TensorFlow学习——Tensorflow Object Detection API（win10，CPU）

英文链接地址：https://github.com/tensorflow/models/tree/master/object_detection

确保安装了如下的库：

Tensorflow Object Detection API depends on the following libraries:

• Protobuf 2.6
• Pillow 1.0
• lxml
• tf Slim (which is included in the "tensorflow/models" checkout)
• Jupyter notebook
• Matplotlib
• Tensorflow

模型下载链接：https://github.com/tensorflow/models    (内涵模型各模块的简介，建议使用Chrome浏览器下载 ，下载文档文件名字为：models-master.zip )

下载模型存在自己本地目录下（D:\TensorFlow\TensorFlow Object Detection API Tutorial），我作了解压并在该目录下重命名为model文件夹

还需打开链接：https://github.com/google/protobuf/releases  下载所需版本的，我这里下载的是win版本 protoc-3.4.0-win32.zip,解压生成：bin, include两个文件夹

文件目录：D:\TensorFlow\TensorFlow Object Detection API Tutorial\include      与      D:\TensorFlow\TensorFlow Object Detection API Tutorial\bin （该目录下包含protoc.exe，待会需要用到 协议编译models下的object_detection文件）

由于我的运行环境是win10，用Anaconda装的TensorFlow，因此打开Anaconda带的Anaconda prompt(类似cmd),

如下图，目录切到下载解压后的models目录下，用protoc可执行文件编译目录object_detection/protos下的proto文件，生成Python文件；

并打开Jupyter notebook

打开Object Detection Demo，运行Run All，该文件是从coco上下载数据并生成预训练模型，读者可根据需求自己训练模型。运行完毕后会生成验证测试效果如下：

------------------------------------------------------------上述是运行API中的一个程序文件，用预训练的模型测试几张图片分类效果-----------------------------------------

------------------------------------------------------------以下将用此API以及下载的模型做视频目标检测与定位--------------------------------------------------------------------

将上述文件Download as成py文件，并更改个文件名以便做代码更改，本文更改为：object_detection_tutorial_CONVERT.py （置于目录：D:\TensorFlow\TensorFlow Object Detection API Tutorial\models\object_detection）

接下来对object_detection_tutorial_CONVERT.py内容做修改，改成读摄像头并做目标检测与定位，

更改后的代码：

# coding: utf-8# # Object Detection Demo# Welcome to the object detection inference walkthrough!  This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the [installation instructions](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/installation.md) before you start.# # Imports# In[1]: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 Imageimport cv2                  #add 20170825cap = cv2.VideoCapture(0)   #add 20170825# ## Env setup# In[2]:                                  #delete 20170825# This is needed to display the images.    #delete 20170825#get_ipython().magic('matplotlib inline')   #delete 20170825# This is needed since the notebook is stored in the object_detection folder.  sys.path.append("..")# ## Object detection imports# Here are the imports from the object detection module.# In[3]:from utils import label_map_utilfrom utils import visualization_utils as vis_util# # Model preparation # ## Variables# # Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file.  # # By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.# In[4]:# 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# In[5]:opener = urllib.request.URLopener()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.# In[6]: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 maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine# In[7]: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# In[8]: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)# # Detection# In[9]:# 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)# In[10]:with detection_graph.as_default():  with tf.Session(graph=detection_graph) as sess:    while True:    #for image_path in TEST_IMAGE_PATHS:    #changed 20170825      ret, image_np = cap.read()        # 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)      cv2.imshow('object detection', cv2.resize(image_np,(800,600)))      if cv2.waitKey(25) & 0xFF ==ord('q'):        cv2.destroyAllWindows()        break      #plt.figure(figsize=IMAGE_SIZE)   #delete 20170825      #plt.imshow(image_np)             #delete 20170825# In[ ]:

之后用IDLE或是Spyder运行代码，效果如下。