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原帖标题为：如何使用 TensorFlow Object Detection API 训练和使用目标检测模型
原帖网址为：~~https://verimake.com/topics/125~~ （旧版论坛网址，已失效）

原帖作者为：karb0n（旧版论坛 id = 39，注册于 2020-05-04 19:27:42）
原帖由作者初次发表于 2020-08-01 03:18:16，最后编辑于 2020-08-01 03:18:16（编辑时间可能不准确）

截至 2021-12-18 14:27:30 备份数据库时，原帖已获得 2212 次浏览、0 个点赞、0 条回复

引言

本文以使用 TT100K 数据集训练一个交通标志目标检测器为例，对如何使用 TensorFlow Object Detection API 训练模型和进行目标检测做了一些简单介绍。当然，你也可以自己拍摄或者搜寻其它图片，使用 LabelImg 对图片进行标注，然后仿照本文教程来训练一个能够检测其它物体的目标检测器。

本文分两个部分，第一部分是训练，第二部分是预测。如果你使用的设备的 GPU 显存不大 (例如 < 6 GiB)，或者甚至直接使用 CPU 版本的 TensorFlow，可能无法正常进行训练，但是你可以上网搜寻并下载前人们预训练好的模型文件来进行预测，用于预测的代码可以直接参考第 2.2 节。

目录

训练
1.1 下载 TT100K 数据集
1.2 将 JSON 格式的标注文件转为 csv 文件
1.3 生成 TFRecord 文件
1.4 生成 pbtxt 格式的类别命名文件
1.5 下载预训练模型准备进行迁移学习
1.6 开始训练
1.7 完成训练
检测
2.1 将训练产生的 .ckpt 文件转换为 .pb 文件
2.2 开始检测

1. 训练

1.1 下载 TT100K 数据集

TT100K 数据集是清华-腾讯联合实验室发布的中国交通标志目标检测数据集，打开 TT100K 数据集的网站，点击“Download the Datasets”，下载数据集 Part1（其它部分无需下载，约 17.8 GiB）即可。
下载完成后解压，可以看到这些文件和文件夹

其中，marks 是 TT100K 包含的交通标志类别的示例图片；train 和 test 分别是被拆分出的训练集和测试集；others 是其它一些图片，可以用于训练，也可以用于测试，但是其中的图片不一定每张都包含交通标志；annotations.json 是对数据集的标注文件，里边描述了每张图片里边哪些位置是哪种交通标志，比如下图所示的记录就表示，在图片 train/65575.jpg 中，x∈[1432, 1526], y∈[886, 990] 的矩形范围被标注为 pl20 标签，即这里有个限速20的标志。

1.2 将 JSON 格式的标注文件转为 csv 文件

为便于后续处理，我们编写代码，将 annotations.json 进行处理，将标签转为 train_labels.csv 和 test_labels.csv 两个 csv 文件进行储存。

JSON2CSV.py 的代码

# -*- coding: UTF-8 -*- 
import os
import numpy as np
import json
import csv

splits = ['train', 'test']

for split in splits:
    tt100k_root = 'E:/objDetect/tt100k'  # 这里填你的 TT100K 数据集的根目录

    # 读取 json 文件
    jsonFilePath = os.path.join(tt100k_root, 'data', 'annotations.json')  
    with open(jsonFilePath, 'r', encoding='utf-8') as f:
        labels = json.load(f)
    
    # 读取 ids 文件
    idsFilePath = os.path.join(tt100k_root, 'data', split, 'ids.txt')
    with open(idsFilePath, 'r', encoding='utf-8') as f:
        ids = f.readlines()
        for i in range(len(ids)):
            ids[i] = ids[i].replace('\n','')
    
    # 将 json 中的标签转换格式，并存于变量 annos 中
    annos = ["filename,width,height,class,xmin,ymin,xmax,ymax"]
    for i in ids:
        for obj in labels['imgs'][i]['objects']:
            anno = "%s.jpg,%d,%d,%s,%f,%f,%f,%f"%(i, 2048, 2048,
                                                  obj['category'],
                                                  obj['bbox']['xmin'],
                                                  obj['bbox']['ymin'],
                                                  obj['bbox']['xmax'],
                                                  obj['bbox']['ymax'])
            annos.append(anno)
    
