utility.py 23.5 KB
Newer Older
L
LDOUBLEV 已提交
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
W
WenmuZhou 已提交
16
import os
W
WenmuZhou 已提交
17
import sys
L
LDOUBLEV 已提交
18 19
import cv2
import numpy as np
L
LDOUBLEV 已提交
20 21
import json
from PIL import Image, ImageDraw, ImageFont
22
import math
W
WenmuZhou 已提交
23
from paddle import inference
L
LDOUBLEV 已提交
24 25
import time
from ppocr.utils.logging import get_logger
W
WenmuZhou 已提交
26

L
LDOUBLEV 已提交
27
logger = get_logger()
L
LDOUBLEV 已提交
28 29


30 31
def str2bool(v):
    return v.lower() in ("true", "t", "1")
L
LDOUBLEV 已提交
32 33


W
WenmuZhou 已提交
34
def init_args():
L
LDOUBLEV 已提交
35
    parser = argparse.ArgumentParser()
W
WenmuZhou 已提交
36
    # params for prediction engine
L
LDOUBLEV 已提交
37 38 39
    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)
L
LDOUBLEV 已提交
40
    parser.add_argument("--precision", type=str, default="fp32")
L
LDOUBLEV 已提交
41
    parser.add_argument("--gpu_mem", type=int, default=500)
L
LDOUBLEV 已提交
42

W
WenmuZhou 已提交
43
    # params for text detector
L
LDOUBLEV 已提交
44 45 46
    parser.add_argument("--image_dir", type=str)
    parser.add_argument("--det_algorithm", type=str, default='DB')
    parser.add_argument("--det_model_dir", type=str)
W
WenmuZhou 已提交
47 48
    parser.add_argument("--det_limit_side_len", type=float, default=960)
    parser.add_argument("--det_limit_type", type=str, default='max')
L
LDOUBLEV 已提交
49

W
WenmuZhou 已提交
50
    # DB parmas
L
LDOUBLEV 已提交
51 52
    parser.add_argument("--det_db_thresh", type=float, default=0.3)
    parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
W
WenmuZhou 已提交
53
    parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
L
LDOUBLEV 已提交
54
    parser.add_argument("--max_batch_size", type=int, default=10)
L
LDOUBLEV 已提交
55
    parser.add_argument("--use_dilation", type=bool, default=False)
littletomatodonkey's avatar
littletomatodonkey 已提交
56
    parser.add_argument("--det_db_score_mode", type=str, default="fast")
W
WenmuZhou 已提交
57
    # EAST parmas
L
LDOUBLEV 已提交
58 59 60 61
    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)

W
WenmuZhou 已提交
62
    # SAST parmas
L
licx 已提交
63 64
    parser.add_argument("--det_sast_score_thresh", type=float, default=0.5)
    parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2)
65
    parser.add_argument("--det_sast_polygon", type=bool, default=False)
L
licx 已提交
66

W
WenmuZhou 已提交
67
    # params for text recognizer
L
LDOUBLEV 已提交
68 69
    parser.add_argument("--rec_algorithm", type=str, default='CRNN')
    parser.add_argument("--rec_model_dir", type=str)
T
fix bug  
tink2123 已提交
70 71
    parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
    parser.add_argument("--rec_char_type", type=str, default='ch')
L
LDOUBLEV 已提交
72
    parser.add_argument("--rec_batch_num", type=int, default=6)
T
fix bug  
tink2123 已提交
73
    parser.add_argument("--max_text_length", type=int, default=25)
L
LDOUBLEV 已提交
74 75 76 77
    parser.add_argument(
        "--rec_char_dict_path",
        type=str,
        default="./ppocr/utils/ppocr_keys_v1.txt")
W
WenmuZhou 已提交
78 79
    parser.add_argument("--use_space_char", type=str2bool, default=True)
    parser.add_argument(
T
tink2123 已提交
80
        "--vis_font_path", type=str, default="./doc/fonts/simfang.ttf")
W
WenmuZhou 已提交
81
    parser.add_argument("--drop_score", type=float, default=0.5)
W
WenmuZhou 已提交
82

