mirror of
https://github.com/RapidAI/RapidOCR.git
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136 lines
4.8 KiB
Python
136 lines
4.8 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import math
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import time
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from typing import Any, Dict, List, Union
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import cv2
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import numpy as np
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from rapidocr_onnxruntime.utils import OrtInferSession, read_yaml
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from .utils import (
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CTCLabelDecode,
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TextRecognizerOutput,
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)
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class TextRecognizer:
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def __init__(self, config: Dict[str, Any]):
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self.session = OrtInferSession(config)
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character = None
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if self.session.have_key():
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character = self.session.get_character_list()
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character_path = config.get("rec_keys_path", None)
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self.postprocess_op = CTCLabelDecode(
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character=character, character_path=character_path
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)
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self.rec_batch_num = config["rec_batch_num"]
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self.rec_image_shape = config["rec_img_shape"]
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def __call__(
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self,
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img_list: Union[np.ndarray, List[np.ndarray]],
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return_word_box: bool = False,
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) -> TextRecognizerOutput:
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if isinstance(img_list, np.ndarray):
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img_list = [img_list]
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# Calculate the aspect ratio of all text bars
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width_list = [img.shape[1] / float(img.shape[0]) for img in img_list]
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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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img_num = len(img_list)
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rec_res = [("", 0.0)] * img_num
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batch_num = self.rec_batch_num
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elapse = 0
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for beg_img_no in range(0, img_num, batch_num):
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end_img_no = min(img_num, beg_img_no + batch_num)
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# Parameter Alignment for PaddleOCR
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imgC, imgH, imgW = self.rec_image_shape[:3]
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max_wh_ratio = imgW / imgH
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wh_ratio_list = []
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for ino in range(beg_img_no, end_img_no):
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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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wh_ratio_list.append(wh_ratio)
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norm_img_batch = []
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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[indices[ino]], max_wh_ratio)
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norm_img_batch.append(norm_img[np.newaxis, :])
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norm_img_batch = np.concatenate(norm_img_batch).astype(np.float32)
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starttime = time.time()
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preds = self.session(norm_img_batch)[0]
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line_results, word_results = self.postprocess_op(
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preds,
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return_word_box,
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wh_ratio_list=wh_ratio_list,
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max_wh_ratio=max_wh_ratio,
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)
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for rno, one_res in enumerate(line_results):
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rec_res[indices[beg_img_no + rno]] = (one_res, word_results[rno])
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elapse += time.time() - starttime
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all_line_results, all_word_results = list(zip(*rec_res))
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return TextRecognizerOutput(all_line_results, all_word_results, elapse)
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def resize_norm_img(self, img: np.ndarray, max_wh_ratio: float) -> np.ndarray:
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img_channel, img_height, img_width = self.rec_image_shape
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assert img_channel == img.shape[2]
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img_width = int(img_height * 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(img_height * ratio) > img_width:
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resized_w = img_width
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else:
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resized_w = int(math.ceil(img_height * ratio))
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resized_image = cv2.resize(img, (resized_w, img_height))
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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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resized_image /= 0.5
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padding_im = np.zeros((img_channel, img_height, img_width), dtype=np.float32)
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padding_im[:, :, 0:resized_w] = resized_image
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return padding_im
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--image_path", type=str, help="image_dir|image_path")
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parser.add_argument("--config_path", type=str, default="config.yaml")
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args = parser.parse_args()
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config = read_yaml(args.config_path)
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text_recognizer = TextRecognizer(config)
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img = cv2.imread(args.image_path)
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rec_res, predict_time = text_recognizer(img)
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print(f"rec result: {rec_res}\t cost: {predict_time}s")
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