python训练检测目标的代码

你好，以下是 Python 训练检测目标的代码：

import tensorflow as tf
from object_detection.utils import config_util
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.builders import model_builder

# Load pipeline config and build a detection model
CONFIG_PATH = 'path/to/pipeline.config'
CHECKPOINT_PATH = 'path/to/checkpoint'
pipeline_config = config_util.get_configs_from_pipeline_file(CONFIG_PATH)
model_config = pipeline_config['model']
detection_model = model_builder.build(model_config=model_config, is_training=False)

# Restore checkpoint
ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
ckpt.restore(CHECKPOINT_PATH).expect_partial()

# Load label map data
LABEL_MAP_PATH = 'path/to/label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(LABEL_MAP_PATH, use_display_name=True)

# Load image and run inference
IMAGE_PATH = 'path/to/image.jpg'
image_np = viz_utils.load_image_into_numpy_array(IMAGE_PATH)
input_tensor = tf.convert_to_tensor(image_np)
input_tensor = input_tensor[tf.newaxis, ...]
detections = detection_model(input_tensor)

# Visualize results
viz_utils.visualize_boxes_and_labels_on_image_array(
    image_np,
    detections['detection_boxes'][0].numpy(),
    detections['detection_classes'][0].numpy().astype(int),
    detections['detection_scores'][0].numpy(),
    category_index,
    use_normalized_coordinates=True,
    max_boxes_to_draw=200,
    min_score_thresh=.30,
    agnostic_mode=False)

希望对你有帮助。