utility.py 19.3 KB
Newer Older
LDOUBLEV's avatar
LDOUBLEV committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
# 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.

import argparse
WenmuZhou's avatar
WenmuZhou committed
16
import os
WenmuZhou's avatar
WenmuZhou committed
17
import sys
LDOUBLEV's avatar
LDOUBLEV committed
18
19
import cv2
import numpy as np
LDOUBLEV's avatar
LDOUBLEV committed
20
21
import json
from PIL import Image, ImageDraw, ImageFont
22
import math
WenmuZhou's avatar
WenmuZhou committed
23
from paddle import inference
24
25
26
import time
from ppocr.utils.logging import get_logger
logger = get_logger()
LDOUBLEV's avatar
LDOUBLEV committed
27
28
29
30
31
32
33


def parse_args():
    def str2bool(v):
        return v.lower() in ("true", "t", "1")

    parser = argparse.ArgumentParser()
WenmuZhou's avatar
WenmuZhou committed
34
    # params for prediction engine
LDOUBLEV's avatar
LDOUBLEV committed
35
36
37
    parser.add_argument("--use_gpu", type=str2bool, default=True)
    parser.add_argument("--ir_optim", type=str2bool, default=True)
    parser.add_argument("--use_tensorrt", type=str2bool, default=False)
LDOUBLEV's avatar
LDOUBLEV committed
38
    parser.add_argument("--use_fp16", type=str2bool, default=False)
39
    parser.add_argument("--gpu_mem", type=int, default=500)
LDOUBLEV's avatar
LDOUBLEV committed
40

WenmuZhou's avatar
WenmuZhou committed
41
    # params for text detector
LDOUBLEV's avatar
LDOUBLEV committed
42
43
44
    parser.add_argument("--image_dir", type=str)
    parser.add_argument("--det_algorithm", type=str, default='DB')
    parser.add_argument("--det_model_dir", type=str)
WenmuZhou's avatar
WenmuZhou committed
45
46
    parser.add_argument("--det_limit_side_len", type=float, default=960)
    parser.add_argument("--det_limit_type", type=str, default='max')
LDOUBLEV's avatar
LDOUBLEV committed
47

WenmuZhou's avatar
WenmuZhou committed
48
    # DB parmas
LDOUBLEV's avatar
LDOUBLEV committed
49
50
    parser.add_argument("--det_db_thresh", type=float, default=0.3)
    parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
WenmuZhou's avatar
WenmuZhou committed
51
    parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
LDOUBLEV's avatar
LDOUBLEV committed
52
    parser.add_argument("--max_batch_size", type=int, default=10)
LDOUBLEV's avatar
LDOUBLEV committed
53
    parser.add_argument("--use_dilation", type=bool, default=False)
littletomatodonkey's avatar
littletomatodonkey committed
54
    parser.add_argument("--det_db_score_mode", type=str, default="fast")
WenmuZhou's avatar
WenmuZhou committed
55
    # EAST parmas
LDOUBLEV's avatar
LDOUBLEV committed
56
57
58
59
    parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
    parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
    parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)

WenmuZhou's avatar
WenmuZhou committed
60
    # SAST parmas
licx's avatar
licx committed
61
62
    parser.add_argument("--det_sast_score_thresh", type=float, default=0.5)
    parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2)
63
    parser.add_argument("--det_sast_polygon", type=bool, default=False)
licx's avatar
licx committed
64

WenmuZhou's avatar
WenmuZhou committed
65
    # params for text recognizer
LDOUBLEV's avatar
LDOUBLEV committed
66
67
    parser.add_argument("--rec_algorithm", type=str, default='CRNN')
    parser.add_argument("--rec_model_dir", type=str)
tink2123's avatar
fix bug  
tink2123 committed
68
69
    parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
    parser.add_argument("--rec_char_type", type=str, default='ch')
70
    parser.add_argument("--rec_batch_num", type=int, default=6)
tink2123's avatar
fix bug  
tink2123 committed
71
    parser.add_argument("--max_text_length", type=int, default=25)
LDOUBLEV's avatar
LDOUBLEV committed
72
73
74
75
    parser.add_argument(
        "--rec_char_dict_path",
        type=str,
        default="./ppocr/utils/ppocr_keys_v1.txt")
WenmuZhou's avatar
WenmuZhou committed
76
77
    parser.add_argument("--use_space_char", type=str2bool, default=True)
    parser.add_argument(
tink2123's avatar
tink2123 committed
78
        "--vis_font_path", type=str, default="./doc/fonts/simfang.ttf")
WenmuZhou's avatar
WenmuZhou committed
79
    parser.add_argument("--drop_score", type=float, default=0.5)
WenmuZhou's avatar
WenmuZhou committed
80