    # 把转换格式后的标签写入 csv 文件
    with open('./data/' + split + '_labels.csv','w') as f:
        for anno in annos:
            f.write(anno + '\n')

运行完成后，会发现 ./data/ 目录中生成了两个 csv 文件。比如打开 train_labels.csv 文件，我们能看到类似下图的表格，这就是转换格式后的数据集标签。

1.3 生成 TFRecord 文件

TFRecord 是 TensorFlow 提供的一种统一的数据存储格式，相比训练时直接一张一张读取图片文件，使用 TFRecord 进行读取会更加高效。TF OD API 被设计为需要使用 TFRecord 格式读入数据才能够进行训练，所以我们将数据集转换成 TFRecord 格式的文件。

ConvertCSV2TFRecord.py 的代码

# -*- coding: utf-8 -*-

import os
import io
import pandas as pd
import tensorflow as tf
from PIL import Image
from tqdm import *

os.chdir('E:/objDetect/TensorFlow_OD/models-master/research/')  # 填 TF OD API 上一级的 research 目录的地址
from collections import namedtuple, OrderedDict
from object_detection.utils import dataset_util

# 所有类别的名字
names = ["i1", "i10", "i11", "i12", "i13", "i14", "i15", "i2", "i3", "i4", "i5",
         "il100", "il110", "il50", "il60", "il70", "il80", "il90", "io", "ip", "p1", "p10",
         "p11", "p12", "p13", "p14", "p15", "p16", "p17", "p18", "p19", "p2", "p20", "p21",
         "p22", "p23", "p24", "p25", "p26", "p27", "p28", "p3", "p4", "p5", "p6", "p7", "p8",
         "p9", "pa10", "pa12", "pa13", "pa14", "pa8", "pb", "pc", "pg", "ph1.5", "ph2",
         "ph2.1", "ph2.2", "ph2.4", "ph2.5", "ph2.8", "ph2.9", "ph3", "ph3.2", "ph3.5",
         "ph3.8", "ph4", "ph4.2", "ph4.3", "ph4.5", "ph4.8", "ph5", "ph5.3", "ph5.5",
         "pl10", "pl100", "pl110", "pl120", "pl15", "pl20", "pl25", "pl30", "pl35", "pl40",
         "pl5", "pl50", "pl60", "pl65", "pl70", "pl80", "pl90", "pm10", "pm13", "pm15",
         "pm1.5", "pm2", "pm20", "pm25", "pm30", "pm35", "pm40", "pm46", "pm5", "pm50",
         "pm55", "pm8", "pn", "pne", "po", "pr10", "pr100", "pr20", "pr30", "pr40",
         "pr45", "pr50", "pr60", "pr70", "pr80", "ps", "pw2", "pw2.5", "pw3", "pw3.2",
         "pw3.5", "pw4", "pw4.2", "pw4.5", "w1", "w10", "w12", "w13", "w16", "w18",
         "w20", "w21", "w22", "w24", "w28", "w3", "w30", "w31", "w32", "w34", "w35",
         "w37", "w38", "w41", "w42", "w43", "w44", "w45", "w46", "w47", "w48", "w49",
         "w5", "w50", "w55", "w56", "w57", "w58", "w59", "w60", "w62", "w63", "w66",
         "w8", "wo", "i6", "i7", "i8", "i9", "ilx", "p29", "w29", "w33", "w36", "w39",
         "w4", "w40", "w51", "w52", "w53", "w54", "w6", "w61", "w64", "w65", "w67",
         "w7", "w9", "pax", "pd", "pe", "phx", "plx", "pmx", "pnl", "prx", "pwx",
         "w11", "w14", "w15", "w17", "w19", "w2", "w23", "w25", "w26", "w27", "pl0",
         "pl4", "pl3", "pm2.5", "ph4.4", "pn40", "ph3.3", "ph2.6"]


