import numpy as np 
import os 
import six.moves.urllib as urllib 
import sys 
import tarfile 
import tensorflow as tf 
import zipfile 
 
from collections import defaultdict 
from io import StringIO 
from matplotlib import pyplot as plt 
from PIL import Image 

%matplotlib inline 
sys.path.append("..") 

from utils import label_map_util 
 
from utils import visualization_utils as vis_util 

MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' 
MODEL_FILE = MODEL_NAME + '.tar.gz' 
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/' 
 
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb' 
 
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt') 
 
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()) 

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) 

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) 

PATH_TO_TEST_IMAGES_DIR = 'test_images' 
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ] 
IMAGE_SIZE = (12, 8) 
[python] view plain copy
with detection_graph.as_default(): 
 
 with tf.Session(graph=detection_graph) as sess: 
  for image_path in TEST_IMAGE_PATHS: 
   image = Image.open(image_path) 
   # 这个array在之后会被用来准备为图片加上框和标签 
   image_np = load_image_into_numpy_array(image) 
   # 扩展维度，应为模型期待: [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') 
   # 每个框代表一个物体被侦测到. 
   boxes = detection_graph.get_tensor_by_name('detection_boxes:0') 
   # 每个分值代表侦测到物体的可信度. 
   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') 
   # 执行侦测任务. 
   (boxes, scores, classes, num_detections) = sess.run( 
     [boxes, scores, classes, num_detections], 
     feed_dict={image_tensor: image_np_expanded}) 
   # 图形化. 
   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.figure(figsize=IMAGE_SIZE) 
   plt.imshow(image_np)