mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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615 lines
24 KiB
Python
615 lines
24 KiB
Python
# 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
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import sys
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import cv2
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import numpy as np
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import paddle
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from PIL import Image, ImageDraw, ImageFont
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import math
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from paddle import inference
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import time
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from ppocr.utils.logging import get_logger
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def str2bool(v):
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return v.lower() in ("true", "t", "1")
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def init_args():
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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("--min_subgraph_size", type=int, default=15)
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parser.add_argument("--precision", type=str, default="fp32")
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parser.add_argument("--gpu_mem", type=int, default=500)
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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_limit_side_len", type=float, default=960)
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parser.add_argument("--det_limit_type", type=str, default='max')
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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=0.6)
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parser.add_argument("--det_db_unclip_ratio", type=float, default=1.5)
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parser.add_argument("--max_batch_size", type=int, default=10)
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parser.add_argument("--use_dilation", type=str2bool, default=False)
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parser.add_argument("--det_db_score_mode", type=str, default="fast")
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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=str2bool, default=False)
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# PSE parmas
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parser.add_argument("--det_pse_thresh", type=float, default=0)
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parser.add_argument("--det_pse_box_thresh", type=float, default=0.85)
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parser.add_argument("--det_pse_min_area", type=float, default=16)
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parser.add_argument("--det_pse_box_type", type=str, default='box')
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parser.add_argument("--det_pse_scale", type=int, default=1)
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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_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/fonts/simfang.ttf")
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parser.add_argument("--drop_score", type=float, default=0.5)
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# params for e2e
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parser.add_argument("--e2e_algorithm", type=str, default='PGNet')
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parser.add_argument("--e2e_model_dir", type=str)
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parser.add_argument("--e2e_limit_side_len", type=float, default=768)
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parser.add_argument("--e2e_limit_type", type=str, default='max')
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# PGNet parmas
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parser.add_argument("--e2e_pgnet_score_thresh", type=float, default=0.5)
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parser.add_argument(
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"--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt")
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parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
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parser.add_argument("--e2e_pgnet_mode", type=str, default='fast')
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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=6)
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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("--cpu_threads", type=int, default=10)
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parser.add_argument("--use_pdserving", type=str2bool, default=False)
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parser.add_argument("--warmup", type=str2bool, default=False)
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#
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parser.add_argument(
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"--draw_img_save_dir", type=str, default="./inference_results")
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parser.add_argument("--save_crop_res", type=str2bool, default=False)
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parser.add_argument("--crop_res_save_dir", type=str, default="./output")
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# multi-process
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parser.add_argument("--use_mp", type=str2bool, default=False)
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parser.add_argument("--total_process_num", type=int, default=1)
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parser.add_argument("--process_id", type=int, default=0)
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parser.add_argument("--benchmark", type=str2bool, default=False)
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parser.add_argument("--save_log_path", type=str, default="./log_output/")
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parser.add_argument("--show_log", type=str2bool, default=True)
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parser.add_argument("--use_onnx", type=str2bool, default=False)
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return parser
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def parse_args():
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parser = init_args()
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return parser.parse_args()
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def create_predictor(args, mode, logger):
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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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elif mode == 'table':
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model_dir = args.table_model_dir
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else:
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model_dir = args.e2e_model_dir
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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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if args.use_onnx:
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import onnxruntime as ort
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model_file_path = model_dir
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if not os.path.exists(model_file_path):
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raise ValueError("not find model file path {}".format(
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model_file_path))
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sess = ort.InferenceSession(model_file_path)
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return sess, sess.get_inputs()[0], None, None
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else:
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model_file_path = model_dir + "/inference.pdmodel"
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params_file_path = model_dir + "/inference.pdiparams"
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if not os.path.exists(model_file_path):
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raise ValueError("not find model file path {}".format(
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model_file_path))
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if not os.path.exists(params_file_path):
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raise ValueError("not find params file path {}".format(
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params_file_path))
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config = inference.Config(model_file_path, params_file_path)
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if hasattr(args, 'precision'):
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if args.precision == "fp16" and args.use_tensorrt:
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precision = inference.PrecisionType.Half
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elif args.precision == "int8":
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precision = inference.PrecisionType.Int8
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else:
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precision = inference.PrecisionType.Float32
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else:
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precision = inference.PrecisionType.Float32
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if args.use_gpu:
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gpu_id = get_infer_gpuid()
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if gpu_id is None:
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logger.warning(
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"GPU is not found in current device by nvidia-smi. Please check your device or ignore it if run on jeston."