J
Jethong 已提交
83 84 85 86 87 88 89 90 91
    # 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(
J
Jethong 已提交
92
        "--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt")
J
Jethong 已提交
93
    parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
J
Jethong 已提交
94
    parser.add_argument("--e2e_pgnet_polygon", type=bool, default=True)
J
Jethong 已提交
95
    parser.add_argument("--e2e_pgnet_mode", type=str, default='fast')
J
Jethong 已提交
96

W
WenmuZhou 已提交
97 98 99 100 101
    # 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'])
L
LDOUBLEV 已提交
102
    parser.add_argument("--cls_batch_num", type=int, default=6)
W
WenmuZhou 已提交
103 104 105
    parser.add_argument("--cls_thresh", type=float, default=0.9)

    parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
L
LDOUBLEV 已提交
106
    parser.add_argument("--cpu_threads", type=int, default=10)
W
WenmuZhou 已提交
107
    parser.add_argument("--use_pdserving", type=str2bool, default=False)
L
LDOUBLEV 已提交
108
    parser.add_argument("--warmup", type=str2bool, default=True)
W
WenmuZhou 已提交
109

L
LDOUBLEV 已提交
110
    # multi-process
littletomatodonkey's avatar
littletomatodonkey 已提交
111
    parser.add_argument("--use_mp", type=str2bool, default=False)
112 113
    parser.add_argument("--total_process_num", type=int, default=1)
    parser.add_argument("--process_id", type=int, default=0)
W
WenmuZhou 已提交
114

L
LDOUBLEV 已提交
115 116
    parser.add_argument("--benchmark", type=bool, default=False)
    parser.add_argument("--save_log_path", type=str, default="./log_output/")
D
Double_V 已提交
117

W
WenmuZhou 已提交
118
    parser.add_argument("--show_log", type=str2bool, default=True)
W
WenmuZhou 已提交
119
    return parser
W
WenmuZhou 已提交
120

121

122
def parse_args():
W
WenmuZhou 已提交
123
    parser = init_args()
L
LDOUBLEV 已提交
124 125 126
    return parser.parse_args()


L
LDOUBLEV 已提交
127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196
class Times(object):
    def __init__(self):
        self.time = 0.
        self.st = 0.
        self.et = 0.

    def start(self):
        self.st = time.time()

    def end(self, accumulative=True):
        self.et = time.time()
        if accumulative:
            self.time += self.et - self.st
        else:
            self.time = self.et - self.st

    def reset(self):
        self.time = 0.
        self.st = 0.
        self.et = 0.

    def value(self):
        return round(self.time, 4)


class Timer(Times):
    def __init__(self):
        super(Timer, self).__init__()
        self.total_time = Times()
        self.preprocess_time = Times()
        self.inference_time = Times()
        self.postprocess_time = Times()
        self.img_num = 0

    def info(self, average=False):
        logger.info("----------------------- Perf info -----------------------")
        logger.info("total_time: {}, img_num: {}".format(self.total_time.value(
        ), self.img_num))
        preprocess_time = round(self.preprocess_time.value() / self.img_num,
                                4) if average else self.preprocess_time.value()
        postprocess_time = round(
            self.postprocess_time.value() / self.img_num,
            4) if average else self.postprocess_time.value()
        inference_time = round(self.inference_time.value() / self.img_num,
                               4) if average else self.inference_time.value()

        average_latency = self.total_time.value() / self.img_num
        logger.info("average_latency(ms): {:.2f}, QPS: {:2f}".format(
            average_latency * 1000, 1 / average_latency))
        logger.info(
            "preprocess_latency(ms): {:.2f}, inference_latency(ms): {:.2f}, postprocess_latency(ms): {:.2f}".
            format(preprocess_time * 1000, inference_time * 1000,
                   postprocess_time * 1000))

    def report(self, average=False):
        dic = {}
        dic['preprocess_time'] = round(
            self.preprocess_time.value() / self.img_num,
            4) if average else self.preprocess_time.value()
        dic['postprocess_time'] = round(
            self.postprocess_time.value() / self.img_num,
            4) if average else self.postprocess_time.value()
        dic['inference_time'] = round(
            self.inference_time.value() / self.img_num,
            4) if average else self.inference_time.value()
        dic['img_num'] = self.img_num
        dic['total_time'] = round(self.total_time.value(), 4)
        return dic


