之前文章目標檢測API 已經介紹過API的基本使用，這裡就不贅述了，直接上本次內容的程式碼了，新增的內容並不多。將測試的test.mp4原檔案放到models-master\research\object_detection路徑下，並建立一個detect_video.py檔案，程式碼內容如下：

import os
import cv2
import time
import argparse
import multiprocessing
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
import
 
 matplotlib
# Matplotlib chooses Xwindows backend by default.
matplotlib.use('Agg')
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

'''
視訊目標追蹤
'''

# 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(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

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

#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='')



#import imageio
#imageio.plugins.ffmpeg.download()
# Import everything needed to edit/save/watch video clips
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 = 'test_out.mp4'
clip1 = VideoFileClip("test.mp4").subclip(1,9)
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!s
white_clip.write_videofile(white_output, audio=False)

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

檢測結果：

上面的對現有的視訊中目標進行檢測的，那麼怎樣實時的對現實生活中的目標進行檢測呢？這個其實也很簡單，我們來建立一個object_detection_tutorial_video.py 檔案，具體的程式碼如下：

import numpy as np  
import os  
import six.moves.urllib as urllib  
import sys  
import tarfile  
import tensorflow as tf  
import zipfile  
import matplotlib  
import cv2
# Matplotlib chooses Xwindows backend by default.  
matplotlib.use('Agg')  

from collections import defaultdict  
from io import StringIO  
from matplotlib import pyplot as plt  
from PIL import Image  
from utils import label_map_util  
from utils import visualization_utils as vis_util  

'''
    檢測視訊中的目標
'''

cap = cv2.VideoCapture(0)  #開啟攝像頭

##################### Download Model  
# What model to download.  
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'  
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 if not already downloaded  
if not os.path.exists(PATH_TO_CKPT):  
    print('Downloading model... (This may take over 5 minutes)')  
    opener = urllib.request.URLopener()  
    opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)  
    print('Extracting...')  
    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())  
else:  
    print('Model already downloaded.')  

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

##################### Loading label map  
print('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)  

##################### Detection ###########

print('Detecting...')  
with detection_graph.as_default():  
    with tf.Session(graph=detection_graph) as sess:

        # print(TEST_IMAGE_PATH)
        # image = Image.open(TEST_IMAGE_PATH)
        # image_np = load_image_into_numpy_array(image)
        while True:                              
            ret, image_np = cap.read()           #從攝像頭中獲取每一幀影象
            image_np_expanded = np.expand_dims(image_np, axis=0)
            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
            boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
            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})
             # Print the results of a detection.
            print(scores)
            print(classes)
            print(category_index)
            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)))
            #cv2.waitKey(0)
            if cv2.waitKey(25) & 0xFF == ord('q'):
                cv2.destroyAllWindows()
                break

程式碼中只是添加了攝像頭來獲取每一幀影象，處理方式和靜態的圖片差不多，這裡就不多說了。這裡就不上測試的結果了，大家課可以實際的跑一下程式即可看到結果。