source/use_case/object_detection/src/UseCaseHandler.cc - ml/ethos-u/ml-embedded-evaluation-kit - Gitiles

 /*
  * SPDX-FileCopyrightText: Copyright 2022, 2024 Arm Limited and/or its affiliates
  * <open-source-office@arm.com> SPDX-License-Identifier: Apache-2.0
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
  * You may obtain a copy of the License at
  *
  *     http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing, software
  * distributed under the License is distributed on an "AS IS" BASIS,
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
 #include "UseCaseHandler.hpp"
 #include "DetectorPostProcessing.hpp"
 #include "DetectorPreProcessing.hpp"
 #include "InputFiles.hpp"
 #include "UseCaseCommonUtils.hpp"
 #include "YoloFastestModel.hpp"
 #include "hal.h"
 #include "log_macros.h"

 #include <cinttypes>

 namespace arm {
 namespace app {

 #if VSI_ENABLED
 #include "video_drv.h"                /* Video Driver API. */

     /**
      * @brief Draws a box in the image using the object detection result object.
      *
      * @param[out] imageData    Pointer to the start of the image.
      * @param[in]  width        Image width.
      * @param[in]  height       Image height.
      * @param[in]  result       Object detection result.
      */
     static void DrawBox(uint8_t* imageData,
                         const uint32_t width,
                         const uint32_t height,
                         const OdResults& result)
     {
         UNUSED(height);
         const auto x = result.m_x0;
         const auto y = result.m_y0;
         const auto w = result.m_w;
         const auto h = result.m_h;

         const uint32_t step = width * 3;
         uint8_t* const imStart = imageData + (y * step) + (x * 3);

         uint8_t* dst_0 = imStart;
         uint8_t* dst_1 = imStart + (h * step);

         for (uint32_t i = 0; i < static_cast<uint32_t>(w); ++i) {
             *dst_0 = 255;
             *dst_1 = 255;

             dst_0 += 3;
             dst_1 += 3;
         }

         dst_0 = imStart;
         dst_1 = imStart + (w * 3);

         for (uint32_t j = 0; j < static_cast<uint32_t>(h); ++j) {
             *dst_0 = 255;
             *dst_1 = 255;

             dst_0 += step;
             dst_1 += step;
         }
     }

     void DrawDetectionBoxesVsi(uint8_t* image,
                                const uint32_t imageWidth,
                                const uint32_t imageHeight,
                                const std::vector<OdResults>& results)
     {
         for (const auto& result : results) {
             DrawBox(image, imageWidth, imageHeight, result);
             printf("Detection :: [%d" ", %d"
                    ", %d" ", %d" "]\n",
                    result.m_x0,
                    result.m_y0,
                    result.m_w,
                    result.m_h);
         }
     }

     /* Object detection VSI inference handler. */
     bool ObjectDetectionHandlerVsi(ApplicationContext& ctx)
     {
         /* Image buffer. */
         static uint8_t ImageBuf[IMAGE_DATA_SIZE];
         static uint8_t ImageOut[IMAGE_DATA_SIZE];

         /* Model object creation and initialisation. */
         auto& model = ctx.Get<Model&>("model");

         TfLiteTensor* inputTensor   = model.GetInputTensor(0);
         TfLiteTensor* outputTensor0 = model.GetOutputTensor(0);
         TfLiteTensor* outputTensor1 = model.GetOutputTensor(1);

         if (!inputTensor->dims) {
             printf_err("Invalid input tensor dims\n");
             return false;
         } else if (inputTensor->dims->size < 3) {
             printf_err("Input tensor dimension should be >= 3\n");
             return false;
         }

         TfLiteIntArray* inputShape = model.GetInputShape(0);
         const int inputImgCols = inputShape->data[arm::app::YoloFastestModel::ms_inputColsIdx];
         const int inputImgRows = inputShape->data[arm::app::YoloFastestModel::ms_inputRowsIdx];

