our frame length lines is above the maximum allowed, see if we can
* extend the line length to accommodate the requested frame length.
*/
if (frameLengthLines > mode_.maxFrameLength) {
Duration lineLengthAdjusted = lineLength * frameLengthLines / mode_.maxFrameLength;
lineLength = std::min(mode_.maxLineLength, lineLengthAdjusted);
frameLengthLines = mode_.maxFrameLength;
}
hblank = lineLengthToHblank(lineLength);
vblank = frameLengthLines - mode_.height;
/*
* Limit the exposure to the maximum frame duration requested, and
* re-calculate if it has been clipped.
*/
exposureLines = std::min(frameLengthLines - frameIntegrationDiff_,
CamHelper::exposureLines(exposure, lineLength));
exposure = CamHelper::exposure(exposureLines, lineLength);
return { vblank, hblank };
}
Duration CamHelper::hblankToLineLength(uint32_t hblank) const
{
return (mode_.width + hblank) * (1.0s / mode_.pixelRate);
}
uint32_t CamHelper::lineLengthToHblank(const Duration &lineLength) const
{
return (lineLength * mode_.pixelRate / 1.0s) - mode_.width;
}
Duration CamHelper::lineLengthPckToDuration(uint32_t lineLengthPck) const
{
return lineLengthPck * (1.0s / mode_.pixelRate);
}
void CamHelper::setCameraMode(const CameraMode &mode)
{
mode_ = mode;
if (parser_) {
parser_->reset();
parser_->setBitsPerPixel(mode.bitdepth);
parser_->setLineLengthBytes(0); /* We use SetBufferSize. */
}
}
void CamHelper::getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const
{
/*
* These values are correct for many sensors. Other sensors will
* need to over-ride this function.
*/
exposureDelay = 2;
gainDelay = 1;
vblankDelay = 2;
hblankDelay = 2;
}
bool CamHelper::sensorEmbeddedDataPresent() const
{
return false;
}
double CamHelper::getModeSensitivity([[maybe_unused]] const CameraMode &mode) const
{
/*
* Most sensors have the same sensitivity in every mode, but this
* function can be overridden for those that do not. Note that it is
* called before mode_ is set, so it must return the sensitivity
* of the mode that is passed in.
*/
return 1.0;
}
unsigned int CamHelper::hideFramesStartup() const
{
/*
* The number of frames when a camera first starts that shouldn't be
* displayed as they are invalid in some way.
*/
return 0;
}
unsigned int CamHelper::hideFramesModeSwitch() const
{
/* After a mode switch, many sensors return valid frames immediately. */
return 0;
}
unsigned int CamHelper::mistrustFramesStartup() const
{
/* Many sensors return a single bad frame on start-up. */
return 1;
}
unsigned int CamHelper::mistrustFramesModeSwitch() const
{
/* Many sensors return valid metadata immediately. */
return 0;
}
void CamHelper::parseEmbeddedData(Span<const uint8_t> buffer,
Metadata &metadata)
{
MdParser::RegisterMap registers;
Metadata parsedMetadata;
if (buffer.empty())
return;
if (parser_->parse(buffer, registers) != MdParser::Status::OK) {
LOG(IPARPI, Error) << "Embedded data buffer parsing failed";
return;
}
populateMetadata(registers, parsedMetadata);
metadata.merge(parsedMetadata);
/*
* Overwrite the exposure/gain, line/frame length and sensor temperature values
* in the existing DeviceStatus with values from the parsed embedded buffer.
* Fetch it first in case any other fields were set meaningfully.
*/
DeviceStatus deviceStatus, parsedDeviceStatus;
if (metadata.get("device.status", deviceStatus) ||
parsedMetadata.get("device.status", parsedDeviceStatus)) {
LOG(IPARPI, Error) << "DeviceStatus not found";
return;
}
deviceStatus.shutterSpeed = parsedDeviceStatus.shutterSpeed;
deviceStatus.analogueGain = parsedDeviceStatus.analogueGain;
deviceStatus.frameLength = parsedDeviceStatus.frameLength;
deviceStatus.lineLength = parsedDeviceStatus.lineLength;
if (parsedDeviceStatus.sensorTemperature)
deviceStatus.sensorTemperature = parsedDeviceStatus.sensorTemperature;
LOG(IPARPI, Debug) << "Metadata updated - " << deviceStatus;
metadata.set("device.status", deviceStatus);
}
void CamHelper::populateMetadata([[maybe_unused]] const MdParser::RegisterMap &registers,
[[maybe_unused]] Metadata &metadata) const
{
}
RegisterCamHelper::RegisterCamHelper(char const *camName,
CamHelperCreateFunc createFunc)
{
camHelpers()[std::string(camName)] = createFunc;
}
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* cam_helper.h - helper class providing camera information
*/
#pragma once
#include <memory>
#include <string>
#include <utility>
#include <libcamera/base/span.h>
#include <libcamera/base/utils.h>
#include "controller/camera_mode.h"
#include "controller/controller.h"
#include "controller/metadata.h"
#include "md_parser.h"
#include "libcamera/internal/v4l2_videodevice.h"
namespace RPiController {
/*
* The CamHelper class provides a number of facilities that anyone trying
* to drive a camera will need to know, but which are not provided by the
* standard driver framework. Specifically, it provides:
*
* A "CameraMode" structure to describe extra information about the chosen
* mode of the driver. For example, how it is cropped from the full sensor
* area, how it is scaled, whether pixels are averaged compared to the full
* resolution.
*
* The ability to convert between number of lines of exposure and actual
* exposure time, and to convert between the sensor's gain codes and actual
* gains.
*
* A function to return the number of frames of delay between updating exposure,
* analogue gain and vblanking, and for the changes to take effect. For many
* sensors these take the values 2, 1 and 2 respectively, but sensors that are
* different will need to over-ride the default function provided.
*
* A function to query if the sensor outputs embedded data that can be parsed.
*
* A function to return the sensitivity of a given camera mode.
*
* A parser to parse the embedded data buffers provided by some sensors (for
* example, the imx219 does; the ov5647 doesn't). This allows us to know for
* sure the exposure and gain of the frame we're looking at. CamHelper
* provides functions for converting analogue gains to and from the sensor's
* native gain codes.
*
* Finally, a set of functions that determine how to handle the vagaries of
* different camera modules on start-up or when switching modes. Some
* modules may produce one or more frames that are not yet correctly exposed,
* or where the metadata may be suspect. We have the following functions:
* HideFramesStartup(): Tell the pipeline handler not to return this many
* frames at start-up. This can also be used to hide initial frames
* while the AGC and other algorithms are sorting themselves out.
* HideFramesModeSwitch(): Tell the pipeline handler not to return this
* many frames after a mode switch (other than start-up). Some sensors
* may produce innvalid frames after a mode switch; others may not.
* MistrustFramesStartup(): At start-up a sensor may return frames for
* which we should not run any control algorithms (for example, metadata
* may be invalid).
* MistrustFramesModeSwitch(): The number of frames, after a mode switch
* (other than start-up), for which control algorithms should not run
* (for example, metadata may be unreliable).
*/
class CamHelper
{
public:
static CamHelper *create(std::string const &camName);
CamHelper(std::unique_ptr<MdParser> parser, unsigned int frameIntegrationDiff);
virtual ~CamHelper();
void setCameraMode(const CameraMode &mode);
virtual void prepare(libcamera::Span<const uint8_t> buffer,
Metadata &metadata);
virtual void process(StatisticsPtr &stats, Metadata &metadata);
virtual uint32_t exposureLines(const libcamera::utils::Duration exposure,
const libcamera::utils::Duration lineLength) const;
virtual libcamera::utils::Duration exposure(uint32_t exposureLines,
const libcamera::utils::Duration lineLength) const;
virtual std::pair<uint32_t, uint32_t> getBlanking(libcamera::utils::Duration &exposure,
libcamera::utils::Duration minFrameDuration,
libcamera::utils::Duration maxFrameDuration) const;
libcamera::utils::Duration hblankToLineLength(uint32_t hblank) const;
uint32_t lineLengthToHblank(const libcamera::utils::Duration &duration) const;
libcamera::utils::Duration lineLengthPckToDuration(uint32_t lineLengthPck) const;
virtual uint32_t gainCode(double gain) const = 0;
virtual double gain(uint32_t gainCode) const = 0;
virtual void getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const;
virtual bool sensorEmbeddedDataPresent() const;
virtual double getModeSensitivity(const CameraMode &mode) const;
virtual unsigned int hideFramesStartup() const;
virtual unsigned int hideFramesModeSwitch() const;
virtual unsigned int mistrustFramesStartup() const;
virtual unsigned int mistrustFramesModeSwitch() const;
protected:
void parseEmbeddedData(libcamera::Span<const uint8_t> buffer,
Metadata &metadata);
virtual void populateMetadata(const MdParser::RegisterMap &registers,
Metadata &metadata) const;
std::unique_ptr<MdParser> parser_;
CameraMode mode_;
private:
/*
* Smallest difference between the frame length and integration time,
* in units of lines.
*/
unsigned int frameIntegrationDiff_;
};
/*
* This is for registering camera helpers with the system, so that the
* CamHelper::Create function picks them up automatically.
*/
typedef CamHelper *(*CamHelperCreateFunc)();
struct RegisterCamHelper
{
RegisterCamHelper(char const *camName,
CamHelperCreateFunc createFunc);
};
} /* namespace RPi */
@@ -0,0 +1,115 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* cam_helper_imx219.cpp - camera helper for imx219 sensor
*/
#include <assert.h>
#include <stddef.h>
#include <stdio.h>
#include <stdlib.h>
/*
* We have observed that the imx219 embedded data stream randomly returns junk
* register values. Do not rely on embedded data until this has been resolved.
*/
#define ENABLE_EMBEDDED_DATA 0
#include "cam_helper.h"
#if ENABLE_EMBEDDED_DATA
#include "md_parser.h"
#endif
using namespace RPiController;
/*
* We care about one gain register and a pair of exposure registers. Their I2C
* addresses from the Sony IMX219 datasheet:
*/
constexpr uint32_t gainReg = 0x157;
constexpr uint32_t expHiReg = 0x15a;
constexpr uint32_t expLoReg = 0x15b;
constexpr uint32_t frameLengthHiReg = 0x160;
constexpr uint32_t frameLengthLoReg = 0x161;
constexpr uint32_t lineLengthHiReg = 0x162;
constexpr uint32_t lineLengthLoReg = 0x163;
constexpr std::initializer_list<uint32_t> registerList [[maybe_unused]]
= { expHiReg, expLoReg, gainReg, frameLengthHiReg, frameLengthLoReg,
lineLengthHiReg, lineLengthLoReg };
class CamHelperImx219 : public CamHelper
{
public:
CamHelperImx219();
uint32_t gainCode(double gain) const override;
double gain(uint32_t gainCode) const override;
unsigned int mistrustFramesModeSwitch() const override;
bool sensorEmbeddedDataPresent() const override;
private:
/*
* Smallest difference between the frame length and integration time,
* in units of lines.
*/
static constexpr int frameIntegrationDiff = 4;
void populateMetadata(const MdParser::RegisterMap &registers,
Metadata &metadata) const override;
};
CamHelperImx219::CamHelperImx219()
#if ENABLE_EMBEDDED_DATA
: CamHelper(std::make_unique<MdParserSmia>(registerList), frameIntegrationDiff)
#else
: CamHelper({}, frameIntegrationDiff)
#endif
{
}
uint32_t CamHelperImx219::gainCode(double gain) const
{
return (uint32_t)(256 - 256 / gain);
}
double CamHelperImx219::gain(uint32_t gainCode) const
{
return 256.0 / (256 - gainCode);
}
unsigned int CamHelperImx219::mistrustFramesModeSwitch() const
{
/*
* For reasons unknown, we do occasionally get a bogus metadata frame
* at a mode switch (though not at start-up). Possibly warrants some
* investigation, though not a big deal.
*/
return 1;
}
bool CamHelperImx219::sensorEmbeddedDataPresent() const
{
return ENABLE_EMBEDDED_DATA;
}
void CamHelperImx219::populateMetadata(const MdParser::RegisterMap &registers,
Metadata &metadata) const
{
DeviceStatus deviceStatus;
deviceStatus.lineLength = lineLengthPckToDuration(registers.at(lineLengthHiReg) * 256 +
registers.at(lineLengthLoReg));
deviceStatus.shutterSpeed = exposure(registers.at(expHiReg) * 256 + registers.at(expLoReg),
deviceStatus.lineLength);
deviceStatus.analogueGain = gain(registers.at(gainReg));
deviceStatus.frameLength = registers.at(frameLengthHiReg) * 256 + registers.at(frameLengthLoReg);
metadata.set("device.status", deviceStatus);
}
static CamHelper *create()
{
return new CamHelperImx219();
}
static RegisterCamHelper reg("imx219", &create);
@@ -0,0 +1,68 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2021, Raspberry Pi Ltd
*
* cam_helper_imx290.cpp - camera helper for imx290 sensor
*/
#include <math.h>
#include "cam_helper.h"
using namespace RPiController;
class CamHelperImx290 : public CamHelper
{
public:
CamHelperImx290();
uint32_t gainCode(double gain) const override;
double gain(uint32_t gainCode) const override;
void getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const override;
unsigned int hideFramesModeSwitch() const override;
private:
/*
* Smallest difference between the frame length and integration time,
* in units of lines.
*/
static constexpr int frameIntegrationDiff = 2;
};
CamHelperImx290::CamHelperImx290()
: CamHelper({}, frameIntegrationDiff)
{
}
uint32_t CamHelperImx290::gainCode(double gain) const
{
int code = 66.6667 * log10(gain);
return std::max(0, std::min(code, 0xf0));
}
double CamHelperImx290::gain(uint32_t gainCode) const
{
return pow(10, 0.015 * gainCode);
}
void CamHelperImx290::getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const
{
exposureDelay = 2;
gainDelay = 2;
vblankDelay = 2;
hblankDelay = 2;
}
unsigned int CamHelperImx290::hideFramesModeSwitch() const
{
/* After a mode switch, we seem to get 1 bad frame. */
return 1;
}
static CamHelper *create()
{
return new CamHelperImx290();
}
static RegisterCamHelper reg("imx290", &create);
@@ -0,0 +1,83 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2020, Raspberry Pi Ltd
*
* cam_helper_imx296.cpp - Camera helper for IMX296 sensor
*/
#include <algorithm>
#include <cmath>
#include <stddef.h>
#include "cam_helper.h"
using namespace RPiController;
using libcamera::utils::Duration;
using namespace std::literals::chrono_literals;
class CamHelperImx296 : public CamHelper
{
public:
CamHelperImx296();
uint32_t gainCode(double gain) const override;
double gain(uint32_t gainCode) const override;
uint32_t exposureLines(const Duration exposure, const Duration lineLength) const override;
Duration exposure(uint32_t exposureLines, const Duration lineLength) const override;
void getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const override;
private:
static constexpr uint32_t minExposureLines = 1;
static constexpr uint32_t maxGainCode = 239;
static constexpr Duration timePerLine = 550.0 / 37.125e6 * 1.0s;
/*
* Smallest difference between the frame length and integration time,
* in units of lines.
*/
static constexpr int frameIntegrationDiff = 4;
};
CamHelperImx296::CamHelperImx296()
: CamHelper(nullptr, frameIntegrationDiff)
{
}
uint32_t CamHelperImx296::gainCode(double gain) const
{
uint32_t code = 20 * std::log10(gain) * 10;
return std::min(code, maxGainCode);
}
double CamHelperImx296::gain(uint32_t gainCode) const
{
return std::pow(10.0, gainCode / 200.0);
}
uint32_t CamHelperImx296::exposureLines(const Duration exposure,
[[maybe_unused]] const Duration lineLength) const
{
return std::max<uint32_t>(minExposureLines, (exposure - 14.26us) / timePerLine);
}
Duration CamHelperImx296::exposure(uint32_t exposureLines,
[[maybe_unused]] const Duration lineLength) const
{
return std::max<uint32_t>(minExposureLines, exposureLines) * timePerLine + 14.26us;
}
void CamHelperImx296::getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const
{
exposureDelay = 2;
gainDelay = 2;
vblankDelay = 2;
hblankDelay = 2;
}
static CamHelper *create()
{
return new CamHelperImx296();
}
static RegisterCamHelper reg("imx296", &create);
@@ -0,0 +1,197 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2020, Raspberry Pi Ltd
*
* cam_helper_imx477.cpp - camera helper for imx477 sensor
*/
#include <algorithm>
#include <assert.h>
#include <cmath>
#include <stddef.h>
#include <stdio.h>
#include <stdlib.h>
#include <libcamera/base/log.h>
#include "cam_helper.h"
#include "md_parser.h"
using namespace RPiController;
using namespace libcamera;
using libcamera::utils::Duration;
namespace libcamera {
LOG_DECLARE_CATEGORY(IPARPI)
}
/*
* We care about two gain registers and a pair of exposure registers. Their
* I2C addresses from the Sony IMX477 datasheet:
*/
constexpr uint32_t expHiReg = 0x0202;
constexpr uint32_t expLoReg = 0x0203;
constexpr uint32_t gainHiReg = 0x0204;
constexpr uint32_t gainLoReg = 0x0205;
constexpr uint32_t frameLengthHiReg = 0x0340;
constexpr uint32_t frameLengthLoReg = 0x0341;
constexpr uint32_t lineLengthHiReg = 0x0342;
constexpr uint32_t lineLengthLoReg = 0x0343;
constexpr uint32_t temperatureReg = 0x013a;
constexpr std::initializer_list<uint32_t> registerList =
{ expHiReg, expLoReg, gainHiReg, gainLoReg, frameLengthHiReg, frameLengthLoReg,
lineLengthHiReg, lineLengthLoReg, temperatureReg };
class CamHelperImx477 : public CamHelper
{
public:
CamHelperImx477();
uint32_t gainCode(double gain) const override;
double gain(uint32_t gainCode) const override;
void prepare(libcamera::Span<const uint8_t> buffer, Metadata &metadata) override;
std::pair<uint32_t, uint32_t> getBlanking(Duration &exposure, Duration minFrameDuration,
Duration maxFrameDuration) const override;
void getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const override;
bool sensorEmbeddedDataPresent() const override;
private:
/*
* Smallest difference between the frame length and integration time,
* in units of lines.
*/
static constexpr int frameIntegrationDiff = 22;
/* Maximum frame length allowable for long exposure calculations. */
static constexpr int frameLengthMax = 0xffdc;
/* Largest long exposure scale factor given as a left shift on the frame length. */
static constexpr int longExposureShiftMax = 7;
void populateMetadata(const MdParser::RegisterMap &registers,
Metadata &metadata) const override;
};
CamHelperImx477::CamHelperImx477()
: CamHelper(std::make_unique<MdParserSmia>(registerList), frameIntegrationDiff)
{
}
uint32_t CamHelperImx477::gainCode(double gain) const
{
return static_cast<uint32_t>(1024 - 1024 / gain);
}
double CamHelperImx477::gain(uint32_t gainCode) const
{
return 1024.0 / (1024 - gainCode);
}
void CamHelperImx477::prepare(libcamera::Span<const uint8_t> buffer, Metadata &metadata)
{
MdParser::RegisterMap registers;
DeviceStatus deviceStatus;
if (metadata.get("device.status", deviceStatus)) {
LOG(IPARPI, Error) << "DeviceStatus not found from DelayedControls";
return;
}
parseEmbeddedData(buffer, metadata);
/*
* The DeviceStatus struct is first populated with values obtained from
* DelayedControls. If this reports frame length is > frameLengthMax,
* it means we are using a long exposure mode. Since the long exposure
* scale factor is not returned back through embedded data, we must rely
* on the existing exposure lines and frame length values returned by
* DelayedControls.
*
* Otherwise, all values are updated with what is reported in the
* embedded data.
*/
if (deviceStatus.frameLength > frameLengthMax) {
DeviceStatus parsedDeviceStatus;
metadata.get("device.status", parsedDeviceStatus);
parsedDeviceStatus.shutterSpeed = deviceStatus.shutterSpeed;
parsedDeviceStatus.frameLength = deviceStatus.frameLength;
metadata.set("device.status", parsedDeviceStatus);
LOG(IPARPI, Debug) << "Metadata updated for long exposure: "
<< parsedDeviceStatus;
}
}
std::pair<uint32_t, uint32_t> CamHelperImx477::getBlanking(Duration &exposure,
Duration minFrameDuration,
Duration maxFrameDuration) const
{
uint32_t frameLength, exposureLines;
unsigned int shift = 0;
auto [vblank, hblank] = CamHelper::getBlanking(exposure, minFrameDuration,
maxFrameDuration);
frameLength = mode_.height + vblank;
Duration lineLength = hblankToLineLength(hblank);
/*
* Check if the frame length calculated needs to be setup for long
* exposure mode. This will require us to use a long exposure scale
* factor provided by a shift operation in the sensor.
*/
while (frameLength > frameLengthMax) {
if (++shift > longExposureShiftMax) {
shift = longExposureShiftMax;
frameLength = frameLengthMax;
break;
}
frameLength >>= 1;
}
if (shift) {
/* Account for any rounding in the scaled frame length value. */
frameLength <<= shift;
exposureLines = CamHelperImx477::exposureLines(exposure, lineLength);
exposureLines = std::min(exposureLines, frameLength - frameIntegrationDiff);
exposure = CamHelperImx477::exposure(exposureLines, lineLength);
}
return { frameLength - mode_.height, hblank };
}
void CamHelperImx477::getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const
{
exposureDelay = 2;
gainDelay = 2;
vblankDelay = 3;
hblankDelay = 3;
}
bool CamHelperImx477::sensorEmbeddedDataPresent() const
{
return true;
}
void CamHelperImx477::populateMetadata(const MdParser::RegisterMap &registers,
Metadata &metadata) const
{
DeviceStatus deviceStatus;
deviceStatus.lineLength = lineLengthPckToDuration(registers.at(lineLengthHiReg) * 256 +
registers.at(lineLengthLoReg));
deviceStatus.shutterSpeed = exposure(registers.at(expHiReg) * 256 + registers.at(expLoReg),
deviceStatus.lineLength);
deviceStatus.analogueGain = gain(registers.at(gainHiReg) * 256 + registers.at(gainLoReg));
deviceStatus.frameLength = registers.at(frameLengthHiReg) * 256 + registers.at(frameLengthLoReg);
deviceStatus.sensorTemperature = std::clamp<int8_t>(registers.at(temperatureReg), -20, 80);
metadata.set("device.status", deviceStatus);
}
static CamHelper *create()
{
return new CamHelperImx477();
}
static RegisterCamHelper reg("imx477", &create);
@@ -0,0 +1,196 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Based on cam_helper_imx477.cpp
* Copyright (C) 2020, Raspberry Pi Ltd
*
* cam_helper_imx519.cpp - camera helper for imx519 sensor
* Copyright (C) 2021, Arducam Technology co., Ltd.
*/
#include <assert.h>
#include <cmath>
#include <stddef.h>
#include <stdio.h>
#include <stdlib.h>
#include <libcamera/base/log.h>
#include "cam_helper.h"
#include "md_parser.h"
using namespace RPiController;
using namespace libcamera;
using libcamera::utils::Duration;
namespace libcamera {
LOG_DECLARE_CATEGORY(IPARPI)
}
/*
* We care about two gain registers and a pair of exposure registers. Their
* I2C addresses from the Sony IMX519 datasheet:
*/
constexpr uint32_t expHiReg = 0x0202;
constexpr uint32_t expLoReg = 0x0203;
constexpr uint32_t gainHiReg = 0x0204;
constexpr uint32_t gainLoReg = 0x0205;
constexpr uint32_t frameLengthHiReg = 0x0340;
constexpr uint32_t frameLengthLoReg = 0x0341;
constexpr uint32_t lineLengthHiReg = 0x0342;
constexpr uint32_t lineLengthLoReg = 0x0343;
constexpr std::initializer_list<uint32_t> registerList =
{ expHiReg, expLoReg, gainHiReg, gainLoReg, frameLengthHiReg, frameLengthLoReg,
lineLengthHiReg, lineLengthLoReg };
class CamHelperImx519 : public CamHelper
{
public:
CamHelperImx519();
uint32_t gainCode(double gain) const override;
double gain(uint32_t gainCode) const override;
void prepare(libcamera::Span<const uint8_t> buffer, Metadata &metadata) override;
std::pair<uint32_t, uint32_t> getBlanking(Duration &exposure, Duration minFrameDuration,
Duration maxFrameDuration) const override;
void getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const override;
bool sensorEmbeddedDataPresent() const override;
private:
/*
* Smallest difference between the frame length and integration time,
* in units of lines.
*/
static constexpr int frameIntegrationDiff = 32;
/* Maximum frame length allowable for long exposure calculations. */
static constexpr int frameLengthMax = 0xffdc;
/* Largest long exposure scale factor given as a left shift on the frame length. */
static constexpr int longExposureShiftMax = 7;
void populateMetadata(const MdParser::RegisterMap &registers,
Metadata &metadata) const override;
};
CamHelperImx519::CamHelperImx519()
: CamHelper(std::make_unique<MdParserSmia>(registerList), frameIntegrationDiff)
{
}
uint32_t CamHelperImx519::gainCode(double gain) const
{
return static_cast<uint32_t>(1024 - 1024 / gain);
}
double CamHelperImx519::gain(uint32_t gainCode) const
{
return 1024.0 / (1024 - gainCode);
}
void CamHelperImx519::prepare(libcamera::Span<const uint8_t> buffer, Metadata &metadata)
{
MdParser::RegisterMap registers;
DeviceStatus deviceStatus;
if (metadata.get("device.status", deviceStatus)) {
LOG(IPARPI, Error) << "DeviceStatus not found from DelayedControls";
return;
}
parseEmbeddedData(buffer, metadata);
/*
* The DeviceStatus struct is first populated with values obtained from
* DelayedControls. If this reports frame length is > frameLengthMax,
* it means we are using a long exposure mode. Since the long exposure
* scale factor is not returned back through embedded data, we must rely
* on the existing exposure lines and frame length values returned by
* DelayedControls.
*
* Otherwise, all values are updated with what is reported in the
* embedded data.
*/
if (deviceStatus.frameLength > frameLengthMax) {
DeviceStatus parsedDeviceStatus;
metadata.get("device.status", parsedDeviceStatus);
parsedDeviceStatus.shutterSpeed = deviceStatus.shutterSpeed;
parsedDeviceStatus.frameLength = deviceStatus.frameLength;
metadata.set("device.status", parsedDeviceStatus);
LOG(IPARPI, Debug) << "Metadata updated for long exposure: "
<< parsedDeviceStatus;
}
}
std::pair<uint32_t, uint32_t> CamHelperImx519::getBlanking(Duration &exposure,
Duration minFrameDuration,
Duration maxFrameDuration) const
{
uint32_t frameLength, exposureLines;
unsigned int shift = 0;
auto [vblank, hblank] = CamHelper::getBlanking(exposure, minFrameDuration,
maxFrameDuration);
frameLength = mode_.height + vblank;
Duration lineLength = hblankToLineLength(hblank);
/*
* Check if the frame length calculated needs to be setup for long
* exposure mode. This will require us to use a long exposure scale
* factor provided by a shift operation in the sensor.
*/
while (frameLength > frameLengthMax) {
if (++shift > longExposureShiftMax) {
shift = longExposureShiftMax;
frameLength = frameLengthMax;
break;
}
frameLength >>= 1;
}
if (shift) {
/* Account for any rounding in the scaled frame length value. */
frameLength <<= shift;
exposureLines = CamHelperImx519::exposureLines(exposure, lineLength);
exposureLines = std::min(exposureLines, frameLength - frameIntegrationDiff);
exposure = CamHelperImx519::exposure(exposureLines, lineLength);
}
return { frameLength - mode_.height, hblank };
}
void CamHelperImx519::getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const
{
exposureDelay = 2;
gainDelay = 2;
vblankDelay = 3;
hblankDelay = 3;
}
bool CamHelperImx519::sensorEmbeddedDataPresent() const
{
return true;
}
void CamHelperImx519::populateMetadata(const MdParser::RegisterMap &registers,
Metadata &metadata) const
{
DeviceStatus deviceStatus;
deviceStatus.lineLength = lineLengthPckToDuration(registers.at(lineLengthHiReg) * 256 +
registers.at(lineLengthLoReg));
deviceStatus.shutterSpeed = exposure(registers.at(expHiReg) * 256 + registers.at(expLoReg),
deviceStatus.lineLength);
deviceStatus.analogueGain = gain(registers.at(gainHiReg) * 256 + registers.at(gainLoReg));
deviceStatus.frameLength = registers.at(frameLengthHiReg) * 256 + registers.at(frameLengthLoReg);
metadata.set("device.status", deviceStatus);
}
static CamHelper *create()
{
return new CamHelperImx519();
}
static RegisterCamHelper reg("imx519", &create);
@@ -0,0 +1,359 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2022, Raspberry Pi Ltd
*
* cam_helper_imx708.cpp - camera helper for imx708 sensor
*/
#include <cmath>
#include <stddef.h>
#include <stdio.h>
#include <stdlib.h>
#include <libcamera/base/log.h>
#include "controller/pdaf_data.h"
#include "cam_helper.h"
#include "md_parser.h"
using namespace RPiController;
using namespace libcamera;
using libcamera::utils::Duration;
namespace libcamera {
LOG_DECLARE_CATEGORY(IPARPI)
}
/*
* We care about two gain registers and a pair of exposure registers. Their
* I2C addresses from the Sony imx708 datasheet:
*/
constexpr uint32_t expHiReg = 0x0202;
constexpr uint32_t expLoReg = 0x0203;
constexpr uint32_t gainHiReg = 0x0204;
constexpr uint32_t gainLoReg = 0x0205;
constexpr uint32_t frameLengthHiReg = 0x0340;
constexpr uint32_t frameLengthLoReg = 0x0341;
constexpr uint32_t lineLengthHiReg = 0x0342;
constexpr uint32_t lineLengthLoReg = 0x0343;
constexpr uint32_t temperatureReg = 0x013a;
constexpr std::initializer_list<uint32_t> registerList =
{ expHiReg, expLoReg, gainHiReg, gainLoReg, lineLengthHiReg,
lineLengthLoReg, frameLengthHiReg, frameLengthLoReg, temperatureReg };
class CamHelperImx708 : public CamHelper
{
public:
CamHelperImx708();
uint32_t gainCode(double gain) const override;
double gain(uint32_t gain_code) const override;
void prepare(libcamera::Span<const uint8_t> buffer, Metadata &metadata) override;
void process(StatisticsPtr &stats, Metadata &metadata) override;
std::pair<uint32_t, uint32_t> getBlanking(Duration &exposure, Duration minFrameDuration,
Duration maxFrameDuration) const override;
void getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const override;
bool sensorEmbeddedDataPresent() const override;
double getModeSensitivity(const CameraMode &mode) const override;
unsigned int hideFramesModeSwitch() const override { return 1; } // seems to be required for HDR
private:
/*
* Smallest difference between the frame length and integration time,
* in units of lines.
*/
static constexpr int frameIntegrationDiff = 22;
/* Maximum frame length allowable for long exposure calculations. */
static constexpr int frameLengthMax = 0xffdc;
/* Largest long exposure scale factor given as a left shift on the frame length. */
static constexpr int longExposureShiftMax = 7;
static constexpr int pdafStatsRows = 12;
static constexpr int pdafStatsCols = 16;
void populateMetadata(const MdParser::RegisterMap &registers,
Metadata &metadata) const override;
static bool parsePdafData(const uint8_t *ptr, size_t len, unsigned bpp,
PdafRegions &pdaf);
bool parseAEHist(const uint8_t *ptr, size_t len, unsigned bpp);
void putAGCStatistics(StatisticsPtr stats);
Histogram aeHistLinear_;
uint32_t aeHistAverage_;
bool aeHistValid_;
};
CamHelperImx708::CamHelperImx708()
: CamHelper(std::make_unique<MdParserSmia>(registerList), frameIntegrationDiff),
aeHistLinear_{}, aeHistAverage_(0), aeHistValid_(false)
{
}
uint32_t CamHelperImx708::gainCode(double gain) const
{
return static_cast<uint32_t>(1024 - 1024 / gain);
}
double CamHelperImx708::gain(uint32_t gain_code) const
{
return 1024.0 / (1024 - gain_code);
}
void CamHelperImx708::prepare(libcamera::Span<const uint8_t> buffer, Metadata &metadata)
{
MdParser::RegisterMap registers;
DeviceStatus deviceStatus;
LOG(IPARPI, Debug) << "Embedded buffer size: " << buffer.size();
if (metadata.get("device.status", deviceStatus)) {
LOG(IPARPI, Error) << "DeviceStatus not found from DelayedControls";
return;
}
parseEmbeddedData(buffer, metadata);
/*
* Parse PDAF data, which we expect to occupy the third scanline
* of embedded data. As PDAF is quite sensor-specific, it's parsed here.
*/
size_t bytesPerLine = (mode_.width * mode_.bitdepth) >> 3;
if (buffer.size() > 2 * bytesPerLine) {
PdafRegions pdaf;
if (parsePdafData(&buffer[2 * bytesPerLine],
buffer.size() - 2 * bytesPerLine,
mode_.bitdepth, pdaf))
metadata.set("pdaf.regions", pdaf);
}
/* Parse AE-HIST data where present */
if (buffer.size() > 3 * bytesPerLine) {
aeHistValid_ = parseAEHist(&buffer[3 * bytesPerLine],
buffer.size() - 3 * bytesPerLine,
mode_.bitdepth);
}
/*
* The DeviceStatus struct is first populated with values obtained from
* DelayedControls. If this reports frame length is > frameLengthMax,
* it means we are using a long exposure mode. Since the long exposure
* scale factor is not returned back through embedded data, we must rely
* on the existing exposure lines and frame length values returned by
* DelayedControls.
*
* Otherwise, all values are updated with what is reported in the
* embedded data.
*/
if (deviceStatus.frameLength > frameLengthMax) {
DeviceStatus parsedDeviceStatus;
metadata.get("device.status", parsedDeviceStatus);
parsedDeviceStatus.shutterSpeed = deviceStatus.shutterSpeed;
parsedDeviceStatus.frameLength = deviceStatus.frameLength;
metadata.set("device.status", parsedDeviceStatus);
LOG(IPARPI, Debug) << "Metadata updated for long exposure: "
<< parsedDeviceStatus;
}
}
void CamHelperImx708::process(StatisticsPtr &stats, [[maybe_unused]] Metadata &metadata)
{
if (aeHistValid_)
putAGCStatistics(stats);
}
std::pair<uint32_t, uint32_t> CamHelperImx708::getBlanking(Duration &exposure,
Duration minFrameDuration,
Duration maxFrameDuration) const
{
uint32_t frameLength, exposureLines;
unsigned int shift = 0;
auto [vblank, hblank] = CamHelper::getBlanking(exposure, minFrameDuration,
maxFrameDuration);
frameLength = mode_.height + vblank;
Duration lineLength = hblankToLineLength(hblank);
/*
* Check if the frame length calculated needs to be setup for long
* exposure mode. This will require us to use a long exposure scale
* factor provided by a shift operation in the sensor.
*/
while (frameLength > frameLengthMax) {
if (++shift > longExposureShiftMax) {
shift = longExposureShiftMax;
frameLength = frameLengthMax;
break;
}
frameLength >>= 1;
}
if (shift) {
/* Account for any rounding in the scaled frame length value. */
frameLength <<= shift;
exposureLines = CamHelper::exposureLines(exposure, lineLength);
exposureLines = std::min(exposureLines, frameLength - frameIntegrationDiff);
exposure = CamHelper::exposure(exposureLines, lineLength);
}
return { frameLength - mode_.height, hblank };
}
void CamHelperImx708::getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const
{
exposureDelay = 2;
gainDelay = 2;
vblankDelay = 3;
hblankDelay = 3;
}
bool CamHelperImx708::sensorEmbeddedDataPresent() const
{
return true;
}
double CamHelperImx708::getModeSensitivity(const CameraMode &mode) const
{
/* In binned modes, sensitivity increases by a factor of 2 */
return (mode.width > 2304) ? 1.0 : 2.0;
}
void CamHelperImx708::populateMetadata(const MdParser::RegisterMap &registers,
Metadata &metadata) const
{
DeviceStatus deviceStatus;
deviceStatus.lineLength = lineLengthPckToDuration(registers.at(lineLengthHiReg) * 256 +
registers.at(lineLengthLoReg));
deviceStatus.shutterSpeed = exposure(registers.at(expHiReg) * 256 + registers.at(expLoReg),
deviceStatus.lineLength);
deviceStatus.analogueGain = gain(registers.at(gainHiReg) * 256 + registers.at(gainLoReg));
deviceStatus.frameLength = registers.at(frameLengthHiReg) * 256 + registers.at(frameLengthLoReg);
deviceStatus.sensorTemperature = std::clamp<int8_t>(registers.at(temperatureReg), -20, 80);
metadata.set("device.status", deviceStatus);
}
bool CamHelperImx708::parsePdafData(const uint8_t *ptr, size_t len,
unsigned bpp, PdafRegions &pdaf)
{
size_t step = bpp >> 1; /* bytes per PDAF grid entry */
if (bpp < 10 || bpp > 12 || len < 194 * step || ptr[0] != 0 || ptr[1] >= 0x40) {
LOG(IPARPI, Error) << "PDAF data in unsupported format";
return false;
}
pdaf.init({ pdafStatsCols, pdafStatsRows });
ptr += 2 * step;
for (unsigned i = 0; i < pdafStatsRows; ++i) {
for (unsigned j = 0; j < pdafStatsCols; ++j) {
unsigned c = (ptr[0] << 3) | (ptr[1] >> 5);
int p = (((ptr[1] & 0x0F) - (ptr[1] & 0x10)) << 6) | (ptr[2] >> 2);
PdafData pdafData;
pdafData.conf = c;
pdafData.phase = c ? p : 0;
pdaf.set(libcamera::Point(j, i), { pdafData, 1, 0 });
ptr += step;
}
}
return true;
}
bool CamHelperImx708::parseAEHist(const uint8_t *ptr, size_t len, unsigned bpp)
{
static constexpr unsigned int PipelineBits = Statistics::NormalisationFactorPow2;
uint64_t count = 0, sum = 0;
size_t step = bpp >> 1; /* bytes per histogram bin */
uint32_t hist[128];
if (len < 144 * step)
return false;
/*
* Read the 128 bin linear histogram, which by default covers
* the full range of the HDR shortest exposure (small values are
* expected to dominate, so pixel-value resolution will be poor).
