# 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 os import sys __dir__ = os.path.dirname(os.path.abspath(__file__)) sys.path.append(__dir__) sys.path.append(os.path.abspath(os.path.join(__dir__, '../..'))) import tools.infer.utility as utility from ppocr.utils.utility import initial_logger logger = initial_logger() import cv2 import tools.infer.predict_det as predict_det import tools.infer.predict_rec as predict_rec import copy import numpy as np import math import time from ppocr.utils.utility import get_image_file_list, check_and_read_gif from PIL import Image from tools.infer.utility import draw_ocr from tools.infer.utility import draw_ocr_box_txt class TextSystem(object): def __init__(self, args): self.text_detector = predict_det.TextDetector(args) self.text_recognizer = predict_rec.TextRecognizer(args) def get_rotate_crop_image(self, img, points): ''' img_height, img_width = img.shape[0:2] left = int(np.min(points[:, 0])) right = int(np.max(points[:, 0])) top = int(np.min(points[:, 1])) bottom = int(np.max(points[:, 1])) img_crop = img[top:bottom, left:right, :].copy() points[:, 0] = points[:, 0] - left points[:, 1] = points[:, 1] - top ''' img_crop_width = int( max( np.linalg.norm(points[0] - points[1]), np.linalg.norm(points[2] - points[3]))) img_crop_height = int( max( np.linalg.norm(points[0] - points[3]), np.linalg.norm(points[1] - points[2]))) pts_std = np.float32([[0, 0], [img_crop_width, 0], [img_crop_width, img_crop_height], [0, img_crop_height]]) M = cv2.getPerspectiveTransform(points, pts_std) dst_img = cv2.warpPerspective( img, M, (img_crop_width, img_crop_height), borderMode=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC) dst_img_height, dst_img_width = dst_img.shape[0:2] if dst_img_height * 1.0 / dst_img_width >= 1.5: dst_img = np.rot90(dst_img) return dst_img def print_draw_crop_rec_res(self, img_crop_list, rec_res): bbox_num = len(img_crop_list) for bno in range(bbox_num): cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno]) print(bno, rec_res[bno]) def __call__(self, img): ori_im = img.copy() dt_boxes, elapse = self.text_detector(img) print("dt_boxes num : {}, elapse : {}".format(len(dt_boxes), elapse)) if dt_boxes is None: return None, None img_crop_list = [] dt_boxes = sorted_boxes(dt_boxes) for bno in range(len(dt_boxes)): tmp_box = copy.deepcopy(dt_boxes[bno]) img_crop = self.get_rotate_crop_image(ori_im, tmp_box) img_crop_list.append(img_crop) rec_res, elapse = self.text_recognizer(img_crop_list) print("rec_res num : {}, elapse : {}".format(len(rec_res), elapse)) # self.print_draw_crop_rec_res(img_crop_list, rec_res) return dt_boxes, rec_res def sorted_boxes(dt_boxes): """ Sort text boxes in order from top to bottom, left to right args: dt_boxes(array):detected text boxes with shape [4, 2] return: sorted boxes(array) with shape [4, 2] """ num_boxes = dt_boxes.shape[0] sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0])) _boxes = list(sorted_boxes) for i in range(num_boxes - 1): if abs(_boxes[i+1][0][1] - _boxes[i][0][1]) < 10 and \ (_boxes[i + 1][0][0] < _boxes[i][0][0]): tmp = _boxes[i] _boxes[i] = _boxes[i + 1] _boxes[i + 1] = tmp return _boxes def main(args): image_file_list = get_image_file_list(args.image_dir) text_sys = TextSystem(args) is_visualize = True tackle_img_num = 0 if not args.enable_benchmark: for image_file in image_file_list: img, flag = check_and_read_gif(image_file) if not flag: img = cv2.imread(image_file) if img is None: logger.info("error in loading image:{}".format(image_file)) continue starttime = time.time() tackle_img_num += 1 if not args.use_gpu and args.enable_mkldnn and tackle_img_num % 30 == 0: text_sys = TextSystem(args) dt_boxes, rec_res = text_sys(img) elapse = time.time() - starttime print("Predict time of %s: %.3fs" % (image_file, elapse)) drop_score = 0.5 dt_num = len(dt_boxes) for dno in range(dt_num): text, score = rec_res[dno] if score >= drop_score: text_str = "%s, %.3f" % (text, score) print(text_str) if is_visualize: image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) boxes = dt_boxes txts = [rec_res[i][0] for i in range(len(rec_res))] scores = [rec_res[i][1] for i in range(len(rec_res))] draw_img = draw_ocr( image, boxes, txts, scores, drop_score=drop_score) draw_img_save = "./inference_results/" if not os.path.exists(draw_img_save): os.makedirs(draw_img_save) cv2.imwrite( os.path.join(draw_img_save, os.path.basename(image_file)), draw_img[:, :, ::-1]) print("The visualized image saved in {}".format( os.path.join(draw_img_save, os.path.basename(image_file)))) else: test_num = 10 test_time = 0.0 for i in range(0, test_num + 10): image_file = image_file_list[0] inputs = cv2.imread(image_file) inputs = cv2.resize(inputs, (int(640), int(640))) start_time = time.time() dt_boxes, rec_res = text_sys(inputs) if i >= 10: test_time += time.time() - start_time time.sleep(0.01) fp_message = "FP16" if args.use_fp16 else "FP32" trt_msg = "using tensorrt" if args.use_tensorrt else "not using tensorrt" print("Benchmark\t{0}\t{1}\tbatch size: {2}\ttime(ms): {3}".format( trt_msg, fp_message, args.max_batch_size, 1000 * test_time / test_num)) if __name__ == "__main__": main(utility.parse_args())