def __init__(self, config_file):
self.config_file_path = config_file
# ----- CharNet configuration and initialization -----
cfg.merge_from_file(config_file)
cfg.freeze()
print(cfg)
self.charnet = CharNet()
self.charnet.load_state_dict(torch.load(cfg.WEIGHT))
self.charnet.eval()
if torch.cuda.is_available():
print("[*] Using cuda!")
self.charnet.cuda()
(word_bbox[0], word_bbox[1]), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 255), 1)
return img_word_ins
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Test")
parser.add_argument("config_file", help="path to config file", type=str)
parser.add_argument("image_dir", type=str)
parser.add_argument("results_dir", type=str)
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.freeze()
print(cfg)
charnet = CharNet()
charnet.load_state_dict(torch.load(cfg.WEIGHT))
charnet.eval()
charnet.cuda()
for im_name in tqdm(sorted(os.listdir(args.image_dir))):
#print("Processing {}...".format(im_name))
im_file = os.path.join(args.image_dir, im_name)
im_original = cv2.imread(im_file)
im, scale_w, scale_h, original_w, original_h = resize(
im_original, size=cfg.INPUT_SIZE)
with torch.no_grad():