297 lines
10 KiB
Python
Executable File
297 lines
10 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=2.0)
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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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#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=30)
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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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return parser.parse_args()
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def create_predictor(args, mode):
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if mode == "det":
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model_dir = args.det_model_dir
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else:
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model_dir = args.rec_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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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.disable_glog_info()
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# use zero copy
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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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predictor = create_paddle_predictor(config)
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input_names = predictor.get_input_names()
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input_tensor = predictor.get_input_tensor(input_names[0])
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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, return_img=True):
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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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"""
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img = np.array(img)
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im_shape = img.shape
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im_size_min = np.min(im_shape[0:2])
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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, boxes, txts, scores, draw_txt=True, drop_score=0.5):
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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): image from Image.open
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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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draw_txt(bool): whether draw text or not
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drop_score(float): only scores greater than drop_threshold will be visualized
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return(array):
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the visualized img
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"""
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from PIL import Image, ImageDraw, ImageFont
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img = image.copy()
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draw = ImageDraw.Draw(img)
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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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draw.line([(box[0][0], box[0][1]), (box[1][0], box[1][1])], fill='red')
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draw.line([(box[1][0], box[1][1]), (box[2][0], box[2][1])], fill='red')
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draw.line([(box[2][0], box[2][1]), (box[3][0], box[3][1])], fill='red')
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draw.line([(box[3][0], box[3][1]), (box[0][0], box[0][1])], fill='red')
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draw.line(
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[(box[0][0] - 1, box[0][1] + 1), (box[1][0] - 1, box[1][1] + 1)],
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fill='red')
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draw.line(
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[(box[1][0] - 1, box[1][1] + 1), (box[2][0] - 1, box[2][1] + 1)],
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fill='red')
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draw.line(
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[(box[2][0] - 1, box[2][1] + 1), (box[3][0] - 1, box[3][1] + 1)],
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fill='red')
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draw.line(
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[(box[3][0] - 1, box[3][1] + 1), (box[0][0] - 1, box[0][1] + 1)],
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fill='red')
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if draw_txt:
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img = np.array(resize_img(img, input_size=600))
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txt_img = text_visual(
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txts, scores, img_h=img.shape[0], img_w=600, threshold=drop_score)
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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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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, scores, img_h=400, img_w=600, threshold=0.):
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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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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("./doc/simfang.ttf", 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 = 0, 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:
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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 + 1)), new_txt, txt_color, font=font)
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txt = tmp[img_w // font_size - 4:]
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count += 1
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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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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 + 1)), new_txt, txt_color, font=font)
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count += 1
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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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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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# cv2.imwrite("./draw_txt.jpg", np.array(blank_img))
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return np.array(blank_img)
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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, draw_txt=True)
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cv2.imwrite(img_name, new_img)
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