*/
for (unsigned i = 0; i < 128; ++i) {
if (ptr[3] != 0x55)
return false;
uint32_t c = (ptr[0] << 14) + (ptr[1] << 6) + (ptr[2] >> 2);
hist[i] = c >> 2; /* pixels to quads */
if (i != 0) {
count += c;
sum += c *
(i * (1u << (PipelineBits - 7)) +
(1u << (PipelineBits - 8)));
}
ptr += step;
}
/*
* Now use the first 9 bins of the log histogram (these should be
* subdivisions of the smallest linear bin), to get a more accurate
* average value. Don't assume that AEHIST1_AVERAGE is present.
*/
for (unsigned i = 0; i < 9; ++i) {
if (ptr[3] != 0x55)
return false;
uint32_t c = (ptr[0] << 14) + (ptr[1] << 6) + (ptr[2] >> 2);
count += c;
sum += c *
((3u << PipelineBits) >> (17 - i));
ptr += step;
}
if ((unsigned)((ptr[0] << 12) + (ptr[1] << 4) + (ptr[2] >> 4)) !=
hist[1]) {
LOG(IPARPI, Error) << "Lin/Log histogram mismatch";
return false;
}
aeHistLinear_ = Histogram(hist, 128);
aeHistAverage_ = count ? (sum / count) : 0;
return count != 0;
}
void CamHelperImx708::putAGCStatistics(StatisticsPtr stats)
{
/*
* For HDR mode, copy sensor's AE/AGC statistics over ISP's, so the
* AGC algorithm sees a linear response to exposure and gain changes.
*
* Histogram: Just copy the "raw" histogram over the tone-mapped one,
* although they have different distributions (raw values are lower).
* Tuning should either ignore it, or constrain for highlights only.
*
* Average: Overwrite all regional averages with a global raw average,
* scaled by a fiddle-factor so that a conventional (non-HDR) y_target
* of e.g. 0.17 will map to a suitable level for HDR.
*/
stats->yHist = aeHistLinear_;
constexpr unsigned int HdrHeadroomFactor = 4;
uint64_t v = HdrHeadroomFactor * aeHistAverage_;
for (auto &region : stats->agcRegions) {
region.val.rSum = region.val.gSum = region.val.bSum = region.counted * v;
}
}
static CamHelper *create()
{
return new CamHelperImx708();
}
static RegisterCamHelper reg("imx708", &create);
static RegisterCamHelper regWide("imx708_wide", &create);
static RegisterCamHelper regNoIr("imx708_noir", &create);
static RegisterCamHelper regWideNoIr("imx708_wide_noir", &create);
@@ -0,0 +1,109 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* cam_helper_ov5647.cpp - camera information for ov5647 sensor
*/
#include <assert.h>
#include "cam_helper.h"
using namespace RPiController;
class CamHelperOv5647 : public CamHelper
{
public:
CamHelperOv5647();
uint32_t gainCode(double gain) const override;
double gain(uint32_t gainCode) const override;
void getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const override;
unsigned int hideFramesStartup() const override;
unsigned int hideFramesModeSwitch() const override;
unsigned int mistrustFramesStartup() const override;
unsigned int mistrustFramesModeSwitch() const override;
private:
/*
* Smallest difference between the frame length and integration time,
* in units of lines.
*/
static constexpr int frameIntegrationDiff = 4;
};
/*
* OV5647 doesn't output metadata, so we have to use the "unicam parser" which
* works by counting frames.
*/
CamHelperOv5647::CamHelperOv5647()
: CamHelper({}, frameIntegrationDiff)
{
}
uint32_t CamHelperOv5647::gainCode(double gain) const
{
return static_cast<uint32_t>(gain * 16.0);
}
double CamHelperOv5647::gain(uint32_t gainCode) const
{
return static_cast<double>(gainCode) / 16.0;
}
void CamHelperOv5647::getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const
{
/*
* We run this sensor in a mode where the gain delay is bumped up to
* 2. It seems to be the only way to make the delays "predictable".
*/
exposureDelay = 2;
gainDelay = 2;
vblankDelay = 2;
hblankDelay = 2;
}
unsigned int CamHelperOv5647::hideFramesStartup() const
{
/*
* On startup, we get a couple of under-exposed frames which
* we don't want shown.
*/
return 2;
}
unsigned int CamHelperOv5647::hideFramesModeSwitch() const
{
/*
* After a mode switch, we get a couple of under-exposed frames which
* we don't want shown.
*/
return 2;
}
unsigned int CamHelperOv5647::mistrustFramesStartup() const
{
/*
* First couple of frames are under-exposed and are no good for control
* algos.
*/
return 2;
}
unsigned int CamHelperOv5647::mistrustFramesModeSwitch() const
{
/*
* First couple of frames are under-exposed even after a simple
* mode switch, and are no good for control algos.
*/
return 2;
}
static CamHelper *create()
{
return new CamHelperOv5647();
}
static RegisterCamHelper reg("ov5647", &create);
@@ -0,0 +1,66 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2021, Raspberry Pi Ltd
*
* cam_helper_ov9281.cpp - camera information for ov9281 sensor
*/
#include <assert.h>
#include "cam_helper.h"
using namespace RPiController;
class CamHelperOv9281 : public CamHelper
{
public:
CamHelperOv9281();
uint32_t gainCode(double gain) const override;
double gain(uint32_t gainCode) const override;
void getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const override;
private:
/*
* Smallest difference between the frame length and integration time,
* in units of lines.
*/
static constexpr int frameIntegrationDiff = 4;
};
/*
* OV9281 doesn't output metadata, so we have to use the "unicam parser" which
* works by counting frames.
*/
CamHelperOv9281::CamHelperOv9281()
: CamHelper({}, frameIntegrationDiff)
{
}
uint32_t CamHelperOv9281::gainCode(double gain) const
{
return static_cast<uint32_t>(gain * 16.0);
}
double CamHelperOv9281::gain(uint32_t gainCode) const
{
return static_cast<double>(gainCode) / 16.0;
}
void CamHelperOv9281::getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const
{
/* The driver appears to behave as follows: */
exposureDelay = 2;
gainDelay = 2;
vblankDelay = 2;
hblankDelay = 2;
}
static CamHelper *create()
{
return new CamHelperOv9281();
}
static RegisterCamHelper reg("ov9281", &create);
+155
View File
@@ -0,0 +1,155 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* md_parser.h - image sensor metadata parser interface
*/
#pragma once
#include <initializer_list>
#include <map>
#include <optional>
#include <stdint.h>
#include <libcamera/base/span.h>
/*
* Camera metadata parser class. Usage as shown below.
*
* Setup:
*
* Usually the metadata parser will be made as part of the CamHelper class so
* application code doesn't have to worry which kind to instantiate. But for
* the sake of example let's suppose we're parsing imx219 metadata.
*
* MdParser *parser = new MdParserSmia({ expHiReg, expLoReg, gainReg });
* parser->SetBitsPerPixel(bpp);
* parser->SetLineLengthBytes(pitch);
* parser->SetNumLines(2);
*
* Note 1: if you don't know how many lines there are, the size of the input
* buffer is used as a limit instead.
*
* Note 2: if you don't know the line length, you can leave the line length unset
* (or set to zero) and the parser will hunt for the line start instead.
*
* Then on every frame:
*
* RegisterMap registers;
* if (parser->Parse(buffer, registers) != MdParser::OK)
* much badness;
* Metadata metadata;
* CamHelper::PopulateMetadata(registers, metadata);
*
* (Note that the CamHelper class converts to/from exposure lines and time,
* and gain_code / actual gain.)
*
* If you suspect your embedded data may have changed its layout, change any line
* lengths, number of lines, bits per pixel etc. that are different, and
* then:
*
* parser->Reset();
*
* before calling Parse again.
*/
namespace RPiController {
/* Abstract base class from which other metadata parsers are derived. */
class MdParser
{
public:
using RegisterMap = std::map<uint32_t, uint32_t>;
/*
* Parser status codes:
* OK - success
* NOTFOUND - value such as exposure or gain was not found
* ERROR - all other errors
*/
enum Status {
OK = 0,
NOTFOUND = 1,
ERROR = 2
};
MdParser()
: reset_(true), bitsPerPixel_(0), numLines_(0), lineLengthBytes_(0)
{
}
virtual ~MdParser() = default;
void reset()
{
reset_ = true;
}
void setBitsPerPixel(int bpp)
{
bitsPerPixel_ = bpp;
}
void setNumLines(unsigned int numLines)
{
numLines_ = numLines;
}
void setLineLengthBytes(unsigned int numBytes)
{
lineLengthBytes_ = numBytes;
}
virtual Status parse(libcamera::Span<const uint8_t> buffer,
RegisterMap &registers) = 0;
protected:
bool reset_;
int bitsPerPixel_;
unsigned int numLines_;
unsigned int lineLengthBytes_;
};
/*
* This isn't a full implementation of a metadata parser for SMIA sensors,
* however, it does provide the findRegs function which will prove useful and
* make it easier to implement parsers for other SMIA-like sensors (see
* md_parser_imx219.cpp for an example).
*/
class MdParserSmia final : public MdParser
{
public:
MdParserSmia(std::initializer_list<uint32_t> registerList);
MdParser::Status parse(libcamera::Span<const uint8_t> buffer,
RegisterMap &registers) override;
private:
/* Maps register address to offset in the buffer. */
using OffsetMap = std::map<uint32_t, std::optional<uint32_t>>;
/*
* Note that error codes > 0 are regarded as non-fatal; codes < 0
* indicate a bad data buffer. Status codes are:
* ParseOk - found all registers, much happiness
* MissingRegs - some registers found; should this be a hard error?
* The remaining codes are all hard errors.
*/
enum ParseStatus {
ParseOk = 0,
MissingRegs = 1,
NoLineStart = -1,
IllegalTag = -2,
BadDummy = -3,
BadLineEnd = -4,
BadPadding = -5
};
ParseStatus findRegs(libcamera::Span<const uint8_t> buffer);
OffsetMap offsets_;
};
} /* namespace RPi */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019-2021, Raspberry Pi Ltd
*
* md_parser_smia.cpp - SMIA specification based embedded data parser
*/
#include <libcamera/base/log.h>
#include "md_parser.h"
using namespace RPiController;
using namespace libcamera;
/*
* This function goes through the embedded data to find the offsets (not
* values!), in the data block, where the values of the given registers can
* subsequently be found.
*
* Embedded data tag bytes, from Sony IMX219 datasheet but general to all SMIA
* sensors, I think.
*/
constexpr unsigned int LineStart = 0x0a;
constexpr unsigned int LineEndTag = 0x07;
constexpr unsigned int RegHiBits = 0xaa;
constexpr unsigned int RegLowBits = 0xa5;
constexpr unsigned int RegValue = 0x5a;
constexpr unsigned int RegSkip = 0x55;
MdParserSmia::MdParserSmia(std::initializer_list<uint32_t> registerList)
{
for (auto r : registerList)
offsets_[r] = {};
}
MdParser::Status MdParserSmia::parse(libcamera::Span<const uint8_t> buffer,
RegisterMap &registers)
{
if (reset_) {
/*
* Search again through the metadata for all the registers
* requested.
*/
ASSERT(bitsPerPixel_);
for (const auto &kv : offsets_)
offsets_[kv.first] = {};
ParseStatus ret = findRegs(buffer);
/*
* > 0 means "worked partially but parse again next time",
* < 0 means "hard error".
*
* In either case, we retry parsing on the next frame.
*/
if (ret != ParseOk)
return ERROR;
reset_ = false;
}
/* Populate the register values requested. */
registers.clear();
for (const auto &[reg, offset] : offsets_) {
if (!offset) {
reset_ = true;
return NOTFOUND;
}
registers[reg] = buffer[offset.value()];
}
return OK;
}
MdParserSmia::ParseStatus MdParserSmia::findRegs(libcamera::Span<const uint8_t> buffer)
{
ASSERT(offsets_.size());
if (buffer[0] != LineStart)
return NoLineStart;
unsigned int currentOffset = 1; /* after the LineStart */
unsigned int currentLineStart = 0, currentLine = 0;
unsigned int regNum = 0, regsDone = 0;
while (1) {
int tag = buffer[currentOffset++];
if ((bitsPerPixel_ == 10 &&
(currentOffset + 1 - currentLineStart) % 5 == 0) ||
(bitsPerPixel_ == 12 &&
(currentOffset + 1 - currentLineStart) % 3 == 0)) {
if (buffer[currentOffset++] != RegSkip)
return BadDummy;
}
int dataByte = buffer[currentOffset++];
if (tag == LineEndTag) {
if (dataByte != LineEndTag)
return BadLineEnd;
if (numLines_ && ++currentLine == numLines_)
return MissingRegs;
if (lineLengthBytes_) {
currentOffset = currentLineStart + lineLengthBytes_;
/* Require whole line to be in the buffer (if buffer size set). */
if (buffer.size() &&
currentOffset + lineLengthBytes_ > buffer.size())
return MissingRegs;
if (buffer[currentOffset] != LineStart)
return NoLineStart;
} else {
/* allow a zero line length to mean "hunt for the next line" */
while (currentOffset < buffer.size() &&
buffer[currentOffset] != LineStart)
currentOffset++;
if (currentOffset == buffer.size())
return NoLineStart;
}
/* inc currentOffset to after LineStart */
currentLineStart = currentOffset++;
} else {
if (tag == RegHiBits)
regNum = (regNum & 0xff) | (dataByte << 8);
else if (tag == RegLowBits)
regNum = (regNum & 0xff00) | dataByte;
else if (tag == RegSkip)
regNum++;
else if (tag == RegValue) {
auto reg = offsets_.find(regNum);
if (reg != offsets_.end()) {
offsets_[regNum] = currentOffset - 1;
if (++regsDone == offsets_.size())
return ParseOk;
}
regNum++;
} else
return IllegalTag;
}
}
}
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# SPDX-License-Identifier: CC0-1.0
rpi_ipa_cam_helper_sources = files([
'cam_helper.cpp',
'cam_helper_ov5647.cpp',
'cam_helper_imx219.cpp',
'cam_helper_imx290.cpp',
'cam_helper_imx296.cpp',
'cam_helper_imx477.cpp',
'cam_helper_imx519.cpp',
'cam_helper_imx708.cpp',
'cam_helper_ov9281.cpp',
'md_parser_smia.cpp',
])
rpi_ipa_cam_helper_includes = [
include_directories('..'),
]
rpi_ipa_cam_helper_deps = [
libcamera_private,
]
rpi_ipa_cam_helper_lib = static_library('rpi_ipa_cam_helper', rpi_ipa_cam_helper_sources,
include_directories : rpi_ipa_cam_helper_includes,
dependencies : rpi_ipa_cam_helper_deps)
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2022, Raspberry Pi Ltd
*
* af_algorithm.hpp - auto focus algorithm interface
*/
#pragma once
#include <optional>
#include <libcamera/base/span.h>
#include "algorithm.h"
namespace RPiController {
class AfAlgorithm : public Algorithm
{
public:
AfAlgorithm(Controller *controller)
: Algorithm(controller) {}
/*
* An autofocus algorithm should provide the following calls.
*
* Where a ControlList combines a change of AfMode with other AF
* controls, setMode() should be called first, to ensure the
* algorithm will be in the correct state to handle controls.
*
* setLensPosition() returns true if the mode was AfModeManual and
* the lens position has changed, otherwise returns false. When it
* returns true, hwpos should be sent immediately to the lens driver.
*
* getMode() is provided mainly for validating controls.
* getLensPosition() is provided for populating DeviceStatus.
*/
enum AfRange { AfRangeNormal = 0,
AfRangeMacro,
AfRangeFull,
AfRangeMax };
enum AfSpeed { AfSpeedNormal = 0,
AfSpeedFast,
AfSpeedMax };
enum AfMode { AfModeManual = 0,
AfModeAuto,
AfModeContinuous };
enum AfPause { AfPauseImmediate = 0,
AfPauseDeferred,
AfPauseResume };
virtual void setRange([[maybe_unused]] AfRange range)
{
}
virtual void setSpeed([[maybe_unused]] AfSpeed speed)
{
}
virtual void setMetering([[maybe_unused]] bool use_windows)
{
}
virtual void setWindows([[maybe_unused]] libcamera::Span<libcamera::Rectangle const> const &wins)
{
}
virtual void setMode(AfMode mode) = 0;
virtual AfMode getMode() const = 0;
virtual bool setLensPosition(double dioptres, int32_t *hwpos) = 0;
virtual std::optional<double> getLensPosition() const = 0;
virtual void triggerScan() = 0;
virtual void cancelScan() = 0;
virtual void pause(AfPause pause) = 0;
};
} // namespace RPiController
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2022, Raspberry Pi Ltd
*
* af_status.h - AF control algorithm status
*/
#pragma once
#include <optional>
/*
* The AF algorithm should post the following structure into the image's
* "af.status" metadata. lensSetting should control the lens.
*/
enum class AfState {
Idle = 0,
Scanning,
Focused,
Failed
};
enum class AfPauseState {
Running = 0,
Pausing,
Paused
};
struct AfStatus {
/* state for reporting */
AfState state;
AfPauseState pauseState;
/* lensSetting should be sent to the lens driver, when valid */
std::optional<int> lensSetting;
};
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* agc_algorithm.h - AGC/AEC control algorithm interface
*/
#pragma once
#include <libcamera/base/utils.h>
#include "algorithm.h"
namespace RPiController {
class AgcAlgorithm : public Algorithm
{
public:
AgcAlgorithm(Controller *controller) : Algorithm(controller) {}
/* An AGC algorithm must provide the following: */
virtual unsigned int getConvergenceFrames() const = 0;
virtual void setEv(double ev) = 0;
virtual void setFlickerPeriod(libcamera::utils::Duration flickerPeriod) = 0;
virtual void setFixedShutter(libcamera::utils::Duration fixedShutter) = 0;
virtual void setMaxShutter(libcamera::utils::Duration maxShutter) = 0;
virtual void setFixedAnalogueGain(double fixedAnalogueGain) = 0;
virtual void setMeteringMode(std::string const &meteringModeName) = 0;
virtual void setExposureMode(std::string const &exposureModeName) = 0;
virtual void setConstraintMode(std::string const &contraintModeName) = 0;
virtual void enableAuto() = 0;
virtual void disableAuto() = 0;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* agc_status.h - AGC/AEC control algorithm status
*/
#pragma once
#include <libcamera/base/utils.h>
/*
* The AGC algorithm should post the following structure into the image's
* "agc.status" metadata.
*/
/*
* Note: total_exposure_value will be reported as zero until the algorithm has
* seen statistics and calculated meaningful values. The contents should be
* ignored until then.
*/
struct AgcStatus {
libcamera::utils::Duration totalExposureValue; /* value for all exposure and gain for this image */
libcamera::utils::Duration targetExposureValue; /* (unfiltered) target total exposure AGC is aiming for */
libcamera::utils::Duration shutterTime;
double analogueGain;
char exposureMode[32];
char constraintMode[32];
char meteringMode[32];
double ev;
libcamera::utils::Duration flickerPeriod;
int floatingRegionEnable;
libcamera::utils::Duration fixedShutter;
double fixedAnalogueGain;
double digitalGain;
int locked;
};
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* algorithm.cpp - ISP control algorithms
*/
#include "algorithm.h"
using namespace RPiController;
int Algorithm::read([[maybe_unused]] const libcamera::YamlObject &params)
{
return 0;
}
void Algorithm::initialise()
{
}
void Algorithm::switchMode([[maybe_unused]] CameraMode const &cameraMode,
[[maybe_unused]] Metadata *metadata)
{
}
void Algorithm::prepare([[maybe_unused]] Metadata *imageMetadata)
{
}
void Algorithm::process([[maybe_unused]] StatisticsPtr &stats,
[[maybe_unused]] Metadata *imageMetadata)
{
}
/* For registering algorithms with the system: */
namespace {
std::map<std::string, AlgoCreateFunc> &algorithms()
{
static std::map<std::string, AlgoCreateFunc> algorithms;
return algorithms;
}
} /* namespace */
std::map<std::string, AlgoCreateFunc> const &RPiController::getAlgorithms()
{
return algorithms();
}
RegisterAlgorithm::RegisterAlgorithm(char const *name,
AlgoCreateFunc createFunc)
{
algorithms()[std::string(name)] = createFunc;
}
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* algorithm.h - ISP control algorithm interface
*/
#pragma once
/*
* All algorithms should be derived from this class and made available to the
* Controller.
*/
#include <string>
#include <memory>
#include <map>
#include "libcamera/internal/yaml_parser.h"
#include "controller.h"
namespace RPiController {
/* This defines the basic interface for all control algorithms. */
class Algorithm
{
public:
Algorithm(Controller *controller)
: controller_(controller)
{
}
virtual ~Algorithm() = default;
virtual char const *name() const = 0;
virtual int read(const libcamera::YamlObject &params);
virtual void initialise();
virtual void switchMode(CameraMode const &cameraMode, Metadata *metadata);
virtual void prepare(Metadata *imageMetadata);
virtual void process(StatisticsPtr &stats, Metadata *imageMetadata);
Metadata &getGlobalMetadata() const
{
return controller_->getGlobalMetadata();
}
const std::string &getTarget() const
{
return controller_->getTarget();
}
const Controller::HardwareConfig &getHardwareConfig() const
{
return controller_->getHardwareConfig();
}
private:
Controller *controller_;
};
/*
* This code is for automatic registration of Front End algorithms with the
* system.
*/
typedef Algorithm *(*AlgoCreateFunc)(Controller *controller);
struct RegisterAlgorithm {
RegisterAlgorithm(char const *name, AlgoCreateFunc createFunc);
};
std::map<std::string, AlgoCreateFunc> const &getAlgorithms();
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* alsc_status.h - ALSC (auto lens shading correction) control algorithm status
*/
#pragma once
#include <vector>
/*
* The ALSC algorithm should post the following structure into the image's
* "alsc.status" metadata.
*/
struct AlscStatus {
std::vector<double> r;
std::vector<double> g;
std::vector<double> b;
unsigned int rows;
unsigned int cols;
};
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* awb_algorithm.h - AWB control algorithm interface
*/
#pragma once
#include "algorithm.h"
namespace RPiController {
class AwbAlgorithm : public Algorithm
{
public:
AwbAlgorithm(Controller *controller) : Algorithm(controller) {}
/* An AWB algorithm must provide the following: */
virtual unsigned int getConvergenceFrames() const = 0;
virtual void setMode(std::string const &modeName) = 0;
virtual void setManualGains(double manualR, double manualB) = 0;
virtual void enableAuto() = 0;
virtual void disableAuto() = 0;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* awb_status.h - AWB control algorithm status
*/
#pragma once
/*
* The AWB algorithm places its results into both the image and global metadata,
* under the tag "awb.status".
*/
struct AwbStatus {
char mode[32];
double temperatureK;
double gainR;
double gainG;
double gainB;
};
@@ -0,0 +1,15 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* black_level_status.h - black level control algorithm status
*/
#pragma once
/* The "black level" algorithm stores the black levels to use. */
struct BlackLevelStatus {
uint16_t blackLevelR; /* out of 16 bits */
uint16_t blackLevelG;
uint16_t blackLevelB;
};
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019-2020, Raspberry Pi Ltd
*
* camera_mode.h - description of a particular operating mode of a sensor
*/
#pragma once
#include <libcamera/transform.h>
#include <libcamera/base/utils.h>
/*
* Description of a "camera mode", holding enough information for control
* algorithms to adapt their behaviour to the different modes of the camera,
* including binning, scaling, cropping etc.
*/
struct CameraMode {
/* bit depth of the raw camera output */
uint32_t bitdepth;
/* size in pixels of frames in this mode */
uint16_t width;
uint16_t height;
/* size of full resolution uncropped frame ("sensor frame") */
uint16_t sensorWidth;
uint16_t sensorHeight;
/* binning factor (1 = no binning, 2 = 2-pixel binning etc.) */
uint8_t binX;
uint8_t binY;
/* location of top left pixel in the sensor frame */
uint16_t cropX;
uint16_t cropY;
/* scaling factor (so if uncropped, width*scaleX is sensorWidth) */
double scaleX;
double scaleY;
/* scaling of the noise compared to the native sensor mode */
double noiseFactor;
/* minimum and maximum line time and frame durations */
libcamera::utils::Duration minLineLength;
libcamera::utils::Duration maxLineLength;
libcamera::utils::Duration minFrameDuration;
libcamera::utils::Duration maxFrameDuration;
/* any camera transform *not* reflected already in the camera tuning */
libcamera::Transform transform;
/* minimum and maximum frame lengths in units of lines */
uint32_t minFrameLength;
uint32_t maxFrameLength;
/* sensitivity of this mode */
double sensitivity;
/* pixel clock rate */
uint64_t pixelRate;
/* Mode specific shutter speed limits */
libcamera::utils::Duration minShutter;
libcamera::utils::Duration maxShutter;
/* Mode specific analogue gain limits */
double minAnalogueGain;
double maxAnalogueGain;
};
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* ccm_algorithm.h - CCM (colour correction matrix) control algorithm interface
*/
#pragma once
#include "algorithm.h"
namespace RPiController {
class CcmAlgorithm : public Algorithm
{
public:
CcmAlgorithm(Controller *controller) : Algorithm(controller) {}
/* A CCM algorithm must provide the following: */
virtual void setSaturation(double saturation) = 0;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* ccm_status.h - CCM (colour correction matrix) control algorithm status
*/
#pragma once
/* The "ccm" algorithm generates an appropriate colour matrix. */
struct CcmStatus {
double matrix[9];
double saturation;
};
@@ -0,0 +1,22 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* contrast_algorithm.h - contrast (gamma) control algorithm interface
*/
#pragma once
#include "algorithm.h"
namespace RPiController {
class ContrastAlgorithm : public Algorithm
{
public:
ContrastAlgorithm(Controller *controller) : Algorithm(controller) {}
/* A contrast algorithm must provide the following: */
virtual void setBrightness(double brightness) = 0;
virtual void setContrast(double contrast) = 0;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* contrast_status.h - contrast (gamma) control algorithm status
*/
#pragma once
#include "pwl.h"
/*
* The "contrast" algorithm creates a gamma curve, optionally doing a little bit
* of contrast stretching based on the AGC histogram.
*/
struct ContrastStatus {
RPiController::Pwl gammaCurve;
double brightness;
double contrast;
};
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* controller.cpp - ISP controller
*/
#include <assert.h>
#include <libcamera/base/file.h>
#include <libcamera/base/log.h>
#include "libcamera/internal/yaml_parser.h"
#include "algorithm.h"
#include "controller.h"
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiController)
static const std::map<std::string, Controller::HardwareConfig> HardwareConfigMap = {
{
"bcm2835",
{
/*
* There are only ever 15 AGC regions computed by the firmware
* due to zoning, but the HW defines AGC_REGIONS == 16!
*/
.agcRegions = { 15 , 1 },
.agcZoneWeights = { 15 , 1 },
.awbRegions = { 16, 12 },
.focusRegions = { 4, 3 },
.numHistogramBins = 128,
.numGammaPoints = 33,
.pipelineWidth = 13
}
},
};
Controller::Controller()
: switchModeCalled_(false)
{
}
Controller::~Controller() {}
int Controller::read(char const *filename)
{
File file(filename);
if (!file.open(File::OpenModeFlag::ReadOnly)) {
LOG(RPiController, Warning)
<< "Failed to open tuning file '" << filename << "'";
return -EINVAL;
}
std::unique_ptr<YamlObject> root = YamlParser::parse(file);
double version = (*root)["version"].get<double>(1.0);
target_ = (*root)["target"].get<std::string>("bcm2835");
if (version < 2.0) {
LOG(RPiController, Warning)
<< "This format of the tuning file will be deprecated soon!"
<< " Please use the convert_tuning.py utility to update to version 2.0.";
for (auto const &[key, value] : root->asDict()) {
int ret = createAlgorithm(key, value);
if (ret)
return ret;
}
} else if (version < 3.0) {
if (!root->contains("algorithms")) {
LOG(RPiController, Error)
<< "Tuning file " << filename
<< " does not have an \"algorithms\" list!";
return -EINVAL;
}
for (auto const &rootAlgo : (*root)["algorithms"].asList())
for (auto const &[key, value] : rootAlgo.asDict()) {
int ret = createAlgorithm(key, value);
if (ret)
return ret;
}
} else {
LOG(RPiController, Error)
<< "Unrecognised version " << version
<< " for the tuning file " << filename;
return -EINVAL;
}
return 0;
}
int Controller::createAlgorithm(const std::string &name, const YamlObject &params)
{
auto it = getAlgorithms().find(name);
if (it == getAlgorithms().end()) {
LOG(RPiController, Warning)
<< "No algorithm found for \"" << name << "\"";
return 0;
}
Algorithm *algo = (*it->second)(this);
int ret = algo->read(params);
if (ret)
return ret;
algorithms_.push_back(AlgorithmPtr(algo));
return 0;
}
void Controller::initialise()
{
for (auto &algo : algorithms_)
algo->initialise();
}
void Controller::switchMode(CameraMode const &cameraMode, Metadata *metadata)
{
for (auto &algo : algorithms_)
algo->switchMode(cameraMode, metadata);
switchModeCalled_ = true;
}
void Controller::prepare(Metadata *imageMetadata)
{
assert(switchModeCalled_);
for (auto &algo : algorithms_)
algo->prepare(imageMetadata);
}
void Controller::process(StatisticsPtr stats, Metadata *imageMetadata)
{
assert(switchModeCalled_);
for (auto &algo : algorithms_)
algo->process(stats, imageMetadata);
}
Metadata &Controller::getGlobalMetadata()
{
return globalMetadata_;
}
Algorithm *Controller::getAlgorithm(std::string const &name) const
{
/*
* The passed name must be the entire algorithm name, or must match the
* last part of it with a period (.) just before.
*/
size_t nameLen = name.length();
for (auto &algo : algorithms_) {
char const *algoName = algo->name();
size_t algoNameLen = strlen(algoName);
if (algoNameLen >= nameLen &&
strcasecmp(name.c_str(),
algoName + algoNameLen - nameLen) == 0 &&
(nameLen == algoNameLen ||
algoName[algoNameLen - nameLen - 1] == '.'))
return algo.get();
}
return nullptr;
}
const std::string &Controller::getTarget() const
{
return target_;
}
const Controller::HardwareConfig &Controller::getHardwareConfig() const
{
auto cfg = HardwareConfigMap.find(getTarget());
/*
* This really should not happen, the IPA ought to validate the target
* on initialisation.
*/
ASSERT(cfg != HardwareConfigMap.end());
return cfg->second;
}
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* controller.h - ISP controller interface
*/
#pragma once
/*
* The Controller is simply a container for a collecting together a number of
* "control algorithms" (such as AWB etc.) and for running them all in a
* convenient manner.
*/
#include <vector>
#include <string>
#include "libcamera/internal/yaml_parser.h"
#include "camera_mode.h"
#include "device_status.h"
#include "metadata.h"
#include "statistics.h"
namespace RPiController {
class Algorithm;
typedef std::unique_ptr<Algorithm> AlgorithmPtr;
/*
* The Controller holds a pointer to some global_metadata, which is how
* different controllers and control algorithms within them can exchange
* information. The Prepare function returns a pointer to metadata for this
* specific image, and which should be passed on to the Process function.
*/
class Controller
{
public:
struct HardwareConfig {
libcamera::Size agcRegions;
libcamera::Size agcZoneWeights;
libcamera::Size awbRegions;
libcamera::Size focusRegions;
unsigned int numHistogramBins;
unsigned int numGammaPoints;
unsigned int pipelineWidth;
};
Controller();
~Controller();
int read(char const *filename);
void initialise();
void switchMode(CameraMode const &cameraMode, Metadata *metadata);
void prepare(Metadata *imageMetadata);
void process(StatisticsPtr stats, Metadata *imageMetadata);
Metadata &getGlobalMetadata();
Algorithm *getAlgorithm(std::string const &name) const;
const std::string &getTarget() const;
const HardwareConfig &getHardwareConfig() const;
protected:
int createAlgorithm(const std::string &name, const libcamera::YamlObject &params);
Metadata globalMetadata_;
std::vector<AlgorithmPtr> algorithms_;
bool switchModeCalled_;
private:
std::string target_;
};
} /* namespace RPiController */
@@ -0,0 +1,23 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2021, Raspberry Pi Ltd
*
* denoise.h - Denoise control algorithm interface
*/
#pragma once
#include "algorithm.h"
namespace RPiController {
enum class DenoiseMode { Off, ColourOff, ColourFast, ColourHighQuality };
class DenoiseAlgorithm : public Algorithm
{
public:
DenoiseAlgorithm(Controller *controller) : Algorithm(controller) {}
/* A Denoise algorithm must provide the following: */
virtual void setMode(DenoiseMode mode) = 0;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019-2021, Raspberry Pi Ltd
*
* denoise_status.h - Denoise control algorithm status
*/
#pragma once
/* This stores the parameters required for Denoise. */
struct DenoiseStatus {
double noiseConstant;
double noiseSlope;
double strength;
unsigned int mode;
};
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2021, Raspberry Pi Ltd
*
* device_status.cpp - device (image sensor) status
*/
#include "device_status.h"
using namespace libcamera; /* for the Duration operator<< overload */
std::ostream &operator<<(std::ostream &out, const DeviceStatus &d)
{
out << "Exposure: " << d.shutterSpeed
<< " Frame length: " << d.frameLength
<< " Line length: " << d.lineLength
<< " Gain: " << d.analogueGain;
if (d.aperture)
out << " Aperture: " << *d.aperture;
if (d.lensPosition)
out << " Lens: " << *d.lensPosition;
if (d.flashIntensity)
out << " Flash: " << *d.flashIntensity;
if (d.sensorTemperature)
out << " Temperature: " << *d.sensorTemperature;
return out;
}
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019-2021, Raspberry Pi Ltd
*
* device_status.h - device (image sensor) status
*/
#pragma once
#include <iostream>
#include <optional>
#include <libcamera/base/utils.h>
/*
* Definition of "device metadata" which stores things like shutter time and
* analogue gain that downstream control algorithms will want to know.
*/
struct DeviceStatus {
DeviceStatus()
: shutterSpeed(std::chrono::seconds(0)), frameLength(0),
lineLength(std::chrono::seconds(0)), analogueGain(0.0)
{
}
friend std::ostream &operator<<(std::ostream &out, const DeviceStatus &d);
/* time shutter is open */
libcamera::utils::Duration shutterSpeed;
/* frame length given in number of lines */
uint32_t frameLength;
/* line length for the current frame */
libcamera::utils::Duration lineLength;
double analogueGain;
/* 1.0/distance-in-metres */
std::optional<double> lensPosition;
/* 1/f so that brightness quadruples when this doubles */
std::optional<double> aperture;
/* proportional to brightness with 0 = no flash, 1 = maximum flash */
std::optional<double> flashIntensity;
/* Sensor reported temperature value (in degrees) */
std::optional<double> sensorTemperature;
};
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* dpc_status.h - DPC (defective pixel correction) control algorithm status
*/
#pragma once
/* The "DPC" algorithm sets defective pixel correction strength. */
struct DpcStatus {
int strength; /* 0 = "off", 1 = "normal", 2 = "strong" */
};
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* geq_status.h - GEQ (green equalisation) control algorithm status
*/
#pragma once
/* The "GEQ" algorithm calculates the green equalisation thresholds */
struct GeqStatus {
uint16_t offset;
double slope;
};
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* histogram.cpp - histogram calculations
*/
#include <math.h>
#include <stdio.h>
#include "histogram.h"
using namespace RPiController;
uint64_t Histogram::cumulativeFreq(double bin) const
{
if (bin <= 0)
return 0;
else if (bin >= bins())
return total();
int b = (int)bin;
return cumulative_[b] +
(bin - b) * (cumulative_[b + 1] - cumulative_[b]);
}
double Histogram::quantile(double q, int first, int last) const
{
if (first == -1)
first = 0;
if (last == -1)
last = cumulative_.size() - 2;
assert(first <= last);
uint64_t items = q * total();
while (first < last) /* binary search to find the right bin */
{
int middle = (first + last) / 2;
if (cumulative_[middle + 1] > items)
last = middle; /* between first and middle */
else
first = middle + 1; /* after middle */
}
assert(items >= cumulative_[first] && items <= cumulative_[last + 1]);
double frac = cumulative_[first + 1] == cumulative_[first] ? 0
: (double)(items - cumulative_[first]) /
(cumulative_[first + 1] - cumulative_[first]);
return first + frac;
}
double Histogram::interQuantileMean(double qLo, double qHi) const
{
assert(qHi > qLo);
double pLo = quantile(qLo);
double pHi = quantile(qHi, (int)pLo);
double sumBinFreq = 0, cumulFreq = 0;
for (double pNext = floor(pLo) + 1.0; pNext <= ceil(pHi);
pLo = pNext, pNext += 1.0) {
int bin = floor(pLo);
double freq = (cumulative_[bin + 1] - cumulative_[bin]) *
(std::min(pNext, pHi) - pLo);
sumBinFreq += bin * freq;
cumulFreq += freq;
}
/* add 0.5 to give an average for bin mid-points */
return sumBinFreq / cumulFreq + 0.5;
}
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* histogram.h - histogram calculation interface
*/
#pragma once
#include <stdint.h>
#include <vector>
#include <cassert>
/*
* A simple histogram class, for use in particular to find "quantiles" and
* averages between "quantiles".
