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