[{"id":37312,"web_url":"https://patchwork.libcamera.org/comment/37312/","msgid":"","date":"2025-12-11T15:36:53","subject":"Re: [PATCH v2 2/4] ipa: rpi: controller: awb: Add Neural Network AWB","submitter":{"id":42,"url":"https://patchwork.libcamera.org/api/people/42/","name":"David Plowman","email":"david.plowman@raspberrypi.com"},"content":"Hi\n\nOn Thu, 11 Dec 2025 at 14:28, David Plowman\n wrote:\n>\n> From: Peter Bailey \n>\n> Add an AWB algorithm which uses neural networks.\n>\n> Signed-off-by: Peter Bailey \n> ---\n> meson_options.txt | 5 +\n> src/ipa/rpi/controller/meson.build | 9 +\n> src/ipa/rpi/controller/rpi/awb_nn.cpp | 446 ++++++++++++++++++++++++++\n> 3 files changed, 460 insertions(+)\n> create mode 100644 src/ipa/rpi/controller/rpi/awb_nn.cpp\n>\n> diff --git a/meson_options.txt b/meson_options.txt\n> index 5954e028..89eece52 100644\n> --- a/meson_options.txt\n> +++ b/meson_options.txt\n> @@ -78,6 +78,11 @@ option('qcam',\n> value : 'disabled',\n> description : 'Compile the qcam test application')\n>\n> +option('rpi-awb-nn',\n> + type : 'feature',\n> + value : 'auto',\n> + description : 'Enable the Raspberry Pi Neural Network AWB algorithm')\n> +\n> option('test',\n> type : 'boolean',\n> value : false,\n> diff --git a/src/ipa/rpi/controller/meson.build b/src/ipa/rpi/controller/meson.build\n> index 90d9e285..9c261d99 100644\n> --- a/src/ipa/rpi/controller/meson.build\n> +++ b/src/ipa/rpi/controller/meson.build\n> @@ -33,6 +33,15 @@ rpi_ipa_controller_deps = [\n> libcamera_private,\n> ]\n>\n> +tflite_dep = dependency('tensorflow-lite', required : false)\n\nOops. I edited this to read\n\ntflite_dep = dependency('tensorflow-lite', required : get_option('rpi-awb-nn'))\n\nand then failed to hit the save button. Version 3 incoming shortly!\n\nDavid\n\n> +\n> +if tflite_dep.found()\n> + rpi_ipa_controller_sources += files([\n> + 'rpi/awb_nn.cpp',\n> + ])\n> + rpi_ipa_controller_deps += tflite_dep\n> +endif\n> +\n> rpi_ipa_controller_lib = static_library('rpi_ipa_controller', rpi_ipa_controller_sources,\n> include_directories : libipa_includes,\n> dependencies : rpi_ipa_controller_deps)\n> diff --git a/src/ipa/rpi/controller/rpi/awb_nn.cpp b/src/ipa/rpi/controller/rpi/awb_nn.cpp\n> new file mode 100644\n> index 00000000..35d1270e\n> --- /dev/null\n> +++ b/src/ipa/rpi/controller/rpi/awb_nn.cpp\n> @@ -0,0 +1,446 @@\n> +/* SPDX-License-Identifier: BSD-2-Clause */\n> +/*\n> + * Copyright (C) 2025, Raspberry Pi Ltd\n> + *\n> + * AWB control algorithm using neural network\n> + */\n> +\n> +#include \n> +#include \n> +#include \n> +\n> +#include \n> +#include \n> +\n> +#include \n> +#include \n> +#include \n> +\n> +#include \"../awb_algorithm.h\"\n> +#include \"../awb_status.h\"\n> +#include \"../lux_status.h\"\n> +#include \"libipa/pwl.h\"\n> +\n> +#include \"alsc_status.h\"\n> +#include \"awb.h\"\n> +\n> +using namespace libcamera;\n> +\n> +LOG_DECLARE_CATEGORY(RPiAwb)\n> +\n> +constexpr double kDefaultCT = 4500.0;\n> +\n> +/*\n> + * The neural networks are trained to work on images rendered at a canonical\n> + * colour temperature. That value is 5000K, which must be reproduced here.