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)
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config.enable_use_gpu(args.gpu_mem, 0)
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if args.use_tensorrt:
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config.enable_tensorrt_engine(
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workspace_size=1 << 30,
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precision_mode=precision,
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max_batch_size=args.max_batch_size,
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min_subgraph_size=args.min_subgraph_size)
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# skip the minmum trt subgraph
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if mode == "det":
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min_input_shape = {
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"x": [1, 3, 50, 50],
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"conv2d_92.tmp_0": [1, 120, 20, 20],
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"conv2d_91.tmp_0": [1, 24, 10, 10],
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"conv2d_59.tmp_0": [1, 96, 20, 20],
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"nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
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"nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
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"conv2d_124.tmp_0": [1, 256, 20, 20],
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"nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
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"nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
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"nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
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"elementwise_add_7": [1, 56, 2, 2],
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"nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
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}
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max_input_shape = {
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"x": [1, 3, 1280, 1280],
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"conv2d_92.tmp_0": [1, 120, 400, 400],
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"conv2d_91.tmp_0": [1, 24, 200, 200],
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"conv2d_59.tmp_0": [1, 96, 400, 400],
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"nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
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"conv2d_124.tmp_0": [1, 256, 400, 400],
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"nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
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"nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
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"nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
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"nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
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"elementwise_add_7": [1, 56, 400, 400],
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"nearest_interp_v2_0.tmp_0": [1, 256, 400, 400]
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}
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opt_input_shape = {
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"x": [1, 3, 640, 640],
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"conv2d_92.tmp_0": [1, 120, 160, 160],
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"conv2d_91.tmp_0": [1, 24, 80, 80],
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"conv2d_59.tmp_0": [1, 96, 160, 160],
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"nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
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"nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
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"conv2d_124.tmp_0": [1, 256, 160, 160],
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"nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
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"nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
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"nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
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"elementwise_add_7": [1, 56, 40, 40],
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"nearest_interp_v2_0.tmp_0": [1, 256, 40, 40]
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}
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min_pact_shape = {
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"nearest_interp_v2_26.tmp_0": [1, 256, 20, 20],
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"nearest_interp_v2_27.tmp_0": [1, 64, 20, 20],
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"nearest_interp_v2_28.tmp_0": [1, 64, 20, 20],
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"nearest_interp_v2_29.tmp_0": [1, 64, 20, 20]
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}
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max_pact_shape = {
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"nearest_interp_v2_26.tmp_0": [1, 256, 400, 400],
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"nearest_interp_v2_27.tmp_0": [1, 64, 400, 400],
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"nearest_interp_v2_28.tmp_0": [1, 64, 400, 400],
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"nearest_interp_v2_29.tmp_0": [1, 64, 400, 400]
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}
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opt_pact_shape = {
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"nearest_interp_v2_26.tmp_0": [1, 256, 160, 160],
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"nearest_interp_v2_27.tmp_0": [1, 64, 160, 160],
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"nearest_interp_v2_28.tmp_0": [1, 64, 160, 160],
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"nearest_interp_v2_29.tmp_0": [1, 64, 160, 160]
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}
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min_input_shape.update(min_pact_shape)
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max_input_shape.update(max_pact_shape)
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opt_input_shape.update(opt_pact_shape)
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elif mode == "rec":
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min_input_shape = {"x": [1, 3, 32, 10]}
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max_input_shape = {"x": [args.rec_batch_num, 3, 32, 1024]}
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opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
