测试环境：Win7、Anaconda3、tensorflow、opencv、CPU

一、Anaconda3下安装tensorflow和opencv

1、创建anaconda虚拟环境

[添加链接描述](https://blog.****.net/qq_37902216/article/details/84957240)

conda create -n tf_object python=3.6.7

其中tf_object为虚拟环境名称，可以根据自己喜好起名。

**虚拟环境 activate tf_object  若退出可执行deactivate

2、安装tensorflow

打开Anaconda中的Anaconda Navigator

点击环境，然后选择虚拟环境tf_object

然后选择All，再搜索tensorflow再点击Apply进行安装；opencv的安装按照同样的方式进行安装，具体操作可以搜索相关****博客。

进行验证，是否安装成功！！

打开cmd，再**tf_object环境，然后输入python，再输入

import tensorflow




import cv2

不报错则安装成功！！

二、protoc安装

什么是Protoc？Protoc是用来编译.Proto文件，Protocol Buffers (ProtocolBuffer/ protobuf )是Google公司开发的一种数据描述语言，类似于XML能够将结构化数据序列化，可用于数据存储、通信协议等方面。现阶段支持C++、JAVA、Python等三种编程语言。

Protoc用于编译相关程序运行文件，进入Protoc下载页，下载类似下图中带win32的压缩包。

然后解压这个文件，并记住bin文件夹路径，最好不要出现中文。

三、Git安装

git +网址是目前主流的在线下载指令，在官网找到Windows下载安装，按步骤操作就行，记得选择windows的命令框

四、安装其他包

pip install pillow



pip install lxml



pip install jupyter



pip install matplotlib



pip install requests



pip install moviepy

注意这些需要先**虚拟环境下再安装这些包

五、下载模型并编译

打开cmd输入

git clone http://github.com/tensorflow/models.git

下载后放在某个文件夹内，然后在cmd中进入models/research下，再进行编译

E:\protoc\bin\protoc object_detection\protos\*.proto --python_out=.

其中E:\protoc\bin\protoc表示你解压的protoc路径；object_detection\protos\*.proto --python_out=.是进行编译object_detection\protos\下的所有proto文件，运行成功，会编译生成py文件。

六、运行notebook demo

打开cmd 进入models/research再输入

jupyter-notebook

浏览器自动打开如下

然后新建python3程序，输入以下代码（对之前原始代码进行了些许改进）：

import os




import cv2




import time




import argparse




import multiprocessing




import numpy as np




import tensorflow as tf




from matplotlib import pyplot as plt



%matplotlib inline



 




import six.moves.urllib as urllib




import sys




import tarfile




import zipfile



 




from collections import defaultdict




from io import StringIO




from PIL import Image



 



 




from object_detection.utils import label_map_util




from object_detection.utils import visualization_utils as vis_util



 



 



CWD_PATH = os.getcwd()



# Path to frozen detection graph. This is the actual model that is used for the object detection.



MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'




PATH_TO_CKPT = os.path.join(CWD_PATH, 'object_detection', 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(CWD_PATH, 'object_detection', 'data', 'mscoco_label_map.pbtxt')



 



 



NUM_CLASSES = 90




# 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)



 



 



 



def detect_objects(image_np, sess, detection_graph):



    # 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)



    return image_np



 



 



# First test on images



PATH_TO_TEST_IMAGES_DIR = 'object_detection/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)



 



 



 



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)



 



 



 



 




from PIL import Image




for image_path in TEST_IMAGE_PATHS:



    image = Image.open(image_path)



    image_np = load_image_into_numpy_array(image)



    plt.imshow(image_np)



    print(image.size, image_np.shape)

运行之后出来结果

继续输入代码：

#Load a frozen TF model 



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='')



 



 



 




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)



            image_np = load_image_into_numpy_array(image)



            image_process = detect_objects(image_np, sess, detection_graph)



            print(image_process.shape)



            plt.figure(figsize=IMAGE_SIZE)



            plt.imshow(image_process)

得到图片检测结果如下所示

下面部分是对视频进行检测，继续输入代码

# Import everything needed to edit/save/watch video clips




import imageio



imageio.plugins.ffmpeg.download()



 




from moviepy.editor import VideoFileClip




from IPython.display import HTML



 



 



 



def process_image(image):



    # NOTE: The output you return should be a color image (3 channel) for processing video below




    # you should return the final output (image with lines are drawn on lanes)




    with detection_graph.as_default():



        with tf.Session(graph=detection_graph) as sess:



            image_process = detect_objects(image, sess, detection_graph)



            return image_process



 



 



 



white_output = 'video1_out.mp4'




clip1 = VideoFileClip("video1.mp4").subclip(0,2)



white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!s




%time white_clip.write_videofile(white_output, audio=False)

结果如下

输出视频video1_out.mp4保存到了代码所在文件目录中。

可以查看，输入下面代码

HTML("""




<video width="960" height="540" controls>



  <source src="{0}">



</video>




""".format(white_output))