计算机视觉：改进感兴趣区域分割

使用深度学习模型进行感兴趣区域分割。其中，可以使用Mask R-CNN模型，该模型是在Faster R-CNN的基础上添加了掩码预测分支，可以同时预测目标的类别、边界框和掩码。以下是基于Mask R-CNN模型的Python代码示例：

from mrcnn.config import Config
from mrcnn import model as modellib
from mrcnn import visualize
import cv2

# 配置类
class CustomConfig(Config):
    NAME = "custom"
    IMAGES_PER_GPU = 1
    NUM_CLASSES = 1 + 1     # 背景 + 目标类别数
    STEPS_PER_EPOCH = 100
    DETECTION_MIN_CONFIDENCE = 0.9

# 自定义数据集类
class CustomDataset(utils.Dataset):
    def load_custom(self, dataset_dir, subset):
        self.add_class("custom", 1, "target_class_name")
        assert subset in ["train", "val"]
        dataset_dir = os.path.join(dataset_dir, subset)
        annotations = json.load(open(os.path.join(dataset_dir, "annotations.json")))
        annotations = list(annotations.values())
        annotations = [a for a in annotations if a['regions']]
        for a in annotations:
            polygons = [r['shape_attributes'] for r in a['regions']]
            objects = [s['region_attributes'] for s in a['regions']]
            for o, p in zip(objects, polygons):
                class_name = o['target_class_name'].lower()
                if class_name == '':  # 忽略未标记类别的目标
                    continue
                if class_name not in self.class_info:
                    self.add_class("custom", objects.index(o)+1, class_name)
                label = self.get_class("custom", class_name)
                points = p['all_points_x'], p['all_points_y']
                self.add_polygon("custom", label, points)

    # 加载掩码
    def load_mask(self, image_id):
        image_info = self.image_info[image_id]
        if image_info["source"] != "custom":
            return super(CustomDataset, self).load_mask(image_id)

        mask = np.zeros([image_info["height"], image_info["width"], len(image_info["polygons"])], dtype=np.uint8)
        class_ids = []
        for i, p in enumerate(image_info["polygons"]):
            rr, cc = skimage.draw.polygon(p['all_points_y'], p['all_points_x