def test_load_metadata(self):
# Arrange
files_c = file_service.walk_to_path(Path(config.TRAIN_PARENT_PATH_C),
filename_endswith="metadata.json")
files_d = file_service.walk_to_path(Path(config.TRAIN_PARENT_PATH_D),
filename_endswith="metadata.json")
files = files_c + files_d
for f in files:
batch_data: BatchData = batch_data_loader_service.load_batch_from_path(
f)
break
# Act
assert (len(files) > 0)
def get_video_paths(parent_folder_paths: List[str]):
video_paths = []
for par_path in parent_folder_paths:
files = file_service.walk_to_path(par_path, filename_endswith=".mp4")
video_paths.extend(files)
return video_paths
def get_all_dataframes(output_par_path):
pickles = file_service.walk_to_path(output_par_path, filename_endswith='.pkl')
pickles_filtered = [p for p in pickles if file_service.does_file_have_bytes(p)]
logger.info(f'About to collect all {len(pickles_filtered)} pickle paths in list ...')
all_df = [pd.read_pickle(str(p)) for p in pickles_filtered]
return all_df
def load_history(max_pickles=None) -> pd.DataFrame:
logger.info('About to get persisted fake data ...')
pickle_parent_path: Path = Path(config.SSIM_RND_DIFFS_OUTPUT_PATH,
'data')
pickles = file_service.walk_to_path(pickle_parent_path,
filename_endswith='.pkl')
if max_pickles is not None and len(pickles) > max_pickles:
pickles = pickles[:max_pickles]
df_all = []
for p in pickles:
df = pd.read_pickle(str(p))
df_all.append(df)
return pd.concat(df_all)
def __init__(self,
output_par_path: Path,
max_output_size_mb: int = 1,
max_pickles=None):
self.parent_path = output_par_path
self.max_output_size_mb = max_output_size_mb
file_paths = file_service.walk_to_path(output_par_path,
filename_endswith=".pkl")
if max_pickles is not None and len(file_paths) > max_pickles:
file_paths = file_paths[:max_pickles]
self.path_map = {}
for f in file_paths:
logger.info(f'Loading pickle {f.name} ...')
df = pd.read_pickle(f)
path_set = set(df['path'].tolist())
for p in path_set:
frame_index_list = df[df['path'] == p]['frame_index'].tolist()
self.path_map[Path(p).name] = frame_index_list
self.intialize_new_dataframe()
def test_get_video_frame_face(self):
# Arrange
filename = 'dnrpknwija.mp4'
files = file_service.walk_to_path(Path(config.TRAIN_PARENT_PATH_D),
filename_endswith=filename)
assert (len(files) == 1)
vid_path = files[0]
logger.info(f'vid: {vid_path}')
assert (vid_path.exists())
image, _, _ = video_service.get_single_image_from_vid(vid_path, 0)
# l1: 408,706; r1: 652:950 - swatch
# l2: 397,812; r2: 560,976 - face
red = (255, 0, 0)
green = (0, 255, 0)
l1 = (408, 706)
r1 = (652, 950)
l2 = (397, 812)
r2 = (560, 976)
image_rect_1 = cv2.rectangle(image,
pt1=l1,
pt2=r1,
color=red,
thickness=3)
image_rect_1 = cv2.rectangle(image,
pt1=l2,
pt2=r2,
color=green,
thickness=3)
image_service.show_image(image_rect_1, 'Original')
def get_metadata_json_files(which_drive: str):
if which_drive.lower() == 'c':
return file_service.walk_to_path(Path(config.TRAIN_PARENT_PATH_C), filename_endswith="metadata.json")
else:
return file_service.walk_to_path(Path(config.TRAIN_PARENT_PATH_D), filename_endswith="metadata.json")
def _erase_history():
if config.SSIM_REALS_DATA_OUTPUT_PATH.exists():
files = file_service.walk_to_path(config.SSIM_REALS_DATA_OUTPUT_PATH, filename_endswith='.pkl')
for f in files:
f.unlink()
def __init__(self, output_parent_path: Path):
files = file_service.walk_to_path(output_parent_path,
filename_endswith=".pkl")
self.files = files