Python中的_build_detection_graph()函数解析和使用方法

_build_detection_graph()函数是TensorFlow Object Detection API中的一个函数，主要用于构建目标检测模型的计算图。

该函数的定义如下：

def _build_detection_graph(image_resizer_fn, model_fn, anchor_generator_fn, num_classes,
                           image_shape=None, input_shape=None, apply_scale_factors_to_image=False,
                           max_num_batches=None, retain_original_image_annotation=False,
                           augment_input_data_fn=None):
    """Builds a detection model with input features from image_resizer_fn and
    feature_extractor_fn.

    Args:
      image_resizer_fn: A callable that resizes inputs.
      model_fn: A callable that instantiates the detection model.
      anchor_generator_fn: A callable that instantiates anchor generator.
      num_classes: Number of classes represented in the dataset.
      image_shape: A tensor representing the shape of the input image size.
      input_shape: A tensor representing the shape of the input tensor.
      apply_scale_factors_to_image: (optional) bool indicating whether to apply
        scale factor by image_resizer_fn to image.
      max_num_batches: The maximum number of batches to prefetch into the
        prefetch queue on each device used by the decoder. See tf.data.Dataset
        plugin for more details.
      retain_original_image_annotation: Whether to retain original image in the
        preprocess_result.
      augment_input_data_fn: (optional) A callable that is used to pre-process
        input data. It is fed the deserialized tf.Example and should return a
        dictionary of the same form as input to the model_fn. Mainly used for
        test/dev purposes.

    Returns:
      A dictionary containing:
        detection_model: A DetectionModel (based on Keras) output by
          model_fn.
        image_resizer_fn: The image resizer function.
        concat_preprocessed_inputs: Function to concatenate preprocessed
          features into a tuple or a dict.
        'preprocessed_inputs': A tensor or a dict of tensors as modelfn input.
          input tensors are of shape [(batch, H, W, C), ....] if model_fn takes
          image only as input. or [(batch, D1, .. DN, C), ....] if model_fn takes
          other features as well.
    """

该函数接受一系列参数来构建目标检测模型，其中包括：

- image_resizer_fn: 图像的缩放函数，用于调整图像的大小和形状。

- model_fn: 创建检测模型的函数，返回一个DetectionModel对象。

- anchor_generator_fn: 创建锚框（anchor）生成器的函数。

- num_classes: 数据集中待检测的物体类别数量。

- image_shape: 输入图像的形状。

- input_shape: 输入张量的形状。

- apply_scale_factors_to_image: 是否将缩放因子应用到图像上。

- max_num_batches: 每个设备使用的最大预取批次数。

- retain_original_image_annotation: 是否保留原始图像的注释信息。

- augment_input_data_fn: 预处理输入数据的函数。

返回结果是一个包含以下内容的字典：

- detection_model: 通过model_fn创建的DetectionModel对象。

- image_resizer_fn: 图像缩放函数。

- concat_preprocessed_inputs: 将预处理特征连接成一个元组或字典的函数。

- preprocessed_inputs: 用于模型的预处理输入。

下面是_build_detection_graph()函数的一个示例用法：

pipeline_config_path = 'path/to/pipeline.config'
model_dir = 'path/to/model_directory'

# 加载配置文件
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
with tf.compat.v2.io.gfile.GFile(pipeline_config_path, "r") as f:
    text_format.Parse(f.read(), pipeline_config)

# 加载模型
trained_model = checkpoint_utils.load_checkpoint(model_dir)
detection_model = model_builder.build(pipeline_config.model, is_training=False)

# 调用_build_detection_graph()函数构建计算图
detection_graph = model_builder._build_detection_graph(
    image_resizer_fn=image_resizer_builder.build(pipeline_config.model.image_resizer),
    model_fn=model_builder.build(model_config, is_training=False),
    anchor_generator_fn=anchor_generator_builder.build(model_config.anchor_generator),
    num_classes=model_config.num_classes,
    image_shape=model_config.image_resizer.fixed_shape_resizer.height,
    max_num_batches=pipeline_config.eval_config.max_evals
)

# 在计算图中执行目标检测
def run_inference_for_single_image(image, graph):
    with graph.as_default():
        with tf.compat.v1.Session() as sess:
            # 获取输入和输出张量
            input_tensor = tf.compat.v1.get_default_graph().get_tensor_by_name('Preprocessor/sub:0')
            output_tensors = [
                'num_detections:0',
                'detection_boxes:0',
                'detection_scores:0',
                'detection_classes:0'
            ]
            output_tensors = [tf.compat.v1.get_default_graph().get_tensor_by_name(tensor_name) for tensor_name in output_tensors]

            # 进行推理
            output_dict = sess.run(output_tensors, feed_dict={input_tensor: np.expand_dims(image, axis=0)})

    return output_dict

# 加载图像
image = cv2.imread('path/to/image.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# 执行目标检测
output_dict = run_inference_for_single_image(image, detection_graph)

# 可视化结果
visualize_output(image, output_dict['num_detections'][0], output_dict['detection_boxes'][0], output_dict['detection_scores'][0], output_dict['detection_classes'][0], threshold=0.5)

在上面的例子中，首先加载了模型的配置文件和已训练好的模型。然后使用model_builder._build_detection_graph()函数构建了目标检测模型的计算图。接着使用该计算图进行目标检测，最后将检测结果可视化出来。

需要注意的是，_build_detection_graph()函数是TensorFlow Object Detection API内部使用的函数，一般不需要直接调用该函数来构建计算图。可以使用model_builder.build()函数来构建模型并进行目标检测。以上的例子仅用于展示_build_detection_graph()函数的用法和示例。