\n> + */\n> +constexpr double kNetworkCanonicalCT = 5000.0;\n> +\n> +#define NAME \"rpi.nn.awb\"\n> +\n> +namespace RPiController {\n> +\n> +struct AwbNNConfig {\n> + AwbNNConfig() {}\n> + int read(const libcamera::YamlObject ¶ms, AwbConfig &config);\n> +\n> + /* An empty model will check default locations for model.tflite */\n> + std::string model;\n> + float minTemp;\n> + float maxTemp;\n> +\n> + bool enableNn;\n> +\n> + /* CCM matrix for canonical network CT */\n> + double ccm[9];\n> +};\n> +\n> +class AwbNN : public Awb\n> +{\n> +public:\n> + AwbNN(Controller *controller = NULL);\n> + ~AwbNN();\n> + char const *name() const override;\n> + void initialise() override;\n> + int read(const libcamera::YamlObject ¶ms) override;\n> +\n> +protected:\n> + void doAwb() override;\n> + void prepareStats() override;\n> +\n> +private:\n> + bool isAutoEnabled() const;\n> + AwbNNConfig nnConfig_;\n> + void transverseSearch(double t, double &r, double &b);\n> + RGB processZone(RGB zone, float red_gain, float blue_gain);\n> + void awbNN();\n> + void loadModel();\n> +\n> + libcamera::Size zoneSize_;\n> + std::unique_ptr model_;\n> + std::unique_ptr interpreter_;\n> +};\n> +\n> +int AwbNNConfig::read(const libcamera::YamlObject ¶ms, AwbConfig &config)\n> +{\n> + model = params[\"model\"].get(\"\");\n> + minTemp = params[\"min_temp\"].get(2800.0);\n> + maxTemp = params[\"max_temp\"].get(7600.0);\n> +\n> + for (int i = 0; i < 9; i++)\n> + ccm[i] = params[\"ccm\"][i].get(0.0);\n> +\n> + enableNn = params[\"enable_nn\"].get(1);\n> +\n> + if (enableNn) {\n> + if (!config.hasCtCurve()) {\n> + LOG(RPiAwb, Error) << \"CT curve not specified\";\n> + enableNn = false;\n> + }\n> +\n> + if (!model.empty() && model.find(\".tflite\") == std::string::npos) {\n> + LOG(RPiAwb, Error) << \"Model must be a .tflite file\";\n> + enableNn = false;\n> + }\n> +\n> + bool validCcm = true;\n> + for (int i = 0; i < 9; i++)\n> + if (ccm[i] == 0.0)\n> + validCcm = false;\n> +\n> + if (!validCcm) {\n> + LOG(RPiAwb, Error) << \"CCM not specified or invalid\";\n> + enableNn = false;\n> + }\n> +\n> + if (!enableNn) {\n> + LOG(RPiAwb, Warning) << \"Neural Network AWB mis-configured - switch to Grey method\";\n> + }\n> + }\n> +\n> + if (!enableNn) {\n> + config.sensitivityR = config.sensitivityB = 1.0;\n> + config.greyWorld = true;\n> + }\n> +\n> + return 0;\n> +}\n> +\n> +AwbNN::AwbNN(Controller *controller)\n> + : Awb(controller)\n> +{\n> + zoneSize_ = getHardwareConfig().awbRegions;\n> +}\n> +\n> +AwbNN::~AwbNN()\n> +{\n> +}\n> +\n> +char const *AwbNN::name() const\n> +{\n> + return NAME;\n> +}\n> +\n> +int AwbNN::read(const libcamera::YamlObject ¶ms)\n> +{\n> + int ret;\n> +\n> + ret = config_.read(params);\n> + if (ret)\n> + return ret;\n> +\n> + ret = nnConfig_.read(params, config_);\n> + if (ret)\n> + return ret;\n> +\n> + return 0;\n> +}\n> +\n> +static bool checkTensorShape(TfLiteTensor *tensor, const int *expectedDims, const int expectedDimsSize)\n> +{\n> + if (tensor->dims->size != expectedDimsSize)\n> + return false;\n> +\n> + for (int i = 0; i < tensor->dims->size; i++) {\n> + if (tensor->dims->data[i] != expectedDims[i]) {\n> + return false;\n> + }\n> + }\n> + return true;\n> +}\n> +\n> +static std::string buildDimString(const int *dims, const int dimsSize)\n> +{\n> + std::string s = \"[\";\n> + for (int i = 0; i < dimsSize; i++) {\n> + s += std::to_string(dims[i]);\n> + if (i < dimsSize - 1)\n> + s += \",\";\n> + else\n> + s += \"]\";\n> + }\n> + return s;\n> +}\n> +\n> +void AwbNN::loadModel()\n> +{\n> + std::string