# 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 utility from ppocr.utils.utility import initial_logger logger = initial_logger() import cv2 from ppocr.data.det.east_process import EASTProcessTest from ppocr.data.det.db_process import DBProcessTest from ppocr.postprocess.db_postprocess import DBPostProcess from ppocr.postprocess.east_postprocess import EASTPostPocess from ppocr.utils.utility import get_image_file_list from tools.infer.utility import draw_ocr import copy import numpy as np import math import time import sys import os class TextDetector(object): def __init__(self, args): max_side_len = args.det_max_side_len self.det_algorithm = args.det_algorithm preprocess_params = {'max_side_len': max_side_len} postprocess_params = {} if self.det_algorithm == "DB": self.preprocess_op = DBProcessTest(preprocess_params) postprocess_params["thresh"] = args.det_db_thresh postprocess_params["box_thresh"] = args.det_db_box_thresh postprocess_params["max_candidates"] = 1000 self.postprocess_op = DBPostProcess(postprocess_params) elif self.det_algorithm == "EAST": self.preprocess_op = EASTProcessTest(preprocess_params) postprocess_params["score_thresh"] = args.det_east_score_thresh postprocess_params["cover_thresh"] = args.det_east_cover_thresh postprocess_params["nms_thresh"] = args.det_east_nms_thresh self.postprocess_op = EASTPostPocess(postprocess_params) else: logger.info("unknown det_algorithm:{}".format(self.det_algorithm)) sys.exit(0) self.predictor, self.input_tensor, self.output_tensors =\ utility.create_predictor(args, mode="det") def order_points_clockwise(self, pts): """ reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py # sort the points based on their x-coordinates """ xSorted = pts[np.argsort(pts[:, 0]), :] # grab the left-most and right-most points from the sorted # x-roodinate points leftMost = xSorted[:2, :] rightMost = xSorted[2:, :] # now, sort the left-most coordinates according to their # y-coordinates so we can grab the top-left and bottom-left # points, respectively leftMost = leftMost[np.argsort(leftMost[:, 1]), :] (tl, bl) = leftMost rightMost = rightMost[np.argsort(rightMost[:, 1]), :] (tr, br) = rightMost rect = np.array([tl, tr, br, bl], dtype="float32") return rect def expand_det_res(self, points, bbox_height, bbox_width, img_height, img_width): if bbox_height * 1.0 / bbox_width >= 2.0: expand_w = bbox_width * 0.20 expand_h = bbox_width * 0.20 elif bbox_width * 1.0 / bbox_height >= 3.0: expand_w = bbox_height * 0.20 expand_h = bbox_height * 0.20 else: expand_w = bbox_height * 0.1 expand_h = bbox_height * 0.1 points[0, 0] = int(max((points[0, 0] - expand_w), 0)) points[1, 0] = int(min((points[1, 0] + expand_w), img_width)) points[3, 0] = int(max((points[3, 0] - expand_w), 0)) points[2, 0] = int(min((points[2, 0] + expand_w), img_width)) points[0, 1] = int(max((points[0, 1] - expand_h), 0)) points[1, 1] = int(max((points[1, 1] - expand_h), 0)) points[3, 1] = int(min((points[3, 1] + expand_h), img_height)) points[2, 1] = int(min((points[2, 1] + expand_h), img_height)) return points def filter_tag_det_res(self, dt_boxes, image_shape): img_height, img_width = image_shape[0:2] dt_boxes_new = [] for box in dt_boxes: box = self.order_points_clockwise(box) left = int(np.min(box[:, 0])) right = int(np.max(box[:, 0])) top = int(np.min(box[:, 1])) bottom = int(np.max(box[:, 1])) bbox_height = bottom - top bbox_width = right - left diffh = math.fabs(box[0, 1] - box[1, 1]) diffw = math.fabs(box[0, 0] - box[3, 0]) rect_width = int(np.linalg.norm(box[0] - box[1])) rect_height = int(np.linalg.norm(box[0] - box[3])) if rect_width <= 10 or rect_height <= 10: continue # if diffh <= 10 and diffw <= 10: # box = self.expand_det_res( # copy.deepcopy(box), bbox_height, bbox_width, img_height, # img_width) dt_boxes_new.append(box) dt_boxes = np.array(dt_boxes_new) return dt_boxes def __call__(self, img): ori_im = img.copy() im, ratio_list = self.preprocess_op(img) if im is None: return None, 0 im = im.copy() starttime = time.time() self.input_tensor.copy_from_cpu(im) self.predictor.zero_copy_run() outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) outs_dict = {} if self.det_algorithm == "EAST": outs_dict['f_geo'] = outputs[0] outs_dict['f_score'] = outputs[1] else: outs_dict['maps'] = outputs[0] dt_boxes_list = self.postprocess_op(outs_dict, [ratio_list]) dt_boxes = dt_boxes_list[0] dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape) elapse = time.time() - starttime return dt_boxes, elapse from tools.infer.utility import draw_text_det_res if __name__ == "__main__": args = utility.parse_args() image_file_list = get_image_file_list(args.image_dir) text_detector = TextDetector(args) count = 0 total_time = 0 for image_file in image_file_list: img = cv2.imread(image_file) if img is None: logger.info("error in loading image:{}".format(image_file)) continue dt_boxes, elapse = text_detector(img) if count > 0: total_time += elapse count += 1 print("Predict time of %s:" % image_file, elapse) img_draw = draw_text_det_res(dt_boxes, image_file, return_img=True) save_path = os.path.join("./inference_det/", os.path.basename(image_file)) print("The visualized image saved in {}".format(save_path)) print("Avg Time:", total_time / (count - 1))