代码注销掉了

看代码 是否被注释掉了， 如果没有被注释掉，自己写一段就行，使用opencv挺容易实现的

我也遇到这样的问题

我更喜欢使用cv2的图像，你可以将此代码放在research / object_detection路径为 detect_object.py

文件必须放在这里： model-master/research/object_detection/detect_object.py

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile

from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
import cv2

This is needed since the notebook is stored in the object_detection folder.

sys.path.append("..")
from object_detection.utils import ops as utils_ops

if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')

Object detection imports

Here are the imports from the object detection module.

from utils import label_map_util

from utils import visualization_utils as vis_util

What model to download.

MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

Path to frozen detection graph. This is the actual model that is used for the object detection.

PATH_TO_FROZEN_GRAPH = 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('data', 'mscoco_label_map.pbtxt')

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_FROZEN_GRAPH, 'rb') as fid:

serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')

In[31]:

category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)

Size, in inches, of the output images.

IMAGE_SIZE = (12, 8)

def run_inference_for_single_image(image, graph):
with graph.as_default():

with tf.Session() as sess:
  # Get handles to input and output tensors
  ops = tf.get_default_graph().get_operations()
  all_tensor_names = {output.name for op in ops for output in op.outputs}
  tensor_dict = {}
  for key in [
      'num_detections', 'detection_boxes', 'detection_scores',
      'detection_classes', 'detection_masks'
  ]:
    tensor_name = key + ':0'
    if tensor_name in all_tensor_names:
      tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
          tensor_name)
  if 'detection_masks' in tensor_dict:
    # The following processing is only for single image
    detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
    detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
    # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
    real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
    detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
    detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
    detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
        detection_masks, detection_boxes, image.shape[0], image.shape[1])
    detection_masks_reframed = tf.cast(
        tf.greater(detection_masks_reframed, 0.5), tf.uint8)
    # Follow the convention by adding back the batch dimension
    tensor_dict['detection_masks'] = tf.expand_dims(
        detection_masks_reframed, 0)
  image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

  # Run inference
  output_dict = sess.run(tensor_dict,
                         feed_dict={image_tensor: np.expand_dims(image, 0)})

  # all outputs are float32 numpy arrays, so convert types as appropriate
  output_dict['num_detections'] = int(output_dict['num_detections'][0])
  output_dict['detection_classes'] = output_dict[
      'detection_classes'][0].astype(np.uint8)
  output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
  output_dict['detection_scores'] = output_dict['detection_scores'][0]
  if 'detection_masks' in output_dict:
    output_dict['detection_masks'] = output_dict['detection_masks'][0]

return output_dict

image = cv2.imread("test_images/image1.jpg")

the array based representation of the image will be used later in order to prepare the

result image with boxes and labels on it.

image_np = load_image_into_numpy_array(image)

Expand dimensions since the model expects images to have shape: [1, None, None, 3]

image_np_expanded = np.expand_dims(image, axis=0)

Actual detection.

output_dict = run_inference_for_single_image(image, detection_graph)

vis_util.visualize_boxes_and_labels_on_image_array(

image,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=8)

cv2.imshow("",image)