test: add hand detection model test casetizen_devel

Change-Id: Ia752b12148b672e960e82cf14d8becfc3bba31b8
Signed-off-by: Inki Dae <inki.dae@samsung.com>

2 files changed, 243 insertions, 0 deletions

diff --git a/test/testsuites/machine_learning/object_detection/test_object_detection.cpp b/test/testsuites/machine_learning/object_detection/test_object_detection.cpp
index 151aafd5..6ab0410f 100644
--- a/test/testsuites/machine_learning/object_detection/test_object_detection.cpp
+++ b/test/testsuites/machine_learning/object_detection/test_object_detection.cpp
@@ -24,11 +24,14 @@
 #include "ImageHelper.h"
 #include "mv_face_detection.h"
 #include "mv_face_detection_internal.h"
+#include "mv_hand_detection.h"
+#include "mv_hand_detection_internal.h"
 #include "mv_object_detection.h"
 #include "mv_object_detection_internal.h"
 
 #define IMG_DOG TEST_RES_PATH "/res/inference/images/dog2.jpg"
 #define IMG_FACE TEST_RES_PATH "/res/inference/images/faceDetection.jpg"
+#define IMG_HAND TEST_RES_PATH "/res/inference/images/handDetection.jpg"
 
 using namespace testing;
 using namespace std;
@@ -109,6 +112,43 @@ TEST(FaceDetectionTest, GettingAvailableInferenceEnginesInfoShouldBeOk)
 	ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
 }
 
+TEST(HandDetectionTest, GettingAvailableInferenceEnginesInfoShouldBeOk)
+{
+	mv_object_detection_h handle;
+
+	int ret = mv_hand_detection_create(&handle);
+	ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+	unsigned int engine_count = 0;
+
+	ret = mv_hand_detection_get_engine_count(handle, &engine_count);
+	ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+	ASSERT_GE(engine_count, 1);
+
+	for (unsigned int engine_idx = 0; engine_idx < engine_count; ++engine_idx) {
+		char *engine_type = nullptr;
+
+		ret = mv_hand_detection_get_engine_type(handle, engine_idx, &engine_type);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		unsigned int device_count = 0;
+
+		ret = mv_hand_detection_get_device_count(handle, engine_type, &device_count);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+		ASSERT_GE(device_count, 1);
+
+		for (unsigned int device_idx = 0; device_idx < device_count; ++device_idx) {
+			char *device_type = nullptr;
+
+			ret = mv_hand_detection_get_device_type(handle, engine_type, device_idx, &device_type);
+			ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+		}
+	}
+
+	ret = mv_hand_detection_destroy(handle);
+	ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+}
+
 TEST(ObjectDetectionTest, InferenceShouldBeOk)
 {
 	mv_object_detection_h handle;
@@ -246,3 +286,65 @@ TEST(FaceDetectionTest, InferenceShouldBeOk)
 	ret = mv_destroy_source(mv_source);
 	ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
 }
+
+TEST(HandDetectionTest, InferenceShouldBeOk)
+{
+	mv_object_detection_h handle;
+	vector<test_model_input> test_models {
+		{} // If empty then default model will be used.
+		// TODO.
+	};
+	const int coordinate_answers[][4] = { { 0, 125, 93, 181 }, { 118, 112, 211, 167 }, { 299, 141, 419, 211 } };
+
+	mv_source_h mv_source = NULL;
+	int ret = mv_create_source(&mv_source);
+	ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+	ret = ImageHelper::loadImageToSource(IMG_HAND, mv_source);
+	ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+	for (auto &model : test_models) {
+		ret = mv_hand_detection_create(&handle);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		ret = mv_hand_detection_set_model(handle, model.model_file.c_str(), model.meta_file.c_str(),
+										  model.label_file.c_str(), NULL);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		ret = mv_hand_detection_set_engine(handle, "tflite", "cpu");
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		ret = mv_hand_detection_configure(handle);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		ret = mv_hand_detection_prepare(handle);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		ret = mv_hand_detection_inference(handle, mv_source);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		unsigned long frame_number;
+		unsigned int number_of_objects;
+
+		ret = mv_hand_detection_get_result_count(handle, &frame_number, &number_of_objects);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		for (unsigned int idx = 0; idx < number_of_objects; ++idx) {
+			int left, top, right, bottom;
+
+			int ret = mv_hand_detection_get_bound_box(handle, idx, &left, &top, &right, &bottom);
+			ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+			ASSERT_EQ(coordinate_answers[idx][0], left);
+			ASSERT_EQ(coordinate_answers[idx][1], top);
+			ASSERT_EQ(coordinate_answers[idx][2], right);
+			ASSERT_EQ(coordinate_answers[idx][3], bottom);
+		}
+
+		ret = mv_hand_detection_destroy(handle);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+	}
+
+	ret = mv_destroy_source(mv_source);
+	ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+}
diff --git a/test/testsuites/machine_learning/object_detection/test_object_detection_async.cpp b/test/testsuites/machine_learning/object_detection/test_object_detection_async.cpp
index 94c44246..86d0e271 100644
--- a/test/testsuites/machine_learning/object_detection/test_object_detection_async.cpp
+++ b/test/testsuites/machine_learning/object_detection/test_object_detection_async.cpp
@@ -25,11 +25,14 @@
 #include "ImageHelper.h"
 #include "mv_face_detection.h"
 #include "mv_face_detection_internal.h"
+#include "mv_hand_detection.h"
+#include "mv_hand_detection_internal.h"
 #include "mv_object_detection.h"
 #include "mv_object_detection_internal.h"
 