*/
namespace RPiController {
class Histogram
{
public:
Histogram()
{
cumulative_.push_back(0);
}
template<typename T> Histogram(T *histogram, int num)
{
assert(num);
cumulative_.reserve(num + 1);
cumulative_.push_back(0);
for (int i = 0; i < num; i++)
cumulative_.push_back(cumulative_.back() +
histogram[i]);
}
uint32_t bins() const { return cumulative_.size() - 1; }
uint64_t total() const { return cumulative_[cumulative_.size() - 1]; }
/* Cumulative frequency up to a (fractional) point in a bin. */
uint64_t cumulativeFreq(double bin) const;
/*
* Return the (fractional) bin of the point q (0 <= q <= 1) through the
* histogram. Optionally provide limits to help.
*/
double quantile(double q, int first = -1, int last = -1) const;
/* Return the average histogram bin value between the two quantiles. */
double interQuantileMean(double qLo, double qHi) const;
private:
std::vector<uint64_t> cumulative_;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* lux_status.h - Lux control algorithm status
*/
#pragma once
/*
* The "lux" algorithm looks at the (AGC) histogram statistics of the frame and
* estimates the current lux level of the scene. It does this by a simple ratio
* calculation comparing to a reference image that was taken in known conditions
* with known statistics and a properly measured lux level. There is a slight
* problem with aperture, in that it may be variable without the system knowing
* or being aware of it. In this case an external application may set a
* "current_aperture" value if it wishes, which would be used in place of the
* (presumably meaningless) value in the image metadata.
*/
struct LuxStatus {
double lux;
double aperture;
};
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# SPDX-License-Identifier: CC0-1.0
rpi_ipa_controller_sources = files([
'algorithm.cpp',
'controller.cpp',
'device_status.cpp',
'histogram.cpp',
'pwl.cpp',
'rpi/af.cpp',
'rpi/agc.cpp',
'rpi/alsc.cpp',
'rpi/awb.cpp',
'rpi/black_level.cpp',
'rpi/ccm.cpp',
'rpi/contrast.cpp',
'rpi/dpc.cpp',
'rpi/geq.cpp',
'rpi/lux.cpp',
'rpi/noise.cpp',
'rpi/sdn.cpp',
'rpi/sharpen.cpp',
])
rpi_ipa_controller_deps = [
libcamera_private,
]
rpi_ipa_controller_lib = static_library('rpi_ipa_controller', rpi_ipa_controller_sources,
dependencies : rpi_ipa_controller_deps)
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019-2021, Raspberry Pi Ltd
*
* metadata.h - general metadata class
*/
#pragma once
/* A simple class for carrying arbitrary metadata, for example about an image. */
#include <any>
#include <map>
#include <mutex>
#include <string>
#include <libcamera/base/thread_annotations.h>
namespace RPiController {
class LIBCAMERA_TSA_CAPABILITY("mutex") Metadata
{
public:
Metadata() = default;
Metadata(Metadata const &other)
{
std::scoped_lock otherLock(other.mutex_);
data_ = other.data_;
}
Metadata(Metadata &&other)
{
std::scoped_lock otherLock(other.mutex_);
data_ = std::move(other.data_);
other.data_.clear();
}
template<typename T>
void set(std::string const &tag, T const &value)
{
std::scoped_lock lock(mutex_);
data_[tag] = value;
}
template<typename T>
int get(std::string const &tag, T &value) const
{
std::scoped_lock lock(mutex_);
auto it = data_.find(tag);
if (it == data_.end())
return -1;
value = std::any_cast<T>(it->second);
return 0;
}
void clear()
{
std::scoped_lock lock(mutex_);
data_.clear();
}
Metadata &operator=(Metadata const &other)
{
std::scoped_lock lock(mutex_, other.mutex_);
data_ = other.data_;
return *this;
}
Metadata &operator=(Metadata &&other)
{
std::scoped_lock lock(mutex_, other.mutex_);
data_ = std::move(other.data_);
other.data_.clear();
return *this;
}
void merge(Metadata &other)
{
std::scoped_lock lock(mutex_, other.mutex_);
data_.merge(other.data_);
}
void mergeCopy(const Metadata &other)
{
std::scoped_lock lock(mutex_, other.mutex_);
/*
* If the metadata key exists, ignore this item and copy only
* unique key/value pairs.
*/
data_.insert(other.data_.begin(), other.data_.end());
}
template<typename T>
T *getLocked(std::string const &tag)
{
/*
* This allows in-place access to the Metadata contents,
* for which you should be holding the lock.
*/
auto it = data_.find(tag);
if (it == data_.end())
return nullptr;
return std::any_cast<T>(&it->second);
}
template<typename T>
void setLocked(std::string const &tag, T const &value)
{
/* Use this only if you're holding the lock yourself. */
data_[tag] = value;
}
/*
* Note: use of (lowercase) lock and unlock means you can create scoped
* locks with the standard lock classes.
* e.g. std::lock_guard<RPiController::Metadata> lock(metadata)
*/
void lock() LIBCAMERA_TSA_ACQUIRE() { mutex_.lock(); }
void unlock() LIBCAMERA_TSA_RELEASE() { mutex_.unlock(); }
private:
mutable std::mutex mutex_;
std::map<std::string, std::any> data_;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* noise_status.h - Noise control algorithm status
*/
#pragma once
/* The "noise" algorithm stores an estimate of the noise profile for this image. */
struct NoiseStatus {
double noiseConstant;
double noiseSlope;
};
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2022, Raspberry Pi Ltd
*
* pdaf_data.h - PDAF Metadata
*/
#pragma once
#include <stdint.h>
#include "region_stats.h"
namespace RPiController {
struct PdafData {
/* Confidence, in arbitrary units */
uint16_t conf;
/* Phase error, in s16 Q4 format (S.11.4) */
int16_t phase;
};
using PdafRegions = RegionStats<PdafData>;
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* pwl.cpp - piecewise linear functions
*/
#include <cassert>
#include <cmath>
#include <stdexcept>
#include "pwl.h"
using namespace RPiController;
int Pwl::read(const libcamera::YamlObject &params)
{
if (!params.size() || params.size() % 2)
return -EINVAL;
const auto &list = params.asList();
for (auto it = list.begin(); it != list.end(); it++) {
auto x = it->get<double>();
if (!x)
return -EINVAL;
if (it != list.begin() && *x <= points_.back().x)
return -EINVAL;
auto y = (++it)->get<double>();
if (!y)
return -EINVAL;
points_.push_back(Point(*x, *y));
}
return 0;
}
void Pwl::append(double x, double y, const double eps)
{
if (points_.empty() || points_.back().x + eps < x)
points_.push_back(Point(x, y));
}
void Pwl::prepend(double x, double y, const double eps)
{
if (points_.empty() || points_.front().x - eps > x)
points_.insert(points_.begin(), Point(x, y));
}
Pwl::Interval Pwl::domain() const
{
return Interval(points_[0].x, points_[points_.size() - 1].x);
}
Pwl::Interval Pwl::range() const
{
double lo = points_[0].y, hi = lo;
for (auto &p : points_)
lo = std::min(lo, p.y), hi = std::max(hi, p.y);
return Interval(lo, hi);
}
bool Pwl::empty() const
{
return points_.empty();
}
double Pwl::eval(double x, int *spanPtr, bool updateSpan) const
{
int span = findSpan(x, spanPtr && *spanPtr != -1 ? *spanPtr : points_.size() / 2 - 1);
if (spanPtr && updateSpan)
*spanPtr = span;
return points_[span].y +
(x - points_[span].x) * (points_[span + 1].y - points_[span].y) /
(points_[span + 1].x - points_[span].x);
}
int Pwl::findSpan(double x, int span) const
{
/*
* Pwls are generally small, so linear search may well be faster than
* binary, though could review this if large PWls start turning up.
*/
int lastSpan = points_.size() - 2;
/*
* some algorithms may call us with span pointing directly at the last
* control point
*/
span = std::max(0, std::min(lastSpan, span));
while (span < lastSpan && x >= points_[span + 1].x)
span++;
while (span && x < points_[span].x)
span--;
return span;
}
Pwl::PerpType Pwl::invert(Point const &xy, Point &perp, int &span,
const double eps) const
{
assert(span >= -1);
bool prevOffEnd = false;
for (span = span + 1; span < (int)points_.size() - 1; span++) {
Point spanVec = points_[span + 1] - points_[span];
double t = ((xy - points_[span]) % spanVec) / spanVec.len2();
if (t < -eps) /* off the start of this span */
{
if (span == 0) {
perp = points_[span];
return PerpType::Start;
} else if (prevOffEnd) {
perp = points_[span];
return PerpType::Vertex;
}
} else if (t > 1 + eps) /* off the end of this span */
{
if (span == (int)points_.size() - 2) {
perp = points_[span + 1];
return PerpType::End;
}
prevOffEnd = true;
} else /* a true perpendicular */
{
perp = points_[span] + spanVec * t;
return PerpType::Perpendicular;
}
}
return PerpType::None;
}
Pwl Pwl::inverse(bool *trueInverse, const double eps) const
{
bool appended = false, prepended = false, neither = false;
Pwl inverse;
for (Point const &p : points_) {
if (inverse.empty())
inverse.append(p.y, p.x, eps);
else if (std::abs(inverse.points_.back().x - p.y) <= eps ||
std::abs(inverse.points_.front().x - p.y) <= eps)
/* do nothing */;
else if (p.y > inverse.points_.back().x) {
inverse.append(p.y, p.x, eps);
appended = true;
} else if (p.y < inverse.points_.front().x) {
inverse.prepend(p.y, p.x, eps);
prepended = true;
} else
neither = true;
}
/*
* This is not a proper inverse if we found ourselves putting points
* onto both ends of the inverse, or if there were points that couldn't
* go on either.
*/
if (trueInverse)
*trueInverse = !(neither || (appended && prepended));
return inverse;
}
Pwl Pwl::compose(Pwl const &other, const double eps) const
{
double thisX = points_[0].x, thisY = points_[0].y;
int thisSpan = 0, otherSpan = other.findSpan(thisY, 0);
Pwl result({ { thisX, other.eval(thisY, &otherSpan, false) } });
while (thisSpan != (int)points_.size() - 1) {
double dx = points_[thisSpan + 1].x - points_[thisSpan].x,
dy = points_[thisSpan + 1].y - points_[thisSpan].y;
if (std::abs(dy) > eps &&
otherSpan + 1 < (int)other.points_.size() &&
points_[thisSpan + 1].y >=
other.points_[otherSpan + 1].x + eps) {
/*
* next control point in result will be where this
* function's y reaches the next span in other
*/
thisX = points_[thisSpan].x +
(other.points_[otherSpan + 1].x -
points_[thisSpan].y) *
dx / dy;
thisY = other.points_[++otherSpan].x;
} else if (std::abs(dy) > eps && otherSpan > 0 &&
points_[thisSpan + 1].y <=
other.points_[otherSpan - 1].x - eps) {
/*
* next control point in result will be where this
* function's y reaches the previous span in other
*/
thisX = points_[thisSpan].x +
(other.points_[otherSpan + 1].x -
points_[thisSpan].y) *
dx / dy;
thisY = other.points_[--otherSpan].x;
} else {
/* we stay in the same span in other */
thisSpan++;
thisX = points_[thisSpan].x,
thisY = points_[thisSpan].y;
}
result.append(thisX, other.eval(thisY, &otherSpan, false),
eps);
}
return result;
}
void Pwl::map(std::function<void(double x, double y)> f) const
{
for (auto &pt : points_)
f(pt.x, pt.y);
}
void Pwl::map2(Pwl const &pwl0, Pwl const &pwl1,
std::function<void(double x, double y0, double y1)> f)
{
int span0 = 0, span1 = 0;
double x = std::min(pwl0.points_[0].x, pwl1.points_[0].x);
f(x, pwl0.eval(x, &span0, false), pwl1.eval(x, &span1, false));
while (span0 < (int)pwl0.points_.size() - 1 ||
span1 < (int)pwl1.points_.size() - 1) {
if (span0 == (int)pwl0.points_.size() - 1)
x = pwl1.points_[++span1].x;
else if (span1 == (int)pwl1.points_.size() - 1)
x = pwl0.points_[++span0].x;
else if (pwl0.points_[span0 + 1].x > pwl1.points_[span1 + 1].x)
x = pwl1.points_[++span1].x;
else
x = pwl0.points_[++span0].x;
f(x, pwl0.eval(x, &span0, false), pwl1.eval(x, &span1, false));
}
}
Pwl Pwl::combine(Pwl const &pwl0, Pwl const &pwl1,
std::function<double(double x, double y0, double y1)> f,
const double eps)
{
Pwl result;
map2(pwl0, pwl1, [&](double x, double y0, double y1) {
result.append(x, f(x, y0, y1), eps);
});
return result;
}
void Pwl::matchDomain(Interval const &domain, bool clip, const double eps)
{
int span = 0;
prepend(domain.start, eval(clip ? points_[0].x : domain.start, &span),
eps);
span = points_.size() - 2;
append(domain.end, eval(clip ? points_.back().x : domain.end, &span),
eps);
}
Pwl &Pwl::operator*=(double d)
{
for (auto &pt : points_)
pt.y *= d;
return *this;
}
void Pwl::debug(FILE *fp) const
{
fprintf(fp, "Pwl {\n");
for (auto &p : points_)
fprintf(fp, "\t(%g, %g)\n", p.x, p.y);
fprintf(fp, "}\n");
}
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* pwl.h - piecewise linear functions interface
*/
#pragma once
#include <functional>
#include <math.h>
#include <vector>
#include "libcamera/internal/yaml_parser.h"
namespace RPiController {
class Pwl
{
public:
struct Interval {
Interval(double _start, double _end)
: start(_start), end(_end)
{
}
double start, end;
bool contains(double value)
{
return value >= start && value <= end;
}
double clip(double value)
{
return value < start ? start
: (value > end ? end : value);
}
double len() const { return end - start; }
};
struct Point {
Point() : x(0), y(0) {}
Point(double _x, double _y)
: x(_x), y(_y) {}
double x, y;
Point operator-(Point const &p) const
{
return Point(x - p.x, y - p.y);
}
Point operator+(Point const &p) const
{
return Point(x + p.x, y + p.y);
}
double operator%(Point const &p) const
{
return x * p.x + y * p.y;
}
Point operator*(double f) const { return Point(x * f, y * f); }
Point operator/(double f) const { return Point(x / f, y / f); }
double len2() const { return x * x + y * y; }
double len() const { return sqrt(len2()); }
};
Pwl() {}
Pwl(std::vector<Point> const &points) : points_(points) {}
int read(const libcamera::YamlObject &params);
void append(double x, double y, const double eps = 1e-6);
void prepend(double x, double y, const double eps = 1e-6);
Interval domain() const;
Interval range() const;
bool empty() const;
/*
* Evaluate Pwl, optionally supplying an initial guess for the
* "span". The "span" may be optionally be updated. If you want to know
* the "span" value but don't have an initial guess you can set it to
* -1.
*/
double eval(double x, int *spanPtr = nullptr,
bool updateSpan = true) const;
/*
* Find perpendicular closest to xy, starting from span+1 so you can
* call it repeatedly to check for multiple closest points (set span to
* -1 on the first call). Also returns "pseudo" perpendiculars; see
* PerpType enum.
*/
enum class PerpType {
None, /* no perpendicular found */
Start, /* start of Pwl is closest point */
End, /* end of Pwl is closest point */
Vertex, /* vertex of Pwl is closest point */
Perpendicular /* true perpendicular found */
};
PerpType invert(Point const &xy, Point &perp, int &span,
const double eps = 1e-6) const;
/*
* Compute the inverse function. Indicate if it is a proper (true)
* inverse, or only a best effort (e.g. input was non-monotonic).
*/
Pwl inverse(bool *trueInverse = nullptr, const double eps = 1e-6) const;
/* Compose two Pwls together, doing "this" first and "other" after. */
Pwl compose(Pwl const &other, const double eps = 1e-6) const;
/* Apply function to (x,y) values at every control point. */
void map(std::function<void(double x, double y)> f) const;
/*
* Apply function to (x, y0, y1) values wherever either Pwl has a
* control point.
*/
static void map2(Pwl const &pwl0, Pwl const &pwl1,
std::function<void(double x, double y0, double y1)> f);
/*
* Combine two Pwls, meaning we create a new Pwl where the y values are
* given by running f wherever either has a knot.
*/
static Pwl
combine(Pwl const &pwl0, Pwl const &pwl1,
std::function<double(double x, double y0, double y1)> f,
const double eps = 1e-6);
/*
* Make "this" match (at least) the given domain. Any extension my be
* clipped or linear.
*/
void matchDomain(Interval const &domain, bool clip = true,
const double eps = 1e-6);
Pwl &operator*=(double d);
void debug(FILE *fp = stdout) const;
private:
int findSpan(double x, int span) const;
std::vector<Point> points_;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2022, Raspberry Pi Ltd
*
* region_stats.h - Raspberry Pi region based statistics container
*/
#pragma once
#include <array>
#include <stdint.h>
#include <vector>
#include <libcamera/geometry.h>
namespace RPiController {
template<typename T>
class RegionStats
{
public:
struct Region {
T val;
uint32_t counted;
uint32_t uncounted;
};
RegionStats()
: size_({}), numFloating_(0), default_({})
{
}
void init(const libcamera::Size &size, unsigned int numFloating = 0)
{
size_ = size;
numFloating_ = numFloating;
regions_.clear();
regions_.resize(size_.width * size_.height + numFloating_);
}
void init(unsigned int num)
{
size_ = libcamera::Size(num, 1);
numFloating_ = 0;
regions_.clear();
regions_.resize(num);
}
unsigned int numRegions() const
{
return size_.width * size_.height;
}
unsigned int numFloatingRegions() const
{
return numFloating_;
}
libcamera::Size size() const
{
return size_;
}
void set(unsigned int index, const Region &region)
{
if (index >= numRegions())
return;
set_(index, region);
}
void set(const libcamera::Point &pos, const Region &region)
{
set(pos.y * size_.width + pos.x, region);
}
void setFloating(unsigned int index, const Region &region)
{
if (index >= numFloatingRegions())
return;
set(numRegions() + index, region);
}
const Region &get(unsigned int index) const
{
if (index >= numRegions())
return default_;
return get_(index);
}
const Region &get(const libcamera::Point &pos) const
{
return get(pos.y * size_.width + pos.x);
}
const Region &getFloating(unsigned int index) const
{
if (index >= numFloatingRegions())
return default_;
return get_(numRegions() + index);
}
typename std::vector<Region>::iterator begin() { return regions_.begin(); }
typename std::vector<Region>::iterator end() { return regions_.end(); }
typename std::vector<Region>::const_iterator begin() const { return regions_.begin(); }
typename std::vector<Region>::const_iterator end() const { return regions_.end(); }
private:
void set_(unsigned int index, const Region &region)
{
regions_[index] = region;
}
const Region &get_(unsigned int index) const
{
return regions_[index];
}
libcamera::Size size_;
unsigned int numFloating_;
std::vector<Region> regions_;
Region default_;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2022-2023, Raspberry Pi Ltd
*
* af.cpp - Autofocus control algorithm
*/
#include "af.h"
#include <iomanip>
#include <math.h>
#include <stdlib.h>
#include <libcamera/base/log.h>
#include <libcamera/control_ids.h>
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiAf)
#define NAME "rpi.af"
/*
* Default values for parameters. All may be overridden in the tuning file.
* Many of these values are sensor- or module-dependent; the defaults here
* assume IMX708 in a Raspberry Pi V3 camera with the standard lens.
*
* Here all focus values are in dioptres (1/m). They are converted to hardware
* units when written to status.lensSetting or returned from setLensPosition().
*
* Gain and delay values are relative to the update rate, since much (not all)
* of the delay is in the sensor and (for CDAF) ISP, not the lens mechanism;
* but note that algorithms are updated at no more than 30 Hz.
*/
Af::RangeDependentParams::RangeDependentParams()
: focusMin(0.0),
focusMax(12.0),
focusDefault(1.0)
{
}
Af::SpeedDependentParams::SpeedDependentParams()
: stepCoarse(1.0),
stepFine(0.25),
contrastRatio(0.75),
pdafGain(-0.02),
pdafSquelch(0.125),
maxSlew(2.0),
pdafFrames(20),
dropoutFrames(6),
stepFrames(4)
{
}
Af::CfgParams::CfgParams()
: confEpsilon(8),
confThresh(16),
confClip(512),
skipFrames(5),
map()
{
}
template<typename T>
static void readNumber(T &dest, const libcamera::YamlObject &params, char const *name)
{
auto value = params[name].get<T>();
if (value)
dest = *value;
else
LOG(RPiAf, Warning) << "Missing parameter \"" << name << "\"";
}
void Af::RangeDependentParams::read(const libcamera::YamlObject &params)
{
readNumber<double>(focusMin, params, "min");
readNumber<double>(focusMax, params, "max");
readNumber<double>(focusDefault, params, "default");
}
void Af::SpeedDependentParams::read(const libcamera::YamlObject &params)
{
readNumber<double>(stepCoarse, params, "step_coarse");
readNumber<double>(stepFine, params, "step_fine");
readNumber<double>(contrastRatio, params, "contrast_ratio");
readNumber<double>(pdafGain, params, "pdaf_gain");
readNumber<double>(pdafSquelch, params, "pdaf_squelch");
readNumber<double>(maxSlew, params, "max_slew");
readNumber<uint32_t>(pdafFrames, params, "pdaf_frames");
readNumber<uint32_t>(dropoutFrames, params, "dropout_frames");
readNumber<uint32_t>(stepFrames, params, "step_frames");
}
int Af::CfgParams::read(const libcamera::YamlObject &params)
{
if (params.contains("ranges")) {
auto &rr = params["ranges"];
if (rr.contains("normal"))
ranges[AfRangeNormal].read(rr["normal"]);
else
LOG(RPiAf, Warning) << "Missing range \"normal\"";
ranges[AfRangeMacro] = ranges[AfRangeNormal];
if (rr.contains("macro"))
ranges[AfRangeMacro].read(rr["macro"]);
ranges[AfRangeFull].focusMin = std::min(ranges[AfRangeNormal].focusMin,
ranges[AfRangeMacro].focusMin);
ranges[AfRangeFull].focusMax = std::max(ranges[AfRangeNormal].focusMax,
ranges[AfRangeMacro].focusMax);
ranges[AfRangeFull].focusDefault = ranges[AfRangeNormal].focusDefault;
if (rr.contains("full"))
ranges[AfRangeFull].read(rr["full"]);
} else
LOG(RPiAf, Warning) << "No ranges defined";
if (params.contains("speeds")) {
auto &ss = params["speeds"];
if (ss.contains("normal"))
speeds[AfSpeedNormal].read(ss["normal"]);
else
LOG(RPiAf, Warning) << "Missing speed \"normal\"";
speeds[AfSpeedFast] = speeds[AfSpeedNormal];
if (ss.contains("fast"))
speeds[AfSpeedFast].read(ss["fast"]);
} else
LOG(RPiAf, Warning) << "No speeds defined";
readNumber<uint32_t>(confEpsilon, params, "conf_epsilon");
readNumber<uint32_t>(confThresh, params, "conf_thresh");
readNumber<uint32_t>(confClip, params, "conf_clip");
readNumber<uint32_t>(skipFrames, params, "skip_frames");
if (params.contains("map"))
map.read(params["map"]);
else
LOG(RPiAf, Warning) << "No map defined";
return 0;
}
void Af::CfgParams::initialise()
{
if (map.empty()) {
/* Default mapping from dioptres to hardware setting */
static constexpr double DefaultMapX0 = 0.0;
static constexpr double DefaultMapY0 = 445.0;
static constexpr double DefaultMapX1 = 15.0;
static constexpr double DefaultMapY1 = 925.0;
map.append(DefaultMapX0, DefaultMapY0);
map.append(DefaultMapX1, DefaultMapY1);
}
}
/* Af Algorithm class */
static constexpr unsigned MaxWindows = 10;
Af::Af(Controller *controller)
: AfAlgorithm(controller),
cfg_(),
range_(AfRangeNormal),
speed_(AfSpeedNormal),
mode_(AfAlgorithm::AfModeManual),
pauseFlag_(false),
statsRegion_(0, 0, 0, 0),
windows_(),
useWindows_(false),
phaseWeights_(),
contrastWeights_(),
scanState_(ScanState::Idle),
initted_(false),
ftarget_(-1.0),
fsmooth_(-1.0),
prevContrast_(0.0),
skipCount_(0),
stepCount_(0),
dropCount_(0),
scanMaxContrast_(0.0),
scanMinContrast_(1.0e9),
scanData_(),
reportState_(AfState::Idle)
{
/*
* Reserve space for data, to reduce memory fragmentation. It's too early
* to query the size of the PDAF (from camera) and Contrast (from ISP)
* statistics, but these are plausible upper bounds.
*/
phaseWeights_.w.reserve(16 * 12);
contrastWeights_.w.reserve(getHardwareConfig().focusRegions.width *
getHardwareConfig().focusRegions.height);
scanData_.reserve(32);
}
Af::~Af()
{
}
char const *Af::name() const
{
return NAME;
}
int Af::read(const libcamera::YamlObject &params)
{
return cfg_.read(params);
}
void Af::initialise()
{
cfg_.initialise();
}
void Af::switchMode(CameraMode const &cameraMode, [[maybe_unused]] Metadata *metadata)
{
(void)metadata;
/* Assume that PDAF and Focus stats grids cover the visible area */
statsRegion_.x = (int)cameraMode.cropX;
statsRegion_.y = (int)cameraMode.cropY;
statsRegion_.width = (unsigned)(cameraMode.width * cameraMode.scaleX);
statsRegion_.height = (unsigned)(cameraMode.height * cameraMode.scaleY);
LOG(RPiAf, Debug) << "switchMode: statsRegion: "
<< statsRegion_.x << ','
<< statsRegion_.y << ','
<< statsRegion_.width << ','
<< statsRegion_.height;
invalidateWeights();
if (scanState_ >= ScanState::Coarse && scanState_ < ScanState::Settle) {
/*
* If a scan was in progress, re-start it, as CDAF statistics
* may have changed. Though if the application is just about
* to take a still picture, this will not help...
*/
startProgrammedScan();
}
skipCount_ = cfg_.skipFrames;
}
void Af::computeWeights(RegionWeights *wgts, unsigned rows, unsigned cols)
{
wgts->rows = rows;
wgts->cols = cols;
wgts->sum = 0;
wgts->w.resize(rows * cols);
std::fill(wgts->w.begin(), wgts->w.end(), 0);
if (rows > 0 && cols > 0 && useWindows_ &&
statsRegion_.height >= rows && statsRegion_.width >= cols) {
/*
* Here we just merge all of the given windows, weighted by area.
* \todo Perhaps a better approach might be to find the phase in each
* window and choose either the closest or the highest-confidence one?
* Ensure weights sum to less than (1<<16). 46080 is a "round number"
* below 65536, for better rounding when window size is a simple
* fraction of image dimensions.
*/
const unsigned maxCellWeight = 46080u / (MaxWindows * rows * cols);
const unsigned cellH = statsRegion_.height / rows;
const unsigned cellW = statsRegion_.width / cols;
const unsigned cellA = cellH * cellW;
for (auto &w : windows_) {
for (unsigned r = 0; r < rows; ++r) {
int y0 = std::max(statsRegion_.y + (int)(cellH * r), w.y);
int y1 = std::min(statsRegion_.y + (int)(cellH * (r + 1)),
w.y + (int)(w.height));
if (y0 >= y1)
continue;
y1 -= y0;
for (unsigned c = 0; c < cols; ++c) {
int x0 = std::max(statsRegion_.x + (int)(cellW * c), w.x);
int x1 = std::min(statsRegion_.x + (int)(cellW * (c + 1)),
w.x + (int)(w.width));
if (x0 >= x1)
continue;
unsigned a = y1 * (x1 - x0);
a = (maxCellWeight * a + cellA - 1) / cellA;
wgts->w[r * cols + c] += a;
wgts->sum += a;
}
}
}
}
if (wgts->sum == 0) {
/* Default AF window is the middle 1/2 width of the middle 1/3 height */
for (unsigned r = rows / 3; r < rows - rows / 3; ++r) {
for (unsigned c = cols / 4; c < cols - cols / 4; ++c) {
wgts->w[r * cols + c] = 1;
wgts->sum += 1;
}
}
}
}
void Af::invalidateWeights()
{
phaseWeights_.sum = 0;
contrastWeights_.sum = 0;
}
bool Af::getPhase(PdafRegions const &regions, double &phase, double &conf)
{
libcamera::Size size = regions.size();
if (size.height != phaseWeights_.rows || size.width != phaseWeights_.cols ||
phaseWeights_.sum == 0) {
LOG(RPiAf, Debug) << "Recompute Phase weights " << size.width << 'x' << size.height;
computeWeights(&phaseWeights_, size.height, size.width);
}
uint32_t sumWc = 0;
int64_t sumWcp = 0;
for (unsigned i = 0; i < regions.numRegions(); ++i) {
unsigned w = phaseWeights_.w[i];
if (w) {
const PdafData &data = regions.get(i).val;
unsigned c = data.conf;
if (c >= cfg_.confThresh) {
if (c > cfg_.confClip)
c = cfg_.confClip;
c -= (cfg_.confThresh >> 2);
sumWc += w * c;
c -= (cfg_.confThresh >> 2);
sumWcp += (int64_t)(w * c) * (int64_t)data.phase;
}
}
}
if (0 < phaseWeights_.sum && phaseWeights_.sum <= sumWc) {
phase = (double)sumWcp / (double)sumWc;
conf = (double)sumWc / (double)phaseWeights_.sum;
return true;
} else {
phase = 0.0;
conf = 0.0;
return false;
}
}
double Af::getContrast(const FocusRegions &focusStats)
{
libcamera::Size size = focusStats.size();
if (size.height != contrastWeights_.rows ||
size.width != contrastWeights_.cols || contrastWeights_.sum == 0) {
LOG(RPiAf, Debug) << "Recompute Contrast weights "
<< size.width << 'x' << size.height;
computeWeights(&contrastWeights_, size.height, size.width);
}
uint64_t sumWc = 0;
for (unsigned i = 0; i < focusStats.numRegions(); ++i)
sumWc += contrastWeights_.w[i] * focusStats.get(i).val;
return (contrastWeights_.sum > 0) ? ((double)sumWc / (double)contrastWeights_.sum) : 0.0;
}
void Af::doPDAF(double phase, double conf)
{
/* Apply loop gain */
phase *= cfg_.speeds[speed_].pdafGain;
if (mode_ == AfModeContinuous) {
/*
* PDAF in Continuous mode. Scale down lens movement when
* delta is small or confidence is low, to suppress wobble.
*/
phase *= conf / (conf + cfg_.confEpsilon);
if (std::abs(phase) < cfg_.speeds[speed_].pdafSquelch) {
double a = phase / cfg_.speeds[speed_].pdafSquelch;
phase *= a * a;
}
} else {
/*
* PDAF in triggered-auto mode. Allow early termination when
* phase delta is small; scale down lens movements towards
* the end of the sequence, to ensure a stable image.
*/
if (stepCount_ >= cfg_.speeds[speed_].stepFrames) {
if (std::abs(phase) < cfg_.speeds[speed_].pdafSquelch)
stepCount_ = cfg_.speeds[speed_].stepFrames;
} else
phase *= stepCount_ / cfg_.speeds[speed_].stepFrames;
}
/* Apply slew rate limit. Report failure if out of bounds. */
if (phase < -cfg_.speeds[speed_].maxSlew) {
phase = -cfg_.speeds[speed_].maxSlew;
reportState_ = (ftarget_ <= cfg_.ranges[range_].focusMin) ? AfState::Failed
: AfState::Scanning;
} else if (phase > cfg_.speeds[speed_].maxSlew) {
phase = cfg_.speeds[speed_].maxSlew;
reportState_ = (ftarget_ >= cfg_.ranges[range_].focusMax) ? AfState::Failed
: AfState::Scanning;
} else
reportState_ = AfState::Focused;
ftarget_ = fsmooth_ + phase;
}
bool Af::earlyTerminationByPhase(double phase)
{
if (scanData_.size() > 0 &&
scanData_[scanData_.size() - 1].conf >= cfg_.confEpsilon) {
double oldFocus = scanData_[scanData_.size() - 1].focus;
double oldPhase = scanData_[scanData_.size() - 1].phase;
/*
* Check that the gradient is finite and has the expected sign;
* Interpolate/extrapolate the lens position for zero phase.
* Check that the extrapolation is well-conditioned.
*/
if ((ftarget_ - oldFocus) * (phase - oldPhase) > 0.0) {
double param = phase / (phase - oldPhase);
if (-3.0 <= param && param <= 3.5) {
ftarget_ += param * (oldFocus - ftarget_);
LOG(RPiAf, Debug) << "ETBP: param=" << param;
return true;
}
}
}
return false;
}
double Af::findPeak(unsigned i) const
{
double f = scanData_[i].focus;
if (i > 0 && i + 1 < scanData_.size()) {
double dropLo = scanData_[i].contrast - scanData_[i - 1].contrast;
double dropHi = scanData_[i].contrast - scanData_[i + 1].contrast;
if (0.0 <= dropLo && dropLo < dropHi) {
double param = 0.3125 * (1.0 - dropLo / dropHi) * (1.6 - dropLo / dropHi);
f += param * (scanData_[i - 1].focus - f);
} else if (0.0 <= dropHi && dropHi < dropLo) {
double param = 0.3125 * (1.0 - dropHi / dropLo) * (1.6 - dropHi / dropLo);
f += param * (scanData_[i + 1].focus - f);
}
}
LOG(RPiAf, Debug) << "FindPeak: " << f;
return f;
}
void Af::doScan(double contrast, double phase, double conf)
{
/* Record lens position, contrast and phase values for the current scan */
if (scanData_.empty() || contrast > scanMaxContrast_) {
scanMaxContrast_ = contrast;
scanMaxIndex_ = scanData_.size();
}
if (contrast < scanMinContrast_)
scanMinContrast_ = contrast;
scanData_.emplace_back(ScanRecord{ ftarget_, contrast, phase, conf });
if (scanState_ == ScanState::Coarse) {
if (ftarget_ >= cfg_.ranges[range_].focusMax ||
contrast < cfg_.speeds[speed_].contrastRatio * scanMaxContrast_) {
/*
* Finished course scan, or termination based on contrast.
* Jump to just after max contrast and start fine scan.
*/
ftarget_ = std::min(ftarget_, findPeak(scanMaxIndex_) +
2.0 * cfg_.speeds[speed_].stepFine);
scanState_ = ScanState::Fine;
scanData_.clear();
} else
ftarget_ += cfg_.speeds[speed_].stepCoarse;
} else { /* ScanState::Fine */
if (ftarget_ <= cfg_.ranges[range_].focusMin || scanData_.size() >= 5 ||
contrast < cfg_.speeds[speed_].contrastRatio * scanMaxContrast_) {
/*
* Finished fine scan, or termination based on contrast.
* Use quadratic peak-finding to find best contrast position.
*/
ftarget_ = findPeak(scanMaxIndex_);
scanState_ = ScanState::Settle;
} else
ftarget_ -= cfg_.speeds[speed_].stepFine;
}
stepCount_ = (ftarget_ == fsmooth_) ? 0 : cfg_.speeds[speed_].stepFrames;
}
void Af::doAF(double contrast, double phase, double conf)
{
/* Skip frames at startup and after sensor mode change */
if (skipCount_ > 0) {
LOG(RPiAf, Debug) << "SKIP";
skipCount_--;
return;
}
if (scanState_ == ScanState::Pdaf) {
/*
* Use PDAF closed-loop control whenever available, in both CAF
* mode and (for a limited number of iterations) when triggered.
* If PDAF fails (due to poor contrast, noise or large defocus),
* fall back to a CDAF-based scan. To avoid "nuisance" scans,
* scan only after a number of frames with low PDAF confidence.
*/
if (conf > (dropCount_ ? 1.0 : 0.25) * cfg_.confEpsilon) {
doPDAF(phase, conf);
if (stepCount_ > 0)
stepCount_--;
else if (mode_ != AfModeContinuous)
scanState_ = ScanState::Idle;
dropCount_ = 0;
} else if (++dropCount_ == cfg_.speeds[speed_].dropoutFrames)
startProgrammedScan();
} else if (scanState_ >= ScanState::Coarse && fsmooth_ == ftarget_) {
/*
* Scanning sequence. This means PDAF has become unavailable.
* Allow a delay between steps for CDAF FoM statistics to be
* updated, and a "settling time" at the end of the sequence.
* [A coarse or fine scan can be abandoned if two PDAF samples
* allow direct interpolation of the zero-phase lens position.]
*/
if (stepCount_ > 0)
stepCount_--;
else if (scanState_ == ScanState::Settle) {
if (prevContrast_ >= cfg_.speeds[speed_].contrastRatio * scanMaxContrast_ &&
scanMinContrast_ <= cfg_.speeds[speed_].contrastRatio * scanMaxContrast_)
reportState_ = AfState::Focused;
else
reportState_ = AfState::Failed;
if (mode_ == AfModeContinuous && !pauseFlag_ &&
cfg_.speeds[speed_].dropoutFrames > 0)
scanState_ = ScanState::Pdaf;
else
scanState_ = ScanState::Idle;
scanData_.clear();
} else if (conf >= cfg_.confEpsilon && earlyTerminationByPhase(phase)) {
scanState_ = ScanState::Settle;
stepCount_ = (mode_ == AfModeContinuous) ? 0
: cfg_.speeds[speed_].stepFrames;
} else
doScan(contrast, phase, conf);
}
}
void Af::updateLensPosition()
{
if (scanState_ >= ScanState::Pdaf) {
ftarget_ = std::clamp(ftarget_,
cfg_.ranges[range_].focusMin,
cfg_.ranges[range_].focusMax);
}
if (initted_) {
/* from a known lens position: apply slew rate limit */
fsmooth_ = std::clamp(ftarget_,
fsmooth_ - cfg_.speeds[speed_].maxSlew,
fsmooth_ + cfg_.speeds[speed_].maxSlew);
} else {
/* from an unknown position: go straight to target, but add delay */
fsmooth_ = ftarget_;
initted_ = true;
skipCount_ = cfg_.skipFrames;
}
}
void Af::startAF()
{
/* Use PDAF if the tuning file allows it; else CDAF. */
if (cfg_.speeds[speed_].dropoutFrames > 0 &&
(mode_ == AfModeContinuous || cfg_.speeds[speed_].pdafFrames > 0)) {
if (!initted_) {
ftarget_ = cfg_.ranges[range_].focusDefault;
updateLensPosition();
}
stepCount_ = (mode_ == AfModeContinuous) ? 0 : cfg_.speeds[speed_].pdafFrames;
scanState_ = ScanState::Pdaf;
scanData_.clear();
dropCount_ = 0;
reportState_ = AfState::Scanning;
} else
startProgrammedScan();
}
void Af::startProgrammedScan()
{
ftarget_ = cfg_.ranges[range_].focusMin;
updateLensPosition();
scanState_ = ScanState::Coarse;
scanMaxContrast_ = 0.0;
scanMinContrast_ = 1.0e9;
scanMaxIndex_ = 0;
scanData_.clear();
stepCount_ = cfg_.speeds[speed_].stepFrames;
reportState_ = AfState::Scanning;
}
void Af::goIdle()
{
scanState_ = ScanState::Idle;
reportState_ = AfState::Idle;
scanData_.clear();
}
/*
* PDAF phase data are available in prepare(), but CDAF statistics are not
* available until process(). We are gambling on the availability of PDAF.
