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

 /*
  * SPDX-FileCopyrightText: Copyright 2022 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 "InputFiles.hpp"
 #include "YoloFastestModel.hpp"
 #include "UseCaseCommonUtils.hpp"
 #include "DetectorPostProcessing.hpp"
 #include "DetectorPreProcessing.hpp"
 #include "hal.h"
 #include "log_macros.h"

 #include <cinttypes>

 namespace arm {
 namespace app {

     /**
      * @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 = get_img_array(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"),
                 get_filename(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 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 "InputFiles.hpp"
	#include "YoloFastestModel.hpp"
	#include "UseCaseCommonUtils.hpp"
	#include "DetectorPostProcessing.hpp"
	#include "DetectorPreProcessing.hpp"
	#include "hal.h"
	#include "log_macros.h"

	#include <cinttypes>

	namespace arm {
	namespace app {

	/**
	* @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 = get_img_array(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"),
	get_filename(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 */