infer_det.py 6.38 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
from copy import deepcopy
import json

LDOUBLEV's avatar
LDOUBLEV committed
23
24
25
26
27
import os
import sys
__dir__ = os.path.dirname(__file__)
sys.path.append(__dir__)
sys.path.append(os.path.join(__dir__, '..'))
28
29
30
31
32
33
34
35
36
37


def set_paddle_flags(**kwargs):
    for key, value in kwargs.items():
        if os.environ.get(key, None) is None:
            os.environ[key] = str(value)


# NOTE(paddle-dev): All of these flags should be
# set before `import paddle`. Otherwise, it would
LDOUBLEV's avatar
LDOUBLEV committed
38
# not take any effect.
39
40
41
42
43
set_paddle_flags(
    FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
)

from paddle import fluid
LDOUBLEV's avatar
LDOUBLEV committed
44
from ppocr.utils.utility import create_module, get_image_file_list
45
46
47
import program
from ppocr.utils.save_load import init_model
from ppocr.data.reader_main import reader_main
LDOUBLEV's avatar
LDOUBLEV committed
48
import cv2
49
50
51
52
53

from ppocr.utils.utility import initial_logger
logger = initial_logger()


LDOUBLEV's avatar
LDOUBLEV committed
54
def draw_det_res(dt_boxes, config, img, img_name):
55
56
    if len(dt_boxes) > 0:
        import cv2
LDOUBLEV's avatar
LDOUBLEV committed
57
        src_im = img
58
59
60
        for box in dt_boxes:
            box = box.astype(np.int32).reshape((-1, 1, 2))
            cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
LDOUBLEV's avatar
LDOUBLEV committed
61
        save_det_path = os.path.dirname(config['Global'][
62
63
64
            'save_res_path']) + "/det_results/"
        if not os.path.exists(save_det_path):
            os.makedirs(save_det_path)
LDOUBLEV's avatar
LDOUBLEV committed
65
        save_path = os.path.join(save_det_path, os.path.basename(img_name))
66
67
68
        cv2.imwrite(save_path, src_im)
        logger.info("The detected Image saved in {}".format(save_path))

licx's avatar
licx committed
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
def gen_im_detection(src_im, detections):
    """
    Generate image with detection results.
    """
    im_detection = src_im.copy()

    h, w, _ = im_detection.shape
    thickness = int(max((h + w) / 2000, 1))

    for poly in detections:
        # Draw the first point
        cv2.putText(im_detection, '0', org=(int(poly[0, 0]), int(poly[0, 1])),
                    fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=thickness, color=(255, 0, 0),
                    thickness=thickness)

        cv2.polylines(im_detection, np.array(poly).reshape((1, -1, 2)).astype(np.int32), isClosed=True,
                  color=(0, 0, 255), thickness=thickness)

    return im_detection
88
89
90
91

def main():
    config = program.load_config(FLAGS.config)
    program.merge_config(FLAGS.opt)
littletomatodonkey's avatar
littletomatodonkey committed
92
    logger.info(config)
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123

    # check if set use_gpu=True in paddlepaddle cpu version
    use_gpu = config['Global']['use_gpu']
    program.check_gpu(use_gpu)

    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
    exe = fluid.Executor(place)

    det_model = create_module(config['Architecture']['function'])(params=config)

    startup_prog = fluid.Program()
    eval_prog = fluid.Program()
    with fluid.program_guard(eval_prog, startup_prog):
        with fluid.unique_name.guard():
            _, eval_outputs = det_model(mode="test")
            fetch_name_list = list(eval_outputs.keys())
            eval_fetch_list = [eval_outputs[v].name for v in fetch_name_list]

    eval_prog = eval_prog.clone(for_test=True)
    exe.run(startup_prog)

    # load checkpoints
    checkpoints = config['Global'].get('checkpoints')
    if checkpoints:
        path = checkpoints
        fluid.load(eval_prog, path, exe)
        logger.info("Finish initing model from {}".format(path))
    else:
        raise Exception("{} not exists!".format(checkpoints))

    save_res_path = config['Global']['save_res_path']
LDOUBLEV's avatar
LDOUBLEV committed
124
125
    if not os.path.exists(os.path.dirname(save_res_path)):
        os.makedirs(os.path.dirname(save_res_path))
126
    with open(save_res_path, "wb") as fout:
LDOUBLEV's avatar
LDOUBLEV committed
127

LDOUBLEV's avatar
LDOUBLEV committed
128
        test_reader = reader_main(config=config, mode='test')
129
130
131
132
133
134
135
136
137
138
139
140
        tackling_num = 0
        for data in test_reader():
            img_num = len(data)
            tackling_num = tackling_num + img_num
            logger.info("tackling_num:%d", tackling_num)
            img_list = []
            ratio_list = []
            img_name_list = []
            for ino in range(img_num):
                img_list.append(data[ino][0])
                ratio_list.append(data[ino][1])
                img_name_list.append(data[ino][2])
LDOUBLEV's avatar
LDOUBLEV committed
141

142
143
144
145
146
147
148
149
150
151
            img_list = np.concatenate(img_list, axis=0)
            outs = exe.run(eval_prog,\
                feed={'image': img_list},\
                fetch_list=eval_fetch_list)

            global_params = config['Global']
            postprocess_params = deepcopy(config["PostProcess"])
            postprocess_params.update(global_params)
            postprocess = create_module(postprocess_params['function'])\
                (params=postprocess_params)
LDOUBLEV's avatar
LDOUBLEV committed
152
153
154
155
            if config['Global']['algorithm'] == 'EAST':
                dic = {'f_score': outs[0], 'f_geo': outs[1]}
            elif config['Global']['algorithm'] == 'DB':
                dic = {'maps': outs[0]}
licx's avatar
licx committed
156
157
            elif config['Global']['algorithm'] == 'SAST':
                dic = {'f_score': outs[0], 'f_border': outs[1], 'f_tvo': outs[2], 'f_tco': outs[3]}
LDOUBLEV's avatar
LDOUBLEV committed
158
            else:
licx's avatar
licx committed
159
                raise Exception("only support algorithm: ['EAST', 'DB', 'SAST']")
LDOUBLEV's avatar
LDOUBLEV committed
160
            dt_boxes_list = postprocess(dic, ratio_list)
161
162
163
164
165
166
167
168
169
170
            for ino in range(img_num):
                dt_boxes = dt_boxes_list[ino]
                img_name = img_name_list[ino]
                dt_boxes_json = []
                for box in dt_boxes:
                    tmp_json = {"transcription": ""}
                    tmp_json['points'] = box.tolist()
                    dt_boxes_json.append(tmp_json)
                otstr = img_name + "\t" + json.dumps(dt_boxes_json) + "\n"
                fout.write(otstr.encode())
LDOUBLEV's avatar
LDOUBLEV committed
171
172
                src_img = cv2.imread(img_name)
                draw_det_res(dt_boxes, config, src_img, img_name)
licx's avatar
licx committed
173
                
174
175
176
177
178
179
180
    logger.info("success!")


if __name__ == '__main__':
    parser = program.ArgsParser()
    FLAGS = parser.parse_args()
    main()