         /* Set up pre- and post-processing. */
         arm::app::DetectorPreProcess preProcess =
             arm::app::DetectorPreProcess(inputTensor, true, model.IsDataSigned());

         std::vector<arm::app::OdResults> results;
         const arm::app::object_detection::PostProcessParams postProcessParams{
             inputImgRows,
             inputImgCols,
             arm::app::object_detection::originalImageSize,
             arm::app::object_detection::anchor1,
             arm::app::object_detection::anchor2};
         arm::app::DetectorPostProcess postProcess =
             arm::app::DetectorPostProcess(outputTensor0, outputTensor1, results, postProcessParams);

         const size_t imgSz = inputTensor->bytes < IMAGE_DATA_SIZE ?
                                                                   inputTensor->bytes : IMAGE_DATA_SIZE;

         if (sizeof(ImageBuf) < imgSz) {
             printf_err("Image buffer is insufficient\n");
             return false;
         }

         /* Configure Input Video. */
         if (VideoDrv_Configure(VIDEO_DRV_IN0,
                                arm::app::object_detection::originalImageSize,
                                arm::app::object_detection::originalImageSize,
                                COLOR_RGB888, 24U) != VIDEO_DRV_OK) {
             printf_err("Failed to configure video input\n");
             return false;
         }

         /* Set Input Video buffer. */
         if (VideoDrv_SetBuf(VIDEO_DRV_IN0,  ImageBuf, IMAGE_DATA_SIZE) != VIDEO_DRV_OK) {
             printf_err("Failed to set buffer for video input\n");
             return false;
         }

         /* Set Output Video file (only when using AVH - default: Display) */
         //  if (VideoDrv_SetFile(VIDEO_DRV_OUT0, "output_image.png") != VIDEO_DRV_OK) {
         //      printf_err("Failed to set filename for video output\n");
         //      return 1;
         //  }
         /* Configure Output Video. */
         if (VideoDrv_Configure(VIDEO_DRV_OUT0,
                                arm::app::object_detection::originalImageSize,
                                arm::app::object_detection::originalImageSize,
                                COLOR_RGB888, 24U) != VIDEO_DRV_OK) {
             printf_err("Failed to configure video output\n");
             return false;
         }

         /* Set Output Video buffer. */
         if (VideoDrv_SetBuf(VIDEO_DRV_OUT0, ImageOut, IMAGE_DATA_SIZE) != VIDEO_DRV_OK) {
             printf_err("Failed to set buffer for video output\n");
             return false;
         }

         auto imgCount = ctx.Get<uint32_t>("imgIndex");
         void* imgFrame = nullptr;
         void* outFrame = nullptr;

         while (true) {
 #if VSI_IMAGE_INPUT
             if (VideoDrv_SetFile(VIDEO_DRV_IN0, GetFilePath(imgCount)) != VIDEO_DRV_OK) {
                 printf_err("Failed to set filename for video input\n");
                 return false;
             }
 #endif

             VideoDrv_Status_t status;

             results.clear();

             /* Start video capture (single frame). */
             if (VideoDrv_StreamStart(VIDEO_DRV_IN0, VIDEO_DRV_MODE_SINGLE) != VIDEO_DRV_OK) {
                 printf_err("Failed to start video capture\n");
                 return false;
             }

             /* Wait for video input frame. */
             do {
                 status = VideoDrv_GetStatus(VIDEO_DRV_IN0);
             } while (status.buf_empty != 0U);

             /* Get input video frame buffer. */
             imgFrame = VideoDrv_GetFrameBuf(VIDEO_DRV_IN0);

             /* Run the pre-processing, inference and post-processing. */
             if (!preProcess.DoPreProcess(imgFrame, imgSz)) {
                 printf_err("Pre-processing failed.\n");
                 return false;
             }

             /* Run inference over this image. */
             printf("\rImage %" PRIu32 "; ", ++imgCount);

             if (!model.RunInference()) {
                 printf_err("Inference failed.\n");
                 return false;
             }

             if (!postProcess.DoPostProcess()) {
                 printf_err("Post-processing failed.\n");
                 return false;
             }

             /* Release input frame. */
             VideoDrv_ReleaseFrame(VIDEO_DRV_IN0);

             arm::app::DrawDetectionBoxesVsi(static_cast<uint8_t*>(imgFrame), inputImgCols, inputImgRows, results);

             /* Get output video frame buffer. */
             outFrame = VideoDrv_GetFrameBuf(VIDEO_DRV_OUT0);

             /* Copy image frame with detection boxes to output frame buffer. */
             memcpy(outFrame, imgFrame, IMAGE_DATA_SIZE);

