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Diffstat (limited to 'source/use_case/vww/src/UseCaseHandler.cc')
| -rw-r--r-- | source/use_case/vww/src/UseCaseHandler.cc | 182 |
1 files changed, 182 insertions, 0 deletions
diff --git a/source/use_case/vww/src/UseCaseHandler.cc b/source/use_case/vww/src/UseCaseHandler.cc new file mode 100644 index 0000000..fb2e837 --- /dev/null +++ b/source/use_case/vww/src/UseCaseHandler.cc @@ -0,0 +1,182 @@ +/* + * 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 "VisualWakeWordModel.hpp" +#include "Classifier.hpp" +#include "InputFiles.hpp" +#include "UseCaseCommonUtils.hpp" +#include "hal.h" + +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 = 1; + constexpr uint32_t dataPsnImgStartX = 10; + constexpr uint32_t dataPsnImgStartY = 35; + + constexpr uint32_t dataPsnTxtInfStartX = 150; + constexpr uint32_t dataPsnTxtInfStartY = 70; + + + platform.data_psn->clear(COLOR_BLACK); + time_t infTimeMs = 0; + + 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[2]; + const uint32_t nRows = inputShape->data[1]; + const uint32_t nChannels = (inputShape->size == 4) ? inputShape->data[3] : 1; + + 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( + (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<Classifier&>("classifier"); + classifier.GetClassificationResults(outputTensor, results, + ctx.Get<std::vector <std::string>&>("labels"), 1); + + /* 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, infTimeMs)) { + return false; + } + + profiler.PrintProfilingResult(); + IncrementAppCtxIfmIdx(ctx,"imgIndex"); + + } while (runAll && ctx.Get<uint32_t>("imgIndex") != curImIdx); + + return true; + } + + static bool LoadImageIntoTensor(const uint32_t imIdx, + TfLiteTensor *inputTensor) + { + const size_t copySz = inputTensor->bytes < IMAGE_DATA_SIZE ? + inputTensor->bytes : IMAGE_DATA_SIZE; + if (imIdx >= NUMBER_OF_FILES) { + printf_err("invalid image index %" PRIu32 " (max: %u)\n", imIdx, + NUMBER_OF_FILES - 1); + return false; + } + + const uint32_t nChannels = (inputTensor->dims->size == 4) ? inputTensor->dims->data[3] : 1; + + const uint8_t* srcPtr = get_img_array(imIdx); + auto* dstPtr = (uint8_t*)inputTensor->data.data; + if (1 == nChannels) { + /** + * Visual Wake Word model accepts only one channel => + * Convert image to grayscale here + **/ + for (size_t i = 0; i < copySz; ++i, srcPtr += 3) { + *dstPtr++ = 0.2989*(*srcPtr) + + 0.587*(*(srcPtr+1)) + + 0.114*(*(srcPtr+2)); + } + } else { + memcpy(inputTensor->data.data, srcPtr, copySz); + } + + debug("Image %" PRIu32 " loaded\n", imIdx); + return true; + } + +} /* namespace app */ +} /* namespace arm */
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