首先到github下载相应的Tensorflow模型,以及配置好环境。具体的可参考这篇博客
或者参考Github上,TensorFlow models/research/object_detection
里的安装教程。
这里给出一个视频里面的目标检测代码:
import os import time import argparse import multiprocessing import numpy as np import tensorflow as tf import tarfile from matplotlib import pyplot as plt from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util ''' 视频目标追踪 ''' #1.得到模型 (这里首先下载流模型并在解压在path/to/models/research/object_detection里面) MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17' PATH_TO_CKPT = os.path.join(MODEL_NAME, 'frozen_inference_graph.pb') PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt') print('Loading model...') #load frozen of tensorflow to memeory detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: #文本操作句柄,类似python里面的open() serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') #将图像从od_graph_def导入当前的默认Graph #label map to class name 如预测为5,知道它是对应飞机 NUM_CLASS = 90 print("Loading label map...") label_map = label_map_util.load_labelmap(PATH_TO_LABELS) #得到label map proto categories = label_map_util.convert_label_map_to_categories(label_map, NUM_CLASS) #得到类别 category_index = label_map_util.create_category_index(categories) #2.对视频进行物体检测 def detect_objects(image_np, sess, detection_graph): 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}) #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 from moviepy.editor import VideoFileClip from IPython.display import HTML def process_image(image): 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 = '/home/magic/111_out.mp4' clip1 = VideoFileClip("/home/magic/111.avi") white_clip = clip1.fl_image(process_image) #This function expects color images! white_clip.write_videofile(white_output, audio=False)
#等待一段时间后,得到111_out.mp4,可以去查看效果 我的测试结果如下
![](https://images2018.cnblogs.com/blog/900252/201808/900252-20180827161339950-1700433533.png)