def class_text_to_int(row_label):
    return names.index(row_label) + 1


def split(df, group):
    data = namedtuple('data', ['filename', 'object'])
    gb = df.groupby(group)
    return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]


def create_tf_example(group, path):
    with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
        encoded_jpg = fid.read()
    encoded_jpg_io = io.BytesIO(encoded_jpg)
    image = Image.open(encoded_jpg_io)
    width, height = image.size

    filename = group.filename.encode('utf8')
    image_format = b'jpg'
    xmins = []
    xmaxs = []
    ymins = []
    ymaxs = []
    classes_text = []
    classes = []

    for index, row in group.object.iterrows():
        xmins.append(row['xmin'] / width)
        xmaxs.append(row['xmax'] / width)
        ymins.append(row['ymin'] / height)
        ymaxs.append(row['ymax'] / height)
        classes_text.append(row['class'].encode('utf8'))
        classes.append(class_text_to_int(row['class']))

    tf_example = tf.train.Example(features=tf.train.Features(feature={
        'image/height': dataset_util.int64_feature(height),
        'image/width': dataset_util.int64_feature(width),
        'image/filename': dataset_util.bytes_feature(filename),
        'image/source_id': dataset_util.bytes_feature(filename),
        'image/encoded': dataset_util.bytes_feature(encoded_jpg),
        'image/format': dataset_util.bytes_feature(image_format),
        'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
        'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
        'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
        'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
        'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
        'image/object/class/label': dataset_util.int64_list_feature(classes),
    }))
    return tf_example


def convertTFR(img_path, csv_input, output_path):
    writer = tf.python_io.TFRecordWriter(output_path)
    examples = pd.read_csv(csv_input)
    grouped = split(examples, 'filename')
    for group in tqdm(grouped):
        tf_example = create_tf_example(group, img_path)
        writer.write(tf_example.SerializeToString())

    writer.close()
    print('Successfully created the TFRecords: {}'.format(output_path))


if __name__ == '__main__':
    
    # 转换训练集为TFRecord格式，这里要根据你的图片和csv文件的实际路径来更改
    img_path = 'E:/objDetect/tt100k/data/train/'
    csv_input = 'E:/objDetect/tt100k/data/train_labels.csv'
    output_path = 'E:/objDetect/tt100k/data/TFRecords/train.tfrecord' 
    convertTFR(img_path, csv_input, output_path)
    
    # 转换测试集为TFRecord格式，这里要根据你的图片和csv文件的实际路径来更改
    img_path = 'E:/objDetect/tt100k/data/test/'
    csv_input = 'E:/objDetect/tt100k/data/test_labels.csv'
    output_path = 'E:/objDetect/tt100k/data/TFRecords/test.tfrecord' 
    convertTFR(img_path, csv_input, output_path)