             /* Release output frame. */
             VideoDrv_ReleaseFrame(VIDEO_DRV_OUT0);

             /* Start video output (single frame). */
             VideoDrv_StreamStart(VIDEO_DRV_OUT0, VIDEO_DRV_MODE_SINGLE);

             /* Check for end of stream (when using AVH with file as Video input). */
             if (status.eos != 0U) {
                 while (VideoDrv_GetStatus(VIDEO_DRV_OUT0).buf_empty == 0U);
                 break;
             }
         }

         IncrementAppCtxIfmIdx(ctx, "imgIndex");

         /* De-initialize Video Interface. */
         //VideoDrv_Uninitialize();
         return true;
     }

 #endif

     /**
      * @brief           Presents inference results along using the data presentation
      *                  object.
      * @param[in]       results            Vector of detection results to be displayed.
      * @return          true if successful, false otherwise.
      **/
     static bool
     PresentInferenceResult(const std::vector<object_detection::DetectionResult>& results);

     /**
      * @brief           Draw boxes directly on the LCD for all detected objects.
      * @param[in]       results            Vector of detection results to be displayed.
      * @param[in]       imageStartX        X coordinate where the image starts on the LCD.
      * @param[in]       imageStartY        Y coordinate where the image starts on the LCD.
      * @param[in]       imgDownscaleFactor How much image has been downscaled on LCD.
      **/
     static void DrawDetectionBoxes(const std::vector<object_detection::DetectionResult>& results,
                                    uint32_t imgStartX,
                                    uint32_t imgStartY,
                                    uint32_t imgDownscaleFactor);

     /* Object detection inference handler. */
     bool ObjectDetectionHandler(ApplicationContext& ctx, uint32_t imgIndex, bool runAll)
     {
         auto& profiler = ctx.Get<Profiler&>("profiler");

         constexpr uint32_t dataPsnImgDownscaleFactor = 1;
         constexpr uint32_t dataPsnImgStartX          = 10;
         constexpr uint32_t dataPsnImgStartY          = 35;

         constexpr uint32_t dataPsnTxtInfStartX = 20;
         constexpr uint32_t dataPsnTxtInfStartY = 28;

         hal_lcd_clear(COLOR_BLACK);

         auto& model = ctx.Get<Model&>("model");

         /* If the request has a valid size, set the image index. */
         if (imgIndex < NUMBER_OF_FILES) {
             if (!SetAppCtxIfmIdx(ctx, imgIndex, "imgIndex")) {
                 return false;
             }
         }
         if (!model.IsInited()) {
             printf_err("Model is not initialised! Terminating processing.\n");
             return false;
         }

         auto initialImgIdx = ctx.Get<uint32_t>("imgIndex");

         TfLiteTensor* inputTensor   = model.GetInputTensor(0);
         TfLiteTensor* outputTensor0 = model.GetOutputTensor(0);
         TfLiteTensor* outputTensor1 = model.GetOutputTensor(1);

         if (!inputTensor->dims) {
             printf_err("Invalid input tensor dims\n");
             return false;
         } else if (inputTensor->dims->size < 3) {
             printf_err("Input tensor dimension should be >= 3\n");
             return false;
         }

         TfLiteIntArray* inputShape = model.GetInputShape(0);

         const int inputImgCols = inputShape->data[YoloFastestModel::ms_inputColsIdx];
         const int inputImgRows = inputShape->data[YoloFastestModel::ms_inputRowsIdx];

         /* Set up pre and post-processing. */
         DetectorPreProcess preProcess = DetectorPreProcess(inputTensor, true, model.IsDataSigned());

         std::vector<object_detection::DetectionResult> results;
         const object_detection::PostProcessParams postProcessParams{
             inputImgRows,
             inputImgCols,
             object_detection::originalImageSize,
             object_detection::anchor1,
             object_detection::anchor2};
         DetectorPostProcess postProcess =
             DetectorPostProcess(outputTensor0, outputTensor1, results, postProcessParams);
         do {
             /* Ensure there are no results leftover from previous inference when running all. */
             results.clear();

             /* Strings for presentation/logging. */
             std::string str_inf{"Running inference... "};

             const uint8_t* currImage = GetImgArray(ctx.Get<uint32_t>("imgIndex"));

             auto dstPtr = static_cast<uint8_t*>(inputTensor->data.uint8);
             const size_t copySz =
                 inputTensor->bytes < IMAGE_DATA_SIZE ? inputTensor->bytes : IMAGE_DATA_SIZE;

