def _init_model(self, model_path, gpu_id):
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
self.det_net = self.det_model_manager.object_detector()
self.det_net = nn.DataParallel(self.det_net).cuda()
if model_path is not None:
model_dict = torch.load(model_path)
self.det_net.load_state_dict(model_dict['state_dict'])
self.configer = Configer(config_dict=model_dict['config_dict'])
else:
Log.error('Model Path is not existed.')
exit(1)
self.det_net.eval()
def _init_model(self, model_path, gpu_list):
self.device = torch.device('cpu' if len(gpu_list) == 0 else 'cuda')
model_dict = None
if model_path is not None:
model_dict = torch.load(model_path)
else:
Log.error('Model Path is not existed.')
exit(1)
self.configer = Configer(config_dict=model_dict['config_dict'])
if len(gpu_list) > 0:
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(gpu_id) for gpu_id in gpu_list)
self.pose_model_manager = PoseModelManager(self.configer)
self.pose_net = self.pose_model_manager.multi_pose_detector()
self.pose_net = nn.DataParallel(self.pose_net).to(self.device)
self.pose_net.load_state_dict(model_dict['state_dict'])
self.pose_net.eval()
type=str,
dest='json_file',
help='The json file of Det Parser.')
parser.add_argument('--mask_file',
default=None,
type=str,
dest='mask_file',
help='The mask file of Det Parser.')
parser.add_argument('--image_dir',
default=None,
type=str,
dest='image_dir',
help='The image directory of Det Parser.')
parser.add_argument('--json_dir',
default=None,
type=str,
dest='json_dir',
help='The json directory of Det Parser.')
parser.add_argument('--mask_dir',
default=None,
type=str,
dest='mask_dir',
help='The mask directory of Det Parser.')
args_parser = parser.parse_args()
det_parser = DetParser(Configer(hypes_file=args_parser.hypes_file))
det_parser.parse_img_det(args_parser.image_file, args_parser.json_file,
args_parser.mask_file)
det_parser.parse_dir_det(args_parser.image_dir, args_parser.json_dir,
args_parser.mask_dir)
# print(mask_canvas.shape)
image_canvas = cv2.addWeighted(image_canvas, 0.6, mask_canvas, 0.8, 0)
return image_canvas
if __name__ == '__main__':
# segmention net init
CONFIG_DICT = {
'img_size': (480, 640),
'gpu_id': [0],
'fabby_model': './model/ICNet32Photo640x480_iter_310000.pth',
'trimap_model': './model/trimap_icnet32SimpleV2_iter_120000.pth',
'mat_model': './model/trimap_icnet32SimpleV2_matting_iter_120000.pth'
}
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(
str(gpu_id) for gpu_id in CONFIG_DICT['gpu_id'])
person_segmentor = PersonSegmentor(Configer(config_dict=CONFIG_DICT))
image = cv2.imread('./samples/25962783011.jpg')
print(image.shape)
out = person_segmentor.forward(image)
cv2.imshow("main", person_segmentor.parse(image, out))
cv2.waitKey()
video_reader = VideoReader('./samples/24866_25155.avi')
for image in video_reader:
print(image.shape)
out = person_segmentor.forward(image)
cv2.imshow("main", person_segmentor.parse(image, out))
#cv2.imshow("main", frame)
cv2.waitKey()
dest='hypes_file',
help='The hypes file of pose.')
parser.add_argument('--gt_dir',
default='/home/donny/DataSet/VOC/VOCdevkit/VOC2007',
type=str,
dest='gt_dir',
help='The groundtruth annotations file of voc dets.')
parser.add_argument(
'--json_dir',
default='../../../val/results/det/voc/test_dir/image/json',
type=str,
dest='json_dir',
help='The json dir of predict annotations.')
parser.add_argument('--dataset',
default='VOC2007',
type=str,
dest='dataset',
help='The target dataset.')
args = parser.parse_args()
coco_evaluator = VOCEvaluator(Configer(hypes_file=args.hypes_file))
if args.gt_dir is not None:
pred_dir = coco_evaluator.relabel(args.json_dir)
coco_evaluator.evaluate(pred_dir,
args.gt_dir,
use_07=(args.dataset == 'VOC2007'))
else:
submission_dir = coco_evaluator.relabel(args.json_dir)
Log.info('Submisson file path: {}'.format(submission_dir))
(int(length / 2),
self.configer.get('vis', 'stick_width')), int(angle), 0, 360, 1)
cv2.fillConvexPoly(cur_canvas, polygon, self.configer.get('details', 'color_list')[i])
image_canvas = cv2.addWeighted(image_canvas, 0.4, cur_canvas, 0.6, 0)
return image_canvas
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--hypes_file', default=None, type=str,
dest='hypes_file', help='The file of the hyper parameters.')
parser.add_argument('--image_file', default=None, type=str,
dest='image_file', help='The image file of Pose Parser.')
parser.add_argument('--json_file', default=None, type=str,
dest='json_file', help='The json file of Pose Parser.')
