利用Google Object Detection模块识别图片中的物体

笔者环境win1064位
1.anaconda python 3.5 64为安装包
2.安装tensflow1.0

第一部分（图片物体识别）

• 1.下载tensflow的源码包，并解压

• 2.下载 Protoc protoc-3.4.0-win32.zip
  解压protoc-3.4.0-win32.zip，并将bin文件夹内的protoc.exe拷贝到c:\windows\system32目录下（也可将其添加到环境变量中）

• 3.cd “F:\test\models-master\object_detection”在终端执行

protoc object_detection/protos/*.proto --python_out=.

• 4.在终端执行ipython notebook
• 5.在浏览器打开object_detection_tutorial.ipynb运行
  
  

说明：

1.PATH_TO_TEST_IMAGES_DIR = ‘test_images’ 未测试文件夹
2.MODEL_NAME = ‘ssd_mobilenet_v1_coco_11_06_2017’
mode有四种：

MODEL_NAME = 'ssd_inception_v2_coco_11_06_2017'MODEL_NAME = 'rfcn_resnet101_coco_11_06_2017'MODEL_NAME = 'faster_rcnn_resnet101_coco_11_06_2017'MODEL_NAME = 'faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017'

其中faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017’的准确路最高，但时间也最长

第二部分（视频中物体识别）

参考自github上面的完整代码下面我们在google的object_detection_tutorial.ipynb基础上改造。

1.安装环境准备
- a: 安装a.opencv的cv2包 :
conda install –channel https://conda.anaconda.org/menpo opencv3
可以在python 命令行中import cv2 测试一下
- b: 安装imageio: conda install -c conda-forge imageio
- c: 安装moviepy：pip install moviepy

1. 导入opencv包

import cv2

3.下载视频剪辑程序，此处消耗时间有点长

# Import everything needed to edit/save/watch video clipsimport imageioimageio.plugins.ffmpeg.download()

下载过程如下：

Imageio: 'ffmpeg-win32-v3.2.4.exe' was not found on your computer; downloading it now.Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/ffmpeg/ffmpeg-win32-v3.2.4.exe (34.1 MB)Downloading: 8192/35749888 bytes (0.0%)16384/35749888 bytes (0.0%)32768/35749888 bytes (0.1%)65536/35749888 bytes (0.2%)98304/35749888 bytes (0.3%)131072/35749888 bytes (0.4%)147456/35749888 bytes (0.4%)163840/35749888 bytes (0.5%)180224/35749888 bytes (0.5%)19660********************** (99.7%)35651584/35749888 bytes (99.7%)35667968/35749888 bytes (99.8%)35684352/35749888 bytes (99.8%)35700736/35749888 bytes (99.9%)35717120/35749888 bytes (99.9%)35733504/35749888 bytes (100.0%)35749888/35749888 bytes (100.0%)35749888/35749888 bytes (100.0%)  DoneFile saved as C:\Users\Administrator\AppData\Local\imageio\ffmpeg\ffmpeg-win32-v3.2.4.exe.

4.

from moviepy.editor import VideoFileClipfrom IPython.display import HTML

5.

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    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

6.开启视频标记，将视频video1.mp4放入到“”F:\test\models-master\object_detection” subclip（25,30）代表识别视频中25-30s这一时间段。

white_output = 'video1_out.mp4'clip1 = VideoFileClip("video1.mp4").subclip(25,30)white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!s%time white_clip.write_videofile(white_output, audio=False)

7.在浏览器中展示

HTML("""<video width="960" height="540" controls>  <source src="{0}"></video>""".format(white_output))

8.保存为gif格式

from moviepy.editor import *clip1 = VideoFileClip("video1_out.mp4")clip1.write_gif("final.gif")

本文参考：
http://m.blog.csdn.net/xiaoxiao123jun/article/details/76605928