Face detection demo from Emza Visual Sense
Signed-off-by: Michael Levit michaell@emza-vs.com
Change-Id: I7958b05b5dbe9a785e0f8a241b716c17a9ca976f
diff --git a/tests/use_case/object_detection/InferenceTestYoloFastest.cc b/tests/use_case/object_detection/InferenceTestYoloFastest.cc
new file mode 100644
index 0000000..e6ae573
--- /dev/null
+++ b/tests/use_case/object_detection/InferenceTestYoloFastest.cc
@@ -0,0 +1,124 @@
+/*
+ * 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 "hal.h"
+#include "ImageUtils.hpp"
+#include "YoloFastestModel.hpp"
+#include "TensorFlowLiteMicro.hpp"
+#include "DetectorPostProcessing.hpp"
+#include "InputFiles.hpp"
+#include "UseCaseCommonUtils.hpp"
+#include "DetectionUseCaseUtils.hpp"
+#include "ExpectedObjectDetectionResults.hpp"
+
+#include <catch.hpp>
+
+
+bool RunInference(arm::app::Model& model, const uint8_t imageData[])
+{
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
+ REQUIRE(inputTensor);
+
+ const size_t copySz = inputTensor->bytes < (INPUT_IMAGE_WIDTH*INPUT_IMAGE_HEIGHT) ?
+ inputTensor->bytes :
+ (INPUT_IMAGE_WIDTH*INPUT_IMAGE_HEIGHT);
+
+ arm::app::RgbToGrayscale(imageData,inputTensor->data.uint8,INPUT_IMAGE_WIDTH,INPUT_IMAGE_HEIGHT);
+
+ if(model.IsDataSigned()){
+ convertImgIoInt8(inputTensor->data.data, copySz);
+ }
+
+ return model.RunInference();
+}
+
+template<typename T>
+void TestInference(int imageIdx, arm::app::Model& model, T tolerance) {
+
+ info("Entering TestInference for image %d \n", imageIdx);
+
+ std::vector<arm::app::DetectionResult> results;
+ auto image = get_img_array(imageIdx);
+
+ REQUIRE(RunInference(model, image));
+
+
+ TfLiteTensor* output_arr[2] = {nullptr,nullptr};
+ output_arr[0] = model.GetOutputTensor(0);
+ output_arr[1] = model.GetOutputTensor(1);
+
+ for (int i =0; i < 2; i++) {
+ REQUIRE(output_arr[i]);
+ REQUIRE(tflite::GetTensorData<T>(output_arr[i]));
+ }
+
+ RunPostProcessing(NULL,output_arr,results);
+
+ info("Got %ld boxes \n",results.size());
+
+ std::vector<std::vector<arm::app::DetectionResult>> expected_results;
+ get_expected_ut_results(expected_results);
+
+ /*validate got the same number of boxes */
+ REQUIRE(results.size() == expected_results[imageIdx].size());
+
+
+ for (int i=0; i < (int)results.size(); i++) {
+
+ info("%" PRIu32 ") (%f) -> %s {x=%d,y=%d,w=%d,h=%d}\n", (int)i,
+ results[i].m_normalisedVal, "Detection box:",
+ results[i].m_x0, results[i].m_y0, results[i].m_w, results[i].m_h );
+
+ /*validate confidence and box dimensions */
+ REQUIRE(fabs(results[i].m_normalisedVal - expected_results[imageIdx][i].m_normalisedVal) < 0.1);
+ REQUIRE(static_cast<int>(results[i].m_x0) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_x0)).epsilon(tolerance));
+ REQUIRE(static_cast<int>(results[i].m_y0) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_y0)).epsilon(tolerance));
+ REQUIRE(static_cast<int>(results[i].m_w) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_w)).epsilon(tolerance));
+ REQUIRE(static_cast<int>(results[i].m_h) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_h)).epsilon(tolerance));
+ }
+
+
+}
+
+
+TEST_CASE("Running inference with TensorFlow Lite Micro and YoloFastest", "[YoloFastest]")
+{
+ SECTION("Executing inferences sequentially")
+ {
+ arm::app::YoloFastestModel model{};
+
+ REQUIRE_FALSE(model.IsInited());
+ REQUIRE(model.Init());
+ REQUIRE(model.IsInited());
+
+ for (uint32_t i = 0 ; i < NUMBER_OF_FILES; ++i) {
+ TestInference<uint8_t>(i, model, 1);
+ }
+ }
+
+ for (uint32_t i = 0 ; i < NUMBER_OF_FILES; ++i) {
+ DYNAMIC_SECTION("Executing inference with re-init")
+ {
+ arm::app::YoloFastestModel model{};
+
+ REQUIRE_FALSE(model.IsInited());
+ REQUIRE(model.Init());
+ REQUIRE(model.IsInited());
+
+ TestInference<uint8_t>(i, model, 1);
+ }
+ }
+}