             /* Run the pre-processing, inference and post-processing. */
             if (!preProcess.DoPreProcess(currImage, copySz)) {
                 printf_err("Pre-processing failed.");
                 return false;
             }

             /* Display image on the LCD. */
             hal_lcd_display_image(
                 (arm::app::object_detection::channelsImageDisplayed == 3) ? currImage : dstPtr,
                 inputImgCols,
                 inputImgRows,
                 arm::app::object_detection::channelsImageDisplayed,
                 dataPsnImgStartX,
                 dataPsnImgStartY,
                 dataPsnImgDownscaleFactor);

             /* Display message on the LCD - inference running. */
             hal_lcd_display_text(
                 str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);

             /* Run inference over this image. */
             info("Running inference on image %" PRIu32 " => %s\n",
                  ctx.Get<uint32_t>("imgIndex"),
                  GetFilename(ctx.Get<uint32_t>("imgIndex")));

             if (!RunInference(model, profiler)) {
                 printf_err("Inference failed.");
                 return false;
             }

             if (!postProcess.DoPostProcess()) {
                 printf_err("Post-processing failed.");
                 return false;
             }

             /* Erase. */
             str_inf = std::string(str_inf.size(), ' ');
             hal_lcd_display_text(
                 str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);

             /* Draw boxes. */
             DrawDetectionBoxes(
                 results, dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor);

 #if VERIFY_TEST_OUTPUT
             DumpTensor(modelOutput0);
             DumpTensor(modelOutput1);
 #endif /* VERIFY_TEST_OUTPUT */

             if (!PresentInferenceResult(results)) {
                 return false;
             }

             profiler.PrintProfilingResult();

             IncrementAppCtxIfmIdx(ctx, "imgIndex");

         } while (runAll && ctx.Get<uint32_t>("imgIndex") != initialImgIdx);

         return true;
     }

     static bool
     PresentInferenceResult(const std::vector<object_detection::DetectionResult>& results)
     {
         hal_lcd_set_text_color(COLOR_GREEN);

         /* If profiling is enabled, and the time is valid. */
         info("Final results:\n");
         info("Total number of inferences: 1\n");

         for (uint32_t i = 0; i < results.size(); ++i) {
             info("%" PRIu32 ") (%f) -> %s {x=%d,y=%d,w=%d,h=%d}\n",
                  i,
                  results[i].m_normalisedVal,
                  "Detection box:",
                  results[i].m_x0,
                  results[i].m_y0,
                  results[i].m_w,
                  results[i].m_h);
         }

         return true;
     }

     static void DrawDetectionBoxes(const std::vector<object_detection::DetectionResult>& results,
                                    uint32_t imgStartX,
                                    uint32_t imgStartY,
                                    uint32_t imgDownscaleFactor)
     {
         uint32_t lineThickness = 1;

         for (const auto& result : results) {
             /* Top line. */
             hal_lcd_display_box(imgStartX + result.m_x0 / imgDownscaleFactor,
                                 imgStartY + result.m_y0 / imgDownscaleFactor,
                                 result.m_w / imgDownscaleFactor,
                                 lineThickness,
                                 COLOR_GREEN);
             /* Bot line. */
             hal_lcd_display_box(imgStartX + result.m_x0 / imgDownscaleFactor,
                                 imgStartY + (result.m_y0 + result.m_h) / imgDownscaleFactor -
                                     lineThickness,
                                 result.m_w / imgDownscaleFactor,
                                 lineThickness,
                                 COLOR_GREEN);

             /* Left line. */
             hal_lcd_display_box(imgStartX + result.m_x0 / imgDownscaleFactor,
                                 imgStartY + result.m_y0 / imgDownscaleFactor,
                                 lineThickness,
                                 result.m_h / imgDownscaleFactor,
                                 COLOR_GREEN);
             /* Right line. */
             hal_lcd_display_box(imgStartX + (result.m_x0 + result.m_w) / imgDownscaleFactor -
                                     lineThickness,
                                 imgStartY + result.m_y0 / imgDownscaleFactor,
                                 lineThickness,
                                 result.m_h / imgDownscaleFactor,
                                 COLOR_GREEN);
         }
     }