parser.add_argument('--mask_file', default=None, type=str,
dest='mask_file', help='The mask file of Pose Parser.')
parser.add_argument('--image_dir', default=None, type=str,
dest='image_dir', help='The image directory of Pose Parser.')
parser.add_argument('--json_dir', default=None, type=str,
dest='json_dir', help='The json directory of Pose Parser.')
parser.add_argument('--mask_dir', default=None, type=str,
dest='mask_dir', help='The mask directory of Pose Parser.')
args_parser = parser.parse_args()
pose_parser = PoseParser(Configer(hypes_file=args_parser.hypes_file))
pose_parser.parse_img_pose(args_parser.image_file, args_parser.json_file, args_parser.mask_file)
pose_parser.parse_dir_pose(args_parser.image_dir, args_parser.json_dir, args_parser.mask_dir)
self.seg_running_score.update(predmap[np.newaxis, :, :],
gtmap[np.newaxis, :, :])
img_cnt += 1
Log.info('Evaluate {} images'.format(img_cnt))
Log.info('mIOU: {}'.format(self.seg_running_score.get_mean_iou()))
Log.info('Pixel ACC: {}'.format(
self.seg_running_score.get_pixel_acc()))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--hypes_file',
default=None,
type=str,
dest='hypes_file',
help='The hypes file of pose.')
parser.add_argument('--gt_dir',
default=None,
type=str,
dest='gt_dir',
help='The groundtruth annotations.')
parser.add_argument('--pred_dir',
default=None,
type=str,
dest='pred_dir',
help='The label dir of predict annotations.')
args = parser.parse_args()
ade20k_evaluator = ADE20KEvaluator(Configer(hypes_file=args.hypes_file))
ade20k_evaluator.evaluate(args.pred_dir, args.gt_dir)
parser.add_argument('--logfile_level', default=None, type=str,
dest='logging:logfile_level', help='To set the log level to files.')
parser.add_argument('--stdout_level', default=None, type=str,
dest='logging:stdout_level', help='To set the level to print to screen.')
parser.add_argument('--log_file', default=None, type=str,
dest='logging:log_file', help='The path of log files.')
# *********** Params for test or submission. **********
parser.add_argument('--test_img', default=None, type=str,
dest='test_img', help='The test path of image.')
parser.add_argument('--test_dir', default=None, type=str,
dest='test_dir', help='The test directory of images.')
args_parser = parser.parse_args()
configer = Configer(args_parser=args_parser)
if configer.get('gpu') is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(gpu_id) for gpu_id in configer.get('gpu'))
project_dir = os.path.dirname(os.path.realpath(__file__))
configer.add_value(['project_dir'], project_dir)
Log.init(logfile_level=configer.get('logging', 'logfile_level'),
stdout_level=configer.get('logging', 'stdout_level'),
log_file=configer.get('logging', 'log_file'),
log_format=configer.get('logging', 'log_format'),
rewrite=configer.get('logging', 'rewrite'))
method_selector = MethodSelector(configer)
model = None
parser.add_argument('--hypes_file',
default='../../hypes/seg/coco/mr_fpn_coco_seg.json',
type=str,
dest='hypes_file',
help='The file of the hyper parameters.')
parser.add_argument('--image_file',
default=None,
type=str,
dest='image_file',
help='The image file of Ins Parser.')
parser.add_argument('--json_file',
default=None,
type=str,
dest='json_file',
help='The json file of Det Parser.')
parser.add_argument('--image_dir',
default='/home/donny/DataSet/COCO_INS/train/image',
type=str,
dest='image_dir',
help='The image directory of Ins Parser.')
parser.add_argument('--json_dir',
default='/home/donny/DataSet/COCO_INS/train/json',
type=str,
dest='json_dir',
help='The json directory of Ins Parser.')
args_parser = parser.parse_args()
ins_parser = InsParser(Configer(hypes_file=args_parser.hypes_file))
ins_parser.parse_img_det(args_parser.image_file, args_parser.json_file)
ins_parser.parse_dir_det(args_parser.image_dir, args_parser.json_dir)
parser.add_argument('--hypes_file',
default=None,
type=str,
dest='hypes_file',
help='The file of the hyper parameters.')
parser.add_argument('--image_file',
default=None,
type=str,
dest='image_file',
help='The image file of Seg Parser.')
parser.add_argument('--label_file',
default=None,
type=str,
dest='label_file',
help='The label file of Seg Parser.')
parser.add_argument('--image_dir',
default=None,
type=str,
dest='image_dir',
help='The image directory of Seg Parser.')
parser.add_argument('--label_dir',
default=None,
type=str,
dest='label_dir',
help='The label directory of Seg Parser.')
args_parser = parser.parse_args()
seg_parser = SegParser(Configer(hypes_file=args_parser.hypes_file))
seg_parser.parse_img_seg(args_parser.image_file, args_parser.label_file)
seg_parser.parse_dir_seg(args_parser.image_dir, args_parser.label_dir)