Jethong's avatar
Jethong committed
81
82
83
84
85
86
87
88
89
    # params for e2e
    parser.add_argument("--e2e_algorithm", type=str, default='PGNet')
    parser.add_argument("--e2e_model_dir", type=str)
    parser.add_argument("--e2e_limit_side_len", type=float, default=768)
    parser.add_argument("--e2e_limit_type", type=str, default='max')

    # PGNet parmas
    parser.add_argument("--e2e_pgnet_score_thresh", type=float, default=0.5)
    parser.add_argument(
Jethong's avatar
Jethong committed
90
        "--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt")
Jethong's avatar
Jethong committed
91
    parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
Jethong's avatar
Jethong committed
92
    parser.add_argument("--e2e_pgnet_polygon", type=bool, default=True)
Jethong's avatar
Jethong committed
93
    parser.add_argument("--e2e_pgnet_mode", type=str, default='fast')
Jethong's avatar
Jethong committed
94

WenmuZhou's avatar
WenmuZhou committed
95
96
97
98
99
    # params for text classifier
    parser.add_argument("--use_angle_cls", type=str2bool, default=False)
    parser.add_argument("--cls_model_dir", type=str)
    parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
    parser.add_argument("--label_list", type=list, default=['0', '180'])
100
    parser.add_argument("--cls_batch_num", type=int, default=6)
WenmuZhou's avatar
WenmuZhou committed
101
102
103
    parser.add_argument("--cls_thresh", type=float, default=0.9)

    parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
104
    parser.add_argument("--cpu_threads", type=int, default=10)
WenmuZhou's avatar
WenmuZhou committed
105
106
    parser.add_argument("--use_pdserving", type=str2bool, default=False)

littletomatodonkey's avatar
littletomatodonkey committed
107
    parser.add_argument("--use_mp", type=str2bool, default=False)
108
109
110
    parser.add_argument("--total_process_num", type=int, default=1)
    parser.add_argument("--process_id", type=int, default=0)

LDOUBLEV's avatar
LDOUBLEV committed
111
112
113
    return parser.parse_args()


WenmuZhou's avatar
WenmuZhou committed
114
115
116
117
118
def create_predictor(args, mode, logger):
    if mode == "det":
        model_dir = args.det_model_dir
    elif mode == 'cls':
        model_dir = args.cls_model_dir
Jethong's avatar
Jethong committed
119
    elif mode == 'rec':
WenmuZhou's avatar
WenmuZhou committed
120
        model_dir = args.rec_model_dir
Jethong's avatar
Jethong committed
121
122
    else:
        model_dir = args.e2e_model_dir
WenmuZhou's avatar
WenmuZhou committed
123
124
125
126

    if model_dir is None:
        logger.info("not find {} model file path {}".format(mode, model_dir))
        sys.exit(0)
WenmuZhou's avatar
WenmuZhou committed
127
128
    model_file_path = model_dir + "/inference.pdmodel"
    params_file_path = model_dir + "/inference.pdiparams"
WenmuZhou's avatar
WenmuZhou committed
129
130
131
132
133
134
135
    if not os.path.exists(model_file_path):
        logger.info("not find model file path {}".format(model_file_path))
        sys.exit(0)
    if not os.path.exists(params_file_path):
        logger.info("not find params file path {}".format(params_file_path))
        sys.exit(0)

WenmuZhou's avatar
WenmuZhou committed
136
    config = inference.Config(model_file_path, params_file_path)
WenmuZhou's avatar
WenmuZhou committed
137
138
139

