前提条件：labelimg打标签得到bbox

1.代码

import torch


from segment_anything import SamPredictor, sam_model_registry
import cv2
import numpy as np
import os
import glob
import xml.etree.ElementTree as ET

checkpoint = "./weight/sam_vit_h_4b8939.pth"
model_type = "vit_h"
sam = sam_model_registry[model_type](checkpoint=checkpoint)
sam.to(device='cuda')
predictor = SamPredictor(sam)

image_dir = r"D:\Desktop\mult_test\images"
# 获取图片目录下的所有图片文件路径

image_files = glob.glob(os.path.join(image_dir, '*.[jJpPeEgG]*'))   # 获取任意格式的图片
save_dir = r"D:\Desktop\mult_test\mask"
# 注释文件目录路径
xml_dir = r'D:\Desktop\mult_test\label'

# 遍历图片文件
for image_file in image_files:
    image = cv2.imread(image_file)
    predictor.set_image(image)

    # 获取图片文件名（不包含扩展名）
    image_filename = os.path.splitext(os.path.basename(image_file))[0]

    # 构建注释文件路径
    xml_file = os.path.join(xml_dir,image_filename + '.xml')
    tree = ET.parse(xml_file)
    root = tree.getroot()

    data_list = []
    # 遍历 XML 标注文件中的目标对象
    for object_elem in root.findall('object'):
        # 获取目标对象的边界框坐标
        bbox_elem = object_elem.find('bndbox')
        xmin = int(bbox_elem.find('xmin').text)
        ymin = int(bbox_elem.find('ymin').text)
        xmax = int(bbox_elem.find('xmax').text)
        ymax = int(bbox_elem.find('ymax').text)

        data = [xmin,ymin,xmax,ymax]
        data_list.append(data)

    input_boxes = torch.tensor(data_list, device=predictor.device)
    transformed_boxes = predictor.transform.apply_boxes_torch(input_boxes, image.shape[:2])

    masks, _, _ = predictor.predict_torch(
        point_coords=None,
        point_labels=None,
        boxes=transformed_boxes,
        multimask_output=False,
    )

    first_mask = np.where(masks[0].cpu().numpy()[0, :, :] == 1, 0, 1) * 255
    for i in range(1, len(masks)):
        first_mask &= np.where(masks[i].cpu().numpy()[0, :, :] == 1, 0, 1) * 255

    image_filename = os.path.basename(image_file)
    cv2.imwrite(os.path.join(save_dir, image_filename), first_mask)

2.效果展示