tensorflow推出的object detection模块，为我们做物体检测提供了极大的便利，如果还没有安装该模块，可以参照
http://blog.csdn.net/hanshuobest/article/details/79222685
下面的示例演示如何用已训练的模型做物体检测

import numpy as np
import os
import tensorflow as tf
from collections import defaultdict
from matplotlib import pyplot as plt
from PIL import Image
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_utildef load_image_into_numpy_array(image):(im_width, im_height) = image.sizereturn np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)PATH_TO_CKPT = "H:\\python\\models\\research\\object_detection\\ssd_mobilenet_v1_coco_11_06_2017\\frozen_inference_graph.pb"
PATH_TO_LABELS = "H:\\python\\models\\research\\object_detection\\data\\mscoco_label_map.pbtxt"
NUM_CLASSES = 90
image_path = "test_images\\image1.jpg"# 加载模型进内存
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='')
# 加载标签映射
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)# 开始检测
with detection_graph.as_default():with tf.Session(graph=detection_graph) as sess:image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')num_detections = detection_graph.get_tensor_by_name('num_detections:0')image = Image.open(image_path)image_np = load_image_into_numpy_array(image)image_np_expanded = np.expand_dims(image_np , axis=0)# Actual detection.(boxes, scores, classes, num) = sess.run([detection_boxes, detection_scores, detection_classes, num_detections],feed_dict={image_tensor: image_np_expanded})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)plt.imshow(image_np)plt.show()