def get_pathlists(self) -> (list, list, list, list):
img_src_pathlist = get_all_files_of_extension(dir_path=self.img_src,
extension='png')
ann_src_pathlist = get_all_files_of_extension(
dir_path=self.ann_src, extension=self.ann_extension)
img_dst_pathlist = get_all_files_of_extension(dir_path=self.img_dst,
extension='png')
ann_dst_pathlist = get_all_files_of_extension(
dir_path=self.ann_dst, extension=self.ann_extension)
return img_src_pathlist, ann_src_pathlist, img_dst_pathlist, ann_dst_pathlist
def run(self):
img_dirs = get_all_files_of_extension(dir_path=self.img_dir, extension='png')
annotation_paths = get_all_files_of_extension(dir_path=self.annotation_dir, extension='xml')
for i, annotation_path in zip(range(len(annotation_paths)), annotation_paths):
rootname = get_rootname_from_path(path=annotation_path)
img_path = f"{self.img_dir}/{rootname}.png"
xml_path = f"{self.annotation_dir}/{rootname}.xml"
self.handler.add(key=len(self.handler.annotations), annotation_path=xml_path, img_path=img_path)
self.handler.load_remaining()
def get_camera_settings(self):
json_path_list = get_all_files_of_extension(dir_path=self.data_dir,
extension='json')
camera_settings_path = [
json_path for json_path in json_path_list if '_camera' in json_path
][0]
camera_settings_json = json.load(open(camera_settings_path,
'r'))["camera_settings"]
return camera_settings_json
def load_annotation_paths(self):
annotation_paths = get_all_files_of_extension(
dir_path=self.labelimg_annotation_dir, extension='xml')
for annotation_path in annotation_paths:
rootname = get_rootname_from_path(path=annotation_path)
img_path = f"{self.img_dir}/{rootname}.png"
xml_path = f"{self.labelimg_annotation_dir}/{rootname}.xml"
self.labelimg_annotation_handler.add(key=len(
self.labelimg_annotation_handler.annotations),
annotation_path=xml_path,
img_path=img_path)
def load_from_dir(cls,
img_dir: str,
json_dir: str,
show_pbar: bool = True) -> NDDS_Frame_Handler:
check_dir_exists(json_dir)
check_dir_exists(img_dir)
img_pathlist = get_valid_image_paths(img_dir)
json_path_list = [
path for path in get_all_files_of_extension(dir_path=json_dir,
extension='json')
if not get_filename(path).startswith('_')
]
json_path_list.sort()
handler = NDDS_Frame_Handler()
if show_pbar:
pbar = tqdm(total=len(json_path_list), unit='ann(s)', leave=True)
pbar.set_description(f'Loading {cls.__name__}')
for json_path in json_path_list:
check_file_exists(json_path)
json_rootname = get_rootname_from_path(json_path)
matching_img_path = None
matching_cs_img_path = None
matching_depth_img_path = None
matching_is_img_path = None
for img_path in img_pathlist:
img_rootname = '.'.join(get_filename(img_path).split('.')[:-1])
if img_rootname == json_rootname:
matching_img_path = img_path
elif img_rootname == f'{json_rootname}.cs':
matching_cs_img_path = img_path
elif img_rootname == f'{json_rootname}.depth':
matching_depth_img_path = img_path
elif img_rootname == f'{json_rootname}.is':
matching_is_img_path = img_path
if matching_img_path and matching_cs_img_path and matching_depth_img_path and matching_is_img_path:
break
if matching_img_path is None:
logger.error(
f"Couldn't find image file that matches rootname of {get_filename(json_path)} in {img_dir}"
)
raise FileNotFoundError
frame = NDDS_Frame(
img_path=matching_img_path,