* To expedite feedback control using PDAF, issue the V4L2 lens control from
* prepare(). Conversely, during scans, we must allow an extra frame delay
* between steps, to retrieve CDAF statistics from the previous process()
* so we can terminate the scan early without having to change our minds.
*/
void Af::prepare(Metadata *imageMetadata)
{
/* Initialize for triggered scan or start of CAF mode */
if (scanState_ == ScanState::Trigger)
startAF();
if (initted_) {
/* Get PDAF from the embedded metadata, and run AF algorithm core */
PdafRegions regions;
double phase = 0.0, conf = 0.0;
double oldFt = ftarget_;
double oldFs = fsmooth_;
ScanState oldSs = scanState_;
uint32_t oldSt = stepCount_;
if (imageMetadata->get("pdaf.regions", regions) == 0)
getPhase(regions, phase, conf);
doAF(prevContrast_, phase, conf);
updateLensPosition();
LOG(RPiAf, Debug) << std::fixed << std::setprecision(2)
<< static_cast<unsigned int>(reportState_)
<< " sst" << static_cast<unsigned int>(oldSs)
<< "->" << static_cast<unsigned int>(scanState_)
<< " stp" << oldSt << "->" << stepCount_
<< " ft" << oldFt << "->" << ftarget_
<< " fs" << oldFs << "->" << fsmooth_
<< " cont=" << (int)prevContrast_
<< " phase=" << (int)phase << " conf=" << (int)conf;
}
/* Report status and produce new lens setting */
AfStatus status;
if (pauseFlag_)
status.pauseState = (scanState_ == ScanState::Idle) ? AfPauseState::Paused
: AfPauseState::Pausing;
else
status.pauseState = AfPauseState::Running;
if (mode_ == AfModeAuto && scanState_ != ScanState::Idle)
status.state = AfState::Scanning;
else
status.state = reportState_;
status.lensSetting = initted_ ? std::optional<int>(cfg_.map.eval(fsmooth_))
: std::nullopt;
imageMetadata->set("af.status", status);
}
void Af::process(StatisticsPtr &stats, [[maybe_unused]] Metadata *imageMetadata)
{
(void)imageMetadata;
prevContrast_ = getContrast(stats->focusRegions);
}
/* Controls */
void Af::setRange(AfRange r)
{
LOG(RPiAf, Debug) << "setRange: " << (unsigned)r;
if (r < AfAlgorithm::AfRangeMax)
range_ = r;
}
void Af::setSpeed(AfSpeed s)
{
LOG(RPiAf, Debug) << "setSpeed: " << (unsigned)s;
if (s < AfAlgorithm::AfSpeedMax) {
if (scanState_ == ScanState::Pdaf &&
cfg_.speeds[s].pdafFrames > cfg_.speeds[speed_].pdafFrames)
stepCount_ += cfg_.speeds[s].pdafFrames - cfg_.speeds[speed_].pdafFrames;
speed_ = s;
}
}
void Af::setMetering(bool mode)
{
if (useWindows_ != mode) {
useWindows_ = mode;
invalidateWeights();
}
}
void Af::setWindows(libcamera::Span<libcamera::Rectangle const> const &wins)
{
windows_.clear();
for (auto &w : wins) {
LOG(RPiAf, Debug) << "Window: "
<< w.x << ", "
<< w.y << ", "
<< w.width << ", "
<< w.height;
windows_.push_back(w);
if (windows_.size() >= MaxWindows)
break;
}
if (useWindows_)
invalidateWeights();
}
bool Af::setLensPosition(double dioptres, int *hwpos)
{
bool changed = false;
if (mode_ == AfModeManual) {
LOG(RPiAf, Debug) << "setLensPosition: " << dioptres;
ftarget_ = cfg_.map.domain().clip(dioptres);
changed = !(initted_ && fsmooth_ == ftarget_);
updateLensPosition();
}
if (hwpos)
*hwpos = cfg_.map.eval(fsmooth_);
return changed;
}
std::optional<double> Af::getLensPosition() const
{
/*
* \todo We ought to perform some precise timing here to determine
* the current lens position.
*/
return initted_ ? std::optional<double>(fsmooth_) : std::nullopt;
}
void Af::cancelScan()
{
LOG(RPiAf, Debug) << "cancelScan";
if (mode_ == AfModeAuto)
goIdle();
}
void Af::triggerScan()
{
LOG(RPiAf, Debug) << "triggerScan";
if (mode_ == AfModeAuto && scanState_ == ScanState::Idle)
scanState_ = ScanState::Trigger;
}
void Af::setMode(AfAlgorithm::AfMode mode)
{
LOG(RPiAf, Debug) << "setMode: " << (unsigned)mode;
if (mode_ != mode) {
mode_ = mode;
pauseFlag_ = false;
if (mode == AfModeContinuous)
scanState_ = ScanState::Trigger;
else if (mode != AfModeAuto || scanState_ < ScanState::Coarse)
goIdle();
}
}
AfAlgorithm::AfMode Af::getMode() const
{
return mode_;
}
void Af::pause(AfAlgorithm::AfPause pause)
{
LOG(RPiAf, Debug) << "pause: " << (unsigned)pause;
if (mode_ == AfModeContinuous) {
if (pause == AfPauseResume && pauseFlag_) {
pauseFlag_ = false;
if (scanState_ < ScanState::Coarse)
scanState_ = ScanState::Trigger;
} else if (pause != AfPauseResume && !pauseFlag_) {
pauseFlag_ = true;
if (pause == AfPauseImmediate || scanState_ < ScanState::Coarse)
goIdle();
}
}
}
// Register algorithm with the system.
static Algorithm *create(Controller *controller)
{
return (Algorithm *)new Af(controller);
}
static RegisterAlgorithm reg(NAME, &create);
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2022-2023, Raspberry Pi Ltd
*
* af.h - Autofocus control algorithm
*/
#pragma once
#include "../af_algorithm.h"
#include "../af_status.h"
#include "../pdaf_data.h"
#include "../pwl.h"
/*
* This algorithm implements a hybrid of CDAF and PDAF, favouring PDAF.
*
* Whenever PDAF is available, it is used in a continuous feedback loop.
* When triggered in auto mode, we simply enable AF for a limited number
* of frames (it may terminate early if the delta becomes small enough).
*
* When PDAF confidence is low (due e.g. to low contrast or extreme defocus)
* or PDAF data are absent, fall back to CDAF with a programmed scan pattern.
* A coarse and fine scan are performed, using ISP's CDAF focus FoM to
* estimate the lens position with peak contrast. This is slower due to
* extra latency in the ISP, and requires a settling time between steps.
*
* Some hysteresis is applied to the switch between PDAF and CDAF, to avoid
* "nuisance" scans. During each interval where PDAF is not working, only
* ONE scan will be performed; CAF cannot track objects using CDAF alone.
*
*/
namespace RPiController {
class Af : public AfAlgorithm
{
public:
Af(Controller *controller = NULL);
~Af();
char const *name() const override;
int read(const libcamera::YamlObject &params) override;
void initialise() override;
/* IPA calls */
void switchMode(CameraMode const &cameraMode, Metadata *metadata) override;
void prepare(Metadata *imageMetadata) override;
void process(StatisticsPtr &stats, Metadata *imageMetadata) override;
/* controls */
void setRange(AfRange range) override;
void setSpeed(AfSpeed speed) override;
void setMetering(bool use_windows) override;
void setWindows(libcamera::Span<libcamera::Rectangle const> const &wins) override;
void setMode(AfMode mode) override;
AfMode getMode() const override;
bool setLensPosition(double dioptres, int32_t *hwpos) override;
std::optional<double> getLensPosition() const override;
void triggerScan() override;
void cancelScan() override;
void pause(AfPause pause) override;
private:
enum class ScanState {
Idle = 0,
Trigger,
Pdaf,
Coarse,
Fine,
Settle
};
struct RangeDependentParams {
double focusMin; /* lower (far) limit in dipotres */
double focusMax; /* upper (near) limit in dioptres */
double focusDefault; /* default setting ("hyperfocal") */
RangeDependentParams();
void read(const libcamera::YamlObject &params);
};
struct SpeedDependentParams {
double stepCoarse; /* used for scans */
double stepFine; /* used for scans */
double contrastRatio; /* used for scan termination and reporting */
double pdafGain; /* coefficient for PDAF feedback loop */
double pdafSquelch; /* PDAF stability parameter (device-specific) */
double maxSlew; /* limit for lens movement per frame */
uint32_t pdafFrames; /* number of iterations when triggered */
uint32_t dropoutFrames; /* number of non-PDAF frames to switch to CDAF */
uint32_t stepFrames; /* frames to skip in between steps of a scan */
SpeedDependentParams();
void read(const libcamera::YamlObject &params);
};
struct CfgParams {
RangeDependentParams ranges[AfRangeMax];
SpeedDependentParams speeds[AfSpeedMax];
uint32_t confEpsilon; /* PDAF hysteresis threshold (sensor-specific) */
uint32_t confThresh; /* PDAF confidence cell min (sensor-specific) */
uint32_t confClip; /* PDAF confidence cell max (sensor-specific) */
uint32_t skipFrames; /* frames to skip at start or modeswitch */
Pwl map; /* converts dioptres -> lens driver position */
CfgParams();
int read(const libcamera::YamlObject &params);
void initialise();
};
struct ScanRecord {
double focus;
double contrast;
double phase;
double conf;
};
struct RegionWeights {
unsigned rows;
unsigned cols;
uint32_t sum;
std::vector<uint16_t> w;
RegionWeights()
: rows(0), cols(0), sum(0), w() {}
};
void computeWeights(RegionWeights *wgts, unsigned rows, unsigned cols);
void invalidateWeights();
bool getPhase(PdafRegions const &regions, double &phase, double &conf);
double getContrast(const FocusRegions &focusStats);
void doPDAF(double phase, double conf);
bool earlyTerminationByPhase(double phase);
double findPeak(unsigned index) const;
void doScan(double contrast, double phase, double conf);
void doAF(double contrast, double phase, double conf);
void updateLensPosition();
void startAF();
void startProgrammedScan();
void goIdle();
/* Configuration and settings */
CfgParams cfg_;
AfRange range_;
AfSpeed speed_;
AfMode mode_;
bool pauseFlag_;
libcamera::Rectangle statsRegion_;
std::vector<libcamera::Rectangle> windows_;
bool useWindows_;
RegionWeights phaseWeights_;
RegionWeights contrastWeights_;
/* Working state. */
ScanState scanState_;
bool initted_;
double ftarget_, fsmooth_;
double prevContrast_;
unsigned skipCount_, stepCount_, dropCount_;
unsigned scanMaxIndex_;
double scanMaxContrast_, scanMinContrast_;
std::vector<ScanRecord> scanData_;
AfState reportState_;
};
} // namespace RPiController
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* agc.cpp - AGC/AEC control algorithm
*/
#include <algorithm>
#include <map>
#include <tuple>
#include <libcamera/base/log.h>
#include "../awb_status.h"
#include "../device_status.h"
#include "../histogram.h"
#include "../lux_status.h"
#include "../metadata.h"
#include "agc.h"
using namespace RPiController;
using namespace libcamera;
using libcamera::utils::Duration;
using namespace std::literals::chrono_literals;
LOG_DEFINE_CATEGORY(RPiAgc)
#define NAME "rpi.agc"
int AgcMeteringMode::read(const libcamera::YamlObject &params)
{
const YamlObject &yamlWeights = params["weights"];
for (const auto &p : yamlWeights.asList()) {
auto value = p.get<double>();
if (!value)
return -EINVAL;
weights.push_back(*value);
}
return 0;
}
static std::tuple<int, std::string>
readMeteringModes(std::map<std::string, AgcMeteringMode> &metering_modes,
const libcamera::YamlObject &params)
{
std::string first;
int ret;
for (const auto &[key, value] : params.asDict()) {
AgcMeteringMode meteringMode;
ret = meteringMode.read(value);
if (ret)
return { ret, {} };
metering_modes[key] = std::move(meteringMode);
if (first.empty())
first = key;
}
return { 0, first };
}
int AgcExposureMode::read(const libcamera::YamlObject &params)
{
auto value = params["shutter"].getList<double>();
if (!value)
return -EINVAL;
std::transform(value->begin(), value->end(), std::back_inserter(shutter),
[](double v) { return v * 1us; });
value = params["gain"].getList<double>();
if (!value)
return -EINVAL;
gain = std::move(*value);
if (shutter.size() < 2 || gain.size() < 2) {
LOG(RPiAgc, Error)
<< "AgcExposureMode: must have at least two entries in exposure profile";
return -EINVAL;
}
if (shutter.size() != gain.size()) {
LOG(RPiAgc, Error)
<< "AgcExposureMode: expect same number of exposure and gain entries in exposure profile";
return -EINVAL;
}
return 0;
}
static std::tuple<int, std::string>
readExposureModes(std::map<std::string, AgcExposureMode> &exposureModes,
const libcamera::YamlObject &params)
{
std::string first;
int ret;
for (const auto &[key, value] : params.asDict()) {
AgcExposureMode exposureMode;
ret = exposureMode.read(value);
if (ret)
return { ret, {} };
exposureModes[key] = std::move(exposureMode);
if (first.empty())
first = key;
}
return { 0, first };
}
int AgcConstraint::read(const libcamera::YamlObject &params)
{
std::string boundString = params["bound"].get<std::string>("");
transform(boundString.begin(), boundString.end(),
boundString.begin(), ::toupper);
if (boundString != "UPPER" && boundString != "LOWER") {
LOG(RPiAgc, Error) << "AGC constraint type should be UPPER or LOWER";
return -EINVAL;
}
bound = boundString == "UPPER" ? Bound::UPPER : Bound::LOWER;
auto value = params["q_lo"].get<double>();
if (!value)
return -EINVAL;
qLo = *value;
value = params["q_hi"].get<double>();
if (!value)
return -EINVAL;
qHi = *value;
return yTarget.read(params["y_target"]);
}
static std::tuple<int, AgcConstraintMode>
readConstraintMode(const libcamera::YamlObject &params)
{
AgcConstraintMode mode;
int ret;
for (const auto &p : params.asList()) {
AgcConstraint constraint;
ret = constraint.read(p);
if (ret)
return { ret, {} };
mode.push_back(std::move(constraint));
}
return { 0, mode };
}
static std::tuple<int, std::string>
readConstraintModes(std::map<std::string, AgcConstraintMode> &constraintModes,
const libcamera::YamlObject &params)
{
std::string first;
int ret;
for (const auto &[key, value] : params.asDict()) {
std::tie(ret, constraintModes[key]) = readConstraintMode(value);
if (ret)
return { ret, {} };
if (first.empty())
first = key;
}
return { 0, first };
}
int AgcConfig::read(const libcamera::YamlObject &params)
{
LOG(RPiAgc, Debug) << "AgcConfig";
int ret;
std::tie(ret, defaultMeteringMode) =
readMeteringModes(meteringModes, params["metering_modes"]);
if (ret)
return ret;
std::tie(ret, defaultExposureMode) =
readExposureModes(exposureModes, params["exposure_modes"]);
if (ret)
return ret;
std::tie(ret, defaultConstraintMode) =
readConstraintModes(constraintModes, params["constraint_modes"]);
if (ret)
return ret;
ret = yTarget.read(params["y_target"]);
if (ret)
return ret;
speed = params["speed"].get<double>(0.2);
startupFrames = params["startup_frames"].get<uint16_t>(10);
convergenceFrames = params["convergence_frames"].get<unsigned int>(6);
fastReduceThreshold = params["fast_reduce_threshold"].get<double>(0.4);
baseEv = params["base_ev"].get<double>(1.0);
/* Start with quite a low value as ramping up is easier than ramping down. */
defaultExposureTime = params["default_exposure_time"].get<double>(1000) * 1us;
defaultAnalogueGain = params["default_analogue_gain"].get<double>(1.0);
return 0;
}
Agc::ExposureValues::ExposureValues()
: shutter(0s), analogueGain(0),
totalExposure(0s), totalExposureNoDG(0s)
{
}
Agc::Agc(Controller *controller)
: AgcAlgorithm(controller), meteringMode_(nullptr),
exposureMode_(nullptr), constraintMode_(nullptr),
frameCount_(0), lockCount_(0),
lastTargetExposure_(0s), ev_(1.0), flickerPeriod_(0s),
maxShutter_(0s), fixedShutter_(0s), fixedAnalogueGain_(0.0)
{
memset(&awb_, 0, sizeof(awb_));
/*
* Setting status_.totalExposureValue_ to zero initially tells us
* it's not been calculated yet (i.e. Process hasn't yet run).
*/
memset(&status_, 0, sizeof(status_));
status_.ev = ev_;
}
char const *Agc::name() const
{
return NAME;
}
int Agc::read(const libcamera::YamlObject &params)
{
LOG(RPiAgc, Debug) << "Agc";
int ret = config_.read(params);
if (ret)
return ret;
const Size &size = getHardwareConfig().agcZoneWeights;
for (auto const &modes : config_.meteringModes) {
if (modes.second.weights.size() != size.width * size.height) {
LOG(RPiAgc, Error) << "AgcMeteringMode: Incorrect number of weights";
return -EINVAL;
}
}
/*
* Set the config's defaults (which are the first ones it read) as our
* current modes, until someone changes them. (they're all known to
* exist at this point)
*/
meteringModeName_ = config_.defaultMeteringMode;
meteringMode_ = &config_.meteringModes[meteringModeName_];
exposureModeName_ = config_.defaultExposureMode;
exposureMode_ = &config_.exposureModes[exposureModeName_];
constraintModeName_ = config_.defaultConstraintMode;
constraintMode_ = &config_.constraintModes[constraintModeName_];
/* Set up the "last shutter/gain" values, in case AGC starts "disabled". */
status_.shutterTime = config_.defaultExposureTime;
status_.analogueGain = config_.defaultAnalogueGain;
return 0;
}
void Agc::disableAuto()
{
fixedShutter_ = status_.shutterTime;
fixedAnalogueGain_ = status_.analogueGain;
}
void Agc::enableAuto()
{
fixedShutter_ = 0s;
fixedAnalogueGain_ = 0;
}
unsigned int Agc::getConvergenceFrames() const
{
/*
* If shutter and gain have been explicitly set, there is no
* convergence to happen, so no need to drop any frames - return zero.
*/
if (fixedShutter_ && fixedAnalogueGain_)
return 0;
else
return config_.convergenceFrames;
}
void Agc::setEv(double ev)
{
ev_ = ev;
}
void Agc::setFlickerPeriod(Duration flickerPeriod)
{
flickerPeriod_ = flickerPeriod;
}
void Agc::setMaxShutter(Duration maxShutter)
{
maxShutter_ = maxShutter;
}
void Agc::setFixedShutter(Duration fixedShutter)
{
fixedShutter_ = fixedShutter;
/* Set this in case someone calls disableAuto() straight after. */
status_.shutterTime = limitShutter(fixedShutter_);
}
void Agc::setFixedAnalogueGain(double fixedAnalogueGain)
{
fixedAnalogueGain_ = fixedAnalogueGain;
/* Set this in case someone calls disableAuto() straight after. */
status_.analogueGain = limitGain(fixedAnalogueGain);
}
void Agc::setMeteringMode(std::string const &meteringModeName)
{
meteringModeName_ = meteringModeName;
}
void Agc::setExposureMode(std::string const &exposureModeName)
{
exposureModeName_ = exposureModeName;
}
void Agc::setConstraintMode(std::string const &constraintModeName)
{
constraintModeName_ = constraintModeName;
}
void Agc::switchMode(CameraMode const &cameraMode,
Metadata *metadata)
{
/* AGC expects the mode sensitivity always to be non-zero. */
ASSERT(cameraMode.sensitivity);
housekeepConfig();
/*
* Store the mode in the local state. We must cache the sensitivity of
* of the previous mode for the calculations below.
*/
double lastSensitivity = mode_.sensitivity;
mode_ = cameraMode;
Duration fixedShutter = limitShutter(fixedShutter_);
if (fixedShutter && fixedAnalogueGain_) {
/* We're going to reset the algorithm here with these fixed values. */
fetchAwbStatus(metadata);
double minColourGain = std::min({ awb_.gainR, awb_.gainG, awb_.gainB, 1.0 });
ASSERT(minColourGain != 0.0);
/* This is the equivalent of computeTargetExposure and applyDigitalGain. */
target_.totalExposureNoDG = fixedShutter_ * fixedAnalogueGain_;
target_.totalExposure = target_.totalExposureNoDG / minColourGain;
/* Equivalent of filterExposure. This resets any "history". */
filtered_ = target_;
/* Equivalent of divideUpExposure. */
filtered_.shutter = fixedShutter;
filtered_.analogueGain = fixedAnalogueGain_;
} else if (status_.totalExposureValue) {
/*
* On a mode switch, various things could happen:
* - the exposure profile might change
* - a fixed exposure or gain might be set
* - the new mode's sensitivity might be different
* We cope with the last of these by scaling the target values. After
* that we just need to re-divide the exposure/gain according to the
* current exposure profile, which takes care of everything else.
*/
double ratio = lastSensitivity / cameraMode.sensitivity;
target_.totalExposureNoDG *= ratio;
target_.totalExposure *= ratio;
filtered_.totalExposureNoDG *= ratio;
filtered_.totalExposure *= ratio;
divideUpExposure();
} else {
/*
* We come through here on startup, when at least one of the shutter
* or gain has not been fixed. We must still write those values out so
* that they will be applied immediately. We supply some arbitrary defaults
* for any that weren't set.
*/
/* Equivalent of divideUpExposure. */
filtered_.shutter = fixedShutter ? fixedShutter : config_.defaultExposureTime;
filtered_.analogueGain = fixedAnalogueGain_ ? fixedAnalogueGain_ : config_.defaultAnalogueGain;
}
writeAndFinish(metadata, false);
}
void Agc::prepare(Metadata *imageMetadata)
{
Duration totalExposureValue = status_.totalExposureValue;
AgcStatus delayedStatus;
if (!imageMetadata->get("agc.delayed_status", delayedStatus))
totalExposureValue = delayedStatus.totalExposureValue;
status_.digitalGain = 1.0;
fetchAwbStatus(imageMetadata); /* always fetch it so that Process knows it's been done */
if (status_.totalExposureValue) {
/* Process has run, so we have meaningful values. */
DeviceStatus deviceStatus;
if (imageMetadata->get("device.status", deviceStatus) == 0) {
Duration actualExposure = deviceStatus.shutterSpeed *
deviceStatus.analogueGain;
if (actualExposure) {
status_.digitalGain = totalExposureValue / actualExposure;
LOG(RPiAgc, Debug) << "Want total exposure " << totalExposureValue;
/*
* Never ask for a gain < 1.0, and also impose
* some upper limit. Make it customisable?
*/
status_.digitalGain = std::max(1.0, std::min(status_.digitalGain, 4.0));
LOG(RPiAgc, Debug) << "Actual exposure " << actualExposure;
LOG(RPiAgc, Debug) << "Use digitalGain " << status_.digitalGain;
LOG(RPiAgc, Debug) << "Effective exposure "
<< actualExposure * status_.digitalGain;
/* Decide whether AEC/AGC has converged. */
updateLockStatus(deviceStatus);
}
} else
LOG(RPiAgc, Warning) << name() << ": no device metadata";
imageMetadata->set("agc.status", status_);
}
}
void Agc::process(StatisticsPtr &stats, Metadata *imageMetadata)
{
frameCount_++;
/*
* First a little bit of housekeeping, fetching up-to-date settings and
* configuration, that kind of thing.
*/
housekeepConfig();
/* Get the current exposure values for the frame that's just arrived. */
fetchCurrentExposure(imageMetadata);
/* Compute the total gain we require relative to the current exposure. */
double gain, targetY;
computeGain(stats, imageMetadata, gain, targetY);
/* Now compute the target (final) exposure which we think we want. */
computeTargetExposure(gain);
/*
* Some of the exposure has to be applied as digital gain, so work out
* what that is. This function also tells us whether it's decided to
* "desaturate" the image more quickly.
*/
bool desaturate = applyDigitalGain(gain, targetY);
/* The results have to be filtered so as not to change too rapidly. */
filterExposure(desaturate);
/*
* The last thing is to divide up the exposure value into a shutter time
* and analogue gain, according to the current exposure mode.
*/
divideUpExposure();
/* Finally advertise what we've done. */
writeAndFinish(imageMetadata, desaturate);
}
void Agc::updateLockStatus(DeviceStatus const &deviceStatus)
{
const double errorFactor = 0.10; /* make these customisable? */
const int maxLockCount = 5;
/* Reset "lock count" when we exceed this multiple of errorFactor */
const double resetMargin = 1.5;
/* Add 200us to the exposure time error to allow for line quantisation. */
Duration exposureError = lastDeviceStatus_.shutterSpeed * errorFactor + 200us;
double gainError = lastDeviceStatus_.analogueGain * errorFactor;
Duration targetError = lastTargetExposure_ * errorFactor;
/*
* Note that we don't know the exposure/gain limits of the sensor, so
* the values we keep requesting may be unachievable. For this reason
* we only insist that we're close to values in the past few frames.
*/
if (deviceStatus.shutterSpeed > lastDeviceStatus_.shutterSpeed - exposureError &&
deviceStatus.shutterSpeed < lastDeviceStatus_.shutterSpeed + exposureError &&
deviceStatus.analogueGain > lastDeviceStatus_.analogueGain - gainError &&
deviceStatus.analogueGain < lastDeviceStatus_.analogueGain + gainError &&
status_.targetExposureValue > lastTargetExposure_ - targetError &&
status_.targetExposureValue < lastTargetExposure_ + targetError)
lockCount_ = std::min(lockCount_ + 1, maxLockCount);
else if (deviceStatus.shutterSpeed < lastDeviceStatus_.shutterSpeed - resetMargin * exposureError ||
deviceStatus.shutterSpeed > lastDeviceStatus_.shutterSpeed + resetMargin * exposureError ||
deviceStatus.analogueGain < lastDeviceStatus_.analogueGain - resetMargin * gainError ||
deviceStatus.analogueGain > lastDeviceStatus_.analogueGain + resetMargin * gainError ||
status_.targetExposureValue < lastTargetExposure_ - resetMargin * targetError ||
status_.targetExposureValue > lastTargetExposure_ + resetMargin * targetError)
lockCount_ = 0;
lastDeviceStatus_ = deviceStatus;
lastTargetExposure_ = status_.targetExposureValue;
LOG(RPiAgc, Debug) << "Lock count updated to " << lockCount_;
status_.locked = lockCount_ == maxLockCount;
}
static void copyString(std::string const &s, char *d, size_t size)
{
size_t length = s.copy(d, size - 1);
d[length] = '\0';
}
void Agc::housekeepConfig()
{
/* First fetch all the up-to-date settings, so no one else has to do it. */
status_.ev = ev_;
status_.fixedShutter = limitShutter(fixedShutter_);
status_.fixedAnalogueGain = fixedAnalogueGain_;
status_.flickerPeriod = flickerPeriod_;
LOG(RPiAgc, Debug) << "ev " << status_.ev << " fixedShutter "
<< status_.fixedShutter << " fixedAnalogueGain "
<< status_.fixedAnalogueGain;
/*
* Make sure the "mode" pointers point to the up-to-date things, if
* they've changed.
*/
if (strcmp(meteringModeName_.c_str(), status_.meteringMode)) {
auto it = config_.meteringModes.find(meteringModeName_);
if (it == config_.meteringModes.end())
LOG(RPiAgc, Fatal) << "No metering mode " << meteringModeName_;
meteringMode_ = &it->second;
copyString(meteringModeName_, status_.meteringMode,
sizeof(status_.meteringMode));
}
if (strcmp(exposureModeName_.c_str(), status_.exposureMode)) {
auto it = config_.exposureModes.find(exposureModeName_);
if (it == config_.exposureModes.end())
LOG(RPiAgc, Fatal) << "No exposure profile " << exposureModeName_;
exposureMode_ = &it->second;
copyString(exposureModeName_, status_.exposureMode,
sizeof(status_.exposureMode));
}
if (strcmp(constraintModeName_.c_str(), status_.constraintMode)) {
auto it =
config_.constraintModes.find(constraintModeName_);
if (it == config_.constraintModes.end())
LOG(RPiAgc, Fatal) << "No constraint list " << constraintModeName_;
constraintMode_ = &it->second;
copyString(constraintModeName_, status_.constraintMode,
sizeof(status_.constraintMode));
}
LOG(RPiAgc, Debug) << "exposureMode "
<< exposureModeName_ << " constraintMode "
<< constraintModeName_ << " meteringMode "
<< meteringModeName_;
}
void Agc::fetchCurrentExposure(Metadata *imageMetadata)
{
std::unique_lock<Metadata> lock(*imageMetadata);
DeviceStatus *deviceStatus =
imageMetadata->getLocked<DeviceStatus>("device.status");
if (!deviceStatus)
LOG(RPiAgc, Fatal) << "No device metadata";
current_.shutter = deviceStatus->shutterSpeed;
current_.analogueGain = deviceStatus->analogueGain;
AgcStatus *agcStatus =
imageMetadata->getLocked<AgcStatus>("agc.status");
current_.totalExposure = agcStatus ? agcStatus->totalExposureValue : 0s;
current_.totalExposureNoDG = current_.shutter * current_.analogueGain;
}
void Agc::fetchAwbStatus(Metadata *imageMetadata)
{
awb_.gainR = 1.0; /* in case not found in metadata */
awb_.gainG = 1.0;
awb_.gainB = 1.0;
if (imageMetadata->get("awb.status", awb_) != 0)
LOG(RPiAgc, Debug) << "No AWB status found";
}
static double computeInitialY(StatisticsPtr &stats, AwbStatus const &awb,
std::vector<double> &weights, double gain)
{
constexpr uint64_t maxVal = 1 << Statistics::NormalisationFactorPow2;
ASSERT(weights.size() == stats->agcRegions.numRegions());
/*
* Note how the calculation below means that equal weights give you
* "average" metering (i.e. all pixels equally important).
*/
double rSum = 0, gSum = 0, bSum = 0, pixelSum = 0;
for (unsigned int i = 0; i < stats->agcRegions.numRegions(); i++) {
auto &region = stats->agcRegions.get(i);
double rAcc = std::min<double>(region.val.rSum * gain, (maxVal - 1) * region.counted);
double gAcc = std::min<double>(region.val.gSum * gain, (maxVal - 1) * region.counted);
double bAcc = std::min<double>(region.val.bSum * gain, (maxVal - 1) * region.counted);
rSum += rAcc * weights[i];
gSum += gAcc * weights[i];
bSum += bAcc * weights[i];
pixelSum += region.counted * weights[i];
}
if (pixelSum == 0.0) {
LOG(RPiAgc, Warning) << "computeInitialY: pixelSum is zero";
return 0;
}
double ySum = rSum * awb.gainR * .299 +
gSum * awb.gainG * .587 +
bSum * awb.gainB * .114;
return ySum / pixelSum / maxVal;
}
/*
* We handle extra gain through EV by adjusting our Y targets. However, you
* simply can't monitor histograms once they get very close to (or beyond!)
* saturation, so we clamp the Y targets to this value. It does mean that EV
* increases don't necessarily do quite what you might expect in certain
* (contrived) cases.
*/
static constexpr double EvGainYTargetLimit = 0.9;
static double constraintComputeGain(AgcConstraint &c, const Histogram &h, double lux,
double evGain, double &targetY)
{
targetY = c.yTarget.eval(c.yTarget.domain().clip(lux));
targetY = std::min(EvGainYTargetLimit, targetY * evGain);
double iqm = h.interQuantileMean(c.qLo, c.qHi);
return (targetY * h.bins()) / iqm;
}
void Agc::computeGain(StatisticsPtr &statistics, Metadata *imageMetadata,
double &gain, double &targetY)
{
struct LuxStatus lux = {};
lux.lux = 400; /* default lux level to 400 in case no metadata found */
if (imageMetadata->get("lux.status", lux) != 0)
LOG(RPiAgc, Warning) << "No lux level found";
const Histogram &h = statistics->yHist;
double evGain = status_.ev * config_.baseEv;
/*
* The initial gain and target_Y come from some of the regions. After
* that we consider the histogram constraints.
*/
targetY = config_.yTarget.eval(config_.yTarget.domain().clip(lux.lux));
targetY = std::min(EvGainYTargetLimit, targetY * evGain);
/*
* Do this calculation a few times as brightness increase can be
* non-linear when there are saturated regions.
*/
gain = 1.0;
for (int i = 0; i < 8; i++) {
double initialY = computeInitialY(statistics, awb_, meteringMode_->weights, gain);
double extraGain = std::min(10.0, targetY / (initialY + .001));
gain *= extraGain;
LOG(RPiAgc, Debug) << "Initial Y " << initialY << " target " << targetY
<< " gives gain " << gain;
if (extraGain < 1.01) /* close enough */
break;
}
for (auto &c : *constraintMode_) {
double newTargetY;
double newGain = constraintComputeGain(c, h, lux.lux, evGain, newTargetY);
LOG(RPiAgc, Debug) << "Constraint has target_Y "
<< newTargetY << " giving gain " << newGain;
if (c.bound == AgcConstraint::Bound::LOWER && newGain > gain) {
LOG(RPiAgc, Debug) << "Lower bound constraint adopted";
gain = newGain;
targetY = newTargetY;
} else if (c.bound == AgcConstraint::Bound::UPPER && newGain < gain) {
LOG(RPiAgc, Debug) << "Upper bound constraint adopted";
gain = newGain;
targetY = newTargetY;
}
}
LOG(RPiAgc, Debug) << "Final gain " << gain << " (target_Y " << targetY << " ev "
<< status_.ev << " base_ev " << config_.baseEv
<< ")";
}
void Agc::computeTargetExposure(double gain)
{
if (status_.fixedShutter && status_.fixedAnalogueGain) {
/*
* When ag and shutter are both fixed, we need to drive the
* total exposure so that we end up with a digital gain of at least
* 1/minColourGain. Otherwise we'd desaturate channels causing
* white to go cyan or magenta.
*/
double minColourGain = std::min({ awb_.gainR, awb_.gainG, awb_.gainB, 1.0 });
ASSERT(minColourGain != 0.0);
target_.totalExposure =
status_.fixedShutter * status_.fixedAnalogueGain / minColourGain;
} else {
/*
* The statistics reflect the image without digital gain, so the final
* total exposure we're aiming for is:
*/
target_.totalExposure = current_.totalExposureNoDG * gain;
/* The final target exposure is also limited to what the exposure mode allows. */
Duration maxShutter = status_.fixedShutter
? status_.fixedShutter
: exposureMode_->shutter.back();
maxShutter = limitShutter(maxShutter);
Duration maxTotalExposure =
maxShutter *
(status_.fixedAnalogueGain != 0.0
? status_.fixedAnalogueGain
: exposureMode_->gain.back());
target_.totalExposure = std::min(target_.totalExposure, maxTotalExposure);
}
LOG(RPiAgc, Debug) << "Target totalExposure " << target_.totalExposure;
}
bool Agc::applyDigitalGain(double gain, double targetY)
{
double minColourGain = std::min({ awb_.gainR, awb_.gainG, awb_.gainB, 1.0 });
ASSERT(minColourGain != 0.0);
double dg = 1.0 / minColourGain;
/*
* I think this pipeline subtracts black level and rescales before we
* get the stats, so no need to worry about it.
*/
LOG(RPiAgc, Debug) << "after AWB, target dg " << dg << " gain " << gain
<< " target_Y " << targetY;
/*
* Finally, if we're trying to reduce exposure but the target_Y is
* "close" to 1.0, then the gain computed for that constraint will be
* only slightly less than one, because the measured Y can never be
* larger than 1.0. When this happens, demand a large digital gain so
* that the exposure can be reduced, de-saturating the image much more
* quickly (and we then approach the correct value more quickly from
* below).
*/
bool desaturate = targetY > config_.fastReduceThreshold &&
gain < sqrt(targetY);
if (desaturate)
dg /= config_.fastReduceThreshold;
LOG(RPiAgc, Debug) << "Digital gain " << dg << " desaturate? " << desaturate;
target_.totalExposureNoDG = target_.totalExposure / dg;
LOG(RPiAgc, Debug) << "Target totalExposureNoDG " << target_.totalExposureNoDG;
return desaturate;
}
void Agc::filterExposure(bool desaturate)
{
double speed = config_.speed;
/*
* AGC adapts instantly if both shutter and gain are directly specified
* or we're in the startup phase.
*/
if ((status_.fixedShutter && status_.fixedAnalogueGain) ||
frameCount_ <= config_.startupFrames)
speed = 1.0;
if (!filtered_.totalExposure) {
filtered_.totalExposure = target_.totalExposure;
filtered_.totalExposureNoDG = target_.totalExposureNoDG;
} else {
/*
* If close to the result go faster, to save making so many
* micro-adjustments on the way. (Make this customisable?)