W
WenmuZhou 已提交
197 198 199 200 201
def create_predictor(args, mode, logger):
    if mode == "det":
        model_dir = args.det_model_dir
    elif mode == 'cls':
        model_dir = args.cls_model_dir
J
Jethong 已提交
202
    elif mode == 'rec':
W
WenmuZhou 已提交
203
        model_dir = args.rec_model_dir
W
WenmuZhou 已提交
204 205
    elif mode == 'structure':
        model_dir = args.structure_model_dir
J
Jethong 已提交
206 207
    else:
        model_dir = args.e2e_model_dir
W
WenmuZhou 已提交
208 209 210 211

    if model_dir is None:
        logger.info("not find {} model file path {}".format(mode, model_dir))
        sys.exit(0)
文幕地方's avatar
文幕地方 已提交
212 213
    model_file_path = model_dir + "/inference.pdmodel"
    params_file_path = model_dir + "/inference.pdiparams"
W
WenmuZhou 已提交
214 215 216 217 218 219 220
    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)

W
WenmuZhou 已提交
221
    config = inference.Config(model_file_path, params_file_path)
W
WenmuZhou 已提交
222

L
LDOUBLEV 已提交
223 224 225 226 227 228 229 230 231 232
    if hasattr(args, 'precision'):
        if args.precision == "fp16" and args.use_tensorrt:
            precision = inference.PrecisionType.Half
        elif args.precision == "int8":
            precision = inference.PrecisionType.Int8
        else:
            precision = inference.PrecisionType.Float32
    else:
        precision = inference.PrecisionType.Float32

W
WenmuZhou 已提交
233 234
    if args.use_gpu:
        config.enable_use_gpu(args.gpu_mem, 0)
L
LDOUBLEV 已提交
235 236
        if args.use_tensorrt:
            config.enable_tensorrt_engine(
L
LDOUBLEV 已提交
237 238
                precision_mode=inference.PrecisionType.Float32,
                max_batch_size=args.max_batch_size,
W
WenmuZhou 已提交
239
                min_subgraph_size=3)  # skip the minmum trt subgraph
L
LDOUBLEV 已提交
240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309
        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]}
L
LDOUBLEV 已提交
310 311 312 313
        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]}
L
LDOUBLEV 已提交
314 315 316
        config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
                                          opt_input_shape)

W
WenmuZhou 已提交
317 318
    else:
        config.disable_gpu()
L
LDOUBLEV 已提交
319 320 321
        if hasattr(args, "cpu_threads"):
            config.set_cpu_math_library_num_threads(args.cpu_threads)
        else:
W
WenmuZhou 已提交
322
            # default cpu threads as 10
L
LDOUBLEV 已提交
323
            config.set_cpu_math_library_num_threads(10)
W
WenmuZhou 已提交
324 325 326 327 328
        if args.enable_mkldnn:
            # cache 10 different shapes for mkldnn to avoid memory leak
            config.set_mkldnn_cache_capacity(10)
            config.enable_mkldnn()

L
LDOUBLEV 已提交
329 330
    # enable memory optim
    config.enable_memory_optim()
W
WenmuZhou 已提交
331 332
    config.disable_glog_info()

W
WenmuZhou 已提交
333
    config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
334 335
    if mode == 'structure':
        config.delete_pass("fc_fuse_pass") # not supported for table    
W
WenmuZhou 已提交
336
    config.switch_use_feed_fetch_ops(False)
W
WenmuZhou 已提交
337
    config.switch_ir_optim(True)
338

