From 06fcf7502b5c9b0e30ac06acfa25aa914d936500 Mon Sep 17 00:00:00 2001
From: Michael Levit <michaell@emza-vs.com>
Date: Wed, 12 Jan 2022 11:53:46 +0200
Subject: Face detection demo from Emza Visual Sense Signed-off-by: Michael
 Levit michaell@emza-vs.com

Change-Id: I7958b05b5dbe9a785e0f8a241b716c17a9ca976f
---
 .../object_detection/InferenceTestYoloFastest.cc   | 124 +++++++++++++++++++++
 1 file changed, 124 insertions(+)
 create mode 100644 tests/use_case/object_detection/InferenceTestYoloFastest.cc

(limited to 'tests/use_case/object_detection/InferenceTestYoloFastest.cc')

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);
+        }
+    }
+}
-- 
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