 } /* namespace app */
 } /* namespace arm */
	/*
	* SPDX-FileCopyrightText: Copyright 2022, 2024 Arm Limited and/or its affiliates
	* <open-source-office@arm.com> SPDX-License-Identifier: Apache-2.0
	*
	* Licensed under the Apache License, Version 2.0 (the "License");
	* you may not use this file except in compliance with the License.
	* You may obtain a copy of the License at
	*
	* http://www.apache.org/licenses/LICENSE-2.0
	*
	* Unless required by applicable law or agreed to in writing, software
	* distributed under the License is distributed on an "AS IS" BASIS,
	* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	* See the License for the specific language governing permissions and
	* limitations under the License.
	*/
	#include "UseCaseHandler.hpp"
	#include "DetectorPostProcessing.hpp"
	#include "DetectorPreProcessing.hpp"
	#include "InputFiles.hpp"
	#include "UseCaseCommonUtils.hpp"
	#include "YoloFastestModel.hpp"
	#include "hal.h"
	#include "log_macros.h"

	#include <cinttypes>

	namespace arm {
	namespace app {

	#if VSI_ENABLED
	#include "video_drv.h" /* Video Driver API. */

	/**
	* @brief Draws a box in the image using the object detection result object.
	*
	* @param[out] imageData Pointer to the start of the image.
	* @param[in] width Image width.
	* @param[in] height Image height.
	* @param[in] result Object detection result.
	*/
	static void DrawBox(uint8_t* imageData,
	const uint32_t width,
	const uint32_t height,
	const OdResults& result)
	{
	UNUSED(height);
	const auto x = result.m_x0;
	const auto y = result.m_y0;
	const auto w = result.m_w;
	const auto h = result.m_h;

	const uint32_t step = width * 3;
	uint8_t* const imStart = imageData + (y * step) + (x * 3);

	uint8_t* dst_0 = imStart;
	uint8_t* dst_1 = imStart + (h * step);

	for (uint32_t i = 0; i < static_cast<uint32_t>(w); ++i) {
	*dst_0 = 255;
	*dst_1 = 255;

	dst_0 += 3;
	dst_1 += 3;
	}

	dst_0 = imStart;
	dst_1 = imStart + (w * 3);

	for (uint32_t j = 0; j < static_cast<uint32_t>(h); ++j) {
	*dst_0 = 255;
	*dst_1 = 255;

	dst_0 += step;
	dst_1 += step;
	}
	}

	void DrawDetectionBoxesVsi(uint8_t* image,
	const uint32_t imageWidth,
	const uint32_t imageHeight,
	const std::vector<OdResults>& results)
	{
	for (const auto& result : results) {
	DrawBox(image, imageWidth, imageHeight, result);
	printf("Detection :: [%d" ", %d"
	", %d" ", %d" "]\n",
	result.m_x0,
	result.m_y0,
	result.m_w,
	result.m_h);
	}
	}

	/* Object detection VSI inference handler. */
	bool ObjectDetectionHandlerVsi(ApplicationContext& ctx)
	{
	/* Image buffer. */
	static uint8_t ImageBuf[IMAGE_DATA_SIZE];
	static uint8_t ImageOut[IMAGE_DATA_SIZE];

	/* Model object creation and initialisation. */
	auto& model = ctx.Get<Model&>("model");

	TfLiteTensor* inputTensor = model.GetInputTensor(0);
	TfLiteTensor* outputTensor0 = model.GetOutputTensor(0);
	TfLiteTensor* outputTensor1 = model.GetOutputTensor(1);

	if (!inputTensor->dims) {
	printf_err("Invalid input tensor dims\n");
	return false;
	} else if (inputTensor->dims->size < 3) {
	printf_err("Input tensor dimension should be >= 3\n");
	return false;
	}

	TfLiteIntArray* inputShape = model.GetInputShape(0);
	const int inputImgCols = inputShape->data[arm::app::YoloFastestModel::ms_inputColsIdx];
	const int inputImgRows = inputShape->data[arm::app::YoloFastestModel::ms_inputRowsIdx];

