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ipa: rpi: Add support for the Sony IMX500 camera sensor
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Add a Sony IMX500 camera helper to the IPA. This also includes support
for the on-chip CNN hardware accelerator and parsing of the neural
network data stream returned in the metadata buffer.

Add tuning files for both VC4 and PiSP platforms.

Signed-off-by: Naushir Patuck <naush@raspberrypi.com>
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naushir committed Sep 25, 2024
1 parent 549f80a commit 7330f29
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341 changes: 341 additions & 0 deletions src/ipa/rpi/cam_helper/cam_helper_imx500.cpp
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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2024, Raspberry Pi Ltd
*
* cam_helper_imx500.cpp - camera helper for imx500 sensor
*/

#include <algorithm>
#include <assert.h>
#include <cmath>
#include <fstream>
#include <memory>
#include <stddef.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#include <libcamera/base/log.h>
#include <libcamera/base/span.h>

#include <libcamera/control_ids.h>

#include "imx500_tensor_parser/imx500_tensor_parser.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 IMX500 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 CamHelperImx500 : public CamHelper
{
public:
CamHelperImx500();
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 parseInferenceData(libcamera::Span<const uint8_t> buffer, Metadata &metadata);
void populateMetadata(const MdParser::RegisterMap &registers,
Metadata &metadata) const override;

std::unique_ptr<uint8_t[]> savedInputTensor_;
};

CamHelperImx500::CamHelperImx500()
: CamHelper(std::make_unique<MdParserSmia>(registerList), frameIntegrationDiff)
{
}

uint32_t CamHelperImx500::gainCode(double gain) const
{
return static_cast<uint32_t>(1024 - 1024 / gain);
}

double CamHelperImx500::gain(uint32_t gainCode) const
{
return 1024.0 / (1024 - gainCode);
}

void CamHelperImx500::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;
}

parseInferenceData(buffer, metadata);
}

std::pair<uint32_t, uint32_t> CamHelperImx500::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 = CamHelperImx500::exposureLines(exposure, lineLength);
exposureLines = std::min(exposureLines, frameLength - frameIntegrationDiff);
exposure = CamHelperImx500::exposure(exposureLines, lineLength);
}

return { frameLength - mode_.height, hblank };
}

void CamHelperImx500::getDelays(int &exposureDelay, int &gainDelay,
int &vblankDelay, int &hblankDelay) const
{
exposureDelay = 2;
gainDelay = 2;
vblankDelay = 3;
hblankDelay = 3;
}

bool CamHelperImx500::sensorEmbeddedDataPresent() const
{
return true;
}

void CamHelperImx500::parseInferenceData(libcamera::Span<const uint8_t> buffer,
Metadata &metadata)
{
/* Inference data comes after 2 lines of embedded data. */
constexpr unsigned int StartLine = 2;
size_t bytesPerLine = (mode_.width * mode_.bitdepth) >> 3;
if (hwConfig_.cfeDataBufferStrided)
bytesPerLine = (bytesPerLine + 15) & ~15;

if (buffer.size() <= StartLine * bytesPerLine)
return;

/* Check if an input tensor is needed - this is sticky! */
bool enableInputTensor = false;
metadata.get("cnn.enable_input_tensor", enableInputTensor);

/* Cache the DNN metadata for fast parsing. */
unsigned int tensorBufferSize = buffer.size() - (StartLine * bytesPerLine);
std::unique_ptr<uint8_t[]> cache = std::make_unique<uint8_t[]>(tensorBufferSize);
memcpy(cache.get(), buffer.data() + StartLine * bytesPerLine, tensorBufferSize);
Span<const uint8_t> tensors(cache.get(), tensorBufferSize);

std::unordered_map<TensorType, IMX500Tensors> offsets = RPiController::imx500SplitTensors(tensors);
auto itIn = offsets.find(TensorType::InputTensor);
auto itOut = offsets.find(TensorType::OutputTensor);

if (itIn != offsets.end() && itOut != offsets.end()) {
const unsigned int inputTensorOffset = itIn->second.offset;
const unsigned int outputTensorOffset = itOut->second.offset;
const unsigned int inputTensorSize = outputTensorOffset - inputTensorOffset;
Span<const uint8_t> inputTensor;

