416 lines
14 KiB
Python
Executable File
416 lines
14 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 argparse
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import os, sys
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from ppocr.utils.utility import initial_logger
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logger = initial_logger()
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from paddle.fluid.core import PaddleTensor
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from paddle.fluid.core import AnalysisConfig
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from paddle.fluid.core import create_paddle_predictor
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import cv2
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import numpy as np
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import json
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from PIL import Image, ImageDraw, ImageFont
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import math
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def parse_args():
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def str2bool(v):
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return v.lower() in ("true", "t", "1")
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parser = argparse.ArgumentParser()
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# params for prediction engine
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parser.add_argument("--use_gpu", type=str2bool, default=True)
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parser.add_argument("--ir_optim", type=str2bool, default=True)
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parser.add_argument("--use_tensorrt", type=str2bool, default=False)
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parser.add_argument("--gpu_mem", type=int, default=8000)
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# params for text detector
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parser.add_argument("--image_dir", type=str)
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parser.add_argument("--det_algorithm", type=str, default='DB')
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parser.add_argument("--det_model_dir", type=str)
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parser.add_argument("--det_max_side_len", type=float, default=960)
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# DB parmas
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parser.add_argument("--det_db_thresh", type=float, default=0.3)
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parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
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parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
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# EAST parmas
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parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
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parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
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parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)
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# SAST parmas
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parser.add_argument("--det_sast_score_thresh", type=float, default=0.5)
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parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2)
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parser.add_argument("--det_sast_polygon", type=bool, default=False)
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# params for text recognizer
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parser.add_argument("--rec_algorithm", type=str, default='CRNN')
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parser.add_argument("--rec_model_dir", type=str)
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parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
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parser.add_argument("--rec_char_type", type=str, default='ch')
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parser.add_argument("--rec_batch_num", type=int, default=6)
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parser.add_argument("--max_text_length", type=int, default=25)
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parser.add_argument(
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"--rec_char_dict_path",
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type=str,
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default="./ppocr/utils/ppocr_keys_v1.txt")
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parser.add_argument("--use_space_char", type=str2bool, default=True)
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parser.add_argument(
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"--vis_font_path", type=str, default="./doc/simfang.ttf")
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# params for text classifier
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parser.add_argument("--use_angle_cls", type=str2bool, default=False)
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parser.add_argument("--cls_model_dir", type=str)
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parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
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parser.add_argument("--label_list", type=list, default=['0', '180'])
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parser.add_argument("--cls_batch_num", type=int, default=30)
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parser.add_argument("--cls_thresh", type=float, default=0.9)
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parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
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parser.add_argument("--use_zero_copy_run", type=str2bool, default=False)
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parser.add_argument("--use_pdserving", type=str2bool, default=False)
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return parser.parse_args()
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def create_predictor(args, mode):
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"""
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create predictor for inference
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:param args: params for prediction engine
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:param mode: mode
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:return: predictor
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"""
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if mode == "det":
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model_dir = args.det_model_dir
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elif mode == 'cls':
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model_dir = args.cls_model_dir
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elif mode == 'rec':
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model_dir = args.rec_model_dir
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else:
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raise ValueError(
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"'mode' of create_predictor() can only be one of ['det', 'cls', 'rec']"
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)
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if model_dir is None:
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logger.info("not find {} model file path {}".format(mode, model_dir))
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sys.exit(0)
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model_file_path = model_dir + "/model"
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params_file_path = model_dir + "/params"
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if not os.path.exists(model_file_path):
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logger.info("not find model file path {}".format(model_file_path))
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sys.exit(0)
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if not os.path.exists(params_file_path):
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logger.info("not find params file path {}".format(params_file_path))
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sys.exit(0)
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config = AnalysisConfig(model_file_path, params_file_path)
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if args.use_gpu:
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config.enable_use_gpu(args.gpu_mem, 0)
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else:
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config.disable_gpu()
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config.set_cpu_math_library_num_threads(6)
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if args.enable_mkldnn:
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# cache 10 different shapes for mkldnn to avoid memory leak
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config.set_mkldnn_cache_capacity(10)
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config.enable_mkldnn()
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# config.enable_memory_optim()
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config.disable_glog_info()
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if args.use_zero_copy_run:
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config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
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config.switch_use_feed_fetch_ops(False)
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else:
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config.switch_use_feed_fetch_ops(True)
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predictor = create_paddle_predictor(config)
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input_names = predictor.get_input_names()