	/* Set up pre- and post-processing. */
	arm::app::DetectorPreProcess preProcess =
	arm::app::DetectorPreProcess(inputTensor, true, model.IsDataSigned());

	std::vector<arm::app::OdResults> results;
	const arm::app::object_detection::PostProcessParams postProcessParams{
	inputImgRows,
	inputImgCols,
	arm::app::object_detection::originalImageSize,
	arm::app::object_detection::anchor1,
	arm::app::object_detection::anchor2};
	arm::app::DetectorPostProcess postProcess =
	arm::app::DetectorPostProcess(outputTensor0, outputTensor1, results, postProcessParams);

	const size_t imgSz = inputTensor->bytes < IMAGE_DATA_SIZE ?
	inputTensor->bytes : IMAGE_DATA_SIZE;

	if (sizeof(ImageBuf) < imgSz) {
	printf_err("Image buffer is insufficient\n");
	return false;
	}

	/* Configure Input Video. */
	if (VideoDrv_Configure(VIDEO_DRV_IN0,
	arm::app::object_detection::originalImageSize,
	arm::app::object_detection::originalImageSize,
	COLOR_RGB888, 24U) != VIDEO_DRV_OK) {
	printf_err("Failed to configure video input\n");
	return false;
	}

	/* Set Input Video buffer. */
	if (VideoDrv_SetBuf(VIDEO_DRV_IN0, ImageBuf, IMAGE_DATA_SIZE) != VIDEO_DRV_OK) {
	printf_err("Failed to set buffer for video input\n");
	return false;
	}

	/* Set Output Video file (only when using AVH - default: Display) */
	// if (VideoDrv_SetFile(VIDEO_DRV_OUT0, "output_image.png") != VIDEO_DRV_OK) {
	// printf_err("Failed to set filename for video output\n");
	// return 1;
	// }
	/* Configure Output Video. */
	if (VideoDrv_Configure(VIDEO_DRV_OUT0,
	arm::app::object_detection::originalImageSize,
	arm::app::object_detection::originalImageSize,
	COLOR_RGB888, 24U) != VIDEO_DRV_OK) {
	printf_err("Failed to configure video output\n");
	return false;
	}

	/* Set Output Video buffer. */
	if (VideoDrv_SetBuf(VIDEO_DRV_OUT0, ImageOut, IMAGE_DATA_SIZE) != VIDEO_DRV_OK) {
	printf_err("Failed to set buffer for video output\n");
	return false;
	}

	auto imgCount = ctx.Get<uint32_t>("imgIndex");
	void* imgFrame = nullptr;
	void* outFrame = nullptr;

	while (true) {
	#if VSI_IMAGE_INPUT
	if (VideoDrv_SetFile(VIDEO_DRV_IN0, GetFilePath(imgCount)) != VIDEO_DRV_OK) {
	printf_err("Failed to set filename for video input\n");
	return false;
	}
	#endif

	VideoDrv_Status_t status;

	results.clear();

	/* Start video capture (single frame). */
	if (VideoDrv_StreamStart(VIDEO_DRV_IN0, VIDEO_DRV_MODE_SINGLE) != VIDEO_DRV_OK) {
	printf_err("Failed to start video capture\n");
	return false;
	}

	/* Wait for video input frame. */
	do {
	status = VideoDrv_GetStatus(VIDEO_DRV_IN0);
	} while (status.buf_empty != 0U);

	/* Get input video frame buffer. */
	imgFrame = VideoDrv_GetFrameBuf(VIDEO_DRV_IN0);

	/* Run the pre-processing, inference and post-processing. */
	if (!preProcess.DoPreProcess(imgFrame, imgSz)) {
	printf_err("Pre-processing failed.\n");
	return false;
	}

	/* Run inference over this image. */
	printf("\rImage %" PRIu32 "; ", ++imgCount);

	if (!model.RunInference()) {
	printf_err("Inference failed.\n");
	return false;
	}

	if (!postProcess.DoPostProcess()) {
	printf_err("Post-processing failed.\n");
	return false;
	}

	/* Release input frame. */
	VideoDrv_ReleaseFrame(VIDEO_DRV_IN0);

	arm::app::DrawDetectionBoxesVsi(static_cast<uint8_t*>(imgFrame), inputImgCols, inputImgRows, results);

	/* Get output video frame buffer. */
	outFrame = VideoDrv_GetFrameBuf(VIDEO_DRV_OUT0);