modelPath;\n> + if (getTarget() == \"bcm2835\") {\n> + modelPath = \"/ipa/rpi/vc4/awb_model.tflite\";\n> + } else {\n> + modelPath = \"/ipa/rpi/pisp/awb_model.tflite\";\n> + }\n> +\n> + if (nnConfig_.model.empty()) {\n> + std::string root = utils::libcameraSourcePath();\n> + if (!root.empty()) {\n> + modelPath = root + modelPath;\n> + } else {\n> + modelPath = LIBCAMERA_DATA_DIR + modelPath;\n> + }\n> +\n> + if (!File::exists(modelPath)) {\n> + LOG(RPiAwb, Error) << \"No model file found in standard locations\";\n> + nnConfig_.enableNn = false;\n> + return;\n> + }\n> + } else {\n> + modelPath = nnConfig_.model;\n> + }\n> +\n> + LOG(RPiAwb, Debug) << \"Attempting to load model from: \" << modelPath;\n> +\n> + model_ = tflite::FlatBufferModel::BuildFromFile(modelPath.c_str());\n> +\n> + if (!model_) {\n> + LOG(RPiAwb, Error) << \"Failed to load model from \" << modelPath;\n> + nnConfig_.enableNn = false;\n> + return;\n> + }\n> +\n> + tflite::MutableOpResolver resolver;\n> + tflite::ops::builtin::BuiltinOpResolver builtin_resolver;\n> + resolver.AddAll(builtin_resolver);\n> + tflite::InterpreterBuilder(*model_, resolver)(&interpreter_);\n> + if (!interpreter_) {\n> + LOG(RPiAwb, Error) << \"Failed to build interpreter for model \" << nnConfig_.model;\n> + nnConfig_.enableNn = false;\n> + return;\n> + }\n> +\n> + interpreter_->AllocateTensors();\n> + TfLiteTensor *inputTensor = interpreter_->input_tensor(0);\n> + TfLiteTensor *inputLuxTensor = interpreter_->input_tensor(1);\n> + TfLiteTensor *outputTensor = interpreter_->output_tensor(0);\n> + if (!inputTensor || !inputLuxTensor || !outputTensor) {\n> + LOG(RPiAwb, Error) << \"Model missing input or output tensor\";\n> + nnConfig_.enableNn = false;\n> + return;\n> + }\n> +\n> + const int expectedInputDims[] = { 1, (int)zoneSize_.height, (int)zoneSize_.width, 3 };\n> + const int expectedInputLuxDims[] = { 1 };\n> + const int expectedOutputDims[] = { 1 };\n> +\n> + if (!checkTensorShape(inputTensor, expectedInputDims, 4)) {\n> + LOG(RPiAwb, Error) << \"Model input tensor dimension mismatch. Expected: \" << buildDimString(expectedInputDims, 4)\n> + << \", Got: \" << buildDimString(inputTensor->dims->data, inputTensor->dims->size);\n> + nnConfig_.enableNn = false;\n> + return;\n> + }\n> +\n> + if (!checkTensorShape(inputLuxTensor, expectedInputLuxDims, 1)) {\n> + LOG(RPiAwb, Error) << \"Model input lux tensor dimension mismatch. Expected: \" << buildDimString(expectedInputLuxDims, 1)\n> + << \", Got: \" << buildDimString(inputLuxTensor->dims->data, inputLuxTensor->dims->size);\n> + nnConfig_.enableNn = false;\n> + return;\n> + }\n> +\n> + if (!checkTensorShape(outputTensor, expectedOutputDims, 1)) {\n> + LOG(RPiAwb, Error) << \"Model output tensor dimension mismatch. Expected: \" << buildDimString(expectedOutputDims, 1)\n> + << \", Got: \" << buildDimString(outputTensor->dims->data, outputTensor->dims->size);\n> + nnConfig_.enableNn = false;\n> + return;\n> + }\n> +\n> + if (inputTensor->type != kTfLiteFloat32 || inputLuxTensor->type != kTfLiteFloat32 || outputTensor->type != kTfLiteFloat32) {\n> + LOG(RPiAwb, Error) << \"Model input and output tensors must be float32\";\n> + nnConfig_.enableNn = false;\n> + return;\n> + }\n> +\n> + LOG(RPiAwb, Info) << \"Model loaded successfully from \" << modelPath;\n> + LOG(RPiAwb, Debug) << \"Model