#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # #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 math import cv2 import numpy as np import json import sys from ppocr.utils.utility import initial_logger, check_and_read_gif logger = initial_logger() import tools.infer.utility as utility args = utility.parse_args() if args.use_pdserving is False: from .data_augment import AugmentData from .random_crop_data import RandomCropData from .make_shrink_map import MakeShrinkMap from .make_border_map import MakeBorderMap class DBProcessTrain(object): """ DB pre-process for Train mode """ def __init__(self, params): self.img_set_dir = params['img_set_dir'] self.image_shape = params['image_shape'] def order_points_clockwise(self, pts): rect = np.zeros((4, 2), dtype="float32") s = pts.sum(axis=1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] diff = np.diff(pts, axis=1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] return rect def make_data_dict(self, imgvalue, entry): boxes = [] texts = [] ignores = [] for rect in entry: points = rect['points'] transcription = rect['transcription'] try: box = self.order_points_clockwise( np.array(points).reshape(-1, 2)) if cv2.contourArea(box) > 0: boxes.append(box) texts.append(transcription) ignores.append(transcription in ['*', '###']) except: print('load label failed!') data = { 'image': imgvalue, 'shape': [imgvalue.shape[0], imgvalue.shape[1]], 'polys': np.array(boxes), 'texts': texts, 'ignore_tags': ignores, } return data def NormalizeImage(self, data): im = data['image'] img_mean = [0.485, 0.456, 0.406] img_std = [0.229, 0.224, 0.225] im = im.astype(np.float32, copy=False) im = im / 255 im -= img_mean im /= img_std channel_swap = (2, 0, 1) im = im.transpose(channel_swap) data['image'] = im return data def FilterKeys(self, data): filter_keys = ['polys', 'texts', 'ignore_tags', 'shape'] for key in filter_keys: if key in data: del data[key] return data def convert_label_infor(self, label_infor): label_infor = label_infor.decode() label_infor = label_infor.encode('utf-8').decode('utf-8-sig') substr = label_infor.strip("\n").split("\t") img_path = self.img_set_dir + substr[0] label = json.loads(substr[1]) return img_path, label def __call__(self, label_infor): img_path, gt_label = self.convert_label_infor(label_infor) imgvalue, flag = check_and_read_gif(img_path) if not flag: imgvalue = cv2.imread(img_path) if imgvalue is None: logger.info("{} does not exist!".format(img_path)) return None if len(list(imgvalue.shape)) == 2 or imgvalue.shape[2] == 1: imgvalue = cv2.cvtColor(imgvalue, cv2.COLOR_GRAY2BGR) data = self.make_data_dict(imgvalue, gt_label) data = AugmentData(data) data = RandomCropData(data, self.image_shape[1:]) data = MakeShrinkMap(data) data = MakeBorderMap(data) data = self.NormalizeImage(data) data = self.FilterKeys(data) return data['image'], data['shrink_map'], data['shrink_mask'], data[ 'threshold_map'], data['threshold_mask'] class DBProcessTest(object): """ DB pre-process for Test mode """ def __init__(self, params): super(DBProcessTest, self).__init__() self.resize_type = 0 if 'test_image_shape' in params: self.image_shape = params['test_image_shape'] # print(self.image_shape) self.resize_type = 1 if 'max_side_len' in params: self.max_side_len = params['max_side_len'] else: self.max_side_len = 2400 def resize_image_type0(self, im): """ resize image to a size multiple of 32 which is required by the network args: img(array): array with shape [h, w, c] return(tuple): img, (ratio_h, ratio_w) """ max_side_len = self.max_side_len h, w, _ = im.shape resize_w = w resize_h = h # limit the max side if max(resize_h, resize_w) > max_side_len: if resize_h > resize_w: ratio = float(max_side_len) / resize_h else: ratio = float(max_side_len) / resize_w else: ratio = 1. resize_h = int(resize_h * ratio) resize_w = int(resize_w * ratio) if resize_h % 32 == 0: resize_h = resize_h elif resize_h // 32 <= 1: resize_h = 32 else: resize_h = (resize_h // 32 - 1) * 32 if resize_w % 32 == 0: resize_w = resize_w elif resize_w // 32 <= 1: resize_w = 32 else: resize_w = (resize_w // 32 - 1) * 32 try: if int(resize_w) <= 0 or int(resize_h) <= 0: return None, (None, None) im = cv2.resize(im, (int(resize_w), int(resize_h))) except: print(im.shape, resize_w, resize_h) sys.exit(0) ratio_h = resize_h / float(h) ratio_w = resize_w / float(w) return im, (ratio_h, ratio_w) def resize_image_type1(self, im): resize_h, resize_w = self.image_shape ori_h, ori_w = im.shape[:2] # (h, w, c) im = cv2.resize(im, (int(resize_w), int(resize_h))) ratio_h = float(resize_h) / ori_h ratio_w = float(resize_w) / ori_w return im, (ratio_h, ratio_w) def normalize(self, im): img_mean = [0.485, 0.456, 0.406] img_std = [0.229, 0.224, 0.225] im = im.astype(np.float32, copy=False) im = im / 255 im[:, :, 0] -= img_mean[0] im[:, :, 1] -= img_mean[1] im[:, :, 2] -= img_mean[2] im[:, :, 0] /= img_std[0] im[:, :, 1] /= img_std[1] im[:, :, 2] /= img_std[2] channel_swap = (2, 0, 1) im = im.transpose(channel_swap) return im def __call__(self, im): if self.resize_type == 0: im, (ratio_h, ratio_w) = self.resize_image_type0(im) else: im, (ratio_h, ratio_w) = self.resize_image_type1(im) im = self.normalize(im) im = im[np.newaxis, :] return [im, (ratio_h, ratio_w)]