    if args.use_gpu:
        config.enable_use_gpu(args.gpu_mem, 0)
LDOUBLEV's avatar
LDOUBLEV committed
140
141
        if args.use_tensorrt:
            config.enable_tensorrt_engine(
LDOUBLEV's avatar
LDOUBLEV committed
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
                precision_mode=inference.PrecisionType.Float32,
                max_batch_size=args.max_batch_size,
                min_subgraph_size=3)  # skip the minmum trt subgraph 
        if mode == "det" and "mobile" in model_file_path:
            min_input_shape = {
                "x": [1, 3, 50, 50],
                "conv2d_92.tmp_0": [1, 96, 20, 20],
                "conv2d_91.tmp_0": [1, 96, 10, 10],
                "nearest_interp_v2_1.tmp_0": [1, 96, 10, 10],
                "nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
                "nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
                "nearest_interp_v2_4.tmp_0": [1, 24, 20, 20],
                "nearest_interp_v2_5.tmp_0": [1, 24, 20, 20],
                "elementwise_add_7": [1, 56, 2, 2],
                "nearest_interp_v2_0.tmp_0": [1, 96, 2, 2]
            }
            max_input_shape = {
                "x": [1, 3, 2000, 2000],
                "conv2d_92.tmp_0": [1, 96, 400, 400],
                "conv2d_91.tmp_0": [1, 96, 200, 200],
                "nearest_interp_v2_1.tmp_0": [1, 96, 200, 200],
                "nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
                "nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
                "nearest_interp_v2_4.tmp_0": [1, 24, 400, 400],
                "nearest_interp_v2_5.tmp_0": [1, 24, 400, 400],
                "elementwise_add_7": [1, 56, 400, 400],
                "nearest_interp_v2_0.tmp_0": [1, 96, 400, 400]
            }
            opt_input_shape = {
                "x": [1, 3, 640, 640],
                "conv2d_92.tmp_0": [1, 96, 160, 160],
                "conv2d_91.tmp_0": [1, 96, 80, 80],
                "nearest_interp_v2_1.tmp_0": [1, 96, 80, 80],
                "nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
                "nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
                "nearest_interp_v2_4.tmp_0": [1, 24, 160, 160],
                "nearest_interp_v2_5.tmp_0": [1, 24, 160, 160],
                "elementwise_add_7": [1, 56, 40, 40],
                "nearest_interp_v2_0.tmp_0": [1, 96, 40, 40]
            }
        if mode == "det" and "server" in model_file_path:
            min_input_shape = {
                "x": [1, 3, 50, 50],
                "conv2d_59.tmp_0": [1, 96, 20, 20],
                "nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
                "nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
                "nearest_interp_v2_4.tmp_0": [1, 24, 20, 20],
                "nearest_interp_v2_5.tmp_0": [1, 24, 20, 20]
            }
            max_input_shape = {
                "x": [1, 3, 2000, 2000],
                "conv2d_59.tmp_0": [1, 96, 400, 400],
                "nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
                "nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
                "nearest_interp_v2_4.tmp_0": [1, 24, 400, 400],
                "nearest_interp_v2_5.tmp_0": [1, 24, 400, 400]
            }
            opt_input_shape = {
                "x": [1, 3, 640, 640],
                "conv2d_59.tmp_0": [1, 96, 160, 160],
                "nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
                "nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
                "nearest_interp_v2_4.tmp_0": [1, 24, 160, 160],
                "nearest_interp_v2_5.tmp_0": [1, 24, 160, 160]
            }
        elif mode == "rec":
            min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}
            max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
            opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
        elif mode == "cls":
            min_input_shape = {"x": [args.rec_batch_num, 3, 48, 10]}
            max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]}
            opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
LDOUBLEV's avatar
LDOUBLEV committed
215
216
217
218
        else:
            min_input_shape = {"x": [1, 3, 10, 10]}
            max_input_shape = {"x": [1, 3, 1000, 1000]}
            opt_input_shape = {"x": [1, 3, 500, 500]}
LDOUBLEV's avatar
LDOUBLEV committed
219
220
221
        config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
                                          opt_input_shape)

WenmuZhou's avatar
WenmuZhou committed
222
223
    else:
        config.disable_gpu()
224
225
226
        if hasattr(args, "cpu_threads"):
            config.set_cpu_math_library_num_threads(args.cpu_threads)
        else:
LDOUBLEV's avatar
LDOUBLEV committed
227
228
            # default cpu threads as 10
            config.set_cpu_math_library_num_threads(10)
WenmuZhou's avatar
WenmuZhou committed
229
230
231
232
233
        if args.enable_mkldnn:
            # cache 10 different shapes for mkldnn to avoid memory leak
            config.set_mkldnn_cache_capacity(10)
            config.enable_mkldnn()

LDOUBLEV's avatar
LDOUBLEV committed
234
235
    # enable memory optim
    config.enable_memory_optim()
WenmuZhou's avatar
WenmuZhou committed
236
237
    config.disable_glog_info()

WenmuZhou's avatar
WenmuZhou committed
238
239
    config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
    config.switch_use_feed_fetch_ops(False)
WenmuZhou's avatar
WenmuZhou committed
240