运行此代码，等待一段时间后，即转换完成，可找到下图的两个文件。

1.4 生成 pbtxt 格式的类别命名文件

TF OD API 可通过 pbtxt 格式的文件来读取各个类别对应的名称，我们可以直接使用下边的代码来生成适用于 TT100K 数据集的 pbtxt 文件。

GenerateLabelmap.py 的代码

names = ["i1", "i10", "i11", "i12", "i13",
         "i14", "i15", "i2", "i3", "i4",
         "i5", "il100", "il110", "il50", "il60",
         "il70", "il80", "il90", "io", "ip",
         "p1", "p10", "p11", "p12", "p13",
         "p14", "p15", "p16", "p17", "p18",
         "p19", "p2", "p20", "p21", "p22",
         "p23", "p24", "p25", "p26", "p27",
         "p28", "p3", "p4", "p5", "p6",
         "p7", "p8", "p9", "pa10", "pa12",
         "pa13", "pa14", "pa8", "pb", "pc",
         "pg", "ph1.5", "ph2", "ph2.1", "ph2.2",
         "ph2.4", "ph2.5", "ph2.8", "ph2.9", "ph3",
         "ph3.2", "ph3.5", "ph3.8", "ph4", "ph4.2",
         "ph4.3", "ph4.5", "ph4.8", "ph5", "ph5.3",
         "ph5.5", "pl10", "pl100", "pl110", "pl120",
         "pl15", "pl20", "pl25", "pl30", "pl35",
         "pl40", "pl5", "pl50", "pl60", "pl65",
         "pl70", "pl80", "pl90", "pm10", "pm13",
         "pm15", "pm1.5", "pm2", "pm20", "pm25",
         "pm30", "pm35", "pm40", "pm46", "pm5",
         "pm50", "pm55", "pm8", "pn", "pne",
         "po", "pr10", "pr100", "pr20", "pr30",
         "pr40", "pr45", "pr50", "pr60", "pr70",
         "pr80", "ps", "pw2", "pw2.5", "pw3",
         "pw3.2", "pw3.5", "pw4", "pw4.2", "pw4.5",
         "w1", "w10", "w12", "w13", "w16",
         "w18", "w20", "w21", "w22", "w24",
         "w28", "w3", "w30", "w31", "w32",
         "w34", "w35", "w37", "w38", "w41",
         "w42", "w43", "w44", "w45", "w46",
         "w47", "w48", "w49", "w5", "w50",
         "w55", "w56", "w57", "w58", "w59",
         "w60", "w62", "w63", "w66", "w8",
         "wo", "i6", "i7", "i8", "i9",
         "ilx", "p29", "w29", "w33", "w36",
         "w39", "w4", "w40", "w51", "w52",
         "w53", "w54", "w6", "w61", "w64",
         "w65", "w67", "w7", "w9", "pax",
         "pd", "pe", "phx", "plx", "pmx",
         "pnl", "prx", "pwx", "w11", "w14",
         "w15", "w17", "w19", "w2", "w23",
         "w25", "w26", "w27", "pl0", "pl4",
         "pl3", "pm2.5", "ph4.4", "pn40", "ph3.3",
         "ph2.6"]


with open('./tt100k_label_map.pbtxt', 'w') as f:
    for i in range(len(names)):
        temp = "item{\n    id:%d\n    name:\'%s\'\n}\n\n" % (i+1, names[i])
        f.write(temp)

''' LABEL MAP EXAMPLE:

item{
    id:1
    name:'i1'
}

item{
    id:2
    name:'i10'
}

………………

'''

1.5 下载预训练模型准备进行迁移学习

适用于 TF OD API 格式的数据集已经准备完成了，我们可以准备开始训练了。但是呢，从随机初始化开始训练一个现代的目标检测网络，耗时是非常长的。好在，我们能够下载一些使用其它数据集预训练过的模型来进行迁移学习，这样会大大缩短训练所需的时间。
打开 TensorFlow 1 Detection Model Zoo，根据表格中展示的速度和准确度，选择一个你想使用的模型下载。
本文以 faster_rcnn_resnet101_coco 为例，就是使用 MS COCO 数据集预训练过的 Faster R-CNN with ResNet101 模型。将下载的 faster_rcnn_resnet101_coco_2018_01_28.tar.gz 压缩包解压后，可以得到下图所示的这些文件

我们把 pipeline.config 复制一份，并重命名（例如 faster_rcnn_resnet101_TSR.config），然后打开重命名后的文件，参照文件中的提示，根据我们数据集的类别数量、TFRecord 和 pbtxt 文件的路径，修改这个文件中的部分内容。
例如，一份示例的 faster_rcnn_resnet101_TSR.config 内容如下：