ndds_ann=NDDS_Annotation.load_from_path(json_path),
cs_img_path=matching_cs_img_path,
depth_img_path=matching_depth_img_path,
is_img_path=matching_is_img_path)
handler.append(frame)
if show_pbar:
pbar.update()
return handler
def get_all_object_json_files(self):
json_path_list = get_all_files_of_extension(dir_path=self.data_dir,
extension='json')
json_path_list = [
json_path for json_path in json_path_list
if '_camera' not in json_path
]
json_path_list = [
json_path for json_path in json_path_list
if '_object' not in json_path
]
json_path_list.sort()
return json_path_list
# pbar = tqdm(total=len(frame_result_list), unit='frame(s)')
# for result in frame_result_list:
# for pred in result.pred_list:
# pred.recalculate(
# gt_kpt_3d=kpt_3d,
# corner_3d=corner_3d,
# K=K,
# distortion=np.array([0.001647, -0.105636, -0.002094, -0.006446, 0.000000]),
# units_per_meter=1.0
# )
# pbar.update()
# pbar.close()
# frame_result_list.save_to_path(f'{infer_data_dump}/{model_name}_infer0.json', overwrite=True)
infer_data_dump_dir = 'infer_data_dump0'
infer_data_dump_paths = get_all_files_of_extension(infer_data_dump_dir,
extension='json')
infer_data_dump_paths.sort()
fixed_infer_data_dump_dir = 'infer_data_dump'
make_dir_if_not_exists(fixed_infer_data_dump_dir)
pbar = tqdm(total=len(infer_data_dump_paths), unit='dump(s)', leave=True)
pbar.set_description('Fixing Inference Dumps')
for infer_data_dump_path in infer_data_dump_paths:
results = PVNetFrameResultList.load_from_path(infer_data_dump_path)
for result in results:
for pred in result.pred_list:
pred.recalculate(gt_kpt_3d=kpt_3d,
corner_3d=corner_3d,
K=K,
distortion=distortion,
units_per_meter=1.0)
results.save_to_path(
def load_from_dir(cls, load_dir: str) -> LabelmeAnnotationHandler:
check_dir_exists(load_dir)
json_path_list = get_all_files_of_extension(dir_path=load_dir,
extension='json')
return cls.load_from_pathlist(json_path_list)
coco_dataset = COCO_Dataset.load_from_path(
json_path=
'/home/clayton/workspace/prj/data_keep/data/toyota/from_toyota/20201017/20201017_robot_camera/combined/output.json',
img_dir=
'/home/clayton/workspace/prj/data_keep/data/toyota/from_toyota/20201017/20201017_robot_camera/combined'
)
linemod_ann_sample = linemod_dataset.annotations[0]
kpt_3d = linemod_ann_sample.fps_3d.copy()
kpt_3d.append(linemod_ann_sample.center_3d)
corner_3d = linemod_ann_sample.corner_3d
K = linemod_ann_sample.K
linemod_image_sample = linemod_dataset.images[0]
dsize = (linemod_image_sample.width, linemod_image_sample.height)
weights_dir = '/home/clayton/workspace/git/clean-pvnet/data/model/pvnet/custom'
weight_path_list = get_all_files_of_extension(weights_dir, 'pth')
weight_path_list.sort()
infer_data_dump_dir = '/home/clayton/workspace/prj/data_keep/data/toyota/from_toyota/20201017/20201017_robot_camera/infer_dump'
make_dir_if_not_exists(infer_data_dump_dir)
# delete_all_files_in_dir(infer_data_dump_dir, ask_permission=True)
weights_pbar = tqdm(total=len(weight_path_list), unit='weight(s)')
for weight_path in weight_path_list:
rootname = get_rootname_from_path(weight_path)
weights_pbar.set_description(rootname)
pred_dump_path = f'{infer_data_dump_dir}/{rootname}.json'
if file_exists(pred_dump_path):
weights_pbar.update()
continue
inferer = PVNetInferer(weight_path=weight_path)
inferer.infer_coco_dataset(dataset=coco_dataset,
kpt_3d=kpt_3d,