W
WenmuZhou 已提交
339 340
    # create predictor
    predictor = inference.create_predictor(config)
W
WenmuZhou 已提交
341 342
    input_names = predictor.get_input_names()
    for name in input_names:
W
WenmuZhou 已提交
343
        input_tensor = predictor.get_input_handle(name)
W
WenmuZhou 已提交
344 345 346
    output_names = predictor.get_output_names()
    output_tensors = []
    for output_name in output_names:
W
WenmuZhou 已提交
347
        output_tensor = predictor.get_output_handle(output_name)
W
WenmuZhou 已提交
348
        output_tensors.append(output_tensor)
L
LDOUBLEV 已提交
349
    return predictor, input_tensor, output_tensors, config
W
WenmuZhou 已提交
350 351


J
Jethong 已提交
352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367
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


L
LDOUBLEV 已提交
368
def draw_text_det_res(dt_boxes, img_path):
L
LDOUBLEV 已提交
369 370 371 372
    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)
L
LDOUBLEV 已提交
373
    return src_im
L
LDOUBLEV 已提交
374 375


L
LDOUBLEV 已提交
376 377
def resize_img(img, input_size=600):
    """
L
LDOUBLEV 已提交
378
    resize img and limit the longest side of the image to input_size
L
LDOUBLEV 已提交
379 380 381 382 383
    """
    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)
W
WenmuZhou 已提交
384 385
    img = cv2.resize(img, None, None, fx=im_scale, fy=im_scale)
    return img
L
LDOUBLEV 已提交
386 387


W
WenmuZhou 已提交
388 389 390 391 392
def draw_ocr(image,
             boxes,
             txts=None,
             scores=None,
             drop_score=0.5,
L
LDOUBLEV 已提交
393
             font_path="./doc/fonts/simfang.ttf"):
394 395 396
    """
    Visualize the results of OCR detection and recognition
    args:
L
LDOUBLEV 已提交
397
        image(Image|array): RGB image
398 399 400 401
        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
W
WenmuZhou 已提交
402
        font_path: the path of font which is used to draw text
403 404 405
    return(array):
        the visualized img
    """
L
LDOUBLEV 已提交
406 407
    if scores is None:
        scores = [1] * len(boxes)
W
WenmuZhou 已提交
408 409 410 411
    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])):
L
LDOUBLEV 已提交
412
            continue
W
WenmuZhou 已提交
413
        box = np.reshape(np.array(boxes[i]), [-1, 1, 2]).astype(np.int64)
L
LDOUBLEV 已提交
414
        image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2)
W
WenmuZhou 已提交
415
    if txts is not None:
L
LDOUBLEV 已提交
416
        img = np.array(resize_img(image, input_size=600))
417
        txt_img = text_visual(
W
WenmuZhou 已提交
418 419 420 421 422 423
            txts,
            scores,
            img_h=img.shape[0],
            img_w=600,
            threshold=drop_score,
            font_path=font_path)
424
        img = np.concatenate([np.array(img), np.array(txt_img)], axis=1)
L
LDOUBLEV 已提交
425 426
        return img
    return image
427 428


W
WenmuZhou 已提交
429 430 431 432 433 434
def draw_ocr_box_txt(image,
                     boxes,
                     txts,
                     scores=None,
                     drop_score=0.5,
                     font_path="./doc/simfang.ttf"):
435 436 437
    h, w = image.height, image.width
    img_left = image.copy()
    img_right = Image.new('RGB', (w, h), (255, 255, 255))
438 439