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elif mode == "cls":
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min_input_shape = {"x": [1, 3, 48, 10]}
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max_input_shape = {"x": [args.rec_batch_num, 3, 48, 1024]}
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opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
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else:
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min_input_shape = {"x": [1, 3, 10, 10]}
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max_input_shape = {"x": [1, 3, 512, 512]}
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opt_input_shape = {"x": [1, 3, 256, 256]}
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config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
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opt_input_shape)
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else:
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config.disable_gpu()
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if hasattr(args, "cpu_threads"):
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config.set_cpu_math_library_num_threads(args.cpu_threads)
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else:
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# default cpu threads as 10
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config.set_cpu_math_library_num_threads(10)
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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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if args.precision == "fp16":
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config.enable_mkldnn_bfloat16()
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# enable memory optim
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config.enable_memory_optim()
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config.disable_glog_info()
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config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
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if mode == 'table':
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config.delete_pass("fc_fuse_pass") # not supported for table
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config.switch_use_feed_fetch_ops(False)
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config.switch_ir_optim(True)
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# create predictor
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predictor = inference.create_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_handle(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_handle(output_name)
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output_tensors.append(output_tensor)
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return predictor, input_tensor, output_tensors, config
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def get_infer_gpuid():
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cmd = "env | grep CUDA_VISIBLE_DEVICES"
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env_cuda = os.popen(cmd).readlines()
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if len(env_cuda) == 0:
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return 0
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else:
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gpu_id = env_cuda[0].strip().split("=")[1]
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return int(gpu_id[0])
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def draw_e2e_res(dt_boxes, strs, img_path):
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src_im = cv2.imread(img_path)
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for box, str in zip(dt_boxes, strs):
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box = box.astype(np.int32).reshape((-1, 1, 2))
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cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
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cv2.putText(
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src_im,
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str,
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org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
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fontFace=cv2.FONT_HERSHEY_COMPLEX,
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fontScale=0.7,
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color=(0, 255, 0),
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thickness=1)
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return src_im
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def draw_text_det_res(dt_boxes, img_path):
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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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img = cv2.resize(img, None, None, fx=im_scale, fy=im_scale)
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return img
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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/fonts/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()
|
|
img_right = Image.new('RGB', (w, h), (255, 255, 255))
|
|
|
|
import random
|
|
|
|
random.seed(0)
|
|
draw_left = ImageDraw.Draw(img_left)
|
|
draw_right = ImageDraw.Draw(img_right)
|
|
for idx, (box, txt) in enumerate(zip(boxes, txts)):
|
|
if scores is not None and scores[idx] < drop_score:
|
|
continue
|
|
color = (random.randint(0, 255), random.randint(0, 255),
|
|
random.randint(0, 255))
|
|
draw_left.polygon(box, fill=color)
|
|
draw_right.polygon(
|
|
[
|
|
box[0][0], box[0][1], box[1][0], box[1][1], box[2][0],
|
|
box[2][1], box[3][0], box[3][1]
|
|
],
|
|
outline=color)
|
|
box_height = math.sqrt((box[0][0] - box[3][0])**2 + (box[0][1] - box[3][
|
|
1])**2)
|
|
box_width = math.sqrt((box[0][0] - box[1][0])**2 + (box[0][1] - box[1][
|
|
1])**2)
|
|
if box_height > 2 * box_width:
|
|
font_size = max(int(box_width * 0.9), 10)
|
|
font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
|
|
cur_y = box[0][1]
|
|
for c in txt:
|
|
char_size = font.getsize(c)
|
|
draw_right.text(
|
|
(box[0][0] + 3, cur_y), c, fill=(0, 0, 0), font=font)
|
|
cur_y += char_size[1]
|
|
else:
|
|
font_size = max(int(box_height * 0.8), 10)
|
|
font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
|
|
draw_right.text(
|
|
[box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font)
|
|
img_left = Image.blend(image, img_left, 0.5)
|
|
img_show = Image.new('RGB', (w * 2, h), (255, 255, 255))
|
|
img_show.paste(img_left, (0, 0, w, h))
|
|
img_show.paste(img_right, (w, 0, w * 2, h))
|
|
return np.array(img_show)
|
|
|
|
|
|
def str_count(s):
|
|
"""
|
|
Count the number of Chinese characters,
|
|
a single English character and a single number
|
|
equal to half the length of Chinese characters.