WenmuZhou's avatar
WenmuZhou committed
241
242
    # create predictor
    predictor = inference.create_predictor(config)
WenmuZhou's avatar
WenmuZhou committed
243
244
    input_names = predictor.get_input_names()
    for name in input_names:
WenmuZhou's avatar
WenmuZhou committed
245
        input_tensor = predictor.get_input_handle(name)
WenmuZhou's avatar
WenmuZhou committed
246
247
248
    output_names = predictor.get_output_names()
    output_tensors = []
    for output_name in output_names:
WenmuZhou's avatar
WenmuZhou committed
249
        output_tensor = predictor.get_output_handle(output_name)
WenmuZhou's avatar
WenmuZhou committed
250
251
252
253
        output_tensors.append(output_tensor)
    return predictor, input_tensor, output_tensors


Jethong's avatar
Jethong committed
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
def draw_e2e_res(dt_boxes, strs, img_path):
    src_im = cv2.imread(img_path)
    for box, str in zip(dt_boxes, strs):
        box = box.astype(np.int32).reshape((-1, 1, 2))
        cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
        cv2.putText(
            src_im,
            str,
            org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
            fontFace=cv2.FONT_HERSHEY_COMPLEX,
            fontScale=0.7,
            color=(0, 255, 0),
            thickness=1)
    return src_im


LDOUBLEV's avatar
LDOUBLEV committed
270
def draw_text_det_res(dt_boxes, img_path):
LDOUBLEV's avatar
LDOUBLEV committed
271
272
273
274
    src_im = cv2.imread(img_path)
    for box in dt_boxes:
        box = np.array(box).astype(np.int32).reshape(-1, 2)
        cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
LDOUBLEV's avatar
LDOUBLEV committed
275
    return src_im
LDOUBLEV's avatar
LDOUBLEV committed
276
277


LDOUBLEV's avatar
LDOUBLEV committed
278
279
def resize_img(img, input_size=600):
    """
LDOUBLEV's avatar
LDOUBLEV committed
280
    resize img and limit the longest side of the image to input_size
LDOUBLEV's avatar
LDOUBLEV committed
281
282
283
284
285
    """
    img = np.array(img)
    im_shape = img.shape
    im_size_max = np.max(im_shape[0:2])
    im_scale = float(input_size) / float(im_size_max)
WenmuZhou's avatar
WenmuZhou committed
286
287
    img = cv2.resize(img, None, None, fx=im_scale, fy=im_scale)
    return img
LDOUBLEV's avatar
LDOUBLEV committed
288
289


WenmuZhou's avatar
WenmuZhou committed
290
291
292
293
294
def draw_ocr(image,
             boxes,
             txts=None,
             scores=None,
             drop_score=0.5,
LDOUBLEV's avatar
LDOUBLEV committed
295
             font_path="./doc/fonts/simfang.ttf"):
296
297
298
    """
    Visualize the results of OCR detection and recognition
    args:
LDOUBLEV's avatar
LDOUBLEV committed
299
        image(Image|array): RGB image
300
301
302
303
        boxes(list): boxes with shape(N, 4, 2)
        txts(list): the texts
        scores(list): txxs corresponding scores
        drop_score(float): only scores greater than drop_threshold will be visualized
WenmuZhou's avatar
WenmuZhou committed
304
        font_path: the path of font which is used to draw text
305
306
307
    return(array):
        the visualized img
    """
LDOUBLEV's avatar
LDOUBLEV committed
308
309
    if scores is None:
        scores = [1] * len(boxes)
WenmuZhou's avatar
WenmuZhou committed
310
311
312
313
    box_num = len(boxes)
    for i in range(box_num):
        if scores is not None and (scores[i] < drop_score or
                                   math.isnan(scores[i])):
LDOUBLEV's avatar
LDOUBLEV committed
314
            continue
WenmuZhou's avatar
WenmuZhou committed
315
        box = np.reshape(np.array(boxes[i]), [-1, 1, 2]).astype(np.int64)
LDOUBLEV's avatar
LDOUBLEV committed
316
        image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2)
WenmuZhou's avatar
WenmuZhou committed
317
    if txts is not None:
LDOUBLEV's avatar
LDOUBLEV committed
318
        img = np.array(resize_img(image, input_size=600))
319
        txt_img = text_visual(
WenmuZhou's avatar
WenmuZhou committed
320
321
322
323
324
325
            txts,
            scores,
            img_h=img.shape[0],
            img_w=600,
            threshold=drop_score,
            font_path=font_path)
326
        img = np.concatenate([np.array(img), np.array(txt_img)], axis=1)
LDOUBLEV's avatar
LDOUBLEV committed
327
328
        return img
    return image
329
330