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for name in input_names:
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input_tensor = predictor.get_input_tensor(name)
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output_names = predictor.get_output_names()
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output_tensors = []
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for output_name in output_names:
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output_tensor = predictor.get_output_tensor(output_name)
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output_tensors.append(output_tensor)
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return predictor, input_tensor, output_tensors
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def draw_text_det_res(dt_boxes, img_path):
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"""
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Visualize the results of detection
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:param dt_boxes: The boxes predicted by detection model
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:param img_path: Image path
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:return: Visualized image
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"""
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src_im = cv2.imread(img_path)
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for box in dt_boxes:
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box = np.array(box).astype(np.int32).reshape(-1, 2)
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cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
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return src_im
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def resize_img(img, input_size=600):
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"""
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resize img and limit the longest side of the image to input_size
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"""
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img = np.array(img)
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im_shape = img.shape
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im_size_max = np.max(im_shape[0:2])
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im_scale = float(input_size) / float(im_size_max)
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im = cv2.resize(img, None, None, fx=im_scale, fy=im_scale)
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return im
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def draw_ocr(image,
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boxes,
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txts=None,
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scores=None,
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drop_score=0.5,
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font_path="./doc/simfang.ttf"):
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"""
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Visualize the results of OCR detection and recognition
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args:
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image(Image|array): RGB image
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boxes(list): boxes with shape(N, 4, 2)
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txts(list): the texts
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scores(list): txxs corresponding scores
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drop_score(float): only scores greater than drop_threshold will be visualized
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font_path: the path of font which is used to draw text
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return(array):
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the visualized img
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"""
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if scores is None:
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scores = [1] * len(boxes)
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box_num = len(boxes)
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for i in range(box_num):
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if scores is not None and (scores[i] < drop_score or
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math.isnan(scores[i])):
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continue
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box = np.reshape(np.array(boxes[i]), [-1, 1, 2]).astype(np.int64)
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image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2)
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if txts is not None:
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img = np.array(resize_img(image, input_size=600))
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txt_img = text_visual(
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txts,
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scores,
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img_h=img.shape[0],
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img_w=600,
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threshold=drop_score,
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font_path=font_path)
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img = np.concatenate([np.array(img), np.array(txt_img)], axis=1)
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return img
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return image
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def draw_ocr_box_txt(image,
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boxes,
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txts,
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scores=None,
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drop_score=0.5,
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font_path="./doc/simfang.ttf"):
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h, w = image.height, image.width
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img_left = image.copy()
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img_right = Image.new('RGB', (w, h), (255, 255, 255))
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import random
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random.seed(0)
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draw_left = ImageDraw.Draw(img_left)
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draw_right = ImageDraw.Draw(img_right)
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for idx, (box, txt) in enumerate(zip(boxes, txts)):
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if scores is not None and scores[idx] < drop_score:
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continue
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color = (random.randint(0, 255), random.randint(0, 255),
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random.randint(0, 255))
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draw_left.polygon(box, fill=color)
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draw_right.polygon(
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[
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box[0][0], box[0][1], box[1][0], box[1][1], box[2][0],
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box[2][1], box[3][0], box[3][1]
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],
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outline=color)
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box_height = math.sqrt((box[0][0] - box[3][0])**2 + (box[0][1] - box[3][
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1])**2)
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box_width = math.sqrt((box[0][0] - box[1][0])**2 + (box[0][1] - box[1][
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1])**2)
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if box_height > 2 * box_width:
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font_size = max(int(box_width * 0.9), 10)
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font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
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cur_y = box[0][1]
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for c in txt:
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char_size = font.getsize(c)
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draw_right.text(
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(box[0][0] + 3, cur_y), c, fill=(0, 0, 0), font=font)
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cur_y += char_size[1]
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else:
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font_size = max(int(box_height * 0.8), 10)
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font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
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draw_right.text(
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[box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font)
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img_left = Image.blend(image, img_left, 0.5)
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img_show = Image.new('RGB', (w * 2, h), (255, 255, 255))
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img_show.paste(img_left, (0, 0, w, h))
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img_show.paste(img_right, (w, 0, w * 2, h))
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return np.array(img_show)
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def str_count(s):
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"""
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Count the number of Chinese characters,
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a single English character and a single number
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equal to half the length of Chinese characters.
