| /* |
| * Copyright (c) 2021 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 "Classifier.hpp" |
| #include "InputFiles.hpp" |
| #include "MobileNetModel.hpp" |
| #include "UseCaseCommonUtils.hpp" |
| #include "hal.h" |
| |
| #include <inttypes.h> |
| |
| using ImgClassClassifier = arm::app::Classifier; |
| |
| namespace arm { |
| namespace app { |
| |
| /** |
| * @brief Helper function to load the current image into the input |
| * tensor. |
| * @param[in] imIdx Image index (from the pool of images available |
| * to the application). |
| * @param[out] inputTensor Pointer to the input tensor to be populated. |
| * @return true if tensor is loaded, false otherwise. |
| **/ |
| static bool LoadImageIntoTensor(uint32_t imIdx, TfLiteTensor* inputTensor); |
| |
| /* Image inference classification handler. */ |
| bool ClassifyImageHandler(ApplicationContext& ctx, uint32_t imgIndex, bool runAll) |
| { |
| auto& platform = ctx.Get<hal_platform&>("platform"); |
| auto& profiler = ctx.Get<Profiler&>("profiler"); |
| |
| constexpr uint32_t dataPsnImgDownscaleFactor = 2; |
| constexpr uint32_t dataPsnImgStartX = 10; |
| constexpr uint32_t dataPsnImgStartY = 35; |
| |
| constexpr uint32_t dataPsnTxtInfStartX = 150; |
| constexpr uint32_t dataPsnTxtInfStartY = 40; |
| |
| platform.data_psn->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* outputTensor = model.GetOutputTensor(0); |
| 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::MobileNetModel::ms_inputColsIdx]; |
| const uint32_t nRows = inputShape->data[arm::app::MobileNetModel::ms_inputRowsIdx]; |
| const uint32_t nChannels = inputShape->data[arm::app::MobileNetModel::ms_inputChannelsIdx]; |
| |
| std::vector<ClassificationResult> results; |
| |
| do { |
| /* Strings for presentation/logging. */ |
| std::string str_inf{"Running inference... "}; |
| |
| /* Copy over the data. */ |
| LoadImageIntoTensor(ctx.Get<uint32_t>("imgIndex"), inputTensor); |
| |
| /* Display this image on the LCD. */ |
| platform.data_psn->present_data_image( |
| static_cast<uint8_t *>(inputTensor->data.data), |
| nCols, nRows, nChannels, |
| 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. */ |
| platform.data_psn->present_data_text(str_inf.c_str(), str_inf.size(), |
| dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0); |
| |
| /* 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(), ' '); |
| platform.data_psn->present_data_text(str_inf.c_str(), str_inf.size(), |
| dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0); |
| |
| auto& classifier = ctx.Get<ImgClassClassifier&>("classifier"); |
| classifier.GetClassificationResults(outputTensor, results, |
| ctx.Get<std::vector <std::string>&>("labels"), |
| 5); |
| |
| /* Add results to context for access outside handler. */ |
| ctx.Set<std::vector<ClassificationResult>>("results", results); |
| |
| #if VERIFY_TEST_OUTPUT |
| arm::app::DumpTensor(outputTensor); |
| #endif /* VERIFY_TEST_OUTPUT */ |
| |
| if (!image::PresentInferenceResult(platform, results)) { |
| return false; |
| } |
| |
| profiler.PrintProfilingResult(); |
| |
| IncrementAppCtxIfmIdx(ctx,"imgIndex"); |
| |
| } while (runAll && ctx.Get<uint32_t>("imgIndex") != curImIdx); |
| |
| return true; |
| } |
| |
| static bool LoadImageIntoTensor(uint32_t imIdx, TfLiteTensor* inputTensor) |
| { |
| const size_t copySz = inputTensor->bytes < IMAGE_DATA_SIZE ? |
| inputTensor->bytes : IMAGE_DATA_SIZE; |
| const uint8_t* imgSrc = get_img_array(imIdx); |
| if (nullptr == imgSrc) { |
| printf_err("Failed to get image index %" PRIu32 " (max: %u)\n", imIdx, |
| NUMBER_OF_FILES - 1); |
| return false; |
| } |
| |
| memcpy(inputTensor->data.data, imgSrc, copySz); |
| debug("Image %" PRIu32 " loaded\n", imIdx); |
| return true; |
| } |
| |
| |
| } /* namespace app */ |
| } /* namespace arm */ |