if (itIn->second.valid) {
if (itOut->second.valid) {
/* Valid input and output tensor, get the span directly from the current cache. */
inputTensor = Span<const uint8_t>(cache.get() + inputTensorOffset,
inputTensorSize);
} else {
/*
* Invalid output tensor with valid input tensor.
* This is likely because the DNN takes longer than
* a frame time to generate the output tensor.
*
* In such cases, we don't process the input tensor,
* but simply save it for when the next output
* tensor is valid. This way, we ensure that both
* valid input and output tensors are in lock-step.
*/
savedInputTensor_ = std::make_unique<uint8_t[]>(inputTensorSize);
memcpy(savedInputTensor_.get(), cache.get() + inputTensorOffset,
inputTensorSize);
}
} else if (itOut->second.valid && savedInputTensor_) {
/*
* Invalid input tensor with valid output tensor. This is
* likely because the DNN takes longer than a frame time
* to generate the output tensor.
*
* In such cases, use the previously saved input tensor
* if possible.
*/
inputTensor = Span<const uint8_t>(savedInputTensor_.get(), inputTensorSize);
}

if (inputTensor.size()) {
IMX500InputTensorInfo inputTensorInfo;
if (!imx500ParseInputTensor(inputTensorInfo, inputTensor)) {
CnnInputTensorInfo exported{};
exported.width = inputTensorInfo.width;
exported.height = inputTensorInfo.height;
exported.numChannels = inputTensorInfo.channels;
strncpy(exported.networkName, inputTensorInfo.networkName.c_str(),
sizeof(exported.networkName));
exported.networkName[sizeof(exported.networkName) - 1] = '\0';
metadata.set("cnn.input_tensor_info", exported);
metadata.set("cnn.input_tensor", std::move(inputTensorInfo.data));
metadata.set("cnn.input_tensor_size", inputTensorInfo.size);
}

/* We can now safely clear the saved input tensor. */
savedInputTensor_.reset();
}
}

if (itOut != offsets.end() && itOut->second.valid) {
unsigned int outputTensorOffset = itOut->second.offset;
Span<const uint8_t> outputTensor(cache.get() + outputTensorOffset,
tensorBufferSize - outputTensorOffset);

IMX500OutputTensorInfo outputTensorInfo;
if (!imx500ParseOutputTensor(outputTensorInfo, outputTensor)) {
CnnOutputTensorInfo exported{};
if (outputTensorInfo.numTensors < MaxNumTensors) {
exported.numTensors = outputTensorInfo.numTensors;
for (unsigned int i = 0; i < exported.numTensors; i++) {
exported.info[i].tensorDataNum = outputTensorInfo.tensorDataNum[i];
exported.info[i].numDimensions = outputTensorInfo.numDimensions[i];
for (unsigned int j = 0; j < exported.info[i].numDimensions; j++)
exported.info[i].size[j] = outputTensorInfo.vecDim[i][j].size;
}
} else {
LOG(IPARPI, Debug)
<< "IMX500 output tensor info export failed, numTensors > MaxNumTensors";
}
strncpy(exported.networkName, outputTensorInfo.networkName.c_str(),
sizeof(exported.networkName));
exported.networkName[sizeof(exported.networkName) - 1] = '\0';
metadata.set("cnn.output_tensor_info", exported);
metadata.set("cnn.output_tensor", std::move(outputTensorInfo.data));
metadata.set("cnn.output_tensor_size", outputTensorInfo.totalSize);

auto itKpi = offsets.find(TensorType::Kpi);
if (itKpi != offsets.end()) {
constexpr unsigned int DnnRuntimeOffset = 9;
constexpr unsigned int DspRuntimeOffset = 10;
CnnKpiInfo kpi;

uint8_t *k = cache.get() + itKpi->second.offset;
kpi.dnnRuntime = k[4 * DnnRuntimeOffset + 3] << 24 |
k[4 * DnnRuntimeOffset + 2] << 16 |
k[4 * DnnRuntimeOffset + 1] << 8 |
k[4 * DnnRuntimeOffset];
kpi.dspRuntime = k[4 * DspRuntimeOffset + 3] << 24 |
k[4 * DspRuntimeOffset + 2] << 16 |
k[4 * DspRuntimeOffset + 1] << 8 |
k[4 * DspRuntimeOffset];
metadata.set("cnn.kpi_info", kpi);
}
}
}
}

void CamHelperImx500::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 CamHelperImx500();
}

static RegisterCamHelper reg_imx500("imx500", &create);
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