Question

我使用Tensorflow对象检测API训练了一个更快的rcnn模型，并使用这个推理脚本和我的冻结图：

https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb

我打算将它用于视频中的对象跟踪，但使用此脚本的推断非常慢，因为它一次只处理一个图像而不是一批图像。有没有办法一次对一批图像进行推断？相关的推理功能在这里，我想知道如何修改它以使用一堆图像

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

Answer 1

您可以将一个大小为（batch_size，image_width，image_heigt，3）的图像批处理传递给sess.run命令，而不是仅传递一个大小为（1，image_width，image_heigt，3）的numpy数组。：

output_dict = sess.run(tensor_dict, feed_dict={image_tensor: image_batch})

output_dict将与之前稍有不同，但仍未弄清楚到底有多精确。也许有人可以进一步帮助您？

修改

似乎output_dict获得了另一个索引，该索引对应于您批次中的图像编号。因此，您可以在以下位置找到特定图像的框： output_dict ['detection_boxes'] [image_counter]

Edit2

由于某种原因，这不适用于Mask RCNN ...

Answer 2

如果你运行export_inference_graph.py，你应该能够默认输入批量图像，因为它将image_tensor形状设置为[None，None，None，3]。

python object_detection/export_inference_graph.py \ --input_type image_tensor \ --pipeline_config_path ${PIPELINE_CONFIG_PATH} \ --trained_checkpoint_prefix ${TRAIN_PATH} \ --output_directory output_inference_graph.pb

是否有可以同时在批量图像上运行的Tensorflow对象检测API的推理示例的版本？

2 个答案: