你写一份实例分割读取数据集的代码

好的，以下是一个使用Mask R-CNN模型进行实例分割的示例代码，包括如何读取数据集：

import os
import sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf

# 导入Mask R-CNN库
ROOT_DIR = os.path.abspath("../")
sys.path.append(ROOT_DIR)  # 导入Mask R-CNN库的根目录
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
from mrcnn.config import Config

# 配置数据集路径和类别
class MyConfig(Config):
    NAME = "mydataset"
    IMAGES_PER_GPU = 1
    NUM_CLASSES = 1 + 2  # 背景+2种物体
    STEPS_PER_EPOCH = 100
    DETECTION_MIN_CONFIDENCE = 0.9

class MyDataset(utils.Dataset):
    def load_mydataset(self, dataset_dir, subset):
        self.add_class("mydataset", 1, "object1")
        self.add_class("mydataset", 2, "object2")
        # 读取训练集或验证集
        assert subset in ["train", "val"]
        dataset_dir = os.path.join(dataset_dir, subset)
        # 读取所有图像文件名
        filenames = os.listdir(dataset_dir)
        # 添加图像和标注
        for filename in filenames:
            if not filename.endswith(".jpg"):
                continue