*/
if (filtered_.totalExposure < 1.2 * target_.totalExposure &&
filtered_.totalExposure > 0.8 * target_.totalExposure)
speed = sqrt(speed);
filtered_.totalExposure = speed * target_.totalExposure +
filtered_.totalExposure * (1.0 - speed);
/*
* When desaturing, take a big jump down in totalExposureNoDG,
* which we'll hide with digital gain.
*/
if (desaturate)
filtered_.totalExposureNoDG =
target_.totalExposureNoDG;
else
filtered_.totalExposureNoDG =
speed * target_.totalExposureNoDG +
filtered_.totalExposureNoDG * (1.0 - speed);
}
/*
* We can't let the totalExposureNoDG exposure deviate too far below the
* total exposure, as there might not be enough digital gain available
* in the ISP to hide it (which will cause nasty oscillation).
*/
if (filtered_.totalExposureNoDG <
filtered_.totalExposure * config_.fastReduceThreshold)
filtered_.totalExposureNoDG = filtered_.totalExposure * config_.fastReduceThreshold;
LOG(RPiAgc, Debug) << "After filtering, totalExposure " << filtered_.totalExposure
<< " no dg " << filtered_.totalExposureNoDG;
}
void Agc::divideUpExposure()
{
/*
* Sending the fixed shutter/gain cases through the same code may seem
* unnecessary, but it will make more sense when extend this to cover
* variable aperture.
*/
Duration exposureValue = filtered_.totalExposureNoDG;
Duration shutterTime;
double analogueGain;
shutterTime = status_.fixedShutter ? status_.fixedShutter
: exposureMode_->shutter[0];
shutterTime = limitShutter(shutterTime);
analogueGain = status_.fixedAnalogueGain != 0.0 ? status_.fixedAnalogueGain
: exposureMode_->gain[0];
analogueGain = limitGain(analogueGain);
if (shutterTime * analogueGain < exposureValue) {
for (unsigned int stage = 1;
stage < exposureMode_->gain.size(); stage++) {
if (!status_.fixedShutter) {
Duration stageShutter =
limitShutter(exposureMode_->shutter[stage]);
if (stageShutter * analogueGain >= exposureValue) {
shutterTime = exposureValue / analogueGain;
break;
}
shutterTime = stageShutter;
}
if (status_.fixedAnalogueGain == 0.0) {
if (exposureMode_->gain[stage] * shutterTime >= exposureValue) {
analogueGain = exposureValue / shutterTime;
break;
}
analogueGain = exposureMode_->gain[stage];
analogueGain = limitGain(analogueGain);
}
}
}
LOG(RPiAgc, Debug) << "Divided up shutter and gain are " << shutterTime << " and "
<< analogueGain;
/*
* Finally adjust shutter time for flicker avoidance (require both
* shutter and gain not to be fixed).
*/
if (!status_.fixedShutter && !status_.fixedAnalogueGain &&
status_.flickerPeriod) {
int flickerPeriods = shutterTime / status_.flickerPeriod;
if (flickerPeriods) {
Duration newShutterTime = flickerPeriods * status_.flickerPeriod;
analogueGain *= shutterTime / newShutterTime;
/*
* We should still not allow the ag to go over the
* largest value in the exposure mode. Note that this
* may force more of the total exposure into the digital
* gain as a side-effect.
*/
analogueGain = std::min(analogueGain, exposureMode_->gain.back());
analogueGain = limitGain(analogueGain);
shutterTime = newShutterTime;
}
LOG(RPiAgc, Debug) << "After flicker avoidance, shutter "
<< shutterTime << " gain " << analogueGain;
}
filtered_.shutter = shutterTime;
filtered_.analogueGain = analogueGain;
}
void Agc::writeAndFinish(Metadata *imageMetadata, bool desaturate)
{
status_.totalExposureValue = filtered_.totalExposure;
status_.targetExposureValue = desaturate ? 0s : target_.totalExposureNoDG;
status_.shutterTime = filtered_.shutter;
status_.analogueGain = filtered_.analogueGain;
/*
* Write to metadata as well, in case anyone wants to update the camera
* immediately.
*/
imageMetadata->set("agc.status", status_);
LOG(RPiAgc, Debug) << "Output written, total exposure requested is "
<< filtered_.totalExposure;
LOG(RPiAgc, Debug) << "Camera exposure update: shutter time " << filtered_.shutter
<< " analogue gain " << filtered_.analogueGain;
}
Duration Agc::limitShutter(Duration shutter)
{
/*
* shutter == 0 is a special case for fixed shutter values, and must pass
* through unchanged
*/
if (!shutter)
return shutter;
shutter = std::clamp(shutter, mode_.minShutter, maxShutter_);
return shutter;
}
double Agc::limitGain(double gain) const
{
/*
* Only limit the lower bounds of the gain value to what the sensor limits.
* The upper bound on analogue gain will be made up with additional digital
* gain applied by the ISP.
*
* gain == 0.0 is a special case for fixed shutter values, and must pass
* through unchanged
*/
if (!gain)
return gain;
gain = std::max(gain, mode_.minAnalogueGain);
return gain;
}
/* Register algorithm with the system. */
static Algorithm *create(Controller *controller)
{
return (Algorithm *)new Agc(controller);
}
static RegisterAlgorithm reg(NAME, &create);
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* agc.h - AGC/AEC control algorithm
*/
#pragma once
#include <vector>
#include <mutex>
#include <libcamera/base/utils.h>
#include "../agc_algorithm.h"
#include "../agc_status.h"
#include "../pwl.h"
/* This is our implementation of AGC. */
namespace RPiController {
struct AgcMeteringMode {
std::vector<double> weights;
int read(const libcamera::YamlObject &params);
};
struct AgcExposureMode {
std::vector<libcamera::utils::Duration> shutter;
std::vector<double> gain;
int read(const libcamera::YamlObject &params);
};
struct AgcConstraint {
enum class Bound { LOWER = 0, UPPER = 1 };
Bound bound;
double qLo;
double qHi;
Pwl yTarget;
int read(const libcamera::YamlObject &params);
};
typedef std::vector<AgcConstraint> AgcConstraintMode;
struct AgcConfig {
int read(const libcamera::YamlObject &params);
std::map<std::string, AgcMeteringMode> meteringModes;
std::map<std::string, AgcExposureMode> exposureModes;
std::map<std::string, AgcConstraintMode> constraintModes;
Pwl yTarget;
double speed;
uint16_t startupFrames;
unsigned int convergenceFrames;
double maxChange;
double minChange;
double fastReduceThreshold;
double speedUpThreshold;
std::string defaultMeteringMode;
std::string defaultExposureMode;
std::string defaultConstraintMode;
double baseEv;
libcamera::utils::Duration defaultExposureTime;
double defaultAnalogueGain;
};
class Agc : public AgcAlgorithm
{
public:
Agc(Controller *controller);
char const *name() const override;
int read(const libcamera::YamlObject &params) override;
unsigned int getConvergenceFrames() const override;
void setEv(double ev) override;
void setFlickerPeriod(libcamera::utils::Duration flickerPeriod) override;
void setMaxShutter(libcamera::utils::Duration maxShutter) override;
void setFixedShutter(libcamera::utils::Duration fixedShutter) override;
void setFixedAnalogueGain(double fixedAnalogueGain) override;
void setMeteringMode(std::string const &meteringModeName) override;
void setExposureMode(std::string const &exposureModeName) override;
void setConstraintMode(std::string const &contraintModeName) override;
void enableAuto() override;
void disableAuto() override;
void switchMode(CameraMode const &cameraMode, Metadata *metadata) override;
void prepare(Metadata *imageMetadata) override;
void process(StatisticsPtr &stats, Metadata *imageMetadata) override;
private:
void updateLockStatus(DeviceStatus const &deviceStatus);
AgcConfig config_;
void housekeepConfig();
void fetchCurrentExposure(Metadata *imageMetadata);
void fetchAwbStatus(Metadata *imageMetadata);
void computeGain(StatisticsPtr &statistics, Metadata *imageMetadata,
double &gain, double &targetY);
void computeTargetExposure(double gain);
bool applyDigitalGain(double gain, double targetY);
void filterExposure(bool desaturate);
void divideUpExposure();
void writeAndFinish(Metadata *imageMetadata, bool desaturate);
libcamera::utils::Duration limitShutter(libcamera::utils::Duration shutter);
double limitGain(double gain) const;
AgcMeteringMode *meteringMode_;
AgcExposureMode *exposureMode_;
AgcConstraintMode *constraintMode_;
CameraMode mode_;
uint64_t frameCount_;
AwbStatus awb_;
struct ExposureValues {
ExposureValues();
libcamera::utils::Duration shutter;
double analogueGain;
libcamera::utils::Duration totalExposure;
libcamera::utils::Duration totalExposureNoDG; /* without digital gain */
};
ExposureValues current_; /* values for the current frame */
ExposureValues target_; /* calculate the values we want here */
ExposureValues filtered_; /* these values are filtered towards target */
AgcStatus status_;
int lockCount_;
DeviceStatus lastDeviceStatus_;
libcamera::utils::Duration lastTargetExposure_;
/* Below here the "settings" that applications can change. */
std::string meteringModeName_;
std::string exposureModeName_;
std::string constraintModeName_;
double ev_;
libcamera::utils::Duration flickerPeriod_;
libcamera::utils::Duration maxShutter_;
libcamera::utils::Duration fixedShutter_;
double fixedAnalogueGain_;
};
} /* namespace RPiController */
+865
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* alsc.cpp - ALSC (auto lens shading correction) control algorithm
*/
#include <algorithm>
#include <functional>
#include <math.h>
#include <numeric>
#include <libcamera/base/log.h>
#include <libcamera/base/span.h>
#include "../awb_status.h"
#include "alsc.h"
/* Raspberry Pi ALSC (Auto Lens Shading Correction) algorithm. */
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiAlsc)
#define NAME "rpi.alsc"
static const double InsufficientData = -1.0;
Alsc::Alsc(Controller *controller)
: Algorithm(controller)
{
asyncAbort_ = asyncStart_ = asyncStarted_ = asyncFinished_ = false;
asyncThread_ = std::thread(std::bind(&Alsc::asyncFunc, this));
}
Alsc::~Alsc()
{
{
std::lock_guard<std::mutex> lock(mutex_);
asyncAbort_ = true;
}
asyncSignal_.notify_one();
asyncThread_.join();
}
char const *Alsc::name() const
{
return NAME;
}
static int generateLut(Array2D<double> &lut, const libcamera::YamlObject &params)
{
/* These must be signed ints for the co-ordinate calculations below. */
int X = lut.dimensions().width, Y = lut.dimensions().height;
double cstrength = params["corner_strength"].get<double>(2.0);
if (cstrength <= 1.0) {
LOG(RPiAlsc, Error) << "corner_strength must be > 1.0";
return -EINVAL;
}
double asymmetry = params["asymmetry"].get<double>(1.0);
if (asymmetry < 0) {
LOG(RPiAlsc, Error) << "asymmetry must be >= 0";
return -EINVAL;
}
double f1 = cstrength - 1, f2 = 1 + sqrt(cstrength);
double R2 = X * Y / 4 * (1 + asymmetry * asymmetry);
int num = 0;
for (int y = 0; y < Y; y++) {
for (int x = 0; x < X; x++) {
double dy = y - Y / 2 + 0.5,
dx = (x - X / 2 + 0.5) * asymmetry;
double r2 = (dx * dx + dy * dy) / R2;
lut[num++] =
(f1 * r2 + f2) * (f1 * r2 + f2) /
(f2 * f2); /* this reproduces the cos^4 rule */
}
}
return 0;
}
static int readLut(Array2D<double> &lut, const libcamera::YamlObject &params)
{
if (params.size() != lut.size()) {
LOG(RPiAlsc, Error) << "Invalid number of entries in LSC table";
return -EINVAL;
}
int num = 0;
for (const auto &p : params.asList()) {
auto value = p.get<double>();
if (!value)
return -EINVAL;
lut[num++] = *value;
}
return 0;
}
static int readCalibrations(std::vector<AlscCalibration> &calibrations,
const libcamera::YamlObject &params,
std::string const &name, const Size &size)
{
if (params.contains(name)) {
double lastCt = 0;
for (const auto &p : params[name].asList()) {
auto value = p["ct"].get<double>();
if (!value)
return -EINVAL;
double ct = *value;
if (ct <= lastCt) {
LOG(RPiAlsc, Error)
<< "Entries in " << name << " must be in increasing ct order";
return -EINVAL;
}
AlscCalibration calibration;
calibration.ct = lastCt = ct;
const libcamera::YamlObject &table = p["table"];
if (table.size() != size.width * size.height) {
LOG(RPiAlsc, Error)
<< "Incorrect number of values for ct "
<< ct << " in " << name;
return -EINVAL;
}
int num = 0;
calibration.table.resize(size);
for (const auto &elem : table.asList()) {
value = elem.get<double>();
if (!value)
return -EINVAL;
calibration.table[num++] = *value;
}
calibrations.push_back(std::move(calibration));
LOG(RPiAlsc, Debug)
<< "Read " << name << " calibration for ct " << ct;
}
}
return 0;
}
int Alsc::read(const libcamera::YamlObject &params)
{
config_.tableSize = getHardwareConfig().awbRegions;
config_.framePeriod = params["frame_period"].get<uint16_t>(12);
config_.startupFrames = params["startup_frames"].get<uint16_t>(10);
config_.speed = params["speed"].get<double>(0.05);
double sigma = params["sigma"].get<double>(0.01);
config_.sigmaCr = params["sigma_Cr"].get<double>(sigma);
config_.sigmaCb = params["sigma_Cb"].get<double>(sigma);
config_.minCount = params["min_count"].get<double>(10.0);
config_.minG = params["min_G"].get<uint16_t>(50);
config_.omega = params["omega"].get<double>(1.3);
config_.nIter = params["n_iter"].get<uint32_t>(config_.tableSize.width + config_.tableSize.height);
config_.luminanceStrength =
params["luminance_strength"].get<double>(1.0);
config_.luminanceLut.resize(config_.tableSize, 1.0);
int ret = 0;
if (params.contains("corner_strength"))
ret = generateLut(config_.luminanceLut, params);
else if (params.contains("luminance_lut"))
ret = readLut(config_.luminanceLut, params["luminance_lut"]);
else
LOG(RPiAlsc, Warning)
<< "no luminance table - assume unity everywhere";
if (ret)
return ret;
ret = readCalibrations(config_.calibrationsCr, params, "calibrations_Cr",
config_.tableSize);
if (ret)
return ret;
ret = readCalibrations(config_.calibrationsCb, params, "calibrations_Cb",
config_.tableSize);
if (ret)
return ret;
config_.defaultCt = params["default_ct"].get<double>(4500.0);
config_.threshold = params["threshold"].get<double>(1e-3);
config_.lambdaBound = params["lambda_bound"].get<double>(0.05);
return 0;
}
static double getCt(Metadata *metadata, double defaultCt);
static void getCalTable(double ct, std::vector<AlscCalibration> const &calibrations,
Array2D<double> &calTable);
static void resampleCalTable(const Array2D<double> &calTableIn, CameraMode const &cameraMode,
Array2D<double> &calTableOut);
static void compensateLambdasForCal(const Array2D<double> &calTable,
const Array2D<double> &oldLambdas,
Array2D<double> &newLambdas);
static void addLuminanceToTables(std::array<Array2D<double>, 3> &results,
const Array2D<double> &lambdaR, double lambdaG,
const Array2D<double> &lambdaB,
const Array2D<double> &luminanceLut,
double luminanceStrength);
void Alsc::initialise()
{
frameCount2_ = frameCount_ = framePhase_ = 0;
firstTime_ = true;
ct_ = config_.defaultCt;
const size_t XY = config_.tableSize.width * config_.tableSize.height;
for (auto &r : syncResults_)
r.resize(config_.tableSize);
for (auto &r : prevSyncResults_)
r.resize(config_.tableSize);
for (auto &r : asyncResults_)
r.resize(config_.tableSize);
luminanceTable_.resize(config_.tableSize);
asyncLambdaR_.resize(config_.tableSize);
asyncLambdaB_.resize(config_.tableSize);
/* The lambdas are initialised in the SwitchMode. */
lambdaR_.resize(config_.tableSize);
lambdaB_.resize(config_.tableSize);
/* Temporaries for the computations, but sensible to allocate this up-front! */
for (auto &c : tmpC_)
c.resize(config_.tableSize);
for (auto &m : tmpM_)
m.resize(XY);
}
void Alsc::waitForAysncThread()
{
if (asyncStarted_) {
asyncStarted_ = false;
std::unique_lock<std::mutex> lock(mutex_);
syncSignal_.wait(lock, [&] {
return asyncFinished_;
});
asyncFinished_ = false;
}
}
static bool compareModes(CameraMode const &cm0, CameraMode const &cm1)
{
/*
* Return true if the modes crop from the sensor significantly differently,
* or if the user transform has changed.
*/
if (cm0.transform != cm1.transform)
return true;
int leftDiff = abs(cm0.cropX - cm1.cropX);
int topDiff = abs(cm0.cropY - cm1.cropY);
int rightDiff = fabs(cm0.cropX + cm0.scaleX * cm0.width -
cm1.cropX - cm1.scaleX * cm1.width);
int bottomDiff = fabs(cm0.cropY + cm0.scaleY * cm0.height -
cm1.cropY - cm1.scaleY * cm1.height);
/*
* These thresholds are a rather arbitrary amount chosen to trigger
* when carrying on with the previously calculated tables might be
* worse than regenerating them (but without the adaptive algorithm).
*/
int thresholdX = cm0.sensorWidth >> 4;
int thresholdY = cm0.sensorHeight >> 4;
return leftDiff > thresholdX || rightDiff > thresholdX ||
topDiff > thresholdY || bottomDiff > thresholdY;
}
void Alsc::switchMode(CameraMode const &cameraMode,
[[maybe_unused]] Metadata *metadata)
{
/*
* We're going to start over with the tables if there's any "significant"
* change.
*/
bool resetTables = firstTime_ || compareModes(cameraMode_, cameraMode);
/* Believe the colour temperature from the AWB, if there is one. */
ct_ = getCt(metadata, ct_);
/* Ensure the other thread isn't running while we do this. */
waitForAysncThread();
cameraMode_ = cameraMode;
/*
* We must resample the luminance table like we do the others, but it's
* fixed so we can simply do it up front here.
*/
resampleCalTable(config_.luminanceLut, cameraMode_, luminanceTable_);
if (resetTables) {
/*
* Upon every "table reset", arrange for something sensible to be
* generated. Construct the tables for the previous recorded colour
* temperature. In order to start over from scratch we initialise
* the lambdas, but the rest of this code then echoes the code in
* doAlsc, without the adaptive algorithm.
*/
std::fill(lambdaR_.begin(), lambdaR_.end(), 1.0);
std::fill(lambdaB_.begin(), lambdaB_.end(), 1.0);
Array2D<double> &calTableR = tmpC_[0], &calTableB = tmpC_[1], &calTableTmp = tmpC_[2];
getCalTable(ct_, config_.calibrationsCr, calTableTmp);
resampleCalTable(calTableTmp, cameraMode_, calTableR);
getCalTable(ct_, config_.calibrationsCb, calTableTmp);
resampleCalTable(calTableTmp, cameraMode_, calTableB);
compensateLambdasForCal(calTableR, lambdaR_, asyncLambdaR_);
compensateLambdasForCal(calTableB, lambdaB_, asyncLambdaB_);
addLuminanceToTables(syncResults_, asyncLambdaR_, 1.0, asyncLambdaB_,
luminanceTable_, config_.luminanceStrength);
prevSyncResults_ = syncResults_;
framePhase_ = config_.framePeriod; /* run the algo again asap */
firstTime_ = false;
}
}
void Alsc::fetchAsyncResults()
{
LOG(RPiAlsc, Debug) << "Fetch ALSC results";
asyncFinished_ = false;
asyncStarted_ = false;
syncResults_ = asyncResults_;
}
double getCt(Metadata *metadata, double defaultCt)
{
AwbStatus awbStatus;
awbStatus.temperatureK = defaultCt; /* in case nothing found */
if (metadata->get("awb.status", awbStatus) != 0)
LOG(RPiAlsc, Debug) << "no AWB results found, using "
<< awbStatus.temperatureK;
else
LOG(RPiAlsc, Debug) << "AWB results found, using "
<< awbStatus.temperatureK;
return awbStatus.temperatureK;
}
static void copyStats(RgbyRegions &regions, StatisticsPtr &stats,
AlscStatus const &status)
{
if (!regions.numRegions())
regions.init(stats->awbRegions.size());
const std::vector<double> &rTable = status.r;
const std::vector<double> &gTable = status.g;
const std::vector<double> &bTable = status.b;
for (unsigned int i = 0; i < stats->awbRegions.numRegions(); i++) {
auto r = stats->awbRegions.get(i);
r.val.rSum = static_cast<uint64_t>(r.val.rSum / rTable[i]);
r.val.gSum = static_cast<uint64_t>(r.val.gSum / gTable[i]);
r.val.bSum = static_cast<uint64_t>(r.val.bSum / bTable[i]);
regions.set(i, r);
}
}
void Alsc::restartAsync(StatisticsPtr &stats, Metadata *imageMetadata)
{
LOG(RPiAlsc, Debug) << "Starting ALSC calculation";
/*
* Get the current colour temperature. It's all we need from the
* metadata. Default to the last CT value (which could be the default).
*/
ct_ = getCt(imageMetadata, ct_);
/*
* We have to copy the statistics here, dividing out our best guess of
* the LSC table that the pipeline applied to them.
*/
AlscStatus alscStatus;
if (imageMetadata->get("alsc.status", alscStatus) != 0) {
LOG(RPiAlsc, Warning)
<< "No ALSC status found for applied gains!";
alscStatus.r.resize(config_.tableSize.width * config_.tableSize.height, 1.0);
alscStatus.g.resize(config_.tableSize.width * config_.tableSize.height, 1.0);
alscStatus.b.resize(config_.tableSize.width * config_.tableSize.height, 1.0);
}
copyStats(statistics_, stats, alscStatus);
framePhase_ = 0;
asyncStarted_ = true;
{
std::lock_guard<std::mutex> lock(mutex_);
asyncStart_ = true;
}
asyncSignal_.notify_one();
}
void Alsc::prepare(Metadata *imageMetadata)
{
/*
* Count frames since we started, and since we last poked the async
* thread.
*/
if (frameCount_ < (int)config_.startupFrames)
frameCount_++;
double speed = frameCount_ < (int)config_.startupFrames
? 1.0
: config_.speed;
LOG(RPiAlsc, Debug)
<< "frame count " << frameCount_ << " speed " << speed;
{
std::unique_lock<std::mutex> lock(mutex_);
if (asyncStarted_ && asyncFinished_)
fetchAsyncResults();
}
/* Apply IIR filter to results and program into the pipeline. */
for (unsigned int j = 0; j < syncResults_.size(); j++) {
for (unsigned int i = 0; i < syncResults_[j].size(); i++)
prevSyncResults_[j][i] = speed * syncResults_[j][i] + (1.0 - speed) * prevSyncResults_[j][i];
}
/* Put output values into status metadata. */
AlscStatus status;
status.r = prevSyncResults_[0].data();
status.g = prevSyncResults_[1].data();
status.b = prevSyncResults_[2].data();
imageMetadata->set("alsc.status", status);
}
void Alsc::process(StatisticsPtr &stats, Metadata *imageMetadata)
{
/*
* Count frames since we started, and since we last poked the async
* thread.
*/
if (framePhase_ < (int)config_.framePeriod)
framePhase_++;
if (frameCount2_ < (int)config_.startupFrames)
frameCount2_++;
LOG(RPiAlsc, Debug) << "frame_phase " << framePhase_;
if (framePhase_ >= (int)config_.framePeriod ||
frameCount2_ < (int)config_.startupFrames) {
if (asyncStarted_ == false)
restartAsync(stats, imageMetadata);
}
}
void Alsc::asyncFunc()
{
while (true) {
{
std::unique_lock<std::mutex> lock(mutex_);
asyncSignal_.wait(lock, [&] {
return asyncStart_ || asyncAbort_;
});
asyncStart_ = false;
if (asyncAbort_)
break;
}
doAlsc();
{
std::lock_guard<std::mutex> lock(mutex_);
asyncFinished_ = true;
}
syncSignal_.notify_one();
}
}
void getCalTable(double ct, std::vector<AlscCalibration> const &calibrations,
Array2D<double> &calTable)
{
if (calibrations.empty()) {
std::fill(calTable.begin(), calTable.end(), 1.0);
LOG(RPiAlsc, Debug) << "no calibrations found";
} else if (ct <= calibrations.front().ct) {
calTable = calibrations.front().table;
LOG(RPiAlsc, Debug) << "using calibration for "
<< calibrations.front().ct;
} else if (ct >= calibrations.back().ct) {
calTable = calibrations.back().table;
LOG(RPiAlsc, Debug) << "using calibration for "
<< calibrations.back().ct;
} else {
int idx = 0;
while (ct > calibrations[idx + 1].ct)
idx++;
double ct0 = calibrations[idx].ct, ct1 = calibrations[idx + 1].ct;
LOG(RPiAlsc, Debug)
<< "ct is " << ct << ", interpolating between "
<< ct0 << " and " << ct1;
for (unsigned int i = 0; i < calTable.size(); i++)
calTable[i] =
(calibrations[idx].table[i] * (ct1 - ct) +
calibrations[idx + 1].table[i] * (ct - ct0)) /
(ct1 - ct0);
}
}
void resampleCalTable(const Array2D<double> &calTableIn,
CameraMode const &cameraMode,
Array2D<double> &calTableOut)
{
int X = calTableIn.dimensions().width;
int Y = calTableIn.dimensions().height;
/*
* Precalculate and cache the x sampling locations and phases to save
* recomputing them on every row.
*/
int xLo[X], xHi[X];
double xf[X];
double scaleX = cameraMode.sensorWidth /
(cameraMode.width * cameraMode.scaleX);
double xOff = cameraMode.cropX / (double)cameraMode.sensorWidth;
double x = .5 / scaleX + xOff * X - .5;
double xInc = 1 / scaleX;
for (int i = 0; i < X; i++, x += xInc) {
xLo[i] = floor(x);
xf[i] = x - xLo[i];
xHi[i] = std::min(xLo[i] + 1, X - 1);
xLo[i] = std::max(xLo[i], 0);
if (!!(cameraMode.transform & libcamera::Transform::HFlip)) {
xLo[i] = X - 1 - xLo[i];
xHi[i] = X - 1 - xHi[i];
}
}
/* Now march over the output table generating the new values. */
double scaleY = cameraMode.sensorHeight /
(cameraMode.height * cameraMode.scaleY);
double yOff = cameraMode.cropY / (double)cameraMode.sensorHeight;
double y = .5 / scaleY + yOff * Y - .5;
double yInc = 1 / scaleY;
for (int j = 0; j < Y; j++, y += yInc) {
int yLo = floor(y);
double yf = y - yLo;
int yHi = std::min(yLo + 1, Y - 1);
yLo = std::max(yLo, 0);
if (!!(cameraMode.transform & libcamera::Transform::VFlip)) {
yLo = Y - 1 - yLo;
yHi = Y - 1 - yHi;
}
double const *rowAbove = calTableIn.ptr() + X * yLo;
double const *rowBelow = calTableIn.ptr() + X * yHi;
double *out = calTableOut.ptr() + X * j;
for (int i = 0; i < X; i++) {
double above = rowAbove[xLo[i]] * (1 - xf[i]) +
rowAbove[xHi[i]] * xf[i];
double below = rowBelow[xLo[i]] * (1 - xf[i]) +
rowBelow[xHi[i]] * xf[i];
*(out++) = above * (1 - yf) + below * yf;
}
}
}
/* Calculate chrominance statistics (R/G and B/G) for each region. */
static void calculateCrCb(const RgbyRegions &awbRegion, Array2D<double> &cr,
Array2D<double> &cb, uint32_t minCount, uint16_t minG)
{
for (unsigned int i = 0; i < cr.size(); i++) {
auto s = awbRegion.get(i);
if (s.counted <= minCount || s.val.gSum / s.counted <= minG) {
cr[i] = cb[i] = InsufficientData;
continue;
}
cr[i] = s.val.rSum / (double)s.val.gSum;
cb[i] = s.val.bSum / (double)s.val.gSum;
}
}
static void applyCalTable(const Array2D<double> &calTable, Array2D<double> &C)
{
for (unsigned int i = 0; i < C.size(); i++)
if (C[i] != InsufficientData)
C[i] *= calTable[i];
}
void compensateLambdasForCal(const Array2D<double> &calTable,
const Array2D<double> &oldLambdas,
Array2D<double> &newLambdas)
{
double minNewLambda = std::numeric_limits<double>::max();
for (unsigned int i = 0; i < newLambdas.size(); i++) {
newLambdas[i] = oldLambdas[i] * calTable[i];
minNewLambda = std::min(minNewLambda, newLambdas[i]);
}
for (unsigned int i = 0; i < newLambdas.size(); i++)
newLambdas[i] /= minNewLambda;
}
[[maybe_unused]] static void printCalTable(const Array2D<double> &C)
{
const Size &size = C.dimensions();
printf("table: [\n");
for (unsigned int j = 0; j < size.height; j++) {
for (unsigned int i = 0; i < size.width; i++) {
printf("%5.3f", 1.0 / C[j * size.width + i]);
if (i != size.width - 1 || j != size.height - 1)
printf(",");
}
printf("\n");
}
printf("]\n");
}
/*
* Compute weight out of 1.0 which reflects how similar we wish to make the
* colours of these two regions.
*/
static double computeWeight(double Ci, double Cj, double sigma)
{
if (Ci == InsufficientData || Cj == InsufficientData)
return 0;
double diff = (Ci - Cj) / sigma;
return exp(-diff * diff / 2);
}
/* Compute all weights. */
static void computeW(const Array2D<double> &C, double sigma,
SparseArray<double> &W)
{
size_t XY = C.size();
size_t X = C.dimensions().width;
for (unsigned int i = 0; i < XY; i++) {
/* Start with neighbour above and go clockwise. */
W[i][0] = i >= X ? computeWeight(C[i], C[i - X], sigma) : 0;
W[i][1] = i % X < X - 1 ? computeWeight(C[i], C[i + 1], sigma) : 0;
W[i][2] = i < XY - X ? computeWeight(C[i], C[i + X], sigma) : 0;
W[i][3] = i % X ? computeWeight(C[i], C[i - 1], sigma) : 0;
}
}
/* Compute M, the large but sparse matrix such that M * lambdas = 0. */
static void constructM(const Array2D<double> &C,
const SparseArray<double> &W,
SparseArray<double> &M)
{
size_t XY = C.size();
size_t X = C.dimensions().width;
double epsilon = 0.001;
for (unsigned int i = 0; i < XY; i++) {
/*
* Note how, if C[i] == INSUFFICIENT_DATA, the weights will all
* be zero so the equation is still set up correctly.
*/
int m = !!(i >= X) + !!(i % X < X - 1) + !!(i < XY - X) +
!!(i % X); /* total number of neighbours */
/* we'll divide the diagonal out straight away */
double diagonal = (epsilon + W[i][0] + W[i][1] + W[i][2] + W[i][3]) * C[i];
M[i][0] = i >= X ? (W[i][0] * C[i - X] + epsilon / m * C[i]) / diagonal : 0;
M[i][1] = i % X < X - 1 ? (W[i][1] * C[i + 1] + epsilon / m * C[i]) / diagonal : 0;
M[i][2] = i < XY - X ? (W[i][2] * C[i + X] + epsilon / m * C[i]) / diagonal : 0;
M[i][3] = i % X ? (W[i][3] * C[i - 1] + epsilon / m * C[i]) / diagonal : 0;
}
}
/*
* In the compute_lambda_ functions, note that the matrix coefficients for the
* left/right neighbours are zero down the left/right edges, so we don't need
* need to test the i value to exclude them.
*/
static double computeLambdaBottom(int i, const SparseArray<double> &M,
Array2D<double> &lambda)
{
return M[i][1] * lambda[i + 1] + M[i][2] * lambda[i + lambda.dimensions().width] +
M[i][3] * lambda[i - 1];
}
static double computeLambdaBottomStart(int i, const SparseArray<double> &M,
Array2D<double> &lambda)
{
return M[i][1] * lambda[i + 1] + M[i][2] * lambda[i + lambda.dimensions().width];
}
static double computeLambdaInterior(int i, const SparseArray<double> &M,
Array2D<double> &lambda)
{
return M[i][0] * lambda[i - lambda.dimensions().width] + M[i][1] * lambda[i + 1] +
M[i][2] * lambda[i + lambda.dimensions().width] + M[i][3] * lambda[i - 1];
}
static double computeLambdaTop(int i, const SparseArray<double> &M,
Array2D<double> &lambda)
{
return M[i][0] * lambda[i - lambda.dimensions().width] + M[i][1] * lambda[i + 1] +
M[i][3] * lambda[i - 1];
}
static double computeLambdaTopEnd(int i, const SparseArray<double> &M,
Array2D<double> &lambda)
{
return M[i][0] * lambda[i - lambda.dimensions().width] + M[i][3] * lambda[i - 1];
}
/* Gauss-Seidel iteration with over-relaxation. */
static double gaussSeidel2Sor(const SparseArray<double> &M, double omega,
Array2D<double> &lambda, double lambdaBound)
{
int XY = lambda.size();
int X = lambda.dimensions().width;
const double min = 1 - lambdaBound, max = 1 + lambdaBound;
Array2D<double> oldLambda = lambda;
int i;
lambda[0] = computeLambdaBottomStart(0, M, lambda);
lambda[0] = std::clamp(lambda[0], min, max);
for (i = 1; i < X; i++) {
lambda[i] = computeLambdaBottom(i, M, lambda);
lambda[i] = std::clamp(lambda[i], min, max);
}
for (; i < XY - X; i++) {
lambda[i] = computeLambdaInterior(i, M, lambda);
lambda[i] = std::clamp(lambda[i], min, max);
}
for (; i < XY - 1; i++) {
lambda[i] = computeLambdaTop(i, M, lambda);
lambda[i] = std::clamp(lambda[i], min, max);
}
lambda[i] = computeLambdaTopEnd(i, M, lambda);
lambda[i] = std::clamp(lambda[i], min, max);
/*
* Also solve the system from bottom to top, to help spread the updates
* better.
*/
lambda[i] = computeLambdaTopEnd(i, M, lambda);
lambda[i] = std::clamp(lambda[i], min, max);
for (i = XY - 2; i >= XY - X; i--) {
lambda[i] = computeLambdaTop(i, M, lambda);
lambda[i] = std::clamp(lambda[i], min, max);
}
for (; i >= X; i--) {
lambda[i] = computeLambdaInterior(i, M, lambda);
lambda[i] = std::clamp(lambda[i], min, max);
}
for (; i >= 1; i--) {
lambda[i] = computeLambdaBottom(i, M, lambda);
lambda[i] = std::clamp(lambda[i], min, max);
}
lambda[0] = computeLambdaBottomStart(0, M, lambda);
lambda[0] = std::clamp(lambda[0], min, max);
double maxDiff = 0;
for (i = 0; i < XY; i++) {
lambda[i] = oldLambda[i] + (lambda[i] - oldLambda[i]) * omega;
if (fabs(lambda[i] - oldLambda[i]) > fabs(maxDiff))
maxDiff = lambda[i] - oldLambda[i];
}
return maxDiff;
}
/* Normalise the values so that the smallest value is 1. */
static void normalise(Array2D<double> &results)
{
double minval = *std::min_element(results.begin(), results.end());
std::for_each(results.begin(), results.end(),
[minval](double val) { return val / minval; });
}
/* Rescale the values so that the average value is 1. */
static void reaverage(Array2D<double> &data)
{
double sum = std::accumulate(data.begin(), data.end(), 0.0);
double ratio = 1 / (sum / data.size());
std::for_each(data.begin(), data.end(),
[ratio](double val) { return val * ratio; });
}
static void runMatrixIterations(const Array2D<double> &C,
Array2D<double> &lambda,
const SparseArray<double> &W,
SparseArray<double> &M, double omega,
unsigned int nIter, double threshold, double lambdaBound)
{
constructM(C, W, M);
double lastMaxDiff = std::numeric_limits<double>::max();
for (unsigned int i = 0; i < nIter; i++) {
double maxDiff = fabs(gaussSeidel2Sor(M, omega, lambda, lambdaBound));
if (maxDiff < threshold) {
LOG(RPiAlsc, Debug)
<< "Stop after " << i + 1 << " iterations";
break;
}
/*
* this happens very occasionally (so make a note), though
* doesn't seem to matter
*/
if (maxDiff > lastMaxDiff)
LOG(RPiAlsc, Debug)
<< "Iteration " << i << ": maxDiff gone up "
<< lastMaxDiff << " to " << maxDiff;
lastMaxDiff = maxDiff;
}
/* We're going to normalise the lambdas so the total average is 1. */
reaverage(lambda);
}
static void addLuminanceRb(Array2D<double> &result, const Array2D<double> &lambda,
const Array2D<double> &luminanceLut,
double luminanceStrength)
{
for (unsigned int i = 0; i < result.size(); i++)
result[i] = lambda[i] * ((luminanceLut[i] - 1) * luminanceStrength + 1);
}
static void addLuminanceG(Array2D<double> &result, double lambda,
const Array2D<double> &luminanceLut,
double luminanceStrength)
{
for (unsigned int i = 0; i < result.size(); i++)
result[i] = lambda * ((luminanceLut[i] - 1) * luminanceStrength + 1);
}
void addLuminanceToTables(std::array<Array2D<double>, 3> &results,
const Array2D<double> &lambdaR,
double lambdaG, const Array2D<double> &lambdaB,
const Array2D<double> &luminanceLut,
double luminanceStrength)
{
addLuminanceRb(results[0], lambdaR, luminanceLut, luminanceStrength);
addLuminanceG(results[1], lambdaG, luminanceLut, luminanceStrength);
addLuminanceRb(results[2], lambdaB, luminanceLut, luminanceStrength);
for (auto &r : results)
normalise(r);
}
void Alsc::doAlsc()
{
Array2D<double> &cr = tmpC_[0], &cb = tmpC_[1], &calTableR = tmpC_[2],
&calTableB = tmpC_[3], &calTableTmp = tmpC_[4];
SparseArray<double> &wr = tmpM_[0], &wb = tmpM_[1], &M = tmpM_[2];
/*
* Calculate our R/B ("Cr"/"Cb") colour statistics, and assess which are
* usable.