	/* Copy image frame with detection boxes to output frame buffer. */
	memcpy(outFrame, imgFrame, IMAGE_DATA_SIZE);

	/* Release output frame. */
	VideoDrv_ReleaseFrame(VIDEO_DRV_OUT0);

	/* Start video output (single frame). */
	VideoDrv_StreamStart(VIDEO_DRV_OUT0, VIDEO_DRV_MODE_SINGLE);

	/* Check for end of stream (when using AVH with file as Video input). */
	if (status.eos != 0U) {
	while (VideoDrv_GetStatus(VIDEO_DRV_OUT0).buf_empty == 0U);
	break;
	}
	}

	IncrementAppCtxIfmIdx(ctx, "imgIndex");

	/* De-initialize Video Interface. */
	//VideoDrv_Uninitialize();
	return true;
	}

	#endif

	/**
	* @brief Presents inference results along using the data presentation
	* object.
	* @param[in] results Vector of detection results to be displayed.
	* @return true if successful, false otherwise.
	**/
	static bool
	PresentInferenceResult(const std::vector<object_detection::DetectionResult>& results);

	/**
	* @brief Draw boxes directly on the LCD for all detected objects.
	* @param[in] results Vector of detection results to be displayed.
	* @param[in] imageStartX X coordinate where the image starts on the LCD.
	* @param[in] imageStartY Y coordinate where the image starts on the LCD.
	* @param[in] imgDownscaleFactor How much image has been downscaled on LCD.
	**/
	static void DrawDetectionBoxes(const std::vector<object_detection::DetectionResult>& results,
	uint32_t imgStartX,
	uint32_t imgStartY,
	uint32_t imgDownscaleFactor);

	/* Object detection inference handler. */
	bool ObjectDetectionHandler(ApplicationContext& ctx, uint32_t imgIndex, bool runAll)
	{
	auto& profiler = ctx.Get<Profiler&>("profiler");

	constexpr uint32_t dataPsnImgDownscaleFactor = 1;
	constexpr uint32_t dataPsnImgStartX = 10;
	constexpr uint32_t dataPsnImgStartY = 35;

	constexpr uint32_t dataPsnTxtInfStartX = 20;
	constexpr uint32_t dataPsnTxtInfStartY = 28;

	hal_lcd_clear(COLOR_BLACK);

	auto& model = ctx.Get<Model&>("model");

	/* If the request has a valid size, set the image index. */
	if (imgIndex < NUMBER_OF_FILES) {
	if (!SetAppCtxIfmIdx(ctx, imgIndex, "imgIndex")) {
	return false;
	}
	}
	if (!model.IsInited()) {
	printf_err("Model is not initialised! Terminating processing.\n");
	return false;
	}

	auto initialImgIdx = ctx.Get<uint32_t>("imgIndex");

	TfLiteTensor* inputTensor = model.GetInputTensor(0);
	TfLiteTensor* outputTensor0 = model.GetOutputTensor(0);
	TfLiteTensor* outputTensor1 = model.GetOutputTensor(1);

	if (!inputTensor->dims) {
	printf_err("Invalid input tensor dims\n");
	return false;
	} else if (inputTensor->dims->size < 3) {
	printf_err("Input tensor dimension should be >= 3\n");
	return false;
	}

	TfLiteIntArray* inputShape = model.GetInputShape(0);

	const int inputImgCols = inputShape->data[YoloFastestModel::ms_inputColsIdx];
	const int inputImgRows = inputShape->data[YoloFastestModel::ms_inputRowsIdx];

	/* Set up pre and post-processing. */
	DetectorPreProcess preProcess = DetectorPreProcess(inputTensor, true, model.IsDataSigned());

	std::vector<object_detection::DetectionResult> results;
	const object_detection::PostProcessParams postProcessParams{
	inputImgRows,
	inputImgCols,
	object_detection::originalImageSize,
	object_detection::anchor1,
	object_detection::anchor2};
	DetectorPostProcess postProcess =
	DetectorPostProcess(outputTensor0, outputTensor1, results, postProcessParams);
	do {
	/* Ensure there are no results leftover from previous inference when running all. */
	results.clear();

