tensorflow object detect API 使用，并修改一部分

代码放在tensorflow 提供的modles中的 /models/research/object_detection/models 中，指定的模型要提前下到文件夹，

使用了opencv中从指定的ip地址视频流中截取图片，将识别结果中的第一个写到了制动文件夹中。搭建的环境是Ubuntu16.04

import numpy as np
import os
import sys
import tarfile
import tensorflow as tf
import cv2
import timefrom collections import defaultdict# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("../..")from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util# What model to download.
MODEL_NAME = 'ssdlite_mobilenet_v2_coco_2018_05_09'
#MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
#MODEL_NAME = 'faster_rcnn_inception_resnet_v2_atrous_lowproposals_coco_2018_01_28'
#MODEL_NAME = 'faster_rcnn_resnet50_coco_2018_01_28'
#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/che/Desktop/models/research/object_detection/data', 'mscoco_label_map.pbtxt')#extract the ssd_mobilenet
start = time.clock()
NUM_CLASSES = 90
#opener = 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('http://192.168.12.1:8080/?action=stream')
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'):breakimage_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})class_name = category_index[np.squeeze(classes).astype(np.int32)[0]]['name']f = open('/home/che/Desktop/du.txt','w')print (class_name)f.write(class_name)f.close()# 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 ('One frame detect take time:',end - start)cv2.imshow("capture", image_np)cv2.waitKey(1)
cap.release()
cv2.destroyAllWindows()

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