TensorFlow:Object_Detection_API在Windows10上的配置

安装
假设已配置完tensorflow，并安装好Anaconda3-4.2.0（此版本为python3.5）

从github下载models
tensorflow/models

Protobuf 编译
protobuf下载
我下载的版本是protoc-3.3.0-win32.zip，解压后将bin文件夹中的【protoc.exe】放到C:\Windows
在models目录下shift+右键打开Powershell窗口，输入：

# From tensorflow/models/protoc object_detection/protos/*.proto --python_out=.

不报错即可

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测试
由于电脑中同时有Anaconda2与Anaconda3，在models目录下输入

jupyter-notebook

进入object_detetion中打开【object_detection_tutorial.ipynb】，无法运行，此时的kernel是python2，而windows只有python3.5的tensorflow。如果你的电脑只有python3.5，那么直接run all就可以看到结果。或者将Anaconda3的jupyter-notebook的工作空间变换到models目录下。
这里我用了另一种方法：
新建一个【object_detection_tutorial.py】，将.ipynb中的代码复制到.py中，然后在spyder中运行。以下是代码。功能是取object_detection\test_images中的图片：image1和image2，做目标检测。

import numpy as npimport osimport six.moves.urllib as urllibimport sysimport tarfileimport tensorflow as tfimport zipfilefrom collections import defaultdictfrom io import StringIOfrom matplotlib import pyplot as pltfrom 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_utilfrom 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 modelopener = 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 codedef 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:    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)

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结果
主要时间用于下载模型，目标识别还是蛮快的。

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一些细节

1.模型的选择

Tensorflow detection model zoo
Speed表示速度，COCO mAP表示在COCO数据集上的平均准确率，第一个ssd_mobilenet就是我们默认使用的pre-train模型

# What model to download.MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'#MODEL_NAME = 'faster_rcnn_resnet101_coco_11_06_2017'#MODEL_NAME = 'ssd_inception_v2_coco_11_06_2017'MODEL_FILE = MODEL_NAME + '.tar.gz'DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

2.测试自己的图片

改变【PATH_TO_TEST_IMAGES_DIR】 的路径，要在models-master\object_detection目录下。相应的数量for i in range(1, 3)也要进行改变。

# 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) ]

测试一张图片快7秒，不知道是什么原因。

3.测试视频

安装Opencv3，这里碰到了问题，直接输入

pip install opencv-python

安装好后import cv2报错

ImportError: DLL load failed: 找不到指定的模块

根据网上的解决方法去安装vc++ for visual2015之后并没有解决，下载重装 【opencv_python-3.2.0.8-cp35-cp35m-win_amd64.whl】也无效。把Anaconda2删除只留Anaconda3也不行。无奈之下重新安装Anaconda3，下载最新版的python-opencv，在目录下

pip install opencv_python-3.3.0-cp35-cp35m-win_amd64.whl

import cv2报错：

ImportError: numpy.core.multiarray failed to import

于是更新numpy到1.13.1：

pip install -U numpy

接下来import cv2就没问题了