faster_rcnn_resnet101_TSR.config 文件内容，其中需要更改的地方被 XXX 标记出

# Faster R-CNN with Resnet-101 (v1), configuration for TT100K Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.

model {
  faster_rcnn {
    num_classes: 221   # XXX: configure to fit your dataset
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 600
        max_dimension: 1024
      }
    }
    feature_extractor {
      type: 'faster_rcnn_resnet101'
      first_stage_features_stride: 16
    }
    first_stage_anchor_generator {
      grid_anchor_generator {
        scales: [0.25, 0.5, 1.0, 2.0]
        aspect_ratios: [0.5, 1.0, 2.0]
        height_stride: 16
        width_stride: 16
      }
    }
    first_stage_box_predictor_conv_hyperparams {
      op: CONV
      regularizer {
        l2_regularizer {
          weight: 0.0
        }
      }
      initializer {
        truncated_normal_initializer {
          stddev: 0.01
        }
      }
    }
    first_stage_nms_score_threshold: 0.0
    first_stage_nms_iou_threshold: 0.7
    first_stage_max_proposals: 300
    first_stage_localization_loss_weight: 2.0
    first_stage_objectness_loss_weight: 1.0
    initial_crop_size: 14
    maxpool_kernel_size: 2
    maxpool_stride: 2
    second_stage_box_predictor {
      mask_rcnn_box_predictor {
        use_dropout: false
        dropout_keep_probability: 1.0
        fc_hyperparams {
          op: FC
          regularizer {
            l2_regularizer {
              weight: 0.0
            }
          }
          initializer {
            variance_scaling_initializer {
              factor: 1.0
              uniform: true
              mode: FAN_AVG
            }
          }
        }
      }
    }
    second_stage_post_processing {
      batch_non_max_suppression {
        score_threshold: 0.0
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 300
      }
      score_converter: SOFTMAX
    }
    second_stage_localization_loss_weight: 2.0
    second_stage_classification_loss_weight: 1.0
  }
}

train_config: {
  batch_size: 2  # XXX: configure to fit your GPU, if you have a more powerful GPU, you can try a bigger size
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        manual_step_learning_rate {
          initial_learning_rate: 0.0003
          schedule {
            step: 30000
            learning_rate: .00003
          }
          schedule {
            step: 40000
            learning_rate: .000003
          }
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  gradient_clipping_by_norm: 10.0
  fine_tune_checkpoint: "E:/objDetect/tt100k/pretrained-models/faster_rcnn_resnet101_coco_2018_01_28/model.ckpt"  # XXX: configure to fit your dataset
  from_detection_checkpoint: true
  num_steps: 50000
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "E:/objDetect/tt100k/data/TFRecords/train.tfrecord"  # XXX: configure to fit your dataset
  }
  label_map_path: "E:/objDetect/tt100k/data/TFRecords/tt100k_label_map.pbtxt"  # XXX: configure to fit your dataset
}

eval_config: {
  num_examples: 3071  # XXX: configure to fit your dataset
  batch_size: 1  # XXX: configure to fit your GPU, if you have a more powerful GPU, you can try a bigger size
  num_visualizations: 50
  min_score_threshold: 0.3

  # Note: The below line limits the evaluation process to 10 evaluations.
  # Remove the below line to evaluate indefinitely.
  max_evals: 10
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "E:/objDetect/tt100k/data/TFRecords/test.tfrecord"  # XXX: configure to fit your dataset
  }
  label_map_path: "E:/objDetect/tt100k/data/TFRecords/tt100k_label_map.pbtxt"  # XXX: configure to fit your dataset
  shuffle: false
  num_readers: 1
}

1.6 开始训练

终于把需要用到的文件都准备好了:monkey: ，我们在命令行里进入 research\object_detection\legacy 目录，输入

python train.py --logtostderr --train_dir=E:/objDetect/tt100k/training/ --pipeline_config_path=E:/objDetect/tt100k/faster_rcnn_resnet101_TSR.config

