MLECO-2082: Adding visual wake word use case
MLECO-2083: Refactoring img_class and visual wake word
*Added source files for visual wake word
*Added tests
*Added docs
*Added new images for visual wake word demo
*Refactored common functions in img_class, visual wake word and other usecases
Change-Id: Ibd25854e19a5517f940a8d3086a5d4835fab89e9
Signed-off-by: Éanna Ó Catháin <eanna.ocathain@arm.com>
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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