def test_get_face_rate(self, ):
# Arrange
max_process = 4
batch_data: BatchData = batch_data_loader_service.load_batch(0)
logger.info("Got batch data.")
stopwatch = Stopwatch()
stopwatch.start()
num_videos = 1 # batch_data.size()
for i in range(num_videos):
logger.info(f"Getting {i}th video.")
vid_path = batch_data.get_candidate_file_path(i)
fil = video_service.process_all_video_frames(
vid_path, face_recog_service.get_face_data, max_process)
stopwatch.stop()
for fi in fil:
landmarks = fi['face_info_landmarks']
frame_index = fi['frame_index']
file_path = fi['file_path']
for ndx, land in enumerate(landmarks):
face_image = land['face_image']
image_service.show_image(face_image, 'test')
par_path = os.path.join(config.TRASH_PATH, f"{file_path.stem}")
os.makedirs(par_path, exist_ok=True)
image_path = os.path.join(par_path, f"{frame_index}_{ndx}.png")
logger.info(f"Writing to {image_path}.")
image_converted = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
cv2.imwrite(image_path, image_converted)
logger.info(stopwatch)
def test_get_face(self):
# Arrange
# Batch 8, video 0 has fake
batch_data: BatchData = batch_data_loader_service.load_batch(0)
vid_path = batch_data.get_candidate_file_path(0)
# Act
image, _, _ = video_service.get_single_image_from_vid(vid_path, 150)
image_service.show_image(image)
height, width, _ = image.shape
# Act
face_infos = face_recog_service.get_face_infos(image, height, width)
for fi in face_infos:
title = f"Found {len(face_infos)} face(s)."
face_image, _, _, _, _ = fi
image_service.show_image(face_image, title)
face_lines_image = face_recog_service.add_face_lines(face_image)
image_service.show_image(face_lines_image)
# Assert
assert (len(face_infos) > 0)
def test_mtcn_get_tiny_faces(self):
# Arrange
batch_data: BatchData = batch_data_loader_service.load_batch(0)
root_path = Path(config.SMALL_HEAD_OUTPUT_PATH)
output_path = Path(os.path.join(config.TINY_IMAGE_PATH))
# Act
face_recog_mtcnn_service.find_tiny_faces(root_path, output_path)
def test_batch_load(self):
# Arrange
# Act
batch_data = batch_data_loader_service.load_batch(0)
logger.info(batch_data.df_metadata.head(20))
# Assert
assert (batch_data.__len__() > 0)
def test_get_video_info(self):
# Arrange
bad_data = batch_data_loader_service.load_batch(0)
path_file = bad_data.get_candidate_file_path(1)
logger.info(path_file)
v_cap = cv2.VideoCapture(str(path_file))
def test_display_audio_chart(self):
# Arrange
batch_data: BatchData = batch_data_loader_service.load_batch(8)
vid_path: Path = batch_data.get_candidate_file_path(0)
clip, sample_rate = audio_service.get_audio_clip_from_video(
vid_path=vid_path, start_milli=1000, end_milli=5000)
# Act
audio_service.display_chart(clip, sample_rate)
def test_show_frame(self):
# Arrange
batch_data: BatchData = batch_data_loader_service.load_batch(8)
vid_path: Path = batch_data.get_candidate_file_path(0)
index = 100
# Act
image, _, _ = video_service.get_single_image_from_vid(vid_path, index)
show_image(image, f"Image {index}: {vid_path.name}")
def test_get_differences(self):
# Arrange
batch_data: BatchData = batch_data_loader_service.load_batch(0)
# Act
diffs = face_recog_service.get_face_diffs(batch_data, max_diffs=6000)
mean = float(sum(d['ssim'] for d in diffs)) / len(diffs)
# Assert
logger.info(f"diffs found: {len(diffs)}; Mean: {mean}")
def test_load(self):
# Arrange
batch_data = batch_data_loader_service.load_batch(0)
bdr: BatchDataRow = batch_data.__getitem__(0)
# Act
blazeBright = BlazeBrightSideDataSet(bdr.video_path)
# Assert
assert(blazeBright is not None)
def test_get_file(self):
# Arrange
batch_data = batch_data_loader_service.load_batch(0)
# Act
file_path = batch_data.get_candidate_file_path(0)
logger.info(file_path)
# Assert
assert (os.path.exists(file_path))
def test_get_audio_file(self):
# Arrange
batch_data: BatchData = batch_data_loader_service.load_batch(8)
vid_path: Path = batch_data.get_candidate_file_path(0)
start_millis = 1000
end_millis = 5000
# Act
clip, sample_rate = audio_service.get_audio_clip_from_video(
vid_path=vid_path, start_milli=start_millis, end_milli=end_millis)
logger.info(f"Sample rate: {sample_rate}")
def test_get_subframe_images(self):
# Arrange
batch_data = batch_data_loader_service.load_batch(0)
bdr: BatchDataRow = batch_data.__getitem__(0)
blazeBright = BlazeBrightSideDataSet(bdr.video_path)
# Act
images = blazeBright.get_subframe_images_info(0)
# Assert
assert(len(images) == 3)
image_service.show_image(images[0])
image_service.show_image(images[1])
image_service.show_image(images[2])
def test_mtcnn_get_many_face_recog_expanded(self):
# Arrange
batch_data: BatchData = batch_data_loader_service.load_batch(0)
output_path = Path(config.TRASH_PATH)
expand_frame = True
# output_path = Path(config.TINY_IMAGE_PATH)
# expand_frame = False
stopwatch = Stopwatch()
stopwatch.start()
result = face_recog_mtcnn_service.get_faces(batch_data, output_path,
expand_frame)
stopwatch.stop()
logger.info(f"Elapsed time: {stopwatch}")
# Assert
logger.info(result)
def test_dataset_and_loader(self):
# Arrange
batch_data = batch_data_loader_service.load_batch(0)
batch_row_index = 2
vid_path = batch_data.get_candidate_file_path(batch_row_index)
# Act
blaze_dataset = BlazeDataSet(vid_path, max_process=10)
# batch_path = config.get_train_batch_path(2)
# vid_path = Path(batch_path, "ambabjrwbt.mp4")
# vid_path = Path(batch_path, "aimzesksew.mp4")
# vid_path = Path(batch_path, "aejroilouc.mp4")
# blaze_dataset = BlazeDataSet(vid_path=vid_path, max_process=1)
if len(blaze_dataset.originals) == 0:
raise Exception("Not enough files to process.")
blaze_dataloader = DataLoader(blaze_dataset,
batch_size=60,
shuffle=False,
num_workers=0)
blazeface = BlazeFace()
# Act
all_video_detections = blazeface_detection.batch_detect(
blaze_dataloader, blazeface)
merged_vid_detections = blaze_dataset.merge_sub_frame_detections(
all_video_detections)
output_folder_path = Path(config.SMALL_HEAD_OUTPUT_PATH)
output_folder_path.mkdir(exist_ok=True)
blazeface_detection.save_cropped_blazeface_image(
merged_vid_detections, blaze_dataset, output_folder_path)
batch_data.add_face_detections(batch_row_index, merged_vid_detections)
output_file_path = Path(output_folder_path, 'metadata.pkl')
batch_data.persist(output_file_path)
def pick_image(batch_index: int, video_index: int, frame_index: int): batch_data: BatchData = batch_data_loader_service.load_batch(batch_index) vid_path: Path = batch_data.get_candidate_file_path(video_index) return video_service.get_single_image_from_vid(vid_path, frame_index)
