帮我写一段目标检测函数

好的，以下是一个使用 TensorFlow Object Detection API 实现目标检测的函数示例：

import tensorflow as tf
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

def detect_objects(image_path, model_path, label_path, confidence_threshold=0.5):
    # Load image
    image = tf.io.read_file(image_path)
    image = tf.image.decode_jpeg(image, channels=3)
    image = tf.expand_dims(image, axis=0)

    # Load model
    detection_model = tf.saved_model.load(model_path)
    detection_fn = detection_model.signatures['serving_default']

    # Load label map
    label_map = label_map_util.load_labelmap(label_path)
    categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=90, use_display_name=True)
    category_index = label_map_util.create_category_index(categories)

    # Run inference
    detections = detection_fn(image)

    # Process output
    num_detections = int(detections.pop('num_detections'))
    detections = {key: value[0, :num_detections].numpy() for key, value in detections.items()}
    detections['num_detections'] = num_detections
    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)
    image_np_with_detections = image.numpy().squeeze()

    # Visualize results
    vis_util.visualize_boxes_and_labels_on_image_array(
          image_np_with_detections,
          detections['detection_boxes'],
          detections['detection_classes'],
          detections['detection_scores'],
          category_index,
          use_normalized_coordinates=True,
          max_boxes_to_draw=200,
          min_score_thresh=confidence_threshold,
          agnostic_mode=False)

    # Return image with detections
    return image_np_with_detections

该函数需要传入以下参数：

- `image_path`: 待检测的图像路径
- `model_path`: TensorFlow Object Detection API 模型路径
- `label_path`: 标签映射文件路径
- `confidence_threshold`: 置信度阈值，低于该阈值的检测结果将被过滤掉，默认为 0.5

函数内部使用 TensorFlow 加载图像、模型和标签映射文件，并通过 Object Detection API 进行推理和结果处理。最后，将可视化结果返回。