tf之object detect摄像头物体识别测试

import numpy as npimport osimport six.moves.urllib as urllibimport sysimport tarfileimport tensorflow as tfimport zipfileimport cv2import time  from collections import defaultdictfrom io import StringIOfrom matplotlib import pyplot as pltfrom PIL import Image# This is needed since the notebook is stored in the object_detection folder.sys.path.append("..")from utils import label_map_utilfrom utils import visualization_utils as vis_util# What model to download.MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'#MODEL_NAME = 'faster_rcnn_resnet101_coco_11_06_2017'#MODEL_NAME = 'ssd_inception_v2_coco_11_06_2017'MODEL_FILE = MODEL_NAME + '.tar.gz'# 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('/home/chenqy/tf36/models/object_detection/data', 'mscoco_label_map.pbtxt')#extract the ssd_mobilenetstart = time.clock()NUM_CLASSES = 90opener = 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())end= time.clock()print('load the model',(end-start))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='')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)cap = cv2.VideoCapture(0)with detection_graph.as_default():  with tf.Session(graph=detection_graph) as sess:      writer = tf.summary.FileWriter("logs/", sess.graph)        sess.run(tf.global_variables_initializer())        while(1):        start = time.clock()        ret, frame = cap.read()        if cv2.waitKey(1) & 0xFF == ord('q'):            break        image_np=frame        # the array based representation of the image will be used later in order to prepare the        # result image with boxes and labels on it.        # 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=6)        end = time.clock()        print('frame:',1.0/(end - start))        #print 'frame:',time.time() - start        cv2.imshow("capture", image_np)        cv2.waitKey(1)cap.release()cv2.destroyAllWindows() 

测试比较卡，估计电脑以及虚拟机配置比较差，显示结果：