*/
calculateCrCb(statistics_, cr, cb, config_.minCount, config_.minG);
/*
* Fetch the new calibrations (if any) for this CT. Resample them in
* case the camera mode is not full-frame.
*/
getCalTable(ct_, config_.calibrationsCr, calTableTmp);
resampleCalTable(calTableTmp, cameraMode_, calTableR);
getCalTable(ct_, config_.calibrationsCb, calTableTmp);
resampleCalTable(calTableTmp, cameraMode_, calTableB);
/*
* You could print out the cal tables for this image here, if you're
* tuning the algorithm...
* Apply any calibration to the statistics, so the adaptive algorithm
* makes only the extra adjustments.
*/
applyCalTable(calTableR, cr);
applyCalTable(calTableB, cb);
/* Compute weights between zones. */
computeW(cr, config_.sigmaCr, wr);
computeW(cb, config_.sigmaCb, wb);
/* Run Gauss-Seidel iterations over the resulting matrix, for R and B. */
runMatrixIterations(cr, lambdaR_, wr, M, config_.omega, config_.nIter,
config_.threshold, config_.lambdaBound);
runMatrixIterations(cb, lambdaB_, wb, M, config_.omega, config_.nIter,
config_.threshold, config_.lambdaBound);
/*
* Fold the calibrated gains into our final lambda values. (Note that on
* the next run, we re-start with the lambda values that don't have the
* calibration gains included.)
*/
compensateLambdasForCal(calTableR, lambdaR_, asyncLambdaR_);
compensateLambdasForCal(calTableB, lambdaB_, asyncLambdaB_);
/* Fold in the luminance table at the appropriate strength. */
addLuminanceToTables(asyncResults_, asyncLambdaR_, 1.0,
asyncLambdaB_, luminanceTable_,
config_.luminanceStrength);
}
/* Register algorithm with the system. */
static Algorithm *create(Controller *controller)
{
return (Algorithm *)new Alsc(controller);
}
static RegisterAlgorithm reg(NAME, &create);
+174
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@@ -0,0 +1,174 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* alsc.h - ALSC (auto lens shading correction) control algorithm
*/
#pragma once
#include <array>
#include <mutex>
#include <condition_variable>
#include <thread>
#include <vector>
#include <libcamera/geometry.h>
#include "../algorithm.h"
#include "../alsc_status.h"
#include "../statistics.h"
namespace RPiController {
/* Algorithm to generate automagic LSC (Lens Shading Correction) tables. */
/*
* The Array2D class is a very thin wrapper round std::vector so that it can
* be used in exactly the same way in the code but carries its correct width
* and height ("dimensions") with it.
*/
template<typename T>
class Array2D
{
public:
using Size = libcamera::Size;
const Size &dimensions() const { return dimensions_; }
size_t size() const { return data_.size(); }
const std::vector<T> &data() const { return data_; }
void resize(const Size &dims)
{
dimensions_ = dims;
data_.resize(dims.width * dims.height);
}
void resize(const Size &dims, const T &value)
{
resize(dims);
std::fill(data_.begin(), data_.end(), value);
}
T &operator[](int index) { return data_[index]; }
const T &operator[](int index) const { return data_[index]; }
T *ptr() { return data_.data(); }
const T *ptr() const { return data_.data(); }
auto begin() { return data_.begin(); }
auto end() { return data_.end(); }
private:
Size dimensions_;
std::vector<T> data_;
};
/*
* We'll use the term SparseArray for the large sparse matrices that are
* XY tall but have only 4 non-zero elements on each row.
*/
template<typename T>
using SparseArray = std::vector<std::array<T, 4>>;
struct AlscCalibration {
double ct;
Array2D<double> table;
};
struct AlscConfig {
/* Only repeat the ALSC calculation every "this many" frames */
uint16_t framePeriod;
/* number of initial frames for which speed taken as 1.0 (maximum) */
uint16_t startupFrames;
/* IIR filter speed applied to algorithm results */
double speed;
double sigmaCr;
double sigmaCb;
double minCount;
uint16_t minG;
double omega;
uint32_t nIter;
Array2D<double> luminanceLut;
double luminanceStrength;
std::vector<AlscCalibration> calibrationsCr;
std::vector<AlscCalibration> calibrationsCb;
double defaultCt; /* colour temperature if no metadata found */
double threshold; /* iteration termination threshold */
double lambdaBound; /* upper/lower bound for lambda from a value of 1 */
libcamera::Size tableSize;
};
class Alsc : public Algorithm
{
public:
Alsc(Controller *controller = NULL);
~Alsc();
char const *name() const override;
void initialise() override;
void switchMode(CameraMode const &cameraMode, Metadata *metadata) override;
int read(const libcamera::YamlObject &params) override;
void prepare(Metadata *imageMetadata) override;
void process(StatisticsPtr &stats, Metadata *imageMetadata) override;
private:
/* configuration is read-only, and available to both threads */
AlscConfig config_;
bool firstTime_;
CameraMode cameraMode_;
Array2D<double> luminanceTable_;
std::thread asyncThread_;
void asyncFunc(); /* asynchronous thread function */
std::mutex mutex_;
/* condvar for async thread to wait on */
std::condition_variable asyncSignal_;
/* condvar for synchronous thread to wait on */
std::condition_variable syncSignal_;
/* for sync thread to check if async thread finished (requires mutex) */
bool asyncFinished_;
/* for async thread to check if it's been told to run (requires mutex) */
bool asyncStart_;
/* for async thread to check if it's been told to quit (requires mutex) */
bool asyncAbort_;
/*
* The following are only for the synchronous thread to use:
* for sync thread to note its has asked async thread to run
*/
bool asyncStarted_;
/* counts up to framePeriod before restarting the async thread */
int framePhase_;
/* counts up to startupFrames */
int frameCount_;
/* counts up to startupFrames for Process function */
int frameCount2_;
std::array<Array2D<double>, 3> syncResults_;
std::array<Array2D<double>, 3> prevSyncResults_;
void waitForAysncThread();
/*
* The following are for the asynchronous thread to use, though the main
* thread can set/reset them if the async thread is known to be idle:
*/
void restartAsync(StatisticsPtr &stats, Metadata *imageMetadata);
/* copy out the results from the async thread so that it can be restarted */
void fetchAsyncResults();
double ct_;
RgbyRegions statistics_;
std::array<Array2D<double>, 3> asyncResults_;
Array2D<double> asyncLambdaR_;
Array2D<double> asyncLambdaB_;
void doAlsc();
Array2D<double> lambdaR_;
Array2D<double> lambdaB_;
/* Temporaries for the computations */
std::array<Array2D<double>, 5> tmpC_;
std::array<SparseArray<double>, 3> tmpM_;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* awb.cpp - AWB control algorithm
*/
#include <assert.h>
#include <functional>
#include <libcamera/base/log.h>
#include "../lux_status.h"
#include "awb.h"
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiAwb)
#define NAME "rpi.awb"
/*
* todo - the locking in this algorithm needs some tidying up as has been done
* elsewhere (ALSC and AGC).
*/
int AwbMode::read(const libcamera::YamlObject &params)
{
auto value = params["lo"].get<double>();
if (!value)
return -EINVAL;
ctLo = *value;
value = params["hi"].get<double>();
if (!value)
return -EINVAL;
ctHi = *value;
return 0;
}
int AwbPrior::read(const libcamera::YamlObject &params)
{
auto value = params["lux"].get<double>();
if (!value)
return -EINVAL;
lux = *value;
return prior.read(params["prior"]);
}
static int readCtCurve(Pwl &ctR, Pwl &ctB, const libcamera::YamlObject &params)
{
if (params.size() % 3) {
LOG(RPiAwb, Error) << "AwbConfig: incomplete CT curve entry";
return -EINVAL;
}
if (params.size() < 6) {
LOG(RPiAwb, Error) << "AwbConfig: insufficient points in CT curve";
return -EINVAL;
}
const auto &list = params.asList();
for (auto it = list.begin(); it != list.end(); it++) {
auto value = it->get<double>();
if (!value)
return -EINVAL;
double ct = *value;
assert(it == list.begin() || ct != ctR.domain().end);
value = (++it)->get<double>();
if (!value)
return -EINVAL;
ctR.append(ct, *value);
value = (++it)->get<double>();
if (!value)
return -EINVAL;
ctB.append(ct, *value);
}
return 0;
}
int AwbConfig::read(const libcamera::YamlObject &params)
{
int ret;
bayes = params["bayes"].get<int>(1);
framePeriod = params["frame_period"].get<uint16_t>(10);
startupFrames = params["startup_frames"].get<uint16_t>(10);
convergenceFrames = params["convergence_frames"].get<unsigned int>(3);
speed = params["speed"].get<double>(0.05);
if (params.contains("ct_curve")) {
ret = readCtCurve(ctR, ctB, params["ct_curve"]);
if (ret)
return ret;
/* We will want the inverse functions of these too. */
ctRInverse = ctR.inverse();
ctBInverse = ctB.inverse();
}
if (params.contains("priors")) {
for (const auto &p : params["priors"].asList()) {
AwbPrior prior;
ret = prior.read(p);
if (ret)
return ret;
if (!priors.empty() && prior.lux <= priors.back().lux) {
LOG(RPiAwb, Error) << "AwbConfig: Prior must be ordered in increasing lux value";
return -EINVAL;
}
priors.push_back(prior);
}
if (priors.empty()) {
LOG(RPiAwb, Error) << "AwbConfig: no AWB priors configured";
return ret;
}
}
if (params.contains("modes")) {
for (const auto &[key, value] : params["modes"].asDict()) {
ret = modes[key].read(value);
if (ret)
return ret;
if (defaultMode == nullptr)
defaultMode = &modes[key];
}
if (defaultMode == nullptr) {
LOG(RPiAwb, Error) << "AwbConfig: no AWB modes configured";
return -EINVAL;
}
}
minPixels = params["min_pixels"].get<double>(16.0);
minG = params["min_G"].get<uint16_t>(32);
minRegions = params["min_regions"].get<uint32_t>(10);
deltaLimit = params["delta_limit"].get<double>(0.2);
coarseStep = params["coarse_step"].get<double>(0.2);
transversePos = params["transverse_pos"].get<double>(0.01);
transverseNeg = params["transverse_neg"].get<double>(0.01);
if (transversePos <= 0 || transverseNeg <= 0) {
LOG(RPiAwb, Error) << "AwbConfig: transverse_pos/neg must be > 0";
return -EINVAL;
}
sensitivityR = params["sensitivity_r"].get<double>(1.0);
sensitivityB = params["sensitivity_b"].get<double>(1.0);
if (bayes) {
if (ctR.empty() || ctB.empty() || priors.empty() ||
defaultMode == nullptr) {
LOG(RPiAwb, Warning)
<< "Bayesian AWB mis-configured - switch to Grey method";
bayes = false;
}
}
fast = params[fast].get<int>(bayes); /* default to fast for Bayesian, otherwise slow */
whitepointR = params["whitepoint_r"].get<double>(0.0);
whitepointB = params["whitepoint_b"].get<double>(0.0);
if (bayes == false)
sensitivityR = sensitivityB = 1.0; /* nor do sensitivities make any sense */
return 0;
}
Awb::Awb(Controller *controller)
: AwbAlgorithm(controller)
{
asyncAbort_ = asyncStart_ = asyncStarted_ = asyncFinished_ = false;
mode_ = nullptr;
manualR_ = manualB_ = 0.0;
asyncThread_ = std::thread(std::bind(&Awb::asyncFunc, this));
}
Awb::~Awb()
{
{
std::lock_guard<std::mutex> lock(mutex_);
asyncAbort_ = true;
}
asyncSignal_.notify_one();
asyncThread_.join();
}
char const *Awb::name() const
{
return NAME;
}
int Awb::read(const libcamera::YamlObject &params)
{
return config_.read(params);
}
void Awb::initialise()
{
frameCount_ = framePhase_ = 0;
/*
* Put something sane into the status that we are filtering towards,
* just in case the first few frames don't have anything meaningful in
* them.
*/
if (!config_.ctR.empty() && !config_.ctB.empty()) {
syncResults_.temperatureK = config_.ctR.domain().clip(4000);
syncResults_.gainR = 1.0 / config_.ctR.eval(syncResults_.temperatureK);
syncResults_.gainG = 1.0;
syncResults_.gainB = 1.0 / config_.ctB.eval(syncResults_.temperatureK);
} else {
/* random values just to stop the world blowing up */
syncResults_.temperatureK = 4500;
syncResults_.gainR = syncResults_.gainG = syncResults_.gainB = 1.0;
}
prevSyncResults_ = syncResults_;
asyncResults_ = syncResults_;
}
void Awb::disableAuto()
{
/* Freeze the most recent values, and treat them as manual gains */
manualR_ = syncResults_.gainR = prevSyncResults_.gainR;
manualB_ = syncResults_.gainB = prevSyncResults_.gainB;
syncResults_.gainG = prevSyncResults_.gainG;
syncResults_.temperatureK = prevSyncResults_.temperatureK;
}
void Awb::enableAuto()
{
manualR_ = 0.0;
manualB_ = 0.0;
}
unsigned int Awb::getConvergenceFrames() const
{
/*
* If not in auto mode, there is no convergence
* to happen, so no need to drop any frames - return zero.
*/
if (!isAutoEnabled())
return 0;
else
return config_.convergenceFrames;
}
void Awb::setMode(std::string const &modeName)
{
modeName_ = modeName;
}
void Awb::setManualGains(double manualR, double manualB)
{
/* If any of these are 0.0, we swich back to auto. */
manualR_ = manualR;
manualB_ = manualB;
/*
* If not in auto mode, set these values into the syncResults which
* means that Prepare() will adopt them immediately.
*/
if (!isAutoEnabled()) {
syncResults_.gainR = prevSyncResults_.gainR = manualR_;
syncResults_.gainG = prevSyncResults_.gainG = 1.0;
syncResults_.gainB = prevSyncResults_.gainB = manualB_;
if (config_.bayes) {
/* Also estimate the best corresponding colour temperature from the curves. */
double ctR = config_.ctRInverse.eval(config_.ctRInverse.domain().clip(1 / manualR_));
double ctB = config_.ctBInverse.eval(config_.ctBInverse.domain().clip(1 / manualB_));
prevSyncResults_.temperatureK = (ctR + ctB) / 2;
syncResults_.temperatureK = prevSyncResults_.temperatureK;
}
}
}
void Awb::switchMode([[maybe_unused]] CameraMode const &cameraMode,
Metadata *metadata)
{
/* Let other algorithms know the current white balance values. */
metadata->set("awb.status", prevSyncResults_);
}
bool Awb::isAutoEnabled() const
{
return manualR_ == 0.0 || manualB_ == 0.0;
}
void Awb::fetchAsyncResults()
{
LOG(RPiAwb, Debug) << "Fetch AWB results";
asyncFinished_ = false;
asyncStarted_ = false;
/*
* It's possible manual gains could be set even while the async
* thread was running, so only copy the results if still in auto mode.
*/
if (isAutoEnabled())
syncResults_ = asyncResults_;
}
void Awb::restartAsync(StatisticsPtr &stats, double lux)
{
LOG(RPiAwb, Debug) << "Starting AWB calculation";
/* this makes a new reference which belongs to the asynchronous thread */
statistics_ = stats;
/* store the mode as it could technically change */
auto m = config_.modes.find(modeName_);
mode_ = m != config_.modes.end()
? &m->second
: (mode_ == nullptr ? config_.defaultMode : mode_);
lux_ = lux;
framePhase_ = 0;
asyncStarted_ = true;
size_t len = modeName_.copy(asyncResults_.mode,
sizeof(asyncResults_.mode) - 1);
asyncResults_.mode[len] = '\0';
{
std::lock_guard<std::mutex> lock(mutex_);
asyncStart_ = true;
}
asyncSignal_.notify_one();
}
void Awb::prepare(Metadata *imageMetadata)
{
if (frameCount_ < (int)config_.startupFrames)
frameCount_++;
double speed = frameCount_ < (int)config_.startupFrames
? 1.0
: config_.speed;
LOG(RPiAwb, Debug)
<< "frame_count " << frameCount_ << " speed " << speed;
{
std::unique_lock<std::mutex> lock(mutex_);
if (asyncStarted_ && asyncFinished_)
fetchAsyncResults();
}
/* Finally apply IIR filter to results and put into metadata. */
memcpy(prevSyncResults_.mode, syncResults_.mode,
sizeof(prevSyncResults_.mode));
prevSyncResults_.temperatureK = speed * syncResults_.temperatureK +
(1.0 - speed) * prevSyncResults_.temperatureK;
prevSyncResults_.gainR = speed * syncResults_.gainR +
(1.0 - speed) * prevSyncResults_.gainR;
prevSyncResults_.gainG = speed * syncResults_.gainG +
(1.0 - speed) * prevSyncResults_.gainG;
prevSyncResults_.gainB = speed * syncResults_.gainB +
(1.0 - speed) * prevSyncResults_.gainB;
imageMetadata->set("awb.status", prevSyncResults_);
LOG(RPiAwb, Debug)
<< "Using AWB gains r " << prevSyncResults_.gainR << " g "
<< prevSyncResults_.gainG << " b "
<< prevSyncResults_.gainB;
}
void Awb::process(StatisticsPtr &stats, Metadata *imageMetadata)
{
/* Count frames since we last poked the async thread. */
if (framePhase_ < (int)config_.framePeriod)
framePhase_++;
LOG(RPiAwb, Debug) << "frame_phase " << framePhase_;
/* We do not restart the async thread if we're not in auto mode. */
if (isAutoEnabled() &&
(framePhase_ >= (int)config_.framePeriod ||
frameCount_ < (int)config_.startupFrames)) {
/* Update any settings and any image metadata that we need. */
struct LuxStatus luxStatus = {};
luxStatus.lux = 400; /* in case no metadata */
if (imageMetadata->get("lux.status", luxStatus) != 0)
LOG(RPiAwb, Debug) << "No lux metadata found";
LOG(RPiAwb, Debug) << "Awb lux value is " << luxStatus.lux;
if (asyncStarted_ == false)
restartAsync(stats, luxStatus.lux);
}
}
void Awb::asyncFunc()
{
while (true) {
{
std::unique_lock<std::mutex> lock(mutex_);
asyncSignal_.wait(lock, [&] {
return asyncStart_ || asyncAbort_;
});
asyncStart_ = false;
if (asyncAbort_)
break;
}
doAwb();
{
std::lock_guard<std::mutex> lock(mutex_);
asyncFinished_ = true;
}
syncSignal_.notify_one();
}
}
static void generateStats(std::vector<Awb::RGB> &zones,
RgbyRegions &stats, double minPixels,
double minG)
{
for (auto const &region : stats) {
Awb::RGB zone;
if (region.counted >= minPixels) {
zone.G = region.val.gSum / region.counted;
if (zone.G >= minG) {
zone.R = region.val.rSum / region.counted;
zone.B = region.val.bSum / region.counted;
zones.push_back(zone);
}
}
}
}
void Awb::prepareStats()
{
zones_.clear();
/*
* LSC has already been applied to the stats in this pipeline, so stop
* any LSC compensation. We also ignore config_.fast in this version.
*/
generateStats(zones_, statistics_->awbRegions, config_.minPixels,
config_.minG);
/*
* apply sensitivities, so values appear to come from our "canonical"
* sensor.
*/
for (auto &zone : zones_) {
zone.R *= config_.sensitivityR;
zone.B *= config_.sensitivityB;
}
}
double Awb::computeDelta2Sum(double gainR, double gainB)
{
/*
* Compute the sum of the squared colour error (non-greyness) as it
* appears in the log likelihood equation.
*/
double delta2Sum = 0;
for (auto &z : zones_) {
double deltaR = gainR * z.R - 1 - config_.whitepointR;
double deltaB = gainB * z.B - 1 - config_.whitepointB;
double delta2 = deltaR * deltaR + deltaB * deltaB;
/* LOG(RPiAwb, Debug) << "deltaR " << deltaR << " deltaB " << deltaB << " delta2 " << delta2; */
delta2 = std::min(delta2, config_.deltaLimit);
delta2Sum += delta2;
}
return delta2Sum;
}
Pwl Awb::interpolatePrior()
{
/*
* Interpolate the prior log likelihood function for our current lux
* value.
*/
if (lux_ <= config_.priors.front().lux)
return config_.priors.front().prior;
else if (lux_ >= config_.priors.back().lux)
return config_.priors.back().prior;
else {
int idx = 0;
/* find which two we lie between */
while (config_.priors[idx + 1].lux < lux_)
idx++;
double lux0 = config_.priors[idx].lux,
lux1 = config_.priors[idx + 1].lux;
return Pwl::combine(config_.priors[idx].prior,
config_.priors[idx + 1].prior,
[&](double /*x*/, double y0, double y1) {
return y0 + (y1 - y0) *
(lux_ - lux0) / (lux1 - lux0);
});
}
}
static double interpolateQuadatric(Pwl::Point const &a, Pwl::Point const &b,
Pwl::Point const &c)
{
/*
* Given 3 points on a curve, find the extremum of the function in that
* interval by fitting a quadratic.
*/
const double eps = 1e-3;
Pwl::Point ca = c - a, ba = b - a;
double denominator = 2 * (ba.y * ca.x - ca.y * ba.x);
if (abs(denominator) > eps) {
double numerator = ba.y * ca.x * ca.x - ca.y * ba.x * ba.x;
double result = numerator / denominator + a.x;
return std::max(a.x, std::min(c.x, result));
}
/* has degenerated to straight line segment */
return a.y < c.y - eps ? a.x : (c.y < a.y - eps ? c.x : b.x);
}
double Awb::coarseSearch(Pwl const &prior)
{
points_.clear(); /* assume doesn't deallocate memory */
size_t bestPoint = 0;
double t = mode_->ctLo;
int spanR = 0, spanB = 0;
/* Step down the CT curve evaluating log likelihood. */
while (true) {
double r = config_.ctR.eval(t, &spanR);
double b = config_.ctB.eval(t, &spanB);
double gainR = 1 / r, gainB = 1 / b;
double delta2Sum = computeDelta2Sum(gainR, gainB);
double priorLogLikelihood = prior.eval(prior.domain().clip(t));
double finalLogLikelihood = delta2Sum - priorLogLikelihood;
LOG(RPiAwb, Debug)
<< "t: " << t << " gain R " << gainR << " gain B "
<< gainB << " delta2_sum " << delta2Sum
<< " prior " << priorLogLikelihood << " final "
<< finalLogLikelihood;
points_.push_back(Pwl::Point(t, finalLogLikelihood));
if (points_.back().y < points_[bestPoint].y)
bestPoint = points_.size() - 1;
if (t == mode_->ctHi)
break;
/* for even steps along the r/b curve scale them by the current t */
t = std::min(t + t / 10 * config_.coarseStep, mode_->ctHi);
}
t = points_[bestPoint].x;
LOG(RPiAwb, Debug) << "Coarse search found CT " << t;
/*
* We have the best point of the search, but refine it with a quadratic
* interpolation around its neighbours.
*/
if (points_.size() > 2) {
unsigned long bp = std::min(bestPoint, points_.size() - 2);
bestPoint = std::max(1UL, bp);
t = interpolateQuadatric(points_[bestPoint - 1],
points_[bestPoint],
points_[bestPoint + 1]);
LOG(RPiAwb, Debug)
<< "After quadratic refinement, coarse search has CT "
<< t;
}
return t;
}
void Awb::fineSearch(double &t, double &r, double &b, Pwl const &prior)
{
int spanR = -1, spanB = -1;
config_.ctR.eval(t, &spanR);
config_.ctB.eval(t, &spanB);
double step = t / 10 * config_.coarseStep * 0.1;
int nsteps = 5;
double rDiff = config_.ctR.eval(t + nsteps * step, &spanR) -
config_.ctR.eval(t - nsteps * step, &spanR);
double bDiff = config_.ctB.eval(t + nsteps * step, &spanB) -
config_.ctB.eval(t - nsteps * step, &spanB);
Pwl::Point transverse(bDiff, -rDiff);
if (transverse.len2() < 1e-6)
return;
/*
* unit vector orthogonal to the b vs. r function (pointing outwards
* with r and b increasing)
*/
transverse = transverse / transverse.len();
double bestLogLikelihood = 0, bestT = 0, bestR = 0, bestB = 0;
double transverseRange = config_.transverseNeg + config_.transversePos;
const int maxNumDeltas = 12;
/* a transverse step approximately every 0.01 r/b units */
int numDeltas = floor(transverseRange * 100 + 0.5) + 1;
numDeltas = numDeltas < 3 ? 3 : (numDeltas > maxNumDeltas ? maxNumDeltas : numDeltas);
/*
* Step down CT curve. March a bit further if the transverse range is
* large.
*/
nsteps += numDeltas;
for (int i = -nsteps; i <= nsteps; i++) {
double tTest = t + i * step;
double priorLogLikelihood =
prior.eval(prior.domain().clip(tTest));
double rCurve = config_.ctR.eval(tTest, &spanR);
double bCurve = config_.ctB.eval(tTest, &spanB);
/* x will be distance off the curve, y the log likelihood there */
Pwl::Point points[maxNumDeltas];
int bestPoint = 0;
/* Take some measurements transversely *off* the CT curve. */
for (int j = 0; j < numDeltas; j++) {
points[j].x = -config_.transverseNeg +
(transverseRange * j) / (numDeltas - 1);
Pwl::Point rbTest = Pwl::Point(rCurve, bCurve) +
transverse * points[j].x;
double rTest = rbTest.x, bTest = rbTest.y;
double gainR = 1 / rTest, gainB = 1 / bTest;
double delta2Sum = computeDelta2Sum(gainR, gainB);
points[j].y = delta2Sum - priorLogLikelihood;
LOG(RPiAwb, Debug)
<< "At t " << tTest << " r " << rTest << " b "
<< bTest << ": " << points[j].y;
if (points[j].y < points[bestPoint].y)
bestPoint = j;
}
/*
* We have NUM_DELTAS points transversely across the CT curve,
* now let's do a quadratic interpolation for the best result.
*/
bestPoint = std::max(1, std::min(bestPoint, numDeltas - 2));
Pwl::Point rbTest = Pwl::Point(rCurve, bCurve) +
transverse * interpolateQuadatric(points[bestPoint - 1],
points[bestPoint],
points[bestPoint + 1]);
double rTest = rbTest.x, bTest = rbTest.y;
double gainR = 1 / rTest, gainB = 1 / bTest;
double delta2Sum = computeDelta2Sum(gainR, gainB);
double finalLogLikelihood = delta2Sum - priorLogLikelihood;
LOG(RPiAwb, Debug)
<< "Finally "
<< tTest << " r " << rTest << " b " << bTest << ": "
<< finalLogLikelihood
<< (finalLogLikelihood < bestLogLikelihood ? " BEST" : "");
if (bestT == 0 || finalLogLikelihood < bestLogLikelihood)
bestLogLikelihood = finalLogLikelihood,
bestT = tTest, bestR = rTest, bestB = bTest;
}
t = bestT, r = bestR, b = bestB;
LOG(RPiAwb, Debug)
<< "Fine search found t " << t << " r " << r << " b " << b;
}
void Awb::awbBayes()
{
/*
* May as well divide out G to save computeDelta2Sum from doing it over
* and over.
*/
for (auto &z : zones_)
z.R = z.R / (z.G + 1), z.B = z.B / (z.G + 1);
/*
* Get the current prior, and scale according to how many zones are
* valid... not entirely sure about this.
*/
Pwl prior = interpolatePrior();
prior *= zones_.size() / (double)(statistics_->awbRegions.numRegions());
prior.map([](double x, double y) {
LOG(RPiAwb, Debug) << "(" << x << "," << y << ")";
});
double t = coarseSearch(prior);
double r = config_.ctR.eval(t);
double b = config_.ctB.eval(t);
LOG(RPiAwb, Debug)
<< "After coarse search: r " << r << " b " << b << " (gains r "
<< 1 / r << " b " << 1 / b << ")";
/*
* Not entirely sure how to handle the fine search yet. Mostly the
* estimated CT is already good enough, but the fine search allows us to
* wander transverely off the CT curve. Under some illuminants, where
* there may be more or less green light, this may prove beneficial,
* though I probably need more real datasets before deciding exactly how
* this should be controlled and tuned.
*/
fineSearch(t, r, b, prior);
LOG(RPiAwb, Debug)
<< "After fine search: r " << r << " b " << b << " (gains r "
<< 1 / r << " b " << 1 / b << ")";
/*
* Write results out for the main thread to pick up. Remember to adjust
* the gains from the ones that the "canonical sensor" would require to
* the ones needed by *this* sensor.
*/
asyncResults_.temperatureK = t;
asyncResults_.gainR = 1.0 / r * config_.sensitivityR;
asyncResults_.gainG = 1.0;
asyncResults_.gainB = 1.0 / b * config_.sensitivityB;
}
void Awb::awbGrey()
{
LOG(RPiAwb, Debug) << "Grey world AWB";
/*
* Make a separate list of the derivatives for each of red and blue, so
* that we can sort them to exclude the extreme gains. We could
* consider some variations, such as normalising all the zones first, or
* doing an L2 average etc.
*/
std::vector<RGB> &derivsR(zones_);
std::vector<RGB> derivsB(derivsR);
std::sort(derivsR.begin(), derivsR.end(),
[](RGB const &a, RGB const &b) {
return a.G * b.R < b.G * a.R;
});
std::sort(derivsB.begin(), derivsB.end(),
[](RGB const &a, RGB const &b) {
return a.G * b.B < b.G * a.B;
});
/* Average the middle half of the values. */
int discard = derivsR.size() / 4;
RGB sumR(0, 0, 0), sumB(0, 0, 0);
for (auto ri = derivsR.begin() + discard,
bi = derivsB.begin() + discard;
ri != derivsR.end() - discard; ri++, bi++)
sumR += *ri, sumB += *bi;
double gainR = sumR.G / (sumR.R + 1),
gainB = sumB.G / (sumB.B + 1);
asyncResults_.temperatureK = 4500; /* don't know what it is */
asyncResults_.gainR = gainR;
asyncResults_.gainG = 1.0;
asyncResults_.gainB = gainB;
}
void Awb::doAwb()
{
prepareStats();
LOG(RPiAwb, Debug) << "Valid zones: " << zones_.size();
if (zones_.size() > config_.minRegions) {
if (config_.bayes)
awbBayes();
else
awbGrey();
LOG(RPiAwb, Debug)
<< "CT found is "
<< asyncResults_.temperatureK
<< " with gains r " << asyncResults_.gainR
<< " and b " << asyncResults_.gainB;
}
/*
* we're done with these; we may as well relinquish our hold on the
* pointer.
*/
statistics_.reset();
}
/* Register algorithm with the system. */
static Algorithm *create(Controller *controller)
{
return (Algorithm *)new Awb(controller);
}
static RegisterAlgorithm reg(NAME, &create);
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* awb.h - AWB control algorithm
*/
#pragma once
#include <mutex>
#include <condition_variable>
#include <thread>
#include "../awb_algorithm.h"
#include "../pwl.h"
#include "../awb_status.h"
#include "../statistics.h"
namespace RPiController {
/* Control algorithm to perform AWB calculations. */
struct AwbMode {
int read(const libcamera::YamlObject &params);
double ctLo; /* low CT value for search */
double ctHi; /* high CT value for search */
};
struct AwbPrior {
int read(const libcamera::YamlObject &params);
double lux; /* lux level */
Pwl prior; /* maps CT to prior log likelihood for this lux level */
};
struct AwbConfig {
AwbConfig() : defaultMode(nullptr) {}
int read(const libcamera::YamlObject &params);
/* Only repeat the AWB calculation every "this many" frames */
uint16_t framePeriod;
/* number of initial frames for which speed taken as 1.0 (maximum) */
uint16_t startupFrames;
unsigned int convergenceFrames; /* approx number of frames to converge */
double speed; /* IIR filter speed applied to algorithm results */
bool fast; /* "fast" mode uses a 16x16 rather than 32x32 grid */
Pwl ctR; /* function maps CT to r (= R/G) */
Pwl ctB; /* function maps CT to b (= B/G) */
Pwl ctRInverse; /* inverse of ctR */
Pwl ctBInverse; /* inverse of ctB */
/* table of illuminant priors at different lux levels */
std::vector<AwbPrior> priors;
/* AWB "modes" (determines the search range) */
std::map<std::string, AwbMode> modes;
AwbMode *defaultMode; /* mode used if no mode selected */
/*
* minimum proportion of pixels counted within AWB region for it to be
* "useful"
*/
double minPixels;
/* minimum G value of those pixels, to be regarded a "useful" */
uint16_t minG;
/*
* number of AWB regions that must be "useful" in order to do the AWB
* calculation
*/
uint32_t minRegions;
/* clamp on colour error term (so as not to penalise non-grey excessively) */
double deltaLimit;
/* step size control in coarse search */
double coarseStep;
/* how far to wander off CT curve towards "more purple" */
double transversePos;
/* how far to wander off CT curve towards "more green" */
double transverseNeg;
/*
* red sensitivity ratio (set to canonical sensor's R/G divided by this
* sensor's R/G)
*/
double sensitivityR;
/*
* blue sensitivity ratio (set to canonical sensor's B/G divided by this
* sensor's B/G)
*/
double sensitivityB;
/* The whitepoint (which we normally "aim" for) can be moved. */
double whitepointR;
double whitepointB;
bool bayes; /* use Bayesian algorithm */
};
class Awb : public AwbAlgorithm
{
public:
Awb(Controller *controller = NULL);
~Awb();
char const *name() const override;
void initialise() override;
int read(const libcamera::YamlObject &params) override;
unsigned int getConvergenceFrames() const override;
void setMode(std::string const &name) override;
void setManualGains(double manualR, double manualB) override;
void enableAuto() override;
void disableAuto() override;
void switchMode(CameraMode const &cameraMode, Metadata *metadata) override;
void prepare(Metadata *imageMetadata) override;
void process(StatisticsPtr &stats, Metadata *imageMetadata) override;
struct RGB {
RGB(double r = 0, double g = 0, double b = 0)
: R(r), G(g), B(b)
{
}
double R, G, B;
RGB &operator+=(RGB const &other)
{
R += other.R, G += other.G, B += other.B;
return *this;
}
};
private:
bool isAutoEnabled() const;
/* configuration is read-only, and available to both threads */
AwbConfig config_;
std::thread asyncThread_;
void asyncFunc(); /* asynchronous thread function */
std::mutex mutex_;
/* condvar for async thread to wait on */
std::condition_variable asyncSignal_;
/* condvar for synchronous thread to wait on */
std::condition_variable syncSignal_;
/* for sync thread to check if async thread finished (requires mutex) */
bool asyncFinished_;
/* for async thread to check if it's been told to run (requires mutex) */
bool asyncStart_;
/* for async thread to check if it's been told to quit (requires mutex) */
bool asyncAbort_;
/*
* The following are only for the synchronous thread to use:
* for sync thread to note its has asked async thread to run
*/
bool asyncStarted_;
/* counts up to framePeriod before restarting the async thread */
int framePhase_;
int frameCount_; /* counts up to startup_frames */
AwbStatus syncResults_;
AwbStatus prevSyncResults_;
std::string modeName_;
/*
* The following are for the asynchronous thread to use, though the main
* thread can set/reset them if the async thread is known to be idle:
*/
void restartAsync(StatisticsPtr &stats, double lux);
/* copy out the results from the async thread so that it can be restarted */
void fetchAsyncResults();
StatisticsPtr statistics_;
AwbMode *mode_;
double lux_;
AwbStatus asyncResults_;
void doAwb();
void awbBayes();
void awbGrey();
void prepareStats();
double computeDelta2Sum(double gainR, double gainB);
Pwl interpolatePrior();
double coarseSearch(Pwl const &prior);
void fineSearch(double &t, double &r, double &b, Pwl const &prior);
std::vector<RGB> zones_;
std::vector<Pwl::Point> points_;
/* manual r setting */
double manualR_;
/* manual b setting */
double manualB_;
};
static inline Awb::RGB operator+(Awb::RGB const &a, Awb::RGB const &b)
{
return Awb::RGB(a.R + b.R, a.G + b.G, a.B + b.B);
}
static inline Awb::RGB operator-(Awb::RGB const &a, Awb::RGB const &b)
{
return Awb::RGB(a.R - b.R, a.G - b.G, a.B - b.B);
}
static inline Awb::RGB operator*(double d, Awb::RGB const &rgb)
{
return Awb::RGB(d * rgb.R, d * rgb.G, d * rgb.B);
}
static inline Awb::RGB operator*(Awb::RGB const &rgb, double d)
{
return d * rgb;
}
} /* namespace RPiController */
@@ -0,0 +1,66 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* black_level.cpp - black level control algorithm
*/
#include <math.h>
#include <stdint.h>
#include <libcamera/base/log.h>
#include "../black_level_status.h"
#include "black_level.h"
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiBlackLevel)
#define NAME "rpi.black_level"
BlackLevel::BlackLevel(Controller *controller)
: Algorithm(controller)
{
}
char const *BlackLevel::name() const
{
return NAME;
}
int BlackLevel::read(const libcamera::YamlObject &params)
{
/* 64 in 10 bits scaled to 16 bits */
uint16_t blackLevel = params["black_level"].get<uint16_t>(4096);
blackLevelR_ = params["black_level_r"].get<uint16_t>(blackLevel);
blackLevelG_ = params["black_level_g"].get<uint16_t>(blackLevel);
blackLevelB_ = params["black_level_b"].get<uint16_t>(blackLevel);
LOG(RPiBlackLevel, Debug)
<< " Read black levels red " << blackLevelR_
<< " green " << blackLevelG_
<< " blue " << blackLevelB_;
return 0;
}
void BlackLevel::prepare(Metadata *imageMetadata)
{
/*
* Possibly we should think about doing this in a switchMode or
* something?
