Face detection demo from Emza Visual Sense
Signed-off-by: Michael Levit michaell@emza-vs.com
Change-Id: I7958b05b5dbe9a785e0f8a241b716c17a9ca976f
diff --git a/source/use_case/object_detection/src/UseCaseHandler.cc b/source/use_case/object_detection/src/UseCaseHandler.cc
new file mode 100644
index 0000000..45df4f8
--- /dev/null
+++ b/source/use_case/object_detection/src/UseCaseHandler.cc
@@ -0,0 +1,162 @@
+/*
+ * 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 "DetectionUseCaseUtils.hpp"
+#include "DetectorPostProcessing.hpp"
+#include "hal.h"
+
+#include <inttypes.h>
+
+
+/* used for presentation, original images are read-only"*/
+static uint8_t g_image_buffer[INPUT_IMAGE_WIDTH*INPUT_IMAGE_HEIGHT*FORMAT_MULTIPLY_FACTOR] IFM_BUF_ATTRIBUTE = {};
+
+namespace arm {
+namespace app {
+
+
+ /* Object detection classification handler. */
+ bool ObjectDetectionHandler(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 = 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* 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];
+ const uint32_t nPresentationChannels = FORMAT_MULTIPLY_FACTOR;
+
+ std::vector<DetectionResult> results;
+
+ 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 gryscale */
+#if DISPLAY_RGB_IMAGE
+ memcpy(g_image_buffer,curr_image, INPUT_IMAGE_WIDTH*INPUT_IMAGE_HEIGHT*FORMAT_MULTIPLY_FACTOR);
+#else
+ RgbToGrayscale(curr_image,g_image_buffer,INPUT_IMAGE_WIDTH,INPUT_IMAGE_HEIGHT);
+#endif /*DISPLAY_RGB_IMAGE*/
+
+ RgbToGrayscale(curr_image,inputTensor->data.uint8,INPUT_IMAGE_WIDTH,INPUT_IMAGE_HEIGHT);
+
+
+ /* Display this image on the LCD. */
+ platform.data_psn->present_data_image(
+ g_image_buffer,
+ nCols, nRows, nPresentationChannels,
+ 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);
+
+ /* Detector post-processing*/
+ TfLiteTensor* output_arr[2] = {nullptr,nullptr};
+ output_arr[0] = model.GetOutputTensor(0);
+ output_arr[1] = model.GetOutputTensor(1);
+ RunPostProcessing(g_image_buffer,output_arr,results);
+
+ platform.data_psn->present_data_image(
+ g_image_buffer,
+ nCols, nRows, nPresentationChannels,
+ dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor);
+
+ /*Detector post-processing*/
+
+
+ /* Add results to context for access outside handler. */
+ ctx.Set<std::vector<DetectionResult>>("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;
+ }
+
+} /* namespace app */
+} /* namespace arm */