mirror of
https://github.com/RapidAI/RapidOCR.git
synced 2026-11-10 21:10:55 +08:00
129 lines
4.6 KiB
Python
129 lines
4.6 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import argparse
|
|
import math
|
|
import time
|
|
from pathlib import Path
|
|
from typing import List, Tuple, Union
|
|
|
|
import cv2
|
|
import numpy as np
|
|
|
|
from rapidocr_openvino.utils import OpenVINOInferSession, read_yaml
|
|
|
|
from .utils import CTCLabelDecode
|
|
|
|
DEFAULT_DICT_PATH = str(Path(__file__).parent / "ppocr_keys_v1.txt")
|
|
|
|
|
|
class TextRecognizer:
|
|
def __init__(self, config):
|
|
self.rec_image_shape = config["rec_img_shape"]
|
|
self.rec_batch_num = config["rec_batch_num"]
|
|
|
|
dict_path = config.get("rec_keys_path", None)
|
|
self.character_dict_path = dict_path if dict_path else DEFAULT_DICT_PATH
|
|
self.postprocess_op = CTCLabelDecode(character_path=self.character_dict_path)
|
|
|
|
self.session = OpenVINOInferSession(config)
|
|
|
|
def __call__(
|
|
self,
|
|
img_list: Union[np.ndarray, List[np.ndarray]],
|
|
return_word_box: bool = False,
|
|
) -> Tuple[List[Tuple[str, float]], float]:
|
|
if isinstance(img_list, np.ndarray):
|
|
img_list = [img_list]
|
|
|
|
# Calculate the aspect ratio of all text bars
|
|
width_list = [img.shape[1] / float(img.shape[0]) for img in img_list]
|
|
|
|
# Sorting can speed up the recognition process
|
|
indices = np.argsort(np.array(width_list))
|
|
|
|
img_num = len(img_list)
|
|
rec_res = [("", 0.0)] * img_num
|
|
|
|
batch_num = self.rec_batch_num
|
|
elapse = 0
|
|
for beg_img_no in range(0, img_num, batch_num):
|
|
end_img_no = min(img_num, beg_img_no + batch_num)
|
|
|
|
# Parameter Alignment for PaddleOCR
|
|
imgC, imgH, imgW = self.rec_image_shape[:3]
|
|
max_wh_ratio = imgW / imgH
|
|
wh_ratio_list = []
|
|
for ino in range(beg_img_no, end_img_no):
|
|
h, w = img_list[indices[ino]].shape[0:2]
|
|
wh_ratio = w * 1.0 / h
|
|
max_wh_ratio = max(max_wh_ratio, wh_ratio)
|
|
wh_ratio_list.append(wh_ratio)
|
|
|
|
norm_img_batch = []
|
|
for ino in range(beg_img_no, end_img_no):
|
|
norm_img = self.resize_norm_img(img_list[indices[ino]], max_wh_ratio)
|
|
norm_img_batch.append(norm_img[np.newaxis, :])
|
|
norm_img_batch = np.concatenate(norm_img_batch).astype(np.float32)
|
|
|
|
starttime = time.time()
|
|
preds = self.session(norm_img_batch)
|
|
rec_result = self.postprocess_op(
|
|
preds,
|
|
return_word_box,
|
|
wh_ratio_list=wh_ratio_list,
|
|
max_wh_ratio=max_wh_ratio,
|
|
)
|
|
|
|
for rno, one_res in enumerate(rec_result):
|
|
rec_res[indices[beg_img_no + rno]] = one_res
|
|
elapse += time.time() - starttime
|
|
return rec_res, elapse
|
|
|
|
def resize_norm_img(self, img: np.ndarray, max_wh_ratio: float) -> np.ndarray:
|
|
img_channel, img_height, img_width = self.rec_image_shape
|
|
assert img_channel == img.shape[2]
|
|
|
|
img_width = int(img_height * max_wh_ratio)
|
|
|
|
h, w = img.shape[:2]
|
|
ratio = w / float(h)
|
|
if math.ceil(img_height * ratio) > img_width:
|
|
resized_w = img_width
|
|
else:
|
|
resized_w = int(math.ceil(img_height * ratio))
|
|
|
|
resized_image = cv2.resize(img, (resized_w, img_height))
|
|
resized_image = resized_image.astype("float32")
|
|
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
|
resized_image -= 0.5
|
|
resized_image /= 0.5
|
|
|
|
padding_im = np.zeros((img_channel, img_height, img_width), dtype=np.float32)
|
|
padding_im[:, :, 0:resized_w] = resized_image
|
|
return padding_im
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--image_path", type=str, help="image_dir|image_path")
|
|
parser.add_argument("--config_path", type=str, default="config.yaml")
|
|
args = parser.parse_args()
|
|
|
|
config = read_yaml(args.config_path)
|
|
text_recognizer = TextRecognizer(config)
|
|
|
|
img = cv2.imread(args.image_path)
|
|
rec_res, predict_time = text_recognizer(img)
|
|
print(f"rec result: {rec_res}\t cost: {predict_time}s")
|