|
|
args:
|
|
s(string): the input of string
|
|
return(int):
|
|
the number of Chinese characters
|
|
"""
|
|
import string
|
|
count_zh = count_pu = 0
|
|
s_len = len(s)
|
|
en_dg_count = 0
|
|
for c in s:
|
|
if c in string.ascii_letters or c.isdigit() or c.isspace():
|
|
en_dg_count += 1
|
|
elif c.isalpha():
|
|
count_zh += 1
|
|
else:
|
|
count_pu += 1
|
|
return s_len - math.ceil(en_dg_count / 2)
|
|
|
|
|
|
def text_visual(texts,
|
|
scores,
|
|
img_h=400,
|
|
img_w=600,
|
|
threshold=0.,
|
|
font_path="./doc/simfang.ttf"):
|
|
"""
|
|
create new blank img and draw txt on it
|
|
args:
|
|
texts(list): the text will be draw
|
|
scores(list|None): corresponding score of each txt
|
|
img_h(int): the height of blank img
|
|
img_w(int): the width of blank img
|
|
font_path: the path of font which is used to draw text
|
|
return(array):
|
|
"""
|
|
if scores is not None:
|
|
assert len(texts) == len(
|
|
scores), "The number of txts and corresponding scores must match"
|
|
|
|
def create_blank_img():
|
|
blank_img = np.ones(shape=[img_h, img_w], dtype=np.int8) * 255
|
|
blank_img[:, img_w - 1:] = 0
|
|
blank_img = Image.fromarray(blank_img).convert("RGB")
|
|
draw_txt = ImageDraw.Draw(blank_img)
|
|
return blank_img, draw_txt
|
|
|
|
blank_img, draw_txt = create_blank_img()
|
|
|
|
font_size = 20
|
|
txt_color = (0, 0, 0)
|
|
font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
|
|
|
|
gap = font_size + 5
|
|
txt_img_list = []
|
|
count, index = 1, 0
|
|
for idx, txt in enumerate(texts):
|
|
index += 1
|
|
if scores[idx] < threshold or math.isnan(scores[idx]):
|
|
index -= 1
|
|
continue
|
|
first_line = True
|
|
while str_count(txt) >= img_w // font_size - 4:
|
|
tmp = txt
|
|
txt = tmp[:img_w // font_size - 4]
|
|
if first_line:
|
|
new_txt = str(index) + ': ' + txt
|
|
first_line = False
|
|
else:
|
|
new_txt = ' ' + txt
|
|
draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
|
|
txt = tmp[img_w // font_size - 4:]
|
|
if count >= img_h // gap - 1:
|
|
txt_img_list.append(np.array(blank_img))
|
|
blank_img, draw_txt = create_blank_img()
|
|
count = 0
|
|
count += 1
|
|
if first_line:
|
|
new_txt = str(index) + ': ' + txt + ' ' + '%.3f' % (scores[idx])
|
|
else:
|
|
new_txt = " " + txt + " " + '%.3f' % (scores[idx])
|
|
draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
|
|
# whether add new blank img or not
|
|
if count >= img_h // gap - 1 and idx + 1 < len(texts):
|
|
txt_img_list.append(np.array(blank_img))
|
|
blank_img, draw_txt = create_blank_img()
|
|
count = 0
|
|
count += 1
|
|
txt_img_list.append(np.array(blank_img))
|
|
if len(txt_img_list) == 1:
|
|
blank_img = np.array(txt_img_list[0])
|
|
else:
|
|
blank_img = np.concatenate(txt_img_list, axis=1)
|
|
return np.array(blank_img)
|
|
|
|
|
|
def base64_to_cv2(b64str):
|
|
import base64
|
|
data = base64.b64decode(b64str.encode('utf8'))
|
|
data = np.fromstring(data, np.uint8)
|
|
data = cv2.imdecode(data, cv2.IMREAD_COLOR)
|
|
return data
|
|
|
|
|
|
def draw_boxes(image, boxes, scores=None, drop_score=0.5):
|
|
if scores is None:
|
|
scores = [1] * len(boxes)
|
|
for (box, score) in zip(boxes, scores):
|
|
if score < drop_score:
|
|
continue
|
|
box = np.reshape(np.array(box), [-1, 1, 2]).astype(np.int64)
|
|
image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2)
|
|
return image
|
|
|
|
|
|
def get_rotate_crop_image(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
|
|
'''
|
|
assert len(points) == 4, "shape of points must be 4*2"
|
|
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 check_gpu(use_gpu):
|
|
if use_gpu and not paddle.is_compiled_with_cuda():
|
|
|
|
use_gpu = False
|
|
return use_gpu
|
|
|
|
|
|
if __name__ == '__main__':
|
|
pass
|