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 copywith detection_graph.as_default():   with tf.Session(graph=detection_graph) as sess:   for image_path in TEST_IMAGE_PATHS:    image = Image.open(image_path)    # 這個array在之后會被用來準(zhǔn)備為圖片加上框和標(biāo)簽    image_np = load_image_into_numpy_array(image)    # 擴(kuò)展維度，應(yīng)為模型期待: [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')    # 每個框代表一個物體被偵測到.    boxes = detection_graph.get_tensor_by_name('detection_boxes:0')    # 每個分值代表偵測到物體的可信度.    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')    # 執(zhí)行偵測任務(wù).    (boxes, scores, classes, num_detections) = sess.run(      [boxes, scores, classes, num_detections],      feed_dict={image_tensor: image_np_expanded})    # 圖形化.    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)