xiaoting
GitHub
commit
1bcfd9f1f5
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@ -13,9 +13,9 @@
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# limitations under the License.
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import os
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import sys
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__dir__ = os.path.dirname(__file__)
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.append(os.path.join(__dir__, '../..'))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
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import tools.infer.utility as utility
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from ppocr.utils.utility import initial_logger
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@ -33,14 +33,12 @@ class TextRecognizer(object):
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def __init__(self, args):
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self.predictor, self.input_tensor, self.output_tensors =\
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utility.create_predictor(args, mode="rec")
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image_shape = [int(v) for v in args.rec_image_shape.split(",")]
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self.rec_image_shape = image_shape
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self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
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self.character_type = args.rec_char_type
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self.rec_batch_num = args.rec_batch_num
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self.rec_algorithm = args.rec_algorithm
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char_ops_params = {}
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char_ops_params["character_type"] = args.rec_char_type
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char_ops_params["character_dict_path"] = args.rec_char_dict_path
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char_ops_params = {"character_type": args.rec_char_type,
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"character_dict_path": args.rec_char_dict_path}
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if self.rec_algorithm != "RARE":
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char_ops_params['loss_type'] = 'ctc'
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self.loss_type = 'ctc'
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@ -51,16 +49,16 @@ class TextRecognizer(object):
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def resize_norm_img(self, img, max_wh_ratio):
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imgC, imgH, imgW = self.rec_image_shape
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assert imgC == img.shape[2]
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if self.character_type == "ch":
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imgW = int(32 * max_wh_ratio)
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h = img.shape[0]
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w = img.shape[1]
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imgW = int(math.ceil(32 * max_wh_ratio))
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h, w = img.shape[:2]
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ratio = w / float(h)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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resized_image = cv2.resize(img, (resized_w, imgH))
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resized_image = cv2.resize(img, (resized_w, imgH), interpolation=cv2.INTER_CUBIC)
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resized_image = resized_image.astype('float32')
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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@ -71,7 +69,15 @@ class TextRecognizer(object):
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def __call__(self, img_list):
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img_num = len(img_list)
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rec_res = []
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# Calculate the aspect ratio of all text bars
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width_list = []
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for img in img_list:
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width_list.append(img.shape[1] / float(img.shape[0]))
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# Sorting can speed up the recognition process
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indices = np.argsort(np.array(width_list))
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# rec_res = []
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rec_res = [['', 0.0]] * img_num
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batch_num = self.rec_batch_num
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predict_time = 0
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for beg_img_no in range(0, img_num, batch_num):
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@ -79,11 +85,13 @@ class TextRecognizer(object):
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norm_img_batch = []
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max_wh_ratio = 0
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for ino in range(beg_img_no, end_img_no):
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h, w = img_list[ino].shape[0:2]
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# h, w = img_list[ino].shape[0:2]
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h, w = img_list[indices[ino]].shape[0:2]
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wh_ratio = w * 1.0 / h
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max_wh_ratio = max(max_wh_ratio, wh_ratio)
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for ino in range(beg_img_no, end_img_no):
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norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio)
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# norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio)
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norm_img = self.resize_norm_img(img_list[indices[ino]], max_wh_ratio)
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norm_img = norm_img[np.newaxis, :]
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norm_img_batch.append(norm_img)
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norm_img_batch = np.concatenate(norm_img_batch)
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@ -111,7 +119,8 @@ class TextRecognizer(object):
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blank = probs.shape[1]
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valid_ind = np.where(ind != (blank - 1))[0]
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score = np.mean(probs[valid_ind, ind[valid_ind]])
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rec_res.append([preds_text, score])
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# rec_res.append([preds_text, score])
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rec_res[indices[beg_img_no + rno]] = [preds_text, score]
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else:
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rec_idx_batch = self.output_tensors[0].copy_to_cpu()
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predict_batch = self.output_tensors[1].copy_to_cpu()
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@ -126,19 +135,19 @@ class TextRecognizer(object):
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preds = rec_idx_batch[rno, 1:end_pos[1]]
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score = np.mean(predict_batch[rno, 1:end_pos[1]])
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preds_text = self.char_ops.decode(preds)
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rec_res.append([preds_text, score])
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# rec_res.append([preds_text, score])
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rec_res[indices[beg_img_no + rno]] = [preds_text, score]
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return rec_res, predict_time
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if __name__ == "__main__":
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args = utility.parse_args()
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def main(args):
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image_file_list = get_image_file_list(args.image_dir)
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text_recognizer = TextRecognizer(args)
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valid_image_file_list = []
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img_list = []
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for image_file in image_file_list:
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img = cv2.imread(image_file)
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img = cv2.imread(image_file, cv2.IMREAD_COLOR)
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if img is None:
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logger.info("error in loading image:{}".format(image_file))
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continue
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@ -159,3 +168,7 @@ if __name__ == "__main__":
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print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino]))
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print("Total predict time for %d images:%.3f" %
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(len(img_list), predict_time))
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if __name__ == "__main__":
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main(utility.parse_args())
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@ -75,6 +75,7 @@ class TextSystem(object):
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def __call__(self, img):
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ori_im = img.copy()
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dt_boxes, elapse = self.text_detector(img)
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print("dt_boxes num : {}, elapse : {}".format(len(dt_boxes), elapse))
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if dt_boxes is None:
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return None, None
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img_crop_list = []
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@ -86,6 +87,7 @@ class TextSystem(object):
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img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
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img_crop_list.append(img_crop)
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rec_res, elapse = self.text_recognizer(img_crop_list)
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print("rec_res num : {}, elapse : {}".format(len(rec_res), elapse))
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# self.print_draw_crop_rec_res(img_crop_list, rec_res)
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return dt_boxes, rec_res
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