204 lines
7.3 KiB
Python
Executable File
204 lines
7.3 KiB
Python
Executable File
# 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 os
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import sys
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import subprocess
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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.abspath(os.path.join(__dir__, '../..')))
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os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
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import cv2
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import copy
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import numpy as np
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import time
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from PIL import Image
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import tools.infer.utility as utility
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import tools.infer.predict_rec as predict_rec
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import tools.infer.predict_det as predict_det
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import tools.infer.predict_cls as predict_cls
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from ppocr.utils.utility import get_image_file_list, check_and_read_gif
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from ppocr.utils.logging import get_logger
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from tools.infer.utility import draw_ocr_box_txt
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logger = get_logger()
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class TextSystem(object):
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def __init__(self, args):
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self.text_detector = predict_det.TextDetector(args)
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self.text_recognizer = predict_rec.TextRecognizer(args)
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self.use_angle_cls = args.use_angle_cls
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self.drop_score = args.drop_score
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if self.use_angle_cls:
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self.text_classifier = predict_cls.TextClassifier(args)
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def get_rotate_crop_image(self, img, points):
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'''
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img_height, img_width = img.shape[0:2]
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left = int(np.min(points[:, 0]))
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right = int(np.max(points[:, 0]))
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top = int(np.min(points[:, 1]))
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bottom = int(np.max(points[:, 1]))
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img_crop = img[top:bottom, left:right, :].copy()
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points[:, 0] = points[:, 0] - left
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points[:, 1] = points[:, 1] - top
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'''
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img_crop_width = int(
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max(
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np.linalg.norm(points[0] - points[1]),
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np.linalg.norm(points[2] - points[3])))
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img_crop_height = int(
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max(
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np.linalg.norm(points[0] - points[3]),
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np.linalg.norm(points[1] - points[2])))
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pts_std = np.float32([[0, 0], [img_crop_width, 0],
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[img_crop_width, img_crop_height],
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[0, img_crop_height]])
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M = cv2.getPerspectiveTransform(points, pts_std)
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dst_img = cv2.warpPerspective(
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img,
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M, (img_crop_width, img_crop_height),
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borderMode=cv2.BORDER_REPLICATE,
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flags=cv2.INTER_CUBIC)
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dst_img_height, dst_img_width = dst_img.shape[0:2]
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if dst_img_height * 1.0 / dst_img_width >= 1.5:
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dst_img = np.rot90(dst_img)
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return dst_img
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def print_draw_crop_rec_res(self, img_crop_list, rec_res):
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bbox_num = len(img_crop_list)
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for bno in range(bbox_num):
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cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno])
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logger.info(bno, rec_res[bno])
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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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logger.info("dt_boxes num : {}, elapse : {}".format(
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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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dt_boxes = sorted_boxes(dt_boxes)
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for bno in range(len(dt_boxes)):
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tmp_box = copy.deepcopy(dt_boxes[bno])
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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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if self.use_angle_cls:
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img_crop_list, angle_list, elapse = self.text_classifier(
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img_crop_list)
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logger.info("cls num : {}, elapse : {}".format(
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len(img_crop_list), elapse))
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rec_res, elapse = self.text_recognizer(img_crop_list)
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logger.info("rec_res num : {}, elapse : {}".format(
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len(rec_res), elapse))
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# self.print_draw_crop_rec_res(img_crop_list, rec_res)
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filter_boxes, filter_rec_res = [], []
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for box, rec_reuslt in zip(dt_boxes, rec_res):
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text, score = rec_reuslt
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if score >= self.drop_score:
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filter_boxes.append(box)
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filter_rec_res.append(rec_reuslt)
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return filter_boxes, filter_rec_res
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def sorted_boxes(dt_boxes):
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"""
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Sort text boxes in order from top to bottom, left to right
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args:
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dt_boxes(array):detected text boxes with shape [4, 2]
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return:
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sorted boxes(array) with shape [4, 2]
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"""
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num_boxes = dt_boxes.shape[0]
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sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
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_boxes = list(sorted_boxes)
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for i in range(num_boxes - 1):
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if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
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(_boxes[i + 1][0][0] < _boxes[i][0][0]):
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tmp = _boxes[i]
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_boxes[i] = _boxes[i + 1]
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_boxes[i + 1] = tmp
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return _boxes
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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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image_file_list = image_file_list[args.process_id::args.total_process_num]
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text_sys = TextSystem(args)
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is_visualize = True
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font_path = args.vis_font_path
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drop_score = args.drop_score
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for image_file in image_file_list:
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img, flag = check_and_read_gif(image_file)
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if not flag:
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img = cv2.imread(image_file)
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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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starttime = time.time()
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dt_boxes, rec_res = text_sys(img)
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elapse = time.time() - starttime
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logger.info("Predict time of %s: %.3fs" % (image_file, elapse))
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for text, score in rec_res:
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logger.info("{}, {:.3f}".format(text, score))
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if is_visualize:
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image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
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boxes = dt_boxes
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txts = [rec_res[i][0] for i in range(len(rec_res))]
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scores = [rec_res[i][1] for i in range(len(rec_res))]
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draw_img = draw_ocr_box_txt(
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image,
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boxes,
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txts,
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scores,
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drop_score=drop_score,
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font_path=font_path)
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draw_img_save = "./inference_results/"
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if not os.path.exists(draw_img_save):
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os.makedirs(draw_img_save)
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cv2.imwrite(
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os.path.join(draw_img_save, os.path.basename(image_file)),
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draw_img[:, :, ::-1])
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logger.info("The visualized image saved in {}".format(
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os.path.join(draw_img_save, os.path.basename(image_file))))
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if __name__ == "__main__":
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args = utility.parse_args()
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if args.use_mp:
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p_list = []
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total_process_num = args.total_process_num
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for process_id in range(total_process_num):
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cmd = [sys.executable, "-u"] + sys.argv + [
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"--process_id={}".format(process_id),
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"--use_mp={}".format(False)
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]
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p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
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p_list.append(p)
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for p in p_list:
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p.wait()
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else:
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main(args)
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