    import random
L
LDOUBLEV 已提交
440

441 442 443
    random.seed(0)
    draw_left = ImageDraw.Draw(img_left)
    draw_right = ImageDraw.Draw(img_right)
W
WenmuZhou 已提交
444 445 446
    for idx, (box, txt) in enumerate(zip(boxes, txts)):
        if scores is not None and scores[idx] < drop_score:
            continue
T
tink2123 已提交
447 448
        color = (random.randint(0, 255), random.randint(0, 255),
                 random.randint(0, 255))
449
        draw_left.polygon(box, fill=color)
T
tink2123 已提交
450 451 452 453 454 455 456 457 458 459
        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)
460 461
        if box_height > 2 * box_width:
            font_size = max(int(box_width * 0.9), 10)
W
WenmuZhou 已提交
462
            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
463 464 465
            cur_y = box[0][1]
            for c in txt:
                char_size = font.getsize(c)
T
tink2123 已提交
466 467
                draw_right.text(
                    (box[0][0] + 3, cur_y), c, fill=(0, 0, 0), font=font)
468 469 470
                cur_y += char_size[1]
        else:
            font_size = max(int(box_height * 0.8), 10)
W
WenmuZhou 已提交
471
            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
T
tink2123 已提交
472 473
            draw_right.text(
                [box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font)
474 475 476 477
    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))
478 479 480
    return np.array(img_show)


481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504
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)


W
WenmuZhou 已提交
505 506 507 508 509 510
def text_visual(texts,
                scores,
                img_h=400,
                img_w=600,
                threshold=0.,
                font_path="./doc/simfang.ttf"):
511 512 513 514 515 516 517
    """
    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
W
WenmuZhou 已提交
518
        font_path: the path of font which is used to draw text
519 520 521 522 523 524 525 526 527
    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
L
LDOUBLEV 已提交
528 529
        blank_img = Image.fromarray(blank_img).convert("RGB")
        draw_txt = ImageDraw.Draw(blank_img)
530
        return blank_img, draw_txt
L
LDOUBLEV 已提交
531

532 533 534 535
    blank_img, draw_txt = create_blank_img()

    font_size = 20
    txt_color = (0, 0, 0)
W
WenmuZhou 已提交
536
    font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
537 538 539

    gap = font_size + 5
    txt_img_list = []
L
LDOUBLEV 已提交
540
    count, index = 1, 0
541 542
    for idx, txt in enumerate(texts):
        index += 1
L
LDOUBLEV 已提交
543
        if scores[idx] < threshold or math.isnan(scores[idx]):
544 545 546 547 548 549 550 551 552 553 554
            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
L
LDOUBLEV 已提交
555
            draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
556 557 558 559 560
            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
L
LDOUBLEV 已提交
561
            count += 1
562 563 564
        if first_line:
            new_txt = str(index) + ': ' + txt + '   ' + '%.3f' % (scores[idx])
        else:
L
LDOUBLEV 已提交
565
            new_txt = "  " + txt + "  " + '%.3f' % (scores[idx])
L
LDOUBLEV 已提交
566
        draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
567
        # whether add new blank img or not
L
LDOUBLEV 已提交
568
        if count >= img_h // gap - 1 and idx + 1 < len(texts):
569 570 571
            txt_img_list.append(np.array(blank_img))
            blank_img, draw_txt = create_blank_img()
            count = 0
L
LDOUBLEV 已提交
572
        count += 1
573 574 575 576 577 578
    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)
L
LDOUBLEV 已提交
579 580


D
dyning 已提交
581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599
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


L
LDOUBLEV 已提交
600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
def get_current_memory_mb(gpu_id=None):
    """
    It is used to Obtain the memory usage of the CPU and GPU during the running of the program.
    And this function Current program is time-consuming.
    """
    import pynvml
    import psutil
    import GPUtil
    pid = os.getpid()
    p = psutil.Process(pid)
    info = p.memory_full_info()
    cpu_mem = info.uss / 1024. / 1024.
    gpu_mem = 0
    gpu_percent = 0
    if gpu_id is not None:
        GPUs = GPUtil.getGPUs()
        gpu_load = GPUs[gpu_id].load
        gpu_percent = gpu_load
        pynvml.nvmlInit()
        handle = pynvml.nvmlDeviceGetHandleByIndex(0)
        meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
        gpu_mem = meminfo.used / 1024. / 1024.
    return round(cpu_mem, 4), round(gpu_mem, 4), round(gpu_percent, 4)


L
LDOUBLEV 已提交
625
if __name__ == '__main__':
L
LDOUBLEV 已提交
626
    pass