	/* Strings for presentation/logging. */
	std::string str_inf{"Running inference... "};

	const uint8_t* currImage = GetImgArray(ctx.Get<uint32_t>("imgIndex"));

	auto dstPtr = static_cast<uint8_t*>(inputTensor->data.uint8);
	const size_t copySz =
	inputTensor->bytes < IMAGE_DATA_SIZE ? inputTensor->bytes : IMAGE_DATA_SIZE;

	/* Run the pre-processing, inference and post-processing. */
	if (!preProcess.DoPreProcess(currImage, copySz)) {
	printf_err("Pre-processing failed.");
	return false;
	}

	/* Display image on the LCD. */
	hal_lcd_display_image(
	(arm::app::object_detection::channelsImageDisplayed == 3) ? currImage : dstPtr,
	inputImgCols,
	inputImgRows,
	arm::app::object_detection::channelsImageDisplayed,
	dataPsnImgStartX,
	dataPsnImgStartY,
	dataPsnImgDownscaleFactor);

	/* Display message on the LCD - inference running. */
	hal_lcd_display_text(
	str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);

	/* Run inference over this image. */
	info("Running inference on image %" PRIu32 " => %s\n",
	ctx.Get<uint32_t>("imgIndex"),
	GetFilename(ctx.Get<uint32_t>("imgIndex")));

	if (!RunInference(model, profiler)) {
	printf_err("Inference failed.");
	return false;
	}

	if (!postProcess.DoPostProcess()) {
	printf_err("Post-processing failed.");
	return false;
	}

	/* Erase. */
	str_inf = std::string(str_inf.size(), ' ');
	hal_lcd_display_text(
	str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);

	/* Draw boxes. */
	DrawDetectionBoxes(
	results, dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor);

	#if VERIFY_TEST_OUTPUT
	DumpTensor(modelOutput0);
	DumpTensor(modelOutput1);
	#endif /* VERIFY_TEST_OUTPUT */

	if (!PresentInferenceResult(results)) {
	return false;
	}

	profiler.PrintProfilingResult();

	IncrementAppCtxIfmIdx(ctx, "imgIndex");

	} while (runAll && ctx.Get<uint32_t>("imgIndex") != initialImgIdx);

	return true;
	}

	static bool
	PresentInferenceResult(const std::vector<object_detection::DetectionResult>& results)
	{
	hal_lcd_set_text_color(COLOR_GREEN);

	/* If profiling is enabled, and the time is valid. */
	info("Final results:\n");
	info("Total number of inferences: 1\n");

	for (uint32_t i = 0; i < results.size(); ++i) {
	info("%" PRIu32 ") (%f) -> %s {x=%d,y=%d,w=%d,h=%d}\n",
	i,
	results[i].m_normalisedVal,
	"Detection box:",
	results[i].m_x0,
	results[i].m_y0,
	results[i].m_w,
	results[i].m_h);
	}

	return true;
	}

	static void DrawDetectionBoxes(const std::vector<object_detection::DetectionResult>& results,
	uint32_t imgStartX,
	uint32_t imgStartY,
	uint32_t imgDownscaleFactor)
	{
	uint32_t lineThickness = 1;

	for (const auto& result : results) {
	/* Top line. */
	hal_lcd_display_box(imgStartX + result.m_x0 / imgDownscaleFactor,
	imgStartY + result.m_y0 / imgDownscaleFactor,
	result.m_w / imgDownscaleFactor,
	lineThickness,
	COLOR_GREEN);
	/* Bot line. */
	hal_lcd_display_box(imgStartX + result.m_x0 / imgDownscaleFactor,
	imgStartY + (result.m_y0 + result.m_h) / imgDownscaleFactor -
	lineThickness,
	result.m_w / imgDownscaleFactor,
	lineThickness,
	COLOR_GREEN);

	/* Left line. */
	hal_lcd_display_box(imgStartX + result.m_x0 / imgDownscaleFactor,
	imgStartY + result.m_y0 / imgDownscaleFactor,
	lineThickness,
	result.m_h / imgDownscaleFactor,
	COLOR_GREEN);
	/* Right line. */
	hal_lcd_display_box(imgStartX + (result.m_x0 + result.m_w) / imgDownscaleFactor -
	lineThickness,
	imgStartY + result.m_y0 / imgDownscaleFactor,
	lineThickness,
	result.m_h / imgDownscaleFactor,
	COLOR_GREEN);
	}
	}

	} /* namespace app */
	} /* namespace arm */