gistfile1.txt

def run_inference_for_multiple_images2(images, graph):
    output_dicts = []
    with graph.as_default():
        with tf.Session() as sess:      
            for image in images:
#                 print (image)
                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'
#                     print (tensor_name)
                    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:
                    print ('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)        
                    tensor_dict['detection_masks'] = tf.expand_dims(detection_masks_reframed, 0)


                image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')      

                output_dict = sess.run(tensor_dict, feed_dict={image_tensor: np.expand_dims(image, 0)})    
#                 print (output_dict)
                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]
                output_dicts.append(output_dict)
    return output_dicts
    
    
    
    image_nps = []
for image_path in TEST_IMAGE_PATHS[:5]:
    image = Image.open(image_path)  
    image_np = load_image_into_numpy_array(image)      
    image_nps.append(image_np)


output_dicts = run_inference_for_multiple_images2(image_nps, detection_graph)  
# print (output_dicts)
for output_dict, image_np in zip(output_dicts, image_nps):
    print ('hi')
    vis_util.visualize_boxes_and_labels_on_image_array(
      image_np,
      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)
    plt.figure(figsize=IMAGE_SIZE)
    plt.imshow(image_np)
    plt.show()