utility.py 23.4 KB
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# 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
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import os
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import sys
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import cv2
import numpy as np
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import json
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
from ppocr.utils.logging import get_logger
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logger = get_logger()
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def str2bool(v):
    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)
    parser.add_argument("--ir_optim", type=str2bool, default=True)
    parser.add_argument("--use_tensorrt", type=str2bool, default=False)
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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)
    parser.add_argument("--det_algorithm", type=str, default='DB')
    parser.add_argument("--det_model_dir", type=str)
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    parser.add_argument("--det_limit_side_len", type=float, default=960)
    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)
    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=1.6)
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    parser.add_argument("--max_batch_size", type=int, default=10)
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    parser.add_argument("--use_dilation", type=bool, 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)
    parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
    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)
    parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2)
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    parser.add_argument("--det_sast_polygon", type=bool, default=False)
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    # params for text recognizer
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    parser.add_argument("--rec_algorithm", type=str, default='CRNN')
    parser.add_argument("--rec_model_dir", type=str)
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    parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
    parser.add_argument("--rec_char_type", type=str, default='ch')
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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(
        "--rec_char_dict_path",
        type=str,
        default="./ppocr/utils/ppocr_keys_v1.txt")
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    parser.add_argument("--use_space_char", type=str2bool, default=True)
    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
    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(
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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_polygon", type=bool, default=True)
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    parser.add_argument("--e2e_pgnet_mode", type=str, default='fast')
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    # 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'])
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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)

    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=True)
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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)
    parser.add_argument("--process_id", type=int, default=0)
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    parser.add_argument("--benchmark", type=bool, default=False)
    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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    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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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


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def create_predictor(args, mode, logger):
    if mode == "det":
        model_dir = args.det_model_dir
    elif mode == 'cls':
        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 == 'structure':
        model_dir = args.structure_model_dir
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    else:
        model_dir = args.e2e_model_dir
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    if model_dir is None:
        logger.info("not find {} model file path {}".format(mode, model_dir))
        sys.exit(0)
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    model_file_path = model_dir + "/inference.pdmodel"
    params_file_path = model_dir + "/inference.pdiparams"
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    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)

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    config = inference.Config(model_file_path, params_file_path)
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    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

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    if args.use_gpu:
        config.enable_use_gpu(args.gpu_mem, 0)
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        if args.use_tensorrt:
            config.enable_tensorrt_engine(
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                precision_mode=inference.PrecisionType.Float32,
                max_batch_size=args.max_batch_size,
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                min_subgraph_size=3)  # skip the minmum trt subgraph
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        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]}
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        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]}
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        config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
                                          opt_input_shape)

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    else:
        config.disable_gpu()
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        if hasattr(args, "cpu_threads"):
            config.set_cpu_math_library_num_threads(args.cpu_threads)
        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:
            # cache 10 different shapes for mkldnn to avoid memory leak
            config.set_mkldnn_cache_capacity(10)
            config.enable_mkldnn()

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    # enable memory optim
    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")
    config.switch_use_feed_fetch_ops(False)
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    config.switch_ir_optim(True)
    if mode == 'structure':
        config.switch_ir_optim(False)
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    # create predictor
    predictor = inference.create_predictor(config)
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    input_names = predictor.get_input_names()
    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()
    output_tensors = []
    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 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


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def draw_text_det_res(dt_boxes, img_path):
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    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)
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    return src_im
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def resize_img(img, input_size=600):
    """
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    resize img and limit the longest side of the image to input_size
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    """
    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)
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    img = cv2.resize(img, None, None, fx=im_scale, fy=im_scale)
    return img
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def draw_ocr(image,
             boxes,
             txts=None,
             scores=None,
             drop_score=0.5,
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             font_path="./doc/fonts/simfang.ttf"):
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    """
    Visualize the results of OCR detection and recognition
    args:
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        image(Image|array): RGB image
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        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
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        font_path: the path of font which is used to draw text
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    return(array):
        the visualized img
    """
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    if scores is None:
        scores = [1] * len(boxes)
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    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])):
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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,
            scores,
            img_h=img.shape[0],
            img_w=600,
            threshold=drop_score,
            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
    return image
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def draw_ocr_box_txt(image,
                     boxes,
                     txts,
                     scores=None,
                     drop_score=0.5,
                     font_path="./doc/simfang.ttf"):
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    h, w = image.height, image.width
    img_left = image.copy()
    img_right = Image.new('RGB', (w, h), (255, 255, 255))
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    import random
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    random.seed(0)
    draw_left = ImageDraw.Draw(img_left)
    draw_right = ImageDraw.Draw(img_right)
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    for idx, (box, txt) in enumerate(zip(boxes, txts)):
        if scores is not None and scores[idx] < drop_score:
            continue
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        color = (random.randint(0, 255), random.randint(0, 255),
                 random.randint(0, 255))
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        draw_left.polygon(box, fill=color)
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        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)
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        if box_height > 2 * box_width:
            font_size = max(int(box_width * 0.9), 10)
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            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
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            cur_y = box[0][1]
            for c in txt:
                char_size = font.getsize(c)
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                draw_right.text(
                    (box[0][0] + 3, cur_y), c, fill=(0, 0, 0), font=font)
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                cur_y += char_size[1]
        else:
            font_size = max(int(box_height * 0.8), 10)
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            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
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            draw_right.text(
                [box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font)
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    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))
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    return np.array(img_show)


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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)


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def text_visual(texts,
                scores,
                img_h=400,
                img_w=600,
                threshold=0.,
                font_path="./doc/simfang.ttf"):
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    """
    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
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        font_path: the path of font which is used to draw text
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    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
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        blank_img = Image.fromarray(blank_img).convert("RGB")
        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()

    font_size = 20
    txt_color = (0, 0, 0)
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    font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
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    gap = font_size + 5
    txt_img_list = []
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    count, index = 1, 0
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    for idx, txt in enumerate(texts):
        index += 1
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        if scores[idx] < threshold or math.isnan(scores[idx]):
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            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
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            draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
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            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
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            count += 1
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        if first_line:
            new_txt = str(index) + ': ' + txt + '   ' + '%.3f' % (scores[idx])
        else:
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            new_txt = "  " + txt + "  " + '%.3f' % (scores[idx])
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        draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
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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))
            blank_img, draw_txt = create_blank_img()
            count = 0
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        count += 1
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    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)
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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


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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)


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if __name__ == '__main__':
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    pass