*/
struct BlackLevelStatus status;
status.blackLevelR = blackLevelR_;
status.blackLevelG = blackLevelG_;
status.blackLevelB = blackLevelB_;
imageMetadata->set("black_level.status", status);
}
/* Register algorithm with the system. */
static Algorithm *create(Controller *controller)
{
return new BlackLevel(controller);
}
static RegisterAlgorithm reg(NAME, &create);
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* black_level.h - black level control algorithm
*/
#pragma once
#include "../algorithm.h"
#include "../black_level_status.h"
/* This is our implementation of the "black level algorithm". */
namespace RPiController {
class BlackLevel : public Algorithm
{
public:
BlackLevel(Controller *controller);
char const *name() const override;
int read(const libcamera::YamlObject &params) override;
void prepare(Metadata *imageMetadata) override;
private:
double blackLevelR_;
double blackLevelG_;
double blackLevelB_;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* ccm.cpp - CCM (colour correction matrix) control algorithm
*/
#include <libcamera/base/log.h>
#include "../awb_status.h"
#include "../ccm_status.h"
#include "../lux_status.h"
#include "../metadata.h"
#include "ccm.h"
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiCcm)
/*
* This algorithm selects a CCM (Colour Correction Matrix) according to the
* colour temperature estimated by AWB (interpolating between known matricies as
* necessary). Additionally the amount of colour saturation can be controlled
* both according to the current estimated lux level and according to a
* saturation setting that is exposed to applications.
*/
#define NAME "rpi.ccm"
Matrix::Matrix()
{
memset(m, 0, sizeof(m));
}
Matrix::Matrix(double m0, double m1, double m2, double m3, double m4, double m5,
double m6, double m7, double m8)
{
m[0][0] = m0, m[0][1] = m1, m[0][2] = m2, m[1][0] = m3, m[1][1] = m4,
m[1][2] = m5, m[2][0] = m6, m[2][1] = m7, m[2][2] = m8;
}
int Matrix::read(const libcamera::YamlObject &params)
{
double *ptr = (double *)m;
if (params.size() != 9) {
LOG(RPiCcm, Error) << "Wrong number of values in CCM";
return -EINVAL;
}
for (const auto &param : params.asList()) {
auto value = param.get<double>();
if (!value)
return -EINVAL;
*ptr++ = *value;
}
return 0;
}
Ccm::Ccm(Controller *controller)
: CcmAlgorithm(controller), saturation_(1.0) {}
char const *Ccm::name() const
{
return NAME;
}
int Ccm::read(const libcamera::YamlObject &params)
{
int ret;
if (params.contains("saturation")) {
ret = config_.saturation.read(params["saturation"]);
if (ret)
return ret;
}
for (auto &p : params["ccms"].asList()) {
auto value = p["ct"].get<double>();
if (!value)
return -EINVAL;
CtCcm ctCcm;
ctCcm.ct = *value;
ret = ctCcm.ccm.read(p["ccm"]);
if (ret)
return ret;
if (!config_.ccms.empty() && ctCcm.ct <= config_.ccms.back().ct) {
LOG(RPiCcm, Error)
<< "CCM not in increasing colour temperature order";
return -EINVAL;
}
config_.ccms.push_back(std::move(ctCcm));
}
if (config_.ccms.empty()) {
LOG(RPiCcm, Error) << "No CCMs specified";
return -EINVAL;
}
return 0;
}
void Ccm::setSaturation(double saturation)
{
saturation_ = saturation;
}
void Ccm::initialise()
{
}
template<typename T>
static bool getLocked(Metadata *metadata, std::string const &tag, T &value)
{
T *ptr = metadata->getLocked<T>(tag);
if (ptr == nullptr)
return false;
value = *ptr;
return true;
}
Matrix calculateCcm(std::vector<CtCcm> const &ccms, double ct)
{
if (ct <= ccms.front().ct)
return ccms.front().ccm;
else if (ct >= ccms.back().ct)
return ccms.back().ccm;
else {
int i = 0;
for (; ct > ccms[i].ct; i++)
;
double lambda =
(ct - ccms[i - 1].ct) / (ccms[i].ct - ccms[i - 1].ct);
return lambda * ccms[i].ccm + (1.0 - lambda) * ccms[i - 1].ccm;
}
}
Matrix applySaturation(Matrix const &ccm, double saturation)
{
Matrix RGB2Y(0.299, 0.587, 0.114, -0.169, -0.331, 0.500, 0.500, -0.419,
-0.081);
Matrix Y2RGB(1.000, 0.000, 1.402, 1.000, -0.345, -0.714, 1.000, 1.771,
0.000);
Matrix S(1, 0, 0, 0, saturation, 0, 0, 0, saturation);
return Y2RGB * S * RGB2Y * ccm;
}
void Ccm::prepare(Metadata *imageMetadata)
{
bool awbOk = false, luxOk = false;
struct AwbStatus awb = {};
awb.temperatureK = 4000; /* in case no metadata */
struct LuxStatus lux = {};
lux.lux = 400; /* in case no metadata */
{
/* grab mutex just once to get everything */
std::lock_guard<Metadata> lock(*imageMetadata);
awbOk = getLocked(imageMetadata, "awb.status", awb);
luxOk = getLocked(imageMetadata, "lux.status", lux);
}
if (!awbOk)
LOG(RPiCcm, Warning) << "no colour temperature found";
if (!luxOk)
LOG(RPiCcm, Warning) << "no lux value found";
Matrix ccm = calculateCcm(config_.ccms, awb.temperatureK);
double saturation = saturation_;
struct CcmStatus ccmStatus;
ccmStatus.saturation = saturation;
if (!config_.saturation.empty())
saturation *= config_.saturation.eval(
config_.saturation.domain().clip(lux.lux));
ccm = applySaturation(ccm, saturation);
for (int j = 0; j < 3; j++)
for (int i = 0; i < 3; i++)
ccmStatus.matrix[j * 3 + i] =
std::max(-8.0, std::min(7.9999, ccm.m[j][i]));
LOG(RPiCcm, Debug)
<< "colour temperature " << awb.temperatureK << "K";
LOG(RPiCcm, Debug)
<< "CCM: " << ccmStatus.matrix[0] << " " << ccmStatus.matrix[1]
<< " " << ccmStatus.matrix[2] << " "
<< ccmStatus.matrix[3] << " " << ccmStatus.matrix[4]
<< " " << ccmStatus.matrix[5] << " "
<< ccmStatus.matrix[6] << " " << ccmStatus.matrix[7]
<< " " << ccmStatus.matrix[8];
imageMetadata->set("ccm.status", ccmStatus);
}
/* Register algorithm with the system. */
static Algorithm *create(Controller *controller)
{
return (Algorithm *)new Ccm(controller);
;
}
static RegisterAlgorithm reg(NAME, &create);
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* ccm.h - CCM (colour correction matrix) control algorithm
*/
#pragma once
#include <vector>
#include "../ccm_algorithm.h"
#include "../pwl.h"
namespace RPiController {
/* Algorithm to calculate colour matrix. Should be placed after AWB. */
struct Matrix {
Matrix(double m0, double m1, double m2, double m3, double m4, double m5,
double m6, double m7, double m8);
Matrix();
double m[3][3];
int read(const libcamera::YamlObject &params);
};
static inline Matrix operator*(double d, Matrix const &m)
{
return Matrix(m.m[0][0] * d, m.m[0][1] * d, m.m[0][2] * d,
m.m[1][0] * d, m.m[1][1] * d, m.m[1][2] * d,
m.m[2][0] * d, m.m[2][1] * d, m.m[2][2] * d);
}
static inline Matrix operator*(Matrix const &m1, Matrix const &m2)
{
Matrix m;
for (int i = 0; i < 3; i++)
for (int j = 0; j < 3; j++)
m.m[i][j] = m1.m[i][0] * m2.m[0][j] +
m1.m[i][1] * m2.m[1][j] +
m1.m[i][2] * m2.m[2][j];
return m;
}
static inline Matrix operator+(Matrix const &m1, Matrix const &m2)
{
Matrix m;
for (int i = 0; i < 3; i++)
for (int j = 0; j < 3; j++)
m.m[i][j] = m1.m[i][j] + m2.m[i][j];
return m;
}
struct CtCcm {
double ct;
Matrix ccm;
};
struct CcmConfig {
std::vector<CtCcm> ccms;
Pwl saturation;
};
class Ccm : public CcmAlgorithm
{
public:
Ccm(Controller *controller = NULL);
char const *name() const override;
int read(const libcamera::YamlObject &params) override;
void setSaturation(double saturation) override;
void initialise() override;
void prepare(Metadata *imageMetadata) override;
private:
CcmConfig config_;
double saturation_;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* contrast.cpp - contrast (gamma) control algorithm
*/
#include <stdint.h>
#include <libcamera/base/log.h>
#include "../contrast_status.h"
#include "../histogram.h"
#include "contrast.h"
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiContrast)
/*
* This is a very simple control algorithm which simply retrieves the results of
* AGC and AWB via their "status" metadata, and applies digital gain to the
* colour channels in accordance with those instructions. We take care never to
* apply less than unity gains, as that would cause fully saturated pixels to go
* off-white.
*/
#define NAME "rpi.contrast"
Contrast::Contrast(Controller *controller)
: ContrastAlgorithm(controller), brightness_(0.0), contrast_(1.0)
{
}
char const *Contrast::name() const
{
return NAME;
}
int Contrast::read(const libcamera::YamlObject &params)
{
// enable adaptive enhancement by default
config_.ceEnable = params["ce_enable"].get<int>(1);
// the point near the bottom of the histogram to move
config_.loHistogram = params["lo_histogram"].get<double>(0.01);
// where in the range to try and move it to
config_.loLevel = params["lo_level"].get<double>(0.015);
// but don't move by more than this
config_.loMax = params["lo_max"].get<double>(500);
// equivalent values for the top of the histogram...
config_.hiHistogram = params["hi_histogram"].get<double>(0.95);
config_.hiLevel = params["hi_level"].get<double>(0.95);
config_.hiMax = params["hi_max"].get<double>(2000);
return config_.gammaCurve.read(params["gamma_curve"]);
}
void Contrast::setBrightness(double brightness)
{
brightness_ = brightness;
}
void Contrast::setContrast(double contrast)
{
contrast_ = contrast;
}
void Contrast::initialise()
{
/*
* Fill in some default values as Prepare will run before Process gets
* called.
*/
status_.brightness = brightness_;
status_.contrast = contrast_;
status_.gammaCurve = config_.gammaCurve;
}
void Contrast::prepare(Metadata *imageMetadata)
{
imageMetadata->set("contrast.status", status_);
}
Pwl computeStretchCurve(Histogram const &histogram,
ContrastConfig const &config)
{
Pwl enhance;
enhance.append(0, 0);
/*
* If the start of the histogram is rather empty, try to pull it down a
* bit.
*/
double histLo = histogram.quantile(config.loHistogram) *
(65536 / histogram.bins());
double levelLo = config.loLevel * 65536;
LOG(RPiContrast, Debug)
<< "Move histogram point " << histLo << " to " << levelLo;
histLo = std::max(levelLo,
std::min(65535.0, std::min(histLo, levelLo + config.loMax)));
LOG(RPiContrast, Debug)
<< "Final values " << histLo << " -> " << levelLo;
enhance.append(histLo, levelLo);
/*
* Keep the mid-point (median) in the same place, though, to limit the
* apparent amount of global brightness shift.
*/
double mid = histogram.quantile(0.5) * (65536 / histogram.bins());
enhance.append(mid, mid);
/*
* If the top to the histogram is empty, try to pull the pixel values
* there up.
*/
double histHi = histogram.quantile(config.hiHistogram) *
(65536 / histogram.bins());
double levelHi = config.hiLevel * 65536;
LOG(RPiContrast, Debug)
<< "Move histogram point " << histHi << " to " << levelHi;
histHi = std::min(levelHi,
std::max(0.0, std::max(histHi, levelHi - config.hiMax)));
LOG(RPiContrast, Debug)
<< "Final values " << histHi << " -> " << levelHi;
enhance.append(histHi, levelHi);
enhance.append(65535, 65535);
return enhance;
}
Pwl applyManualContrast(Pwl const &gammaCurve, double brightness,
double contrast)
{
Pwl newGammaCurve;
LOG(RPiContrast, Debug)
<< "Manual brightness " << brightness << " contrast " << contrast;
gammaCurve.map([&](double x, double y) {
newGammaCurve.append(
x, std::max(0.0, std::min(65535.0,
(y - 32768) * contrast +
32768 + brightness)));
});
return newGammaCurve;
}
void Contrast::process(StatisticsPtr &stats,
[[maybe_unused]] Metadata *imageMetadata)
{
Histogram &histogram = stats->yHist;
/*
* We look at the histogram and adjust the gamma curve in the following
* ways: 1. Adjust the gamma curve so as to pull the start of the
* histogram down, and possibly push the end up.
*/
Pwl gammaCurve = config_.gammaCurve;
if (config_.ceEnable) {
if (config_.loMax != 0 || config_.hiMax != 0)
gammaCurve = computeStretchCurve(histogram, config_).compose(gammaCurve);
/*
* We could apply other adjustments (e.g. partial equalisation)
* based on the histogram...?
*/
}
/*
* 2. Finally apply any manually selected brightness/contrast
* adjustment.
*/
if (brightness_ != 0 || contrast_ != 1.0)
gammaCurve = applyManualContrast(gammaCurve, brightness_, contrast_);
/*
* And fill in the status for output. Use more points towards the bottom
* of the curve.
*/
status_.brightness = brightness_;
status_.contrast = contrast_;
status_.gammaCurve = std::move(gammaCurve);
}
/* Register algorithm with the system. */
static Algorithm *create(Controller *controller)
{
return (Algorithm *)new Contrast(controller);
}
static RegisterAlgorithm reg(NAME, &create);
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* contrast.h - contrast (gamma) control algorithm
*/
#pragma once
#include <mutex>
#include "../contrast_algorithm.h"
#include "../pwl.h"
namespace RPiController {
/*
* Back End algorithm to appaly correct digital gain. Should be placed after
* Back End AWB.
*/
struct ContrastConfig {
bool ceEnable;
double loHistogram;
double loLevel;
double loMax;
double hiHistogram;
double hiLevel;
double hiMax;
Pwl gammaCurve;
};
class Contrast : public ContrastAlgorithm
{
public:
Contrast(Controller *controller = NULL);
char const *name() const override;
int read(const libcamera::YamlObject &params) override;
void setBrightness(double brightness) override;
void setContrast(double contrast) override;
void initialise() override;
void prepare(Metadata *imageMetadata) override;
void process(StatisticsPtr &stats, Metadata *imageMetadata) override;
private:
ContrastConfig config_;
double brightness_;
double contrast_;
ContrastStatus status_;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* dpc.cpp - DPC (defective pixel correction) control algorithm
*/
#include <libcamera/base/log.h>
#include "dpc.h"
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiDpc)
/*
* We use the lux status so that we can apply stronger settings in darkness (if
* necessary).
*/
#define NAME "rpi.dpc"
Dpc::Dpc(Controller *controller)
: Algorithm(controller)
{
}
char const *Dpc::name() const
{
return NAME;
}
int Dpc::read(const libcamera::YamlObject &params)
{
config_.strength = params["strength"].get<int>(1);
if (config_.strength < 0 || config_.strength > 2) {
LOG(RPiDpc, Error) << "Bad strength value";
return -EINVAL;
}
return 0;
}
void Dpc::prepare(Metadata *imageMetadata)
{
DpcStatus dpcStatus = {};
/* Should we vary this with lux level or analogue gain? TBD. */
dpcStatus.strength = config_.strength;
LOG(RPiDpc, Debug) << "strength " << dpcStatus.strength;
imageMetadata->set("dpc.status", dpcStatus);
}
/* Register algorithm with the system. */
static Algorithm *create(Controller *controller)
{
return (Algorithm *)new Dpc(controller);
}
static RegisterAlgorithm reg(NAME, &create);
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* dpc.h - DPC (defective pixel correction) control algorithm
*/
#pragma once
#include "../algorithm.h"
#include "../dpc_status.h"
namespace RPiController {
/* Back End algorithm to apply appropriate GEQ settings. */
struct DpcConfig {
int strength;
};
class Dpc : public Algorithm
{
public:
Dpc(Controller *controller);
char const *name() const override;
int read(const libcamera::YamlObject &params) override;
void prepare(Metadata *imageMetadata) override;
private:
DpcConfig config_;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2020, Raspberry Pi Ltd
*
* focus.h - focus algorithm
*/
#pragma once
#include "../algorithm.h"
#include "../metadata.h"
/*
* The "focus" algorithm. All it does it print out a version of the
* focus contrast measure; there is no actual auto-focus mechanism to
* control.
*/
namespace RPiController {
class Focus : public Algorithm
{
public:
Focus(Controller *controller);
char const *name() const override;
void process(StatisticsPtr &stats, Metadata *imageMetadata) override;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* geq.cpp - GEQ (green equalisation) control algorithm
*/
#include <libcamera/base/log.h>
#include "../device_status.h"
#include "../lux_status.h"
#include "../pwl.h"
#include "geq.h"
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiGeq)
/*
* We use the lux status so that we can apply stronger settings in darkness (if
* necessary).
*/
#define NAME "rpi.geq"
Geq::Geq(Controller *controller)
: Algorithm(controller)
{
}
char const *Geq::name() const
{
return NAME;
}
int Geq::read(const libcamera::YamlObject &params)
{
config_.offset = params["offset"].get<uint16_t>(0);
config_.slope = params["slope"].get<double>(0.0);
if (config_.slope < 0.0 || config_.slope >= 1.0) {
LOG(RPiGeq, Error) << "Bad slope value";
return -EINVAL;
}
if (params.contains("strength")) {
int ret = config_.strength.read(params["strength"]);
if (ret)
return ret;
}
return 0;
}
void Geq::prepare(Metadata *imageMetadata)
{
LuxStatus luxStatus = {};
luxStatus.lux = 400;
if (imageMetadata->get("lux.status", luxStatus))
LOG(RPiGeq, Warning) << "no lux data found";
DeviceStatus deviceStatus;
deviceStatus.analogueGain = 1.0; /* in case not found */
if (imageMetadata->get("device.status", deviceStatus))
LOG(RPiGeq, Warning)
<< "no device metadata - use analogue gain of 1x";
GeqStatus geqStatus = {};
double strength = config_.strength.empty()
? 1.0
: config_.strength.eval(config_.strength.domain().clip(luxStatus.lux));
strength *= deviceStatus.analogueGain;
double offset = config_.offset * strength;
double slope = config_.slope * strength;
geqStatus.offset = std::min(65535.0, std::max(0.0, offset));
geqStatus.slope = std::min(.99999, std::max(0.0, slope));
LOG(RPiGeq, Debug)
<< "offset " << geqStatus.offset << " slope "
<< geqStatus.slope << " (analogue gain "
<< deviceStatus.analogueGain << " lux "
<< luxStatus.lux << ")";
imageMetadata->set("geq.status", geqStatus);
}
/* Register algorithm with the system. */
static Algorithm *create(Controller *controller)
{
return (Algorithm *)new Geq(controller);
}
static RegisterAlgorithm reg(NAME, &create);
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* geq.h - GEQ (green equalisation) control algorithm
*/
#pragma once
#include "../algorithm.h"
#include "../geq_status.h"
namespace RPiController {
/* Back End algorithm to apply appropriate GEQ settings. */
struct GeqConfig {
uint16_t offset;
double slope;
Pwl strength; /* lux to strength factor */
};
class Geq : public Algorithm
{
public:
Geq(Controller *controller);
char const *name() const override;
int read(const libcamera::YamlObject &params) override;
void prepare(Metadata *imageMetadata) override;
private:
GeqConfig config_;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* lux.cpp - Lux control algorithm
*/
#include <math.h>
#include <libcamera/base/log.h>
#include "../device_status.h"
#include "lux.h"
using namespace RPiController;
using namespace libcamera;
using namespace std::literals::chrono_literals;
LOG_DEFINE_CATEGORY(RPiLux)
#define NAME "rpi.lux"
Lux::Lux(Controller *controller)
: Algorithm(controller)
{
/*
* Put in some defaults as there will be no meaningful values until
* Process has run.
*/
status_.aperture = 1.0;
status_.lux = 400;
}
char const *Lux::name() const
{
return NAME;
}
int Lux::read(const libcamera::YamlObject &params)
{
auto value = params["reference_shutter_speed"].get<double>();
if (!value)
return -EINVAL;
referenceShutterSpeed_ = *value * 1.0us;
value = params["reference_gain"].get<double>();
if (!value)
return -EINVAL;
referenceGain_ = *value;
referenceAperture_ = params["reference_aperture"].get<double>(1.0);
value = params["reference_Y"].get<double>();
if (!value)
return -EINVAL;
referenceY_ = *value;
value = params["reference_lux"].get<double>();
if (!value)
return -EINVAL;
referenceLux_ = *value;
currentAperture_ = referenceAperture_;
return 0;
}
void Lux::setCurrentAperture(double aperture)
{
currentAperture_ = aperture;
}
void Lux::prepare(Metadata *imageMetadata)
{
std::unique_lock<std::mutex> lock(mutex_);
imageMetadata->set("lux.status", status_);
}
void Lux::process(StatisticsPtr &stats, Metadata *imageMetadata)
{
DeviceStatus deviceStatus;
if (imageMetadata->get("device.status", deviceStatus) == 0) {
double currentGain = deviceStatus.analogueGain;
double currentAperture = deviceStatus.aperture.value_or(currentAperture_);
double currentY = stats->yHist.interQuantileMean(0, 1);
double gainRatio = referenceGain_ / currentGain;
double shutterSpeedRatio =
referenceShutterSpeed_ / deviceStatus.shutterSpeed;
double apertureRatio = referenceAperture_ / currentAperture;
double yRatio = currentY * (65536 / stats->yHist.bins()) / referenceY_;
double estimatedLux = shutterSpeedRatio * gainRatio *
apertureRatio * apertureRatio *
yRatio * referenceLux_;
LuxStatus status;
status.lux = estimatedLux;
status.aperture = currentAperture;
LOG(RPiLux, Debug) << ": estimated lux " << estimatedLux;
{
std::unique_lock<std::mutex> lock(mutex_);
status_ = status;
}
/*
* Overwrite the metadata here as well, so that downstream
* algorithms get the latest value.
*/
imageMetadata->set("lux.status", status);
} else
LOG(RPiLux, Warning) << ": no device metadata";
}
/* Register algorithm with the system. */
static Algorithm *create(Controller *controller)
{
return (Algorithm *)new Lux(controller);
}
static RegisterAlgorithm reg(NAME, &create);
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* lux.h - Lux control algorithm
*/
#pragma once
#include <mutex>
#include <libcamera/base/utils.h>
#include "../lux_status.h"
#include "../algorithm.h"
/* This is our implementation of the "lux control algorithm". */
namespace RPiController {
class Lux : public Algorithm
{
public:
Lux(Controller *controller);
char const *name() const override;
int read(const libcamera::YamlObject &params) override;
void prepare(Metadata *imageMetadata) override;
void process(StatisticsPtr &stats, Metadata *imageMetadata) override;
void setCurrentAperture(double aperture);
private:
/*
* These values define the conditions of the reference image, against
* which we compare the new image.
*/
libcamera::utils::Duration referenceShutterSpeed_;
double referenceGain_;
double referenceAperture_; /* units of 1/f */
double referenceY_; /* out of 65536 */
double referenceLux_;
double currentAperture_;
LuxStatus status_;
std::mutex mutex_;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* noise.cpp - Noise control algorithm
*/
#include <math.h>
#include <libcamera/base/log.h>
#include "../device_status.h"
#include "../noise_status.h"
#include "noise.h"
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiNoise)
#define NAME "rpi.noise"
Noise::Noise(Controller *controller)
: Algorithm(controller), modeFactor_(1.0)
{
}
char const *Noise::name() const
{
return NAME;
}
void Noise::switchMode(CameraMode const &cameraMode,
[[maybe_unused]] Metadata *metadata)
{
/*
* For example, we would expect a 2x2 binned mode to have a "noise
* factor" of sqrt(2x2) = 2. (can't be less than one, right?)
*/
modeFactor_ = std::max(1.0, cameraMode.noiseFactor);
}
int Noise::read(const libcamera::YamlObject &params)
{
auto value = params["reference_constant"].get<double>();
if (!value)
return -EINVAL;
referenceConstant_ = *value;
value = params["reference_slope"].get<double>();
if (!value)
return -EINVAL;
referenceSlope_ = *value;
return 0;
}
void Noise::prepare(Metadata *imageMetadata)
{
struct DeviceStatus deviceStatus;
deviceStatus.analogueGain = 1.0; /* keep compiler calm */
if (imageMetadata->get("device.status", deviceStatus) == 0) {
/*
* There is a slight question as to exactly how the noise
* profile, specifically the constant part of it, scales. For
* now we assume it all scales the same, and we'll revisit this
* if it proves substantially wrong. NOTE: we may also want to
* make some adjustments based on the camera mode (such as
* binning), if we knew how to discover it...
*/
double factor = sqrt(deviceStatus.analogueGain) / modeFactor_;
struct NoiseStatus status;
status.noiseConstant = referenceConstant_ * factor;
status.noiseSlope = referenceSlope_ * factor;
imageMetadata->set("noise.status", status);
LOG(RPiNoise, Debug)
<< "constant " << status.noiseConstant
<< " slope " << status.noiseSlope;
} else
LOG(RPiNoise, Warning) << " no metadata";
}
/* Register algorithm with the system. */
static Algorithm *create(Controller *controller)
{
return new Noise(controller);
}
static RegisterAlgorithm reg(NAME, &create);
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* noise.h - Noise control algorithm
*/
#pragma once
#include "../algorithm.h"
#include "../noise_status.h"
/* This is our implementation of the "noise algorithm". */
namespace RPiController {
class Noise : public Algorithm
{
public:
Noise(Controller *controller);
char const *name() const override;
void switchMode(CameraMode const &cameraMode, Metadata *metadata) override;
int read(const libcamera::YamlObject &params) override;
void prepare(Metadata *imageMetadata) override;
private:
/* the noise profile for analogue gain of 1.0 */
double referenceConstant_;
double referenceSlope_;
double modeFactor_;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019-2021, Raspberry Pi Ltd
*
* sdn.cpp - SDN (spatial denoise) control algorithm
*/
#include <libcamera/base/log.h>
#include "../denoise_status.h"
#include "../noise_status.h"
#include "sdn.h"
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiSdn)
/*
* Calculate settings for the spatial denoise block using the noise profile in
* the image metadata.
*/
#define NAME "rpi.sdn"
Sdn::Sdn(Controller *controller)
: DenoiseAlgorithm(controller), mode_(DenoiseMode::ColourOff)
{
}
char const *Sdn::name() const
{
return NAME;
}
int Sdn::read(const libcamera::YamlObject &params)
{
deviation_ = params["deviation"].get<double>(3.2);
strength_ = params["strength"].get<double>(0.75);
return 0;
}
void Sdn::initialise()
{
}
void Sdn::prepare(Metadata *imageMetadata)
{
struct NoiseStatus noiseStatus = {};
noiseStatus.noiseSlope = 3.0; /* in case no metadata */
if (imageMetadata->get("noise.status", noiseStatus) != 0)
LOG(RPiSdn, Warning) << "no noise profile found";
LOG(RPiSdn, Debug)
<< "Noise profile: constant " << noiseStatus.noiseConstant
<< " slope " << noiseStatus.noiseSlope;
struct DenoiseStatus status;
status.noiseConstant = noiseStatus.noiseConstant * deviation_;
status.noiseSlope = noiseStatus.noiseSlope * deviation_;
status.strength = strength_;
status.mode = static_cast<std::underlying_type_t<DenoiseMode>>(mode_);
imageMetadata->set("denoise.status", status);
LOG(RPiSdn, Debug)
<< "programmed constant " << status.noiseConstant
<< " slope " << status.noiseSlope
<< " strength " << status.strength;
}
void Sdn::setMode(DenoiseMode mode)
{
/* We only distinguish between off and all other modes. */
mode_ = mode;
}
/* Register algorithm with the system. */
static Algorithm *create(Controller *controller)
{
return (Algorithm *)new Sdn(controller);
}
static RegisterAlgorithm reg(NAME, &create);
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* sdn.h - SDN (spatial denoise) control algorithm
*/
#pragma once
#include "../algorithm.h"
#include "../denoise_algorithm.h"
namespace RPiController {
/* Algorithm to calculate correct spatial denoise (SDN) settings. */
class Sdn : public DenoiseAlgorithm
{
public:
Sdn(Controller *controller = NULL);
char const *name() const override;
int read(const libcamera::YamlObject &params) override;
void initialise() override;
void prepare(Metadata *imageMetadata) override;
void setMode(DenoiseMode mode) override;
private:
double deviation_;
double strength_;
DenoiseMode mode_;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* sharpen.cpp - sharpening control algorithm
*/
#include <math.h>
#include <libcamera/base/log.h>
#include "../sharpen_status.h"
#include "sharpen.h"
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiSharpen)
#define NAME "rpi.sharpen"
Sharpen::Sharpen(Controller *controller)
: SharpenAlgorithm(controller), userStrength_(1.0)
{
}
char const *Sharpen::name() const
{
return NAME;
}
void Sharpen::switchMode(CameraMode const &cameraMode,
[[maybe_unused]] Metadata *metadata)
{
/* can't be less than one, right? */
modeFactor_ = std::max(1.0, cameraMode.noiseFactor);
}
int Sharpen::read(const libcamera::YamlObject &params)
{
threshold_ = params["threshold"].get<double>(1.0);
strength_ = params["strength"].get<double>(1.0);
limit_ = params["limit"].get<double>(1.0);
LOG(RPiSharpen, Debug)
<< "Read threshold " << threshold_
<< " strength " << strength_
<< " limit " << limit_;
return 0;
}
void Sharpen::setStrength(double strength)
{
/*
* Note that this function is how an application sets the overall
* sharpening "strength". We call this the "user strength" field
* as there already is a strength_ field - being an internal gain
* parameter that gets passed to the ISP control code. Negative
* values are not allowed - coerce them to zero (no sharpening).
*/
userStrength_ = std::max(0.0, strength);
}
void Sharpen::prepare(Metadata *imageMetadata)
{
/*
* The userStrength_ affects the algorithm's internal gain directly, but
* we adjust the limit and threshold less aggressively. Using a sqrt
* function is an arbitrary but gentle way of accomplishing this.
*/
double userStrengthSqrt = sqrt(userStrength_);
struct SharpenStatus status;
/*
* Binned modes seem to need the sharpening toned down with this
* pipeline, thus we use the modeFactor_ here. Also avoid
* divide-by-zero with the userStrengthSqrt.
*/
status.threshold = threshold_ * modeFactor_ /
std::max(0.01, userStrengthSqrt);
status.strength = strength_ / modeFactor_ * userStrength_;
status.limit = limit_ / modeFactor_ * userStrengthSqrt;
/* Finally, report any application-supplied parameters that were used. */
status.userStrength = userStrength_;
imageMetadata->set("sharpen.status", status);
}
/* Register algorithm with the system. */
static Algorithm *create(Controller *controller)
{
return new Sharpen(controller);
}
static RegisterAlgorithm reg(NAME, &create);
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* sharpen.h - sharpening control algorithm
*/
#pragma once
#include "../sharpen_algorithm.h"
#include "../sharpen_status.h"
/* This is our implementation of the "sharpen algorithm". */
namespace RPiController {
class Sharpen : public SharpenAlgorithm
{
public:
Sharpen(Controller *controller);
char const *name() const override;
void switchMode(CameraMode const &cameraMode, Metadata *metadata) override;
int read(const libcamera::YamlObject &params) override;
void setStrength(double strength) override;
void prepare(Metadata *imageMetadata) override;
private:
double threshold_;
double strength_;
double limit_;
double modeFactor_;
double userStrength_;
};
} /* namespace RPiController */
@@ -0,0 +1,21 @@
/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2020, Raspberry Pi Ltd
*
* sharpen_algorithm.h - sharpness control algorithm interface
*/
#pragma once
#include "algorithm.h"
namespace RPiController {
class SharpenAlgorithm : public Algorithm
{
public:
SharpenAlgorithm(Controller *controller) : Algorithm(controller) {}
/* A sharpness control algorithm must provide the following: */
virtual void setStrength(double strength) = 0;
};
} /* namespace RPiController */
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* sharpen_status.h - Sharpen control algorithm status
*/
#pragma once
/* The "sharpen" algorithm stores the strength to use. */
struct SharpenStatus {
/* controls the smallest level of detail (or noise!) that sharpening will pick up */
double threshold;
/* the rate at which the sharpening response ramps once above the threshold */
double strength;
/* upper limit of the allowed sharpening response */
double limit;
/* The sharpening strength requested by the user or application. */
double userStrength;
};
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2022, Raspberry Pi Ltd
*
* statistics.h - Raspberry Pi generic statistics structure
*/
#pragma once
#include <memory>
#include <stdint.h>
#include <vector>
#include "histogram.h"
#include "region_stats.h"
namespace RPiController {
struct RgbySums {
RgbySums(uint64_t _rSum = 0, uint64_t _gSum = 0, uint64_t _bSum = 0, uint64_t _ySum = 0)
: rSum(_rSum), gSum(_gSum), bSum(_bSum), ySum(_ySum)
{
}
uint64_t rSum;
uint64_t gSum;
uint64_t bSum;
uint64_t ySum;
};
using RgbyRegions = RegionStats<RgbySums>;
using FocusRegions = RegionStats<uint64_t>;
struct Statistics {
/*
* All region based statistics are normalised to 16-bits, giving a
* maximum value of (1 << NormalisationFactorPow2) - 1.
*/
static constexpr unsigned int NormalisationFactorPow2 = 16;
/*
* Positioning of the AGC statistics gathering in the pipeline:
* Pre-WB correction or post-WB correction.
* Assume this is post-LSC.
*/
enum class AgcStatsPos { PreWb, PostWb };
const AgcStatsPos agcStatsPos;
/*
* Positioning of the AWB/ALSC statistics gathering in the pipeline:
* Pre-LSC or post-LSC.