WenmuZhou's avatar
WenmuZhou committed
331
332
333
334
335
336
def draw_ocr_box_txt(image,
                     boxes,
                     txts,
                     scores=None,
                     drop_score=0.5,
                     font_path="./doc/simfang.ttf"):
337
338
339
    h, w = image.height, image.width
    img_left = image.copy()
    img_right = Image.new('RGB', (w, h), (255, 255, 255))
340
341

    import random
LDOUBLEV's avatar
LDOUBLEV committed
342

343
344
345
    random.seed(0)
    draw_left = ImageDraw.Draw(img_left)
    draw_right = ImageDraw.Draw(img_right)
WenmuZhou's avatar
WenmuZhou committed
346
347
348
    for idx, (box, txt) in enumerate(zip(boxes, txts)):
        if scores is not None and scores[idx] < drop_score:
            continue
tink2123's avatar
tink2123 committed
349
350
        color = (random.randint(0, 255), random.randint(0, 255),
                 random.randint(0, 255))
351
        draw_left.polygon(box, fill=color)
tink2123's avatar
tink2123 committed
352
353
354
355
356
357
358
359
360
361
        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)
362
363
        if box_height > 2 * box_width:
            font_size = max(int(box_width * 0.9), 10)
WenmuZhou's avatar
WenmuZhou committed
364
            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
365
366
367
            cur_y = box[0][1]
            for c in txt:
                char_size = font.getsize(c)
tink2123's avatar
tink2123 committed
368
369
                draw_right.text(
                    (box[0][0] + 3, cur_y), c, fill=(0, 0, 0), font=font)
370
371
372
                cur_y += char_size[1]
        else:
            font_size = max(int(box_height * 0.8), 10)
WenmuZhou's avatar
WenmuZhou committed
373
            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
tink2123's avatar
tink2123 committed
374
375
            draw_right.text(
                [box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font)
376
377
378
379
    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))
380
381
382
    return np.array(img_show)


383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
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)


WenmuZhou's avatar
WenmuZhou committed
407
408
409
410
411
412
def text_visual(texts,
                scores,
                img_h=400,
                img_w=600,
                threshold=0.,
                font_path="./doc/simfang.ttf"):
413
414
415
416
417
418
419
    """
    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
WenmuZhou's avatar
WenmuZhou committed
420
        font_path: the path of font which is used to draw text
421
422
423
424
425
426
427
428
429
    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
LDOUBLEV's avatar
LDOUBLEV committed
430
431
        blank_img = Image.fromarray(blank_img).convert("RGB")
        draw_txt = ImageDraw.Draw(blank_img)
432
        return blank_img, draw_txt
LDOUBLEV's avatar
LDOUBLEV committed
433

434
435
436
437
    blank_img, draw_txt = create_blank_img()

    font_size = 20
    txt_color = (0, 0, 0)
WenmuZhou's avatar
WenmuZhou committed
438
    font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
439
440
441

    gap = font_size + 5
    txt_img_list = []
LDOUBLEV's avatar
LDOUBLEV committed
442
    count, index = 1, 0
443
444
    for idx, txt in enumerate(texts):
        index += 1
LDOUBLEV's avatar
LDOUBLEV committed
445
        if scores[idx] < threshold or math.isnan(scores[idx]):
446
447
448
449
450
451
452
453
454
455
456
            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
LDOUBLEV's avatar
LDOUBLEV committed
457
            draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
458
459
460
461
462
            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
LDOUBLEV's avatar
LDOUBLEV committed
463
            count += 1
464
465
466
        if first_line:
            new_txt = str(index) + ': ' + txt + '   ' + '%.3f' % (scores[idx])
        else:
LDOUBLEV's avatar
LDOUBLEV committed
467
            new_txt = "  " + txt + "  " + '%.3f' % (scores[idx])
LDOUBLEV's avatar
LDOUBLEV committed
468
        draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
469
        # whether add new blank img or not
LDOUBLEV's avatar
LDOUBLEV committed
470
        if count >= img_h // gap - 1 and idx + 1 < len(texts):
471
472
473
            txt_img_list.append(np.array(blank_img))
            blank_img, draw_txt = create_blank_img()
            count = 0
LDOUBLEV's avatar
LDOUBLEV committed
474
        count += 1
475
476
477
478
479
480
    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)
LDOUBLEV's avatar
LDOUBLEV committed
481
482


dyning's avatar
dyning committed
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
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


LDOUBLEV's avatar
LDOUBLEV committed
502
if __name__ == '__main__':
LDOUBLEV's avatar
LDOUBLEV committed
503
    pass