 #define IMG_DOG TEST_RES_PATH "/res/inference/images/dog2.jpg"
 #define IMG_FACE TEST_RES_PATH "/res/inference/images/faceDetection.jpg"
+#define IMG_HAND TEST_RES_PATH "/res/inference/images/handDetection.jpg"
 #define MAX_INFERENCE_ITERATION 50
 
 using namespace testing;
@@ -309,4 +312,142 @@ TEST(FaceDetectionAsyncTest, InferenceShouldBeOkWithDestroyFirst)
 
 		thread_handle->join();
 	}
+}
+
+void hand_detection_callback(void *user_data)
+{
+	mv_hand_detection_h handle = static_cast<mv_object_detection_h>(user_data);
+	const int coordinate_answers[][4] = { { 0, 125, 93, 181 }, { 118, 112, 211, 167 }, { 299, 141, 419, 211 } };
+
+	bool is_loop_exit = false;
+
+	while (!is_loop_exit) {
+		unsigned long frame_number;
+		unsigned int number_of_objects;
+
+		int ret = mv_hand_detection_get_result_count(handle, &frame_number, &number_of_objects);
+		if (ret == MEDIA_VISION_ERROR_INVALID_OPERATION)
+			break;
+
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		for (unsigned int idx = 0; idx < number_of_objects; ++idx) {
+			int left, top, right, bottom;
+
+			int ret = mv_hand_detection_get_bound_box(handle, idx, &left, &top, &right, &bottom);
+			ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+			ASSERT_EQ(coordinate_answers[idx][0], left);
+			ASSERT_EQ(coordinate_answers[idx][1], top);
+			ASSERT_EQ(coordinate_answers[idx][2], right);
+			ASSERT_EQ(coordinate_answers[idx][3], bottom);
+
+			if (frame_number > MAX_INFERENCE_ITERATION - 10)
+				is_loop_exit = true;
+		}
+	}
+}
+
+TEST(HandDetectionAsyncTest, InferenceShouldBeOk)
+{
+	mv_hand_detection_h handle;
+	vector<test_model_input> test_models {
+		{} // If empty then default model will be used.
+		// TODO.
+	};
+
+	for (auto &model : test_models) {
+		int ret = mv_hand_detection_create(&handle);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		ret = mv_hand_detection_set_model(handle, model.model_file.c_str(), model.meta_file.c_str(),
+										  model.label_file.c_str(), NULL);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		ret = mv_hand_detection_set_engine(handle, "tflite", "cpu");
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		ret = mv_hand_detection_configure(handle);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		ret = mv_hand_detection_prepare(handle);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		unique_ptr<thread> thread_handle;
+
+		for (unsigned int iter = 0; iter < MAX_INFERENCE_ITERATION; ++iter) {
+			mv_source_h mv_source = NULL;
+			ret = mv_create_source(&mv_source);
+			ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+			ret = ImageHelper::loadImageToSource(IMG_HAND, mv_source);
+			ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+			ret = mv_hand_detection_inference_async(handle, mv_source);
+			ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+			if (iter == 0)
+				thread_handle = make_unique<thread>(&hand_detection_callback, static_cast<void *>(handle));
+
+			ret = mv_destroy_source(mv_source);
+			ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+		}
+
+		thread_handle->join();
+
+		ret = mv_hand_detection_destroy(handle);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+	}
+}
+
+TEST(HandDetectionAsyncTest, InferenceShouldBeOkWithDestroyFirst)
+{
+	mv_hand_detection_h handle;
+	vector<test_model_input> test_models {
+		{} // If empty then default model will be used.
+		// TODO.
+	};
+
+	for (auto &model : test_models) {
+		int ret = mv_hand_detection_create(&handle);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		ret = mv_hand_detection_set_model(handle, model.model_file.c_str(), model.meta_file.c_str(),
+										  model.label_file.c_str(), NULL);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		ret = mv_hand_detection_set_engine(handle, "tflite", "cpu");
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		ret = mv_hand_detection_configure(handle);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		ret = mv_hand_detection_prepare(handle);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		unique_ptr<thread> thread_handle;
+
+		for (unsigned int iter = 0; iter < MAX_INFERENCE_ITERATION; ++iter) {
+			mv_source_h mv_source = NULL;
+			ret = mv_create_source(&mv_source);
+			ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+			ret = ImageHelper::loadImageToSource(IMG_HAND, mv_source);
+			ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+			ret = mv_hand_detection_inference_async(handle, mv_source);
+			ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+			if (iter == 0)
+				thread_handle = make_unique<thread>(&hand_detection_callback, static_cast<void *>(handle));
+
+			ret = mv_destroy_source(mv_source);
+			ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+		}
+
+		ret = mv_hand_detection_destroy(handle);
+		ASSERT_EQ(ret, MEDIA_VISION_ERROR_NONE);
+
+		thread_handle->join();
+	}
 }
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