*/
enum class ColourStatsPos { PreLsc, PostLsc };
const ColourStatsPos colourStatsPos;
Statistics(AgcStatsPos a, ColourStatsPos c)
: agcStatsPos(a), colourStatsPos(c)
{
}
/* Histogram statistics. Not all histograms may be populated! */
Histogram rHist;
Histogram gHist;
Histogram bHist;
Histogram yHist;
/* Row sums for flicker avoidance. */
std::vector<RgbySums> rowSums;
/* Region based colour sums. */
RgbyRegions agcRegions;
RgbyRegions awbRegions;
/* Region based focus FoM. */
FocusRegions focusRegions;
};
using StatisticsPtr = std::shared_ptr<Statistics>;
} /* namespace RPiController */
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# SPDX-License-Identifier: CC0-1.0
subdir('cam_helper')
subdir('controller')
foreach pipeline : pipelines
pipeline = pipeline.split('/')
if pipeline.length() < 2 or pipeline[0] != 'rpi'
continue
endif
subdir(pipeline[1])
endforeach
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{
"version": 2.0,
"target": "bcm2835",
"algorithms": [
{
"rpi.black_level":
{
"black_level": 4096
}
},
{
"rpi.dpc": { }
},
{
"rpi.lux":
{
"reference_shutter_speed": 27685,
"reference_gain": 1.0,
"reference_aperture": 1.0,
"reference_lux": 998,
"reference_Y": 12744
}
},
{
"rpi.noise":
{
"reference_constant": 0,
"reference_slope": 3.67
}
},
{
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}
+402
View File
@@ -0,0 +1,402 @@
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+200
View File
@@ -0,0 +1,200 @@
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+537
View File
@@ -0,0 +1,537 @@
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+233
View File
@@ -0,0 +1,233 @@
{
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+413
View File
@@ -0,0 +1,413 @@
{
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{
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]
}
+518
View File
@@ -0,0 +1,518 @@
{
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+429
View File
@@ -0,0 +1,429 @@
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+479
View File
@@ -0,0 +1,479 @@
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+516
View File
@@ -0,0 +1,516 @@
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View File
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+556
View File
@@ -0,0 +1,556 @@
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+556
View File
@@ -0,0 +1,556 @@
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+459
View File
@@ -0,0 +1,459 @@
{
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}
+459
View File
@@ -0,0 +1,459 @@
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1.529, 1.526, 1.522, 1.506, 1.489, 1.473, 1.458, 1.456, 1.456, 1.458, 1.474, 1.493, 1.513, 1.531, 1.541, 1.544,
1.527, 1.523, 1.511, 1.491, 1.474, 1.459, 1.445, 1.441, 1.441, 1.446, 1.461, 1.479, 1.499, 1.521, 1.536, 1.541,
1.524, 1.515, 1.498, 1.477, 1.459, 1.444, 1.431, 1.426, 1.426, 1.435, 1.446, 1.466, 1.487, 1.507, 1.528, 1.538,
1.522, 1.512, 1.491, 1.468, 1.447, 1.431, 1.423, 1.417, 1.418, 1.425, 1.435, 1.455, 1.479, 1.499, 1.523, 1.537,
1.522, 1.509, 1.485, 1.463, 1.441, 1.423, 1.416, 1.413, 1.415, 1.418, 1.429, 1.449, 1.473, 1.495, 1.521, 1.538,
1.522, 1.508, 1.483, 1.461, 1.438, 1.421, 1.413, 1.412, 1.412, 1.415, 1.428, 1.447, 1.471, 1.493, 1.519, 1.538,
1.522, 1.509, 1.484, 1.462, 1.439, 1.421, 1.414, 1.411, 1.412, 1.416, 1.428, 1.447, 1.471, 1.493, 1.519, 1.537,
1.523, 1.511, 1.487, 1.465, 1.443, 1.424, 1.417, 1.413, 1.415, 1.419, 1.429, 1.451, 1.473, 1.494, 1.519, 1.536,
1.524, 1.514, 1.493, 1.471, 1.451, 1.434, 1.424, 1.419, 1.419, 1.428, 1.437, 1.457, 1.477, 1.498, 1.521, 1.538,
1.527, 1.521, 1.503, 1.481, 1.462, 1.449, 1.434, 1.429, 1.429, 1.437, 1.451, 1.469, 1.488, 1.508, 1.527, 1.539,
1.529, 1.527, 1.515, 1.495, 1.477, 1.462, 1.449, 1.444, 1.444, 1.451, 1.467, 1.481, 1.499, 1.519, 1.535, 1.543,
1.534, 1.531, 1.527, 1.512, 1.492, 1.476, 1.463, 1.461, 1.461, 1.464, 1.479, 1.495, 1.515, 1.533, 1.543, 1.546
]
},
{
"ct": 5000,
"table":
[
2.603, 2.599, 2.591, 2.567, 2.539, 2.515, 2.489, 2.489, 2.489, 2.491, 2.516, 2.543, 2.574, 2.597, 2.614, 2.617,
2.596, 2.591, 2.571, 2.542, 2.516, 2.489, 2.464, 2.458, 2.458, 2.469, 2.492, 2.518, 2.547, 2.576, 2.602, 2.614,
2.591, 2.576, 2.546, 2.519, 2.489, 2.464, 2.437, 2.427, 2.427, 2.441, 2.467, 2.492, 2.525, 2.553, 2.586, 2.605,
2.588, 2.568, 2.534, 2.503, 2.472, 2.437, 2.423, 2.409, 2.411, 2.425, 2.441, 2.475, 2.513, 2.541, 2.577, 2.602,
2.588, 2.565, 2.527, 2.494, 2.461, 2.425, 2.409, 2.399, 2.403, 2.409, 2.431, 2.466, 2.503, 2.534, 2.571, 2.601,
2.586, 2.561, 2.525, 2.491, 2.454, 2.418, 2.399, 2.396, 2.395, 2.402, 2.424, 2.461, 2.501, 2.531, 2.567, 2.599,
2.583, 2.559, 2.525, 2.491, 2.454, 2.418, 2.398, 2.393, 2.393, 2.401, 2.423, 2.459, 2.498, 2.531, 2.566, 2.597,
2.583, 2.559, 2.526, 2.494, 2.458, 2.421, 2.404, 2.397, 2.399, 2.404, 2.426, 2.461, 2.501, 2.531, 2.566, 2.596,
2.583, 2.563, 2.531, 2.501, 2.469, 2.435, 2.419, 2.405, 2.404, 2.422, 2.435, 2.471, 2.505, 2.537, 2.572, 2.596,
2.585, 2.571, 2.539, 2.516, 2.486, 2.458, 2.435, 2.424, 2.424, 2.435, 2.459, 2.489, 2.521, 2.546, 2.579, 2.601,
2.589, 2.578, 2.557, 2.532, 2.506, 2.483, 2.458, 2.449, 2.449, 2.459, 2.485, 2.507, 2.535, 2.563, 2.591, 2.605,
2.589, 2.586, 2.575, 2.551, 2.525, 2.503, 2.481, 2.476, 2.476, 2.481, 2.504, 2.526, 2.555, 2.583, 2.604, 2.611
]
}
],
"calibrations_Cb": [
{
"ct": 3000,
"table":
[
3.311, 3.339, 3.369, 3.374, 3.371, 3.363, 3.356, 3.353, 3.353, 3.353, 3.357, 3.362, 3.362, 3.356, 3.328, 3.311,
3.321, 3.354, 3.374, 3.374, 3.368, 3.359, 3.352, 3.349, 3.347, 3.347, 3.349, 3.357, 3.361, 3.359, 3.343, 3.324,
3.334, 3.368, 3.375, 3.374, 3.365, 3.356, 3.349, 3.347, 3.346, 3.346, 3.347, 3.349, 3.358, 3.361, 3.357, 3.336,
3.346, 3.378, 3.378, 3.369, 3.363, 3.358, 3.351, 3.348, 3.347, 3.346, 3.347, 3.348, 3.354, 3.364, 3.363, 3.345,
3.351, 3.381, 3.381, 3.368, 3.361, 3.357, 3.349, 3.347, 3.347, 3.345, 3.345, 3.347, 3.353, 3.364, 3.364, 3.347,
3.353, 3.379, 3.379, 3.366, 3.359, 3.351, 3.348, 3.343, 3.342, 3.342, 3.343, 3.345, 3.351, 3.363, 3.363, 3.347,
3.353, 3.376, 3.376, 3.363, 3.351, 3.347, 3.343, 3.338, 3.336, 3.338, 3.339, 3.343, 3.351, 3.361, 3.361, 3.347,
3.351, 3.374, 3.374, 3.359, 3.351, 3.345, 3.338, 3.334, 3.333, 3.334, 3.336, 3.339, 3.347, 3.358, 3.358, 3.345,
3.346, 3.368, 3.368, 3.359, 3.349, 3.343, 3.336, 3.332, 3.327, 3.331, 3.333, 3.337, 3.346, 3.356, 3.356, 3.341,
3.336, 3.362, 3.364, 3.359, 3.351, 3.342, 3.334, 3.324, 3.324, 3.325, 3.329, 3.336, 3.346, 3.351, 3.351, 3.333,
3.324, 3.349, 3.359, 3.358, 3.352, 3.341, 3.329, 3.323, 3.321, 3.322, 3.326, 3.336, 3.346, 3.347, 3.339, 3.319,
3.311, 3.328, 3.352, 3.354, 3.352, 3.341, 3.329, 3.321, 3.319, 3.321, 3.324, 3.338, 3.343, 3.343, 3.319, 3.312
]
},
{
"ct": 5000,
"table":
[
1.634, 1.647, 1.665, 1.668, 1.668, 1.664, 1.662, 1.662, 1.661, 1.661, 1.661, 1.663, 1.663, 1.659, 1.643, 1.636,
1.639, 1.656, 1.668, 1.669, 1.668, 1.666, 1.664, 1.663, 1.663, 1.661, 1.661, 1.662, 1.663, 1.662, 1.654, 1.642,
1.645, 1.663, 1.669, 1.668, 1.667, 1.667, 1.667, 1.668, 1.668, 1.665, 1.662, 1.661, 1.662, 1.664, 1.661, 1.649,
1.651, 1.669, 1.669, 1.667, 1.666, 1.668, 1.669, 1.672, 1.672, 1.668, 1.665, 1.661, 1.661, 1.665, 1.665, 1.655,
1.654, 1.669, 1.669, 1.666, 1.666, 1.669, 1.672, 1.673, 1.673, 1.671, 1.666, 1.661, 1.661, 1.665, 1.665, 1.659,
1.654, 1.669, 1.669, 1.666, 1.666, 1.669, 1.671, 1.673, 1.672, 1.669, 1.667, 1.661, 1.661, 1.665, 1.665, 1.659,
1.654, 1.668, 1.668, 1.664, 1.663, 1.667, 1.669, 1.671, 1.669, 1.668, 1.665, 1.661, 1.661, 1.663, 1.663, 1.659,
1.653, 1.665, 1.665, 1.661, 1.661, 1.664, 1.667, 1.668, 1.668, 1.665, 1.661, 1.658, 1.659, 1.662, 1.662, 1.657,
1.651, 1.664, 1.664, 1.659, 1.659, 1.661, 1.663, 1.663, 1.662, 1.661, 1.658, 1.656, 1.657, 1.662, 1.662, 1.655,
1.645, 1.661, 1.663, 1.661, 1.659, 1.659, 1.659, 1.657, 1.657, 1.656, 1.654, 1.655, 1.656, 1.661, 1.661, 1.649,
1.641, 1.654, 1.661, 1.661, 1.659, 1.657, 1.655, 1.653, 1.652, 1.651, 1.652, 1.653, 1.657, 1.658, 1.655, 1.644,
1.635, 1.645, 1.661, 1.661, 1.661, 1.655, 1.653, 1.649, 1.648, 1.647, 1.651, 1.653, 1.657, 1.657, 1.646, 1.638
]
}
],
"luminance_lut":
[
3.535, 3.279, 3.049, 2.722, 2.305, 1.958, 1.657, 1.647, 1.647, 1.656, 1.953, 2.289, 2.707, 3.058, 3.325, 3.589,
3.379, 3.157, 2.874, 2.421, 1.973, 1.735, 1.472, 1.388, 1.388, 1.471, 1.724, 1.963, 2.409, 2.877, 3.185, 3.416,
3.288, 3.075, 2.696, 2.169, 1.735, 1.472, 1.311, 1.208, 1.208, 1.306, 1.471, 1.724, 2.159, 2.695, 3.092, 3.321,
3.238, 3.001, 2.534, 1.981, 1.572, 1.311, 1.207, 1.082, 1.082, 1.204, 1.306, 1.563, 1.973, 2.529, 3.008, 3.259,
3.211, 2.938, 2.414, 1.859, 1.468, 1.221, 1.082, 1.036, 1.031, 1.079, 1.217, 1.463, 1.851, 2.403, 2.931, 3.229,
3.206, 2.904, 2.356, 1.802, 1.421, 1.181, 1.037, 1.002, 1.002, 1.032, 1.175, 1.414, 1.793, 2.343, 2.899, 3.223,
3.206, 2.904, 2.356, 1.802, 1.421, 1.181, 1.037, 1.005, 1.005, 1.032, 1.175, 1.414, 1.793, 2.343, 2.899, 3.223,
3.211, 2.936, 2.417, 1.858, 1.468, 1.222, 1.083, 1.037, 1.032, 1.083, 1.218, 1.463, 1.848, 2.403, 2.932, 3.226,
3.234, 2.997, 2.536, 1.979, 1.569, 1.311, 1.206, 1.084, 1.084, 1.204, 1.305, 1.565, 1.966, 2.524, 2.996, 3.251,
3.282, 3.069, 2.697, 2.166, 1.731, 1.471, 1.311, 1.207, 1.207, 1.305, 1.466, 1.729, 2.158, 2.689, 3.077, 3.304,
3.369, 3.146, 2.873, 2.415, 1.964, 1.722, 1.471, 1.382, 1.382, 1.466, 1.722, 1.964, 2.408, 2.871, 3.167, 3.401,
3.524, 3.253, 3.025, 2.691, 2.275, 1.939, 1.657, 1.628, 1.628, 1.654, 1.936, 2.275, 2.687, 3.029, 3.284, 3.574
],
"sigma": 0.00195,
"sigma_Cb": 0.00241
}
},
{
"rpi.contrast":
{
"ce_enable": 1,
"gamma_curve":
[
0, 0,
1024, 5040,
2048, 9338,
3072, 12356,
4096, 15312,
5120, 18051,
6144, 20790,
7168, 23193,
8192, 25744,
9216, 27942,
10240, 30035,
11264, 32005,
12288, 33975,
13312, 35815,
14336, 37600,
15360, 39168,
16384, 40642,
18432, 43379,
20480, 45749,
22528, 47753,
24576, 49621,
26624, 51253,
28672, 52698,
30720, 53796,
32768, 54876,
36864, 57012,
40960, 58656,
45056, 59954,
49152, 61183,
53248, 62355,
57344, 63419,
61440, 64476,
65535, 65535
]
}
},
{
"rpi.ccm":
{
"ccms": [
{
"ct": 2750,
"ccm":
[
1.13004, 0.36392, -0.49396,
-0.45885, 1.68171, -0.22286,
-0.06473, -0.86962, 1.93435
]
},
{
"ct": 2940,
"ccm":
[
1.29876, 0.09627, -0.39503,
-0.43085, 1.60258, -0.17172,
-0.02638, -0.92581, 1.95218
]
},
{
"ct": 3650,
"ccm":
[
1.57729, -0.29734, -0.27995,
-0.42965, 1.66231, -0.23265,
-0.02183, -0.62331, 1.64514
]
},
{
"ct": 4625,
"ccm":
[
1.52145, -0.22382, -0.29763,
-0.40445, 1.82186, -0.41742,
-0.05732, -0.56222, 1.61954
]
},
{
"ct": 5715,
"ccm":
[
1.67851, -0.39193, -0.28658,
-0.37169, 1.72949, -0.35781,
-0.09556, -0.41951, 1.51508
]
}
]
}
},
{
"rpi.sharpen": { }
},
{
"rpi.af":
{
"ranges":
{
"normal":
{
"min": 0.0,
"max": 12.0,
"default": 1.0
},
"macro":
{
"min": 4.0,
"max": 32.0,
"default": 6.0
}
},
"speeds":
{
"normal":
{
"step_coarse": 2.0,
"step_fine": 0.5,
"contrast_ratio": 0.75,
"pdaf_gain": -0.03,
"pdaf_squelch": 0.2,
"max_slew": 4.0,
"pdaf_frames": 20,
"dropout_frames": 6,
"step_frames": 4
},
"fast":
{
"step_coarse": 2.0,
"step_fine": 0.5,
"contrast_ratio": 0.75,
"pdaf_gain": -0.05,
"pdaf_squelch": 0.2,
"max_slew": 5.0,
"pdaf_frames": 16,
"dropout_frames": 6,
"step_frames": 4
}
},
"conf_epsilon": 8,
"conf_thresh": 12,
"conf_clip": 512,
"skip_frames": 5,
"map": [ 0.0, 420, 35.0, 920 ]
}
}
]
}
+26
View File
@@ -0,0 +1,26 @@
# SPDX-License-Identifier: CC0-1.0
conf_files = files([
'imx219.json',
'imx219_noir.json',
'imx290.json',
'imx296.json',
'imx296_mono.json',
'imx378.json',
'imx477.json',
'imx477_noir.json',
'imx477_scientific.json',
'imx519.json',
'imx708.json',
'imx708_noir.json',
'imx708_wide.json',
'imx708_wide_noir.json',
'ov5647.json',
'ov5647_noir.json',
'ov9281_mono.json',
'se327m12.json',
'uncalibrated.json',
])
install_data(conf_files,
install_dir : ipa_data_dir / 'rpi' / 'vc4')
+487
View File
@@ -0,0 +1,487 @@
{
"version": 2.0,
"target": "bcm2835",
"algorithms": [
{
"rpi.black_level":
{
"black_level": 1024
}
},
{
"rpi.dpc": { }
},
{
"rpi.lux":
{
"reference_shutter_speed": 21663,
"reference_gain": 1.0,
"reference_aperture": 1.0,
"reference_lux": 987,
"reference_Y": 8961
}
},
{
"rpi.noise":
{
"reference_constant": 0,
"reference_slope": 4.25
}
},
{
"rpi.geq":
{
"offset": 401,
"slope": 0.05619
}
},
{
"rpi.sdn": { }
},
{
"rpi.awb":
{
"priors": [
{
"lux": 0,
"prior":
[
2000, 1.0,
3000, 0.0,
13000, 0.0
]
},
{
"lux": 800,
"prior":
[
2000, 0.0,
6000, 2.0,
13000, 2.0
]
},
{
"lux": 1500,
"prior":
[
2000, 0.0,
4000, 1.0,
6000, 6.0,
6500, 7.0,
7000, 1.0,
13000, 1.0
]
}
],
"modes":
{
"auto":
{
"lo": 2500,
"hi": 8000
},
"incandescent":
{
"lo": 2500,
"hi": 3000
},
"tungsten":
{
"lo": 3000,
"hi": 3500
},
"fluorescent":
{
"lo": 4000,
"hi": 4700
},
"indoor":
{
"lo": 3000,
"hi": 5000
},
"daylight":
{
"lo": 5500,
"hi": 6500
},
"cloudy":
{
"lo": 7000,
"hi": 8600
}
},
"bayes": 1,
"ct_curve":
[
2500.0, 1.0289, 0.4503,
2803.0, 0.9428, 0.5108,
2914.0, 0.9406, 0.5127,
3605.0, 0.8261, 0.6249,
4540.0, 0.7331, 0.7533,
5699.0, 0.6715, 0.8627,
8625.0, 0.6081, 1.0012
],
"sensitivity_r": 1.05,
"sensitivity_b": 1.05,
"transverse_pos": 0.0321,
"transverse_neg": 0.04313
}
},
{
"rpi.agc":
{
"metering_modes":
{
"centre-weighted":
{
"weights": [ 3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0 ]
},
"spot":
{
"weights": [ 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
},
"matrix":
{
"weights": [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
}
},
"exposure_modes":
{
"normal":
{
"shutter": [ 100, 10000, 30000, 60000, 66666 ],
"gain": [ 1.0, 2.0, 4.0, 6.0, 8.0 ]
},
"short":
{
"shutter": [ 100, 5000, 10000, 20000, 33333 ],
"gain": [ 1.0, 2.0, 4.0, 6.0, 8.0 ]
},
"long":
{
"shutter": [ 100, 10000, 30000, 60000, 120000 ],
"gain": [ 1.0, 2.0, 4.0, 6.0, 12.0 ]
}
},
"constraint_modes":
{
"normal": [
{
"bound": "LOWER",
"q_lo": 0.98,
"q_hi": 1.0,
"y_target":
[
0, 0.5,
1000, 0.5
]
}
],
"highlight": [
{
"bound": "LOWER",
"q_lo": 0.98,
"q_hi": 1.0,
"y_target":
[
0, 0.5,
1000, 0.5
]
},
{
"bound": "UPPER",
"q_lo": 0.98,
"q_hi": 1.0,
"y_target":
[
0, 0.8,
1000, 0.8
]
}
],
"shadows": [
{
"bound": "LOWER",
"q_lo": 0.0,
"q_hi": 0.5,
"y_target":
[
0, 0.17,
1000, 0.17
]
}
]
},
"y_target":
[
0, 0.16,
1000, 0.165,
10000, 0.17
],
"base_ev": 1.25
}
},
{
"rpi.alsc":
{
"omega": 1.3,
"n_iter": 100,
"luminance_strength": 0.5,
"calibrations_Cr": [
{
"ct": 3000,
"table":
[
1.105, 1.103, 1.093, 1.083, 1.071, 1.065, 1.065, 1.065, 1.066, 1.069, 1.072, 1.077, 1.084, 1.089, 1.093, 1.093,
1.103, 1.096, 1.084, 1.072, 1.059, 1.051, 1.047, 1.047, 1.051, 1.053, 1.059, 1.067, 1.075, 1.082, 1.085, 1.086,
1.096, 1.084, 1.072, 1.059, 1.051, 1.045, 1.039, 1.038, 1.039, 1.045, 1.049, 1.057, 1.063, 1.072, 1.081, 1.082,
1.092, 1.075, 1.061, 1.052, 1.045, 1.039, 1.036, 1.035, 1.035, 1.039, 1.044, 1.049, 1.056, 1.063, 1.072, 1.081,
1.092, 1.073, 1.058, 1.048, 1.043, 1.038, 1.035, 1.033, 1.033, 1.035, 1.039, 1.044, 1.051, 1.057, 1.069, 1.078,
1.091, 1.068, 1.054, 1.045, 1.041, 1.038, 1.035, 1.032, 1.032, 1.032, 1.036, 1.041, 1.045, 1.055, 1.069, 1.078,
1.091, 1.068, 1.052, 1.043, 1.041, 1.038, 1.035, 1.032, 1.031, 1.032, 1.034, 1.036, 1.043, 1.055, 1.069, 1.078,
1.092, 1.068, 1.052, 1.047, 1.042, 1.041, 1.038, 1.035, 1.032, 1.032, 1.035, 1.039, 1.043, 1.055, 1.071, 1.079,
1.092, 1.073, 1.057, 1.051, 1.047, 1.047, 1.044, 1.041, 1.038, 1.038, 1.039, 1.043, 1.051, 1.059, 1.076, 1.083,
1.092, 1.081, 1.068, 1.058, 1.056, 1.056, 1.053, 1.052, 1.049, 1.048, 1.048, 1.051, 1.059, 1.066, 1.083, 1.085,
1.091, 1.087, 1.081, 1.068, 1.065, 1.064, 1.062, 1.062, 1.061, 1.056, 1.056, 1.056, 1.064, 1.069, 1.084, 1.089,
1.091, 1.089, 1.085, 1.079, 1.069, 1.068, 1.067, 1.067, 1.067, 1.063, 1.061, 1.063, 1.068, 1.069, 1.081, 1.092
]
},
{
"ct": 5000,
"table":
[
1.486, 1.484, 1.468, 1.449, 1.427, 1.403, 1.399, 1.399, 1.399, 1.404, 1.413, 1.433, 1.454, 1.473, 1.482, 1.488,
1.484, 1.472, 1.454, 1.431, 1.405, 1.381, 1.365, 1.365, 1.367, 1.373, 1.392, 1.411, 1.438, 1.458, 1.476, 1.481,
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+403
View File
@@ -0,0 +1,403 @@
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+123
View File
@@ -0,0 +1,123 @@
{
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+418
View File
@@ -0,0 +1,418 @@
{
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"table":
[
1.481, 1.471, 1.449, 1.429, 1.416, 1.404, 1.394, 1.389, 1.389, 1.389, 1.392, 1.397, 1.404, 1.416, 1.429, 1.437,
1.472, 1.456, 1.436, 1.418, 1.405, 1.394, 1.389, 1.384, 1.382, 1.382, 1.386, 1.388, 1.398, 1.407, 1.422, 1.429,
1.465, 1.443, 1.426, 1.411, 1.397, 1.389, 1.383, 1.377, 1.377, 1.377, 1.379, 1.384, 1.388, 1.398, 1.411, 1.422,
1.462, 1.441, 1.423, 1.409, 1.395, 1.385, 1.379, 1.376, 1.374, 1.374, 1.375, 1.379, 1.384, 1.394, 1.407, 1.418,
1.461, 1.439, 1.421, 1.407, 1.394, 1.385, 1.381, 1.376, 1.373, 1.373, 1.373, 1.376, 1.381, 1.389, 1.403, 1.415,
1.461, 1.439, 1.419, 1.404, 1.392, 1.384, 1.379, 1.376, 1.373, 1.372, 1.374, 1.375, 1.379, 1.389, 1.401, 1.413,
1.461, 1.438, 1.419, 1.402, 1.389, 1.383, 1.377, 1.375, 1.373, 1.372, 1.372, 1.375, 1.381, 1.388, 1.401, 1.414,
1.462, 1.438, 1.419, 1.403, 1.391, 1.381, 1.377, 1.374, 1.373, 1.373, 1.374, 1.376, 1.381, 1.389, 1.401, 1.414,
1.462, 1.441, 1.423, 1.405, 1.392, 1.383, 1.377, 1.374, 1.373, 1.372, 1.373, 1.376, 1.382, 1.391, 1.402, 1.414,
1.465, 1.444, 1.424, 1.407, 1.393, 1.382, 1.378, 1.373, 1.369, 1.369, 1.372, 1.375, 1.381, 1.389, 1.402, 1.417,
1.469, 1.449, 1.427, 1.413, 1.396, 1.384, 1.381, 1.375, 1.371, 1.371, 1.373, 1.377, 1.385, 1.393, 1.407, 1.422,
1.474, 1.456, 1.436, 1.419, 1.407, 1.391, 1.383, 1.379, 1.377, 1.377, 1.378, 1.381, 1.391, 1.404, 1.422, 1.426
]
},
{
"ct": 5000,
"table":
[
1.742, 1.721, 1.689, 1.661, 1.639, 1.623, 1.613, 1.609, 1.607, 1.606, 1.609, 1.617, 1.626, 1.641, 1.665, 1.681,
1.728, 1.703, 1.672, 1.645, 1.631, 1.614, 1.602, 1.599, 1.596, 1.597, 1.601, 1.608, 1.618, 1.631, 1.653, 1.671,
1.713, 1.691, 1.658, 1.635, 1.618, 1.606, 1.595, 1.591, 1.588, 1.588, 1.591, 1.601, 1.608, 1.624, 1.641, 1.658,
1.707, 1.681, 1.651, 1.627, 1.613, 1.599, 1.591, 1.585, 1.583, 1.584, 1.587, 1.591, 1.601, 1.615, 1.633, 1.655,
1.699, 1.672, 1.644, 1.622, 1.606, 1.593, 1.586, 1.581, 1.579, 1.581, 1.583, 1.587, 1.597, 1.611, 1.631, 1.652,
1.697, 1.665, 1.637, 1.617, 1.601, 1.589, 1.584, 1.579, 1.577, 1.578, 1.581, 1.585, 1.597, 1.607, 1.627, 1.652,
1.697, 1.662, 1.634, 1.613, 1.599, 1.591, 1.583, 1.578, 1.576, 1.576, 1.579, 1.586, 1.597, 1.607, 1.628, 1.653,
1.697, 1.662, 1.633, 1.613, 1.598, 1.589, 1.582, 1.578, 1.576, 1.577, 1.582, 1.589, 1.598, 1.611, 1.635, 1.655,
1.701, 1.666, 1.636, 1.616, 1.602, 1.589, 1.583, 1.578, 1.577, 1.581, 1.583, 1.591, 1.601, 1.617, 1.639, 1.659,
1.708, 1.671, 1.641, 1.618, 1.603, 1.591, 1.584, 1.581, 1.578, 1.581, 1.585, 1.594, 1.604, 1.622, 1.646, 1.666,
1.714, 1.681, 1.648, 1.622, 1.608, 1.599, 1.591, 1.584, 1.583, 1.584, 1.589, 1.599, 1.614, 1.629, 1.653, 1.673,
1.719, 1.691, 1.659, 1.631, 1.618, 1.606, 1.596, 1.591, 1.591, 1.593, 1.599, 1.608, 1.623, 1.642, 1.665, 1.681
]
}
],
"calibrations_Cb": [
{
"ct": 4000,
"table":
[
2.253, 2.267, 2.289, 2.317, 2.342, 2.359, 2.373, 2.381, 2.381, 2.378, 2.368, 2.361, 2.344, 2.337, 2.314, 2.301,
2.262, 2.284, 2.314, 2.335, 2.352, 2.371, 2.383, 2.391, 2.393, 2.391, 2.381, 2.368, 2.361, 2.342, 2.322, 2.308,
2.277, 2.303, 2.321, 2.346, 2.364, 2.381, 2.391, 2.395, 2.397, 2.397, 2.395, 2.381, 2.367, 2.354, 2.332, 2.321,
2.277, 2.304, 2.327, 2.349, 2.369, 2.388, 2.393, 2.396, 2.396, 2.398, 2.396, 2.391, 2.376, 2.359, 2.339, 2.328,
2.279, 2.311, 2.327, 2.354, 2.377, 2.389, 2.393, 2.397, 2.397, 2.398, 2.395, 2.393, 2.382, 2.363, 2.344, 2.332,
2.282, 2.311, 2.329, 2.354, 2.377, 2.386, 2.396, 2.396, 2.395, 2.396, 2.397, 2.394, 2.383, 2.367, 2.346, 2.333,
2.283, 2.314, 2.333, 2.353, 2.375, 2.389, 2.394, 2.395, 2.395, 2.395, 2.396, 2.394, 2.386, 2.368, 2.354, 2.336,
2.287, 2.309, 2.331, 2.352, 2.373, 2.386, 2.394, 2.395, 2.395, 2.396, 2.396, 2.394, 2.384, 2.371, 2.354, 2.339,
2.289, 2.307, 2.326, 2.347, 2.369, 2.385, 2.392, 2.397, 2.398, 2.398, 2.397, 2.392, 2.383, 2.367, 2.352, 2.337,
2.286, 2.303, 2.322, 2.342, 2.361, 2.379, 2.389, 2.394, 2.397, 2.398, 2.396, 2.389, 2.381, 2.366, 2.346, 2.332,
2.284, 2.291, 2.312, 2.329, 2.351, 2.372, 2.381, 2.389, 2.393, 2.394, 2.389, 2.385, 2.374, 2.362, 2.338, 2.325,
2.283, 2.288, 2.305, 2.319, 2.339, 2.365, 2.374, 2.381, 2.384, 2.386, 2.385, 2.379, 2.368, 2.342, 2.325, 2.318
]
},
{
"ct": 5000,
"table":
[
1.897, 1.919, 1.941, 1.969, 1.989, 2.003, 2.014, 2.019, 2.019, 2.017, 2.014, 2.008, 1.999, 1.988, 1.968, 1.944,
1.914, 1.932, 1.957, 1.982, 1.998, 2.014, 2.023, 2.029, 2.031, 2.029, 2.022, 2.014, 2.006, 1.995, 1.976, 1.955,
1.925, 1.951, 1.974, 1.996, 2.013, 2.027, 2.035, 2.039, 2.039, 2.038, 2.035, 2.026, 2.015, 2.002, 1.984, 1.963,
1.932, 1.958, 1.986, 2.007, 2.024, 2.034, 2.041, 2.041, 2.045, 2.045, 2.042, 2.033, 2.023, 2.009, 1.995, 1.971,
1.942, 1.964, 1.994, 2.012, 2.029, 2.038, 2.043, 2.046, 2.047, 2.046, 2.045, 2.039, 2.029, 2.014, 1.997, 1.977,
1.946, 1.974, 1.999, 2.015, 2.031, 2.041, 2.046, 2.047, 2.048, 2.047, 2.044, 2.041, 2.031, 2.019, 1.999, 1.978,
1.948, 1.975, 2.002, 2.018, 2.031, 2.041, 2.046, 2.047, 2.048, 2.048, 2.045, 2.041, 2.029, 2.019, 1.998, 1.978,
1.948, 1.973, 2.002, 2.018, 2.029, 2.042, 2.045, 2.048, 2.048, 2.048, 2.044, 2.037, 2.027, 2.014, 1.993, 1.978,
1.945, 1.969, 1.998, 2.015, 2.028, 2.037, 2.045, 2.046, 2.047, 2.044, 2.039, 2.033, 2.022, 2.008, 1.989, 1.971,
1.939, 1.964, 1.991, 2.011, 2.024, 2.032, 2.036, 2.042, 2.042, 2.039, 2.035, 2.024, 2.012, 1.998, 1.977, 1.964,
1.932, 1.953, 1.981, 2.006, 2.016, 2.024, 2.028, 2.031, 2.034, 2.031, 2.024, 2.015, 2.005, 1.989, 1.966, 1.955,
1.928, 1.944, 1.973, 1.999, 2.007, 2.016, 2.019, 2.025, 2.026, 2.025, 2.017, 2.008, 1.997, 1.975, 1.958, 1.947
]
}
],
"luminance_lut":
[
1.877, 1.597, 1.397, 1.269, 1.191, 1.131, 1.093, 1.078, 1.071, 1.069, 1.086, 1.135, 1.221, 1.331, 1.474, 1.704,
1.749, 1.506, 1.334, 1.229, 1.149, 1.088, 1.058, 1.053, 1.051, 1.046, 1.053, 1.091, 1.163, 1.259, 1.387, 1.587,
1.661, 1.451, 1.295, 1.195, 1.113, 1.061, 1.049, 1.048, 1.047, 1.049, 1.049, 1.066, 1.124, 1.211, 1.333, 1.511,
1.615, 1.411, 1.267, 1.165, 1.086, 1.052, 1.047, 1.047, 1.047, 1.049, 1.052, 1.056, 1.099, 1.181, 1.303, 1.471,
1.576, 1.385, 1.252, 1.144, 1.068, 1.049, 1.044, 1.044, 1.045, 1.049, 1.053, 1.054, 1.083, 1.163, 1.283, 1.447,
1.561, 1.373, 1.245, 1.135, 1.064, 1.049, 1.044, 1.044, 1.044, 1.046, 1.048, 1.054, 1.073, 1.153, 1.271, 1.432,
1.571, 1.377, 1.242, 1.137, 1.066, 1.055, 1.052, 1.051, 1.051, 1.049, 1.047, 1.048, 1.068, 1.148, 1.271, 1.427,
1.582, 1.396, 1.259, 1.156, 1.085, 1.068, 1.059, 1.054, 1.049, 1.045, 1.041, 1.043, 1.074, 1.157, 1.284, 1.444,
1.623, 1.428, 1.283, 1.178, 1.105, 1.074, 1.069, 1.063, 1.056, 1.048, 1.046, 1.051, 1.094, 1.182, 1.311, 1.473,
1.691, 1.471, 1.321, 1.213, 1.135, 1.088, 1.073, 1.069, 1.063, 1.059, 1.053, 1.071, 1.129, 1.222, 1.351, 1.521,
1.808, 1.543, 1.371, 1.253, 1.174, 1.118, 1.085, 1.072, 1.067, 1.064, 1.071, 1.106, 1.176, 1.274, 1.398, 1.582,
1.969, 1.666, 1.447, 1.316, 1.223, 1.166, 1.123, 1.094, 1.089, 1.097, 1.118, 1.163, 1.239, 1.336, 1.471, 1.681
],
"sigma": 0.00218,
"sigma_Cb": 0.00194
}
},
{
"rpi.contrast":
{
"ce_enable": 1,
"gamma_curve":
[
0, 0,
1024, 5040,
2048, 9338,
3072, 12356,
4096, 15312,
5120, 18051,
6144, 20790,
7168, 23193,
8192, 25744,
9216, 27942,
10240, 30035,
11264, 32005,
12288, 33975,
13312, 35815,
14336, 37600,
15360, 39168,
16384, 40642,
18432, 43379,
20480, 45749,
22528, 47753,
24576, 49621,
26624, 51253,
28672, 52698,
30720, 53796,
32768, 54876,
36864, 57012,
40960, 58656,
45056, 59954,
49152, 61183,
53248, 62355,
57344, 63419,
61440, 64476,
65535, 65535
]
}
},
{
"rpi.ccm":
{
"ccms": [
{
"ct": 2900,
"ccm":
[
1.44924, -0.12935, -0.31989,
-0.65839, 1.95441, -0.29602,
0.18344, -1.22282, 2.03938
]
},
{
"ct": 3000,
"ccm":
[
1.38736, 0.07714, -0.46451,
-0.59691, 1.84335, -0.24644,
0.10092, -1.30441, 2.20349
]
},
{
"ct": 3600,
"ccm":
[
1.51261, -0.27921, -0.23339,
-0.55129, 1.83241, -0.28111,
0.11649, -0.93195, 1.81546
]
},
{
"ct": 4600,
"ccm":
[
1.47082, -0.18523, -0.28559,
-0.48923, 1.95126, -0.46203,
0.07951, -0.83987, 1.76036
]
},
{
"ct": 5800,
"ccm":
[
1.57294, -0.36229, -0.21065,
-0.42272, 1.80305, -0.38032,
0.03671, -0.66862, 1.63191
]
},
{
"ct": 8100,
"ccm":
[
1.58803, -0.09912, -0.48891,
-0.42594, 2.22303, -0.79709,
-0.00621, -0.90516, 1.91137
]
}
]
}
},
{
"rpi.sharpen":
{
"threshold": 2.0,
"strength": 0.5,
"limit": 0.5
}
}
]
}
+118
View File
@@ -0,0 +1,118 @@
{
"version": 2.0,
"target": "bcm2835",
"algorithms": [
{
"rpi.black_level":
{
"black_level": 4096
}
},
{
"rpi.awb":
{
"use_derivatives": 0,
"bayes": 0
}
},
{
"rpi.agc":
{
"metering_modes":
{
"centre-weighted":
{
"weights": [ 4, 4, 4, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0 ]
}
},
"exposure_modes":
{
"normal":
{
"shutter": [ 100, 15000, 30000, 60000, 120000 ],
"gain": [ 1.0, 2.0, 3.0, 4.0, 6.0 ]
}
},
"constraint_modes":
{
"normal": [
{
"bound": "LOWER",
"q_lo": 0.98,
"q_hi": 1.0,
"y_target":
[
0, 0.4,
1000, 0.4
]
}
]
},
"y_target":
[
0, 0.16,
1000, 0.165,
10000, 0.17
]
}
},
{
"rpi.ccm":
{
"ccms": [
{
"ct": 4000,
"ccm":
[
2.0, -1.0, 0.0,
-0.5, 2.0, -0.5,
0, -1.0, 2.0
]
}
]
}
},
{
"rpi.contrast":
{
"ce_enable": 0,
"gamma_curve":
[
0, 0,
1024, 5040,
2048, 9338,
3072, 12356,
4096, 15312,
5120, 18051,
6144, 20790,
7168, 23193,
8192, 25744,
9216, 27942,
10240, 30035,
11264, 32005,
12288, 33975,
13312, 35815,
14336, 37600,
15360, 39168,
16384, 40642,
18432, 43379,
20480, 45749,
22528, 47753,
24576, 49621,
26624, 51253,
28672, 52698,
30720, 53796,
32768, 54876,
36864, 57012,
40960, 58656,
45056, 59954,
49152, 61183,
53248, 62355,
57344, 63419,
61440, 64476,
65535, 65535
]
}
}
]
}
+47
View File
@@ -0,0 +1,47 @@
# SPDX-License-Identifier: CC0-1.0
ipa_name = 'ipa_rpi_vc4'
vc4_ipa_deps = [
libcamera_private,
libatomic,
]
vc4_ipa_libs = [
rpi_ipa_cam_helper_lib,
rpi_ipa_controller_lib
]
vc4_ipa_includes = [
ipa_includes,
libipa_includes,
]
vc4_ipa_sources = files([
'raspberrypi.cpp',
])
vc4_ipa_includes += include_directories('..')
mod = shared_module(ipa_name,
[vc4_ipa_sources, libcamera_generated_ipa_headers],
name_prefix : '',
include_directories : vc4_ipa_includes,
dependencies : vc4_ipa_deps,
link_with : libipa,
link_whole : vc4_ipa_libs,
install : true,
install_dir : ipa_install_dir)
if ipa_sign_module
custom_target(ipa_name + '.so.sign',
input : mod,
output : ipa_name + '.so.sign',
command : [ipa_sign, ipa_priv_key, '@INPUT@', '@OUTPUT@'],
install : false,
build_by_default : true)
endif
subdir('data')
ipa_names += ipa_name

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