| /* |
| * Copyright (c) 2022 Arm Limited. All rights reserved. |
| * 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 "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<arm::app::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<arm::app::object_detection::DetectionResult>& results, |
| uint32_t imgStartX, |
| uint32_t imgStartY, |
| uint32_t imgDownscaleFactor); |
| |
| /* Object detection classification 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 = 150; |
| constexpr uint32_t dataPsnTxtInfStartY = 40; |
| |
| 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 curImIdx = ctx.Get<uint32_t>("imgIndex"); |
| |
| TfLiteTensor* inputTensor = model.GetInputTensor(0); |
| |
| 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 uint32_t nCols = inputShape->data[arm::app::YoloFastestModel::ms_inputColsIdx]; |
| const uint32_t nRows = inputShape->data[arm::app::YoloFastestModel::ms_inputRowsIdx]; |
| |
| /* Get pre/post-processing objects. */ |
| auto& postp = ctx.Get<object_detection::DetectorPostprocessing&>("postprocess"); |
| |
| do { |
| /* Strings for presentation/logging. */ |
| std::string str_inf{"Running inference... "}; |
| |
| const uint8_t* curr_image = get_img_array(ctx.Get<uint32_t>("imgIndex")); |
| |
| /* Copy over the data and convert to grayscale */ |
| auto* dstPtr = static_cast<uint8_t*>(inputTensor->data.uint8); |
| const size_t copySz = inputTensor->bytes < IMAGE_DATA_SIZE ? |
| inputTensor->bytes : IMAGE_DATA_SIZE; |
| |
| /* Convert to gray scale and populate input tensor. */ |
| image::RgbToGrayscale(curr_image, dstPtr, copySz); |
| |
| /* Display image on the LCD. */ |
| hal_lcd_display_image( |
| (channelsImageDisplayed == 3) ? curr_image : dstPtr, |
| nCols, nRows, channelsImageDisplayed, |
| dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor); |
| |
| /* If the data is signed. */ |
| if (model.IsDataSigned()) { |
| image::ConvertImgToInt8(inputTensor->data.data, inputTensor->bytes); |
| } |
| |
| /* 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)) { |
| return false; |
| } |
| |
| /* Erase. */ |
| str_inf = std::string(str_inf.size(), ' '); |
| hal_lcd_display_text(str_inf.c_str(), str_inf.size(), |
| dataPsnTxtInfStartX, dataPsnTxtInfStartY, false); |
| |
| /* Detector post-processing*/ |
| std::vector<object_detection::DetectionResult> results; |
| TfLiteTensor* modelOutput0 = model.GetOutputTensor(0); |
| TfLiteTensor* modelOutput1 = model.GetOutputTensor(1); |
| postp.RunPostProcessing( |
| nRows, |
| nCols, |
| modelOutput0, |
| modelOutput1, |
| results); |
| |
| /* Draw boxes. */ |
| DrawDetectionBoxes(results, dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor); |
| |
| #if VERIFY_TEST_OUTPUT |
| arm::app::DumpTensor(modelOutput0); |
| arm::app::DumpTensor(modelOutput1); |
| #endif /* VERIFY_TEST_OUTPUT */ |
| |
| if (!PresentInferenceResult(results)) { |
| return false; |
| } |
| |
| profiler.PrintProfilingResult(); |
| |
| IncrementAppCtxIfmIdx(ctx,"imgIndex"); |
| |
| } while (runAll && ctx.Get<uint32_t>("imgIndex") != curImIdx); |
| |
| return true; |
| } |
| |
| static bool PresentInferenceResult(const std::vector<arm::app::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<arm::app::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 */ |