如何从 Tensorflow 中的 COCO json 生成具有边界框坐标和地面实况坐标的测试图像的 mAP 指标？

如何解决如何从 Tensorflow 中的 COCO json 生成具有边界框坐标和地面实况坐标的测试图像的 mAP 指标？

我刚刚在我的神经网络项目中遵循了这个 walkthrough。

如果我基本上总结本演练，我们训练我们的模型并导出干扰图，如演练部分所示。在这些训练步骤中，正如您猜测的那样，它显示了训练作业的 mAP 指标。在这些步骤之后，在最后一部分“运行干扰测试”中，我们测试我们的图像，并生成已在其上绘制标记边界框的图像。但它不会为每个图像生成像 mAP 等的指标。这部分的源码我看过了，但是没有找到它的配置和方法。

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,-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

我有 COCO json 文件，其中包含图像的真实框信息。有没有办法产生这个指标？

#This code produces images that have labeled bounding Boxes.
for image_path in TEST_IMAGE_PATHS:
    image = Image.open(image_path)
    # 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,3]
    image_np_expanded = np.expand_dims(image_np,axis=0)
    # Actual detection.
    output_dict = run_inference_for_single_image(image_np,detection_graph)
    # Visualization of the results of a detection.
    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)