validation successful - Input Image: \"\n> + << buildDimString(expectedInputDims, 4)\n> + << \", Input Lux: \" << buildDimString(expectedInputLuxDims, 1)\n> + << \", Output: \" << buildDimString(expectedOutputDims, 1) << \" floats\";\n> +}\n> +\n> +void AwbNN::initialise()\n> +{\n> + Awb::initialise();\n> +\n> + if (nnConfig_.enableNn) {\n> + loadModel();\n> + if (!nnConfig_.enableNn) {\n> + LOG(RPiAwb, Warning) << \"Neural Network AWB failed to load - switch to Grey method\";\n> + config_.greyWorld = true;\n> + config_.sensitivityR = config_.sensitivityB = 1.0;\n> + }\n> + }\n> +}\n> +\n> +void AwbNN::prepareStats()\n> +{\n> + zones_.clear();\n> + /*\n> + * LSC has already been applied to the stats in this pipeline, so stop\n> + * any LSC compensation. We also ignore config_.fast in this version.\n> + */\n> + generateStats(zones_, statistics_, 0.0, 0.0, getGlobalMetadata(), 0.0, 0.0, 0.0);\n> + /*\n> + * apply sensitivities, so values appear to come from our \"canonical\"\n> + * sensor.\n> + */\n> + for (auto &zone : zones_) {\n> + zone.R *= config_.sensitivityR;\n> + zone.B *= config_.sensitivityB;\n> + }\n> +}\n> +\n> +void AwbNN::transverseSearch(double t, double &r, double &b)\n> +{\n> + int spanR = -1, spanB = -1;\n> + config_.ctR.eval(t, &spanR);\n> + config_.ctB.eval(t, &spanB);\n> +\n> + const int diff = 10;\n> + double rDiff = config_.ctR.eval(t + diff, &spanR) -\n> + config_.ctR.eval(t - diff, &spanR);\n> + double bDiff = config_.ctB.eval(t + diff, &spanB) -\n> + config_.ctB.eval(t - diff, &spanB);\n> +\n> + ipa::Pwl::Point transverse({ bDiff, -rDiff });\n> + if (transverse.length2() < 1e-6)\n> + return;\n> +\n> + transverse = transverse / transverse.length();\n> + double transverseRange = config_.transverseNeg + config_.transversePos;\n> + const int maxNumDeltas = 12;\n> + int numDeltas = floor(transverseRange * 100 + 0.5) + 1;\n> + numDeltas = numDeltas < 3 ? 3 : (numDeltas > maxNumDeltas ? maxNumDeltas : numDeltas);\n> +\n> + ipa::Pwl::Point points[maxNumDeltas];\n> + int bestPoint = 0;\n> +\n> + for (int i = 0; i < numDeltas; i++) {\n> + points[i][0] = -config_.transverseNeg +\n> + (transverseRange * i) / (numDeltas - 1);\n> + ipa::Pwl::Point rbTest = ipa::Pwl::Point({ r, b }) +\n> + transverse * points[i].x();\n> + double rTest = rbTest.x(), bTest = rbTest.y();\n> + double gainR = 1 / rTest, gainB = 1 / bTest;\n> + double delta2Sum = computeDelta2Sum(gainR, gainB, 0.0, 0.0);\n> + points[i][1] = delta2Sum;\n> + if (points[i].y() < points[bestPoint].y())\n> + bestPoint = i;\n> + }\n> +\n> + bestPoint = std::clamp(bestPoint, 1, numDeltas - 2);\n> + ipa::Pwl::Point rbBest = ipa::Pwl::Point({ r, b }) +\n> + transverse * interpolateQuadatric(points[bestPoint - 1],\n> + points[bestPoint],\n> + points[bestPoint + 1]);\n> + double rBest = rbBest.x(), bBest = rbBest.y();\n> +\n> + r = rBest, b = bBest;\n> +}\n> +\n> +AwbNN::RGB AwbNN::processZone(AwbNN::RGB zone, float redGain, float blueGain)\n> +{\n> + /*\n> + * Renders the pixel at canonical network colour temperature\n> + */\n> + RGB zoneGains = zone;\n> +\n> + zoneGains.R *= redGain;\n> + zoneGains.G *= 1.0;\n> + zoneGains.B *= blueGain;\n> +\n> + RGB zoneCcm;\n> +\n> + zoneCcm.R = nnConfig_.ccm[0] * zoneGains.R + nnConfig_.ccm[1] * zoneGains.G + nnConfig_.ccm[2] * zoneGains.B;\n> + zoneCcm.G = nnConfig_.ccm[3] * zoneGains.R + nnConfig_.ccm[4] * zoneGains.G + nnConfig_.ccm[5] * zoneGains.B;\n> + zoneCcm.B = nnConfig_.ccm[6] * zoneGains.R + nnConfig_.ccm[7] * zoneGains.G + nnConfig_.ccm[8] * zoneGains.B;\n> +\n> + return zoneCcm;\n> +}\n> +\n> +void AwbNN::awbNN()\n> +{\n> + float *inputData = interpreter_->typed_input_tensor(0);\n> + float *inputLux = interpreter_->typed_input_tensor(1);\n> +\n> + float redGain = 1.0 / config_.ctR.eval(kNetworkCanonicalCT);\n> + float blueGain = 1.0 / config_.ctB.eval(kNetworkCanonicalCT);\n> +\n> + for (uint i = 0; i < zoneSize_.height; i++) {\n> + for (uint j = 0; j < zoneSize_.width; j++) {\n> + uint zoneIdx = i * zoneSize_.width + j;\n> +\n> + RGB processedZone = processZone(zones_[zoneIdx] * (1.0 / 65535), redGain, blueGain);\n> + uint baseIdx = zoneIdx * 3;\n> +\n> + inputData[baseIdx + 0] = static_cast(processedZone.R);\n> + inputData[baseIdx + 1] = static_cast(processedZone.G);\n> + inputData[baseIdx + 2] = static_cast(processedZone.B);\n> + }\n> + }\n> +\n> + inputLux[0] = static_cast(lux_);\n> +\n> + TfLiteStatus status = interpreter_->Invoke();\n> + if (status != kTfLiteOk) {\n> + LOG(RPiAwb, Error) << \"Model inference failed with status: \" << status;\n> + return;\n> + }\n> +\n> + float *outputData = interpreter_->typed_output_tensor(0);\n> +\n> + double t = outputData[0];\n> +\n> + LOG(RPiAwb, Debug) << \"Model output temperature: \" << t;\n> +\n> + t = std::clamp(t, mode_->ctLo, mode_->ctHi);\n> +\n> + double r = config_.ctR.eval(t);\n> + double b = config_.ctB.eval(t);\n> +\n> + transverseSearch(t, r, b);\n> +\n> + LOG(RPiAwb, Debug) << \"After transverse search: Temperature: \" << t << \" Red gain: \" << 1.0 / r << \" Blue gain: \" << 1.0 / b;\n> +\n> + asyncResults_.temperatureK = t;\n> + asyncResults_.gainR = 1.0 / r * config_.sensitivityR;\n> + asyncResults_.gainG = 1.0;\n> + asyncResults_.gainB = 1.0 / b * config_.sensitivityB;\n> +}\n> +\n> +void AwbNN::doAwb()\n> +{\n> + prepareStats();\n> + if (zones_.size() == (zoneSize_.width * zoneSize_.height) && nnConfig_.enableNn)\n> + awbNN();\n> + else\n> + awbGrey();\n> + statistics_.reset();\n> +}\n> +\n> +/* Register algorithm with the system. */\n> +static Algorithm *create(Controller *controller)\n> +{\n> + return (Algorithm *)new AwbNN(controller);\n> +}\n> +static RegisterAlgorithm reg(NAME, &create);\n> +\n> +} /* namespace RPiController */\n> --\n> 2.47.3\n>","headers":{"Return-Path":"","X-Original-To":"parsemail@patchwork.libcamera.org","Delivered-To":"parsemail@patchwork.libcamera.org","Received":["from lancelot.ideasonboard.com (lancelot.ideasonboard.com\n\t[92.243.16.209])\n\tby patchwork.libcamera.org (Postfix) with ESMTPS id 539FDBD1F1\n\tfor ;\n\tThu, 11 Dec 2025 15:37:09 +0000 (UTC)","from lancelot.ideasonboard.com (localhost [IPv6:::1])\n\tby lancelot.ideasonboard.com (Postfix) with ESMTP id 5CC4561617;\n\tThu, 11 Dec 2025 16:37:08 +0100 (CET)","from mail-qt1-x830.google.com (mail-qt1-x830.google.com\n\t[IPv6:2607:f8b0:4864:20::830])\n\tby lancelot.ideasonboard.com (Postfix) with ESMTPS id 1A09461603\n\tfor ;\n\tThu, 11 Dec 2025 16:37:06 +0100 (CET)","by mail-qt1-x830.google.com with SMTP id\n\td75a77b69052e-4ee1879e6d9so2402441cf.1\n\tfor ;\n\tThu, 11 Dec 2025 07:37:05 -0800 (PST)"],"Authentication-Results":"lancelot.ideasonboard.com; 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