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args:
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s(string): the input of string
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return(int):
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the number of Chinese characters
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"""
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import string
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count_zh = count_pu = 0
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s_len = len(s)
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en_dg_count = 0
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for c in s:
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if c in string.ascii_letters or c.isdigit() or c.isspace():
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en_dg_count += 1
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elif c.isalpha():
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count_zh += 1
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else:
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count_pu += 1
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return s_len - math.ceil(en_dg_count / 2)
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def text_visual(texts,
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scores,
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img_h=400,
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img_w=600,
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threshold=0.,
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font_path="./doc/simfang.ttf"):
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"""
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create new blank img and draw txt on it
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args:
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texts(list): the text will be draw
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scores(list|None): corresponding score of each txt
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img_h(int): the height of blank img
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img_w(int): the width of blank img
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font_path: the path of font which is used to draw text
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return(array):
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"""
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if scores is not None:
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assert len(texts) == len(
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scores), "The number of txts and corresponding scores must match"
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def create_blank_img():
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blank_img = np.ones(shape=[img_h, img_w], dtype=np.int8) * 255
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blank_img[:, img_w - 1:] = 0
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blank_img = Image.fromarray(blank_img).convert("RGB")
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draw_txt = ImageDraw.Draw(blank_img)
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return blank_img, draw_txt
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blank_img, draw_txt = create_blank_img()
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font_size = 20
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txt_color = (0, 0, 0)
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font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
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gap = font_size + 5
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txt_img_list = []
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count, index = 1, 0
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for idx, txt in enumerate(texts):
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index += 1
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if scores[idx] < threshold or math.isnan(scores[idx]):
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index -= 1
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continue
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first_line = True
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while str_count(txt) >= img_w // font_size - 4:
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tmp = txt
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txt = tmp[:img_w // font_size - 4]
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if first_line:
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new_txt = str(index) + ': ' + txt
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first_line = False
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else:
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new_txt = ' ' + txt
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draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
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txt = tmp[img_w // font_size - 4:]
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if count >= img_h // gap - 1:
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txt_img_list.append(np.array(blank_img))
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blank_img, draw_txt = create_blank_img()
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count = 0
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count += 1
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if first_line:
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new_txt = str(index) + ': ' + txt + ' ' + '%.3f' % (scores[idx])
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else:
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new_txt = " " + txt + " " + '%.3f' % (scores[idx])
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draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
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# whether add new blank img or not
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if count >= img_h // gap - 1 and idx + 1 < len(texts):
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txt_img_list.append(np.array(blank_img))
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blank_img, draw_txt = create_blank_img()
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count = 0
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count += 1
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txt_img_list.append(np.array(blank_img))
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if len(txt_img_list) == 1:
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blank_img = np.array(txt_img_list[0])
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else:
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blank_img = np.concatenate(txt_img_list, axis=1)
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return np.array(blank_img)
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def base64_to_cv2(b64str):
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import base64
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data = base64.b64decode(b64str.encode('utf8'))
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data = np.fromstring(data, np.uint8)
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data = cv2.imdecode(data, cv2.IMREAD_COLOR)
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return data
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def draw_boxes(image, boxes, scores=None, drop_score=0.5):
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if scores is None:
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scores = [1] * len(boxes)
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for (box, score) in zip(boxes, scores):
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if score < drop_score:
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continue
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box = np.reshape(np.array(box), [-1, 1, 2]).astype(np.int64)
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image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2)
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return image
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if __name__ == '__main__':
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test_img = "./doc/test_v2"
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predict_txt = "./doc/predict.txt"
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f = open(predict_txt, 'r')
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data = f.readlines()
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img_path, anno = data[0].strip().split('\t')
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img_name = os.path.basename(img_path)
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img_path = os.path.join(test_img, img_name)
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image = Image.open(img_path)
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data = json.loads(anno)
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boxes, txts, scores = [], [], []
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for dic in data:
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boxes.append(dic['points'])
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txts.append(dic['transcription'])
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scores.append(round(dic['scores'], 3))
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new_img = draw_ocr(image, boxes, txts, scores)
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cv2.imwrite(img_name, new_img)
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