def confirm_folder(path, mode):
# make_dir_if_not_exists(path)
if mode == 'save':
if dir_exists(path):
delete_dir(path)
make_dir(path)
else:
make_dir(path)
def confirm_folder(path, mode):
'''Deletes the directory if exist and create new directory.'''
# make_dir_if_not_exists(path)
if mode == 'save':
if dir_exists(path):
delete_dir(path)
make_dir(path)
else:
make_dir(path)
def run(self):
if not path_exists(self.ndds_dir):
printj.red(f"Input ndds_dir path does not exist./n{self.ndds_dir}")
make_dir_if_not_exists(dir_path=self.output_dir)
coco_data_dir = os.path.join(
self.output_dir,
f"{get_filename_from_path(self.ndds_dir)}_coco_data")
delete_dir_if_exists(dir_path=coco_data_dir)
make_dir(dir_path=coco_data_dir)
ndds_dataset = NDDS_Dataset.load_from_dir(json_dir=self.ndds_dir)
def infer(self, input_type: Union[str, int],
output_type: Union[str, int],
input_path: Union[str, List[str]],
output_path: Union[str, List[str]],
show_max_score_only: bool = False,
show_class_label: bool = True,
show_class_label_score_only: bool = False,
show_keypoint_label: bool = True,
show_legends: bool = False,
show_bbox: bool = True,
show_keypoints: bool = True,
show_segmentation: bool = True,
color_bbox: list = None,
transparent_mask: bool = True,
transparency_alpha: float = 0.3,
ignore_keypoint_idx=None,
gt_path: Union[str, List[str]] = None,
result_json_path: str = None):
"""
Valid options for,
===
input_type:
---
["image", "image_list", "image_directory", "image_directories_list", "video",
"video_list", "video_directory" ]
output_type:
---
["return_summary", "show_image", "write_image", "write_video" ]
Returns
---
The inference result of all data formats.
"""
self.counter = infinite_sequence()
check_value(input_type,
check_from=["image", "image_list", "image_directory", "image_directories_list", "video",
"video_list", "video_directory"])
check_value(value=output_type, check_from=[
"show_image", "write_image", "write_video"])
self.gt_path = gt_path
if self.gt_path:
check_file_exists(gt_path)
with open(gt_path) as json_file:
self.gt_data = json.load(json_file)
if result_json_path is None:
if dir_exists(output_path):
self.result_json_path = f'{output_path}/result.json'
else:
_p = output_path.split('.')
_output_path = '.'.join(_p[:-1])
self.result_json_path = f'{_output_path}.json'
predict_image = partial(self._infer_image,
show_max_score_only=show_max_score_only,
show_class_label=show_class_label,
show_class_label_score_only=show_class_label_score_only,
show_keypoint_label=show_keypoint_label,
show_bbox=show_bbox, show_keypoints=show_keypoints, show_segmentation=show_segmentation,
color_bbox=color_bbox,
transparent_mask=transparent_mask, transparency_alpha=transparency_alpha,
ignore_keypoint_idx=ignore_keypoint_idx,
show_legends=show_legends
# gt_path=gt_path,
)
if input_type == "image":
if file_exists(input_path):
output = predict_image(input_path)
else:
raise Error
if output_type == "return_summary":
return self.pred_dataset
elif output_type == "show_image":
show_image(output)
elif output_type == "write_image":
cv2.imwrite(f'{output_path}', output)
elif output_type == "write_video":
raise NotImplementedError
else:
raise Exception
elif input_type == "image_list":
for image_path in tqdm(input_path, colour='#66cc66'):
output = predict_image(image_path)
if output_type == "show_image":
if show_image(output):
break
elif output_type == "write_image":
make_dir_if_not_exists(output_path)
filename = f.get_filename(image_path)
cv2.imwrite(f'{output_path}/{filename}', output)
elif output_type == "write_video":
raise NotImplementedError
else:
raise Exception
elif input_type == "image_directory":
image_path_list = f.dir_contents_path_list_with_extension(
dirpath=input_path,
extension=[".png", ".jpg", ".jpeg"])
for image_path in tqdm(image_path_list, colour='#66cc66'):
output = predict_image(image_path)
# output = self.draw_gt(gt_path, gt_data, image_path, output)
if output_type == "show_image":
if show_image(output):
break
elif output_type == "write_image":
make_dir_if_not_exists(output_path)
filename = f.get_filename(image_path)
cv2.imwrite(f'{output_path}/{filename}', output)
elif output_type == "write_video":
raise NotImplementedError
else:
raise Exception
elif input_type == "image_directories_list":
for image_directory in tqdm(input_path):
image_path_list = f.dir_contents_path_list_with_extension(
dirpath=input_path,
extension=[".png", ".jpg", ".jpeg"])
for image_path in tqdm(image_path_list, colour='#66cc66'):
output = predict_image(image_path)
if output_type == "show_image":
if show_image(output):
break
elif output_type == "write_image":
filename = f.get_filename(image_path)
directory_name = f.get_directory_name(image_path)
if f.dir_exists(f'{output_path}/{directory_name}'):
f.delete_all_files_in_dir(
f'{output_path}/{directory_name}')
else:
f.make_dir(f'{output_path}/{directory_name}')
cv2.imwrite(
f'{output_path}/{directory_name}/{filename}', output)
elif output_type == "write_video":
raise NotImplementedError
else:
raise Exception
elif input_type == "video":
raise NotImplementedError
elif input_type == "video_list":
raise NotImplementedError
elif input_type == "video_directory":
raise NotImplementedError
else:
raise Exception
# printj.cyan(self.pred_dataset)
self.write_predictions_json()
if self.gt_path:
# print(self.df)
# pip install openpyxl
self.df.to_excel(os.path.abspath(
f'{result_json_path}/../test_data.xlsx'))