Tensorflow object detection API训练自己的目标检测模型 详细配置教程 (一)

Tensorflow object detection API

简单介绍Tensorflow object detection API：

这个API是基于tensorflow构造的开源框架，易于构建、训练和部署目标检测模型。

关于tensorflow安装：自行百度， 教程很多，分CPU,GPU版本的；

环境：

       win10

       pycharm

       anaconda

       python 3.6

       tensorflow gpu

需要的python库有： pillow,  lxml, matplotlib, jupyter, 请自行安装。

第一步：

下载tensorflow object detection API模型

第二步：

下载Protoc： 点击下载Protoc

protoc的作用是将Tensorflow object detection API模型文件中的.pro

文件编译成python文件。window下下载的版本可以是：

下载后解压，可以看到目录如下：

将bin文件夹的路径添加到环境变量：

打开cmd,输入 protoc：输出如下信息则表示添加环境变量成功：

第三步：

将下载的tensorflow object detection文件解压， 文件名可改为model：打开models\research\object_detection\protos，会看到里面有很多的.proto文件，利用Protoc将这些.proto文件编译成py文件，下面介绍具体的做法：

在cmd下， 一路cd到\models\research文件夹下。根据这篇博客的介绍， protos文件夹下的.proto要逐一编译，在这里可以直接用跟快速的方法，直接输入：protoc ./object_detection/protos/*.proto --python_out=. 就可以快速编译所有文件， *.proto相当于匹配所有的.proto文件。

编译完后是这样的：

每一个.proto对应一个python文件。

第四步：

需要添加两个环境变量：

 

-> \models\research

-> \models\research\slim

如图所示：

至此object detection API已经配置完毕：

接下来需要测试一下：

我这里直接用pycharm打开models项目：整个目录如下图所示

在research/object_detection文件夹下新建一个python文件，命名为object_detection_tutorial

文件内容如下：

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image


# # This is needed to display the images.
# %matplotlib inline

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

# from utils import label_map_util
# from utils import visualization_utils as vis_util
from research.object_detection.utils import label_map_util
from research.object_detection.utils import visualization_utils as vis_util

# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

# download model
opener = urllib.request.URLopener()
# 下载模型，如果已经下载好了下面这句代码可以注释掉
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
    file_name = os.path.basename(file.name)
    if 'frozen_inference_graph.pb' in file_name:
        tar_file.extract(file, os.getcwd())

# Load a (frozen) Tensorflow model into memory.
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='')
# Loading label map
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)


# Helper code
def load_image_into_numpy_array(image):
    (im_width, im_height) = image.size
    return np.array(image.getdata()).reshape(
        (im_height, im_width, 3)).astype(np.uint8)


# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3)]

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)

with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        # Definite input and output Tensors for detection_graph
        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.
        detection_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.
        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = detection_graph.get_tensor_by_name('num_detections:0')
        for image_path in TEST_IMAGE_PATHS:
            image = Image.open(image_path)
            # the array based representation of the image will be used later in order to prepare the
            # result image with boxes and labels on it.
            image_np = load_image_into_numpy_array(image)
            # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
            image_np_expanded = np.expand_dims(image_np, 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,
                np.squeeze(boxes),
                np.squeeze(classes).astype(np.int32),
                np.squeeze(scores),
                category_index,
                use_normalized_coordinates=True,
                line_thickness=8)
            plt.figure(figsize=IMAGE_SIZE)
            plt.imshow(image_np)
            plt.show()

这里需要注意： 

# from utils import label_map_util
# from utils import visualization_utils as vis_util
from research.object_detection.utils import label_map_util
from research.object_detection.utils import visualization_utils as vis_util

utils文件需要根据自己的目录作出具体的修改， 比如文件中原始的 

 from utils import label_map_util
 from utils import visualization_utils as vis_util

会报错， 得根据自己的目录做具体的修，我根据自己的目录修改为： 

from research.object_detection.utils import label_map_util
from research.object_detection.utils import visualization_utils as vis_util

这样pycharm就不会提示找不到utils包了：

修改完运行时还是会报错， 是因为还有两个地方需要修改：

第一个就是上面导入的label_mao_util.py文件里面：

# from object_detection.protos import string_int_label_map_pb2
from research.object_detection.protos import string_int_label_map_pb2

   第一行注释掉的时原来文件中的写法， 我根据自己的目录修改为第二行的形式

第二个就是上面导入的visualization_util.py文件里面：

也做相应的修改：

# from object_detection.core import standard_fields as fields
from research.object_detection.core import standard_fields as fields

现在已经全部修改完毕：

接下来可以直接运行刚才创建的object_detection_tutorial.py文件了， 运行可能需要比较久的时间，请耐性等待

最后的结果： 下图为模型中的测试图片

 

 

完毕！

在后面会详细介绍一下如和用自己的数据训练一个目标检测的模型。