就可以开始训练了。（注意：这行指令中的路径都是需要根据你的实际情况来修改的）
在 GTX 1060 GPU 下需要训练接近 11 个小时，所以在运行前请做好这台电脑十几个小时不能另外使用的准备。
train_dir 指定的路径不仅会储存一些训练过程中的 checkpoint，还包含了能够使用 TensorBoard 观察训练过程的文件，所以，我们另开一个命令行，输入

tensorboard --logdir E:/objDetect/tt100k/training/

即可打开 TensorBoard 来观察我们的训练过程。下图是损失函数的变化图表的一个示例。

1.7 完成训练

出去玩了一圈或者睡一觉醒来之后，~~可能还没训练好~~ 终于训练好了，我们在 research\object_detection\legacy 目录下，输入

python eval.py --logtostderr --eval_dir=E:/objDetect/tt100k/evaluating/ --pipeline_config_path=E:/objDetect/tt100k/faster_rcnn_resnet101_TSR.config --checkpoint_dir=E:/objDetect/tt100k/training/

就可以使用 TF OD API 自带的功能来进行性能评估，并生成对随机几张示例图片的预测结果。（注意：这行指令中的路径同样是需要根据你的实际情况来修改的）
也是使用 TensorBoard 来查阅评估结果，另开一个命令行，输入

tensorboard --logdir E:/objDetect/tt100k/evaluating/

即可打开 TensorBoard 来查阅训练好的模型的评估结果。下图是一个示例

因为 TT100K 数据集里数据分布不太均匀，所以直接进行训练产生的模型准确度可能比较一般。如果评估发现模型对交通标志是有一定的检测能力的，就说明训练是有效的。

2. 检测

2.1 将训练产生的 .ckpt 文件转换为 .pb 文件

.ckpt 文件是训练过程中记录的 checkpoint 文件，我们需要将其转换为 .pb 文件才能在后续进行实际的预测或者部署。
在命令行里进入 research\object_detection 目录，输入

python export_inference_graph.py --input_type image_tensor --pipeline_config_path E:/objDetect/tt100k/faster_rcnn_resnet101_TSR.config --trained_checkpoint_prefix E:/objDetect/tt100k/training/model.ckpt-50000 --output_directory E:/objDetect/tt100k/TSRmodels

等待运行完成后，即转换完成。（注意：这行指令中的路径都是需要根据你的实际情况来修改的）

2.2 开始检测

参考下列代码，修改其中的一些文件地址，运行，即可对一张图片进行交通标志目标检测。也可以使用 cv2.VideoCapture 来进行对视频流（视频文件或者摄像头均可）的检测。

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import os
import sys
import cv2
import numpy as np
import tensorflow as tf
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
from PIL import Image
import matplotlib
import matplotlib.pyplot as plt
matplotlib.use('Qt5Agg')

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = 'E:/objDetect/tt100k/TSRmodels/20200414_faster_rcnn_resnet101_TSR.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = 'E:/objDetect/tt100k/data/TFRecords/tt100k_label_map.pbtxt'
# 分类数量
NUM_CLASSES = 221

detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
    label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        
        image = cv2.imread("./7433.jpg")

        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image, axis=0)
        image_tensor = detection_graph.get_tensor_by_name(
            'image_tensor:0')
        # Each box represents a part of the image where a particular object was detected.
        boxes = detection_graph.get_tensor_by_name(
            'detection_boxes:0')
        # Each score represent how level of confidence for each of the objects.
        # Score is shown on the result image, together with the class label.
        scores = detection_graph.get_tensor_by_name(
            'detection_scores:0')
        classes = detection_graph.get_tensor_by_name(
            'detection_classes:0')
        num_detections = detection_graph.get_tensor_by_name(
            'num_detections:0')
        # Actual detection.
        (boxes, scores, classes, num_detections) = sess.run(
            [boxes, scores, classes, num_detections],
            feed_dict={image_tensor: image_np_expanded})
        # Visualization of the results of a detection.
        vis_util.visualize_boxes_and_labels_on_image_array(
            image,
            np.squeeze(boxes),
            np.squeeze(classes).astype(np.int32),
            np.squeeze(scores),
            category_index,
            min_score_thresh=0.4,
            use_normalized_coordinates=True,
            line_thickness=8)

        result = np.asarray(image)
        
        plt.imshow(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
        plt.show()

运行上边代码会显示下边这张图，可以看出，它能够成功检测出这张图片出的三个交通标志。