from predictor import COCODemo
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference")
parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file", type=str,)
parser.add_argument("--root-dir", default="", dest="root_dir", help="path to images", type=str,)
parser.add_argument("opts", help="Modify config options using the command-line", default=None, nargs=argparse.REMAINDER,)
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
assert(cfg.TEST.IMS_PER_BATCH == 1)
img_names = []
if not args.root_dir.endswith('/'):
args.root_dir += '/'
for root, parent, img_files in os.walk(args.root_dir):
for img_file in img_files:
if img_file.lower().endswith(('.jpg', '.png', '.bmp', '.jpeg')):
img_names.append(os.path.join(root, img_file)[len(args.root_dir):])
test_loader = data_loader(cfg, img_names, args.root_dir)
coco_demo = COCODemo(cfg)
start_time = time.time()
end_time = time.time()
for img_idx, img_data in enumerate(test_loader):
img_name, img, img_h, img_w = img_data
print('{}/{}: img_name={}'.format(img_idx, len(test_loader), img_name))
top_predictions = coco_demo.get_top_detections(img[0], img_h[0], img_w[0], img_name[0], topn=None)
print("using time: {:.4f}s".format(time.time() - end_time))
end_time = time.time()
print("Average time {:.4f}s ({:.4f}s/{})".format((end_time - start_time) / len(img_names), (end_time - start_time), len(img_names)))
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
assert (cfg.TEST.IMS_PER_BATCH == 1)
img_names = []
if not args.root_dir.endswith('/'):
args.root_dir += '/'
for root, parent, img_files in os.walk(args.root_dir):
for img_file in img_files:
if img_file.lower().endswith(('.jpg', '.png', '.bmp', '.jpeg')):
img_names.append(
os.path.join(root, img_file)[len(args.root_dir):])
transformer = build_transforms(cfg)
coco_demo = COCODemo(cfg)
start_time = time.time()
end_time = time.time()
for img_idx, img_name in enumerate(img_names[:100000]):
print('{}/{}: img_name={}'.format(img_idx, len(img_names), img_name))
img_ori = cv2.imread(os.path.join(args.root_dir, img_name))
img = transformer(img_ori[:, :, ::-1])
top_predictions = coco_demo.get_top_detections(img,
img_ori.shape[0],
img_ori.shape[1],
img_name,
topn=None)
print("using time: {:.4f}s".format(time.time() - end_time))
end_time = time.time()
print("Average time {:.4f}s ({:.4f}s/{})".format(
(end_time - start_time) / len(img_names), (end_time - start_time),
len(img_names)))
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
# assert(cfg.TEST.IMS_PER_BATCH == 1)
img_list = []
if not args.root_dir.endswith('/'):
args.root_dir += '/'
for root, parent, img_files in os.walk(args.root_dir):
for img_file in img_files:
if img_file.lower().endswith(('.jpg', '.png', '.bmp', '.jpeg')):
img_list.append(
os.path.join(root, img_file)[len(args.root_dir):])
test_loader = data_loader(cfg, img_list, args.root_dir)
coco_demo = COCODemo(cfg, args.root_dir)
start_time = time.time()
end_time = time.time()
for batch_idx, imgs_data in enumerate(test_loader):
imgs_name, imgs, imgs_h, imgs_w = imgs_data
print('batch_idx={}, num_imgs={}'.format(batch_idx, len(imgs_name)))
top_predictions = coco_demo.get_top_detections(imgs,
imgs_h,
imgs_w,
imgs_name,
topn=None)
print("using time: {:.4f}s".format(time.time() - end_time))
end_time = time.time()
print("Average time {:.4f}s ({:.4f}s/{})".format(
(end_time - start_time) / len(img_list), (end_time - start_time),
len(img_list)))
