Tensorflow对象检测api获取按边界框坐标排序的预测

import tensorflow as tf
from object_detection.utils import visualization_utils as viz_utils

model = tf.saved_model.load('path/to/saved_model')
category_index = label_map_util.create_category_index_from_labelmap('path/to/label_map.pbtxt', use_display_name=True)

image_np = load_image('path/to/image.jpg')  # 加载待检测的图像
input_tensor = tf.convert_to_tensor(image_np)
input_tensor = input_tensor[tf.newaxis, ...]
detections = model(input_tensor)

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)
sorted_detections = sorted(zip(detections['detection_boxes'], detections['detection_scores'], detections['detection_classes']), key=lambda x: x[0][1])

viz_utils.visualize_boxes_and_labels_on_image_array(
    image_np,
    sorted_detections,
    detections['detection_classes'],
    detections['detection_scores'],
    category_index,
    use_normalized_coordinates=True,
    max_boxes_to_draw=200,
    min_score_thresh=0.2,
    agnostic_mode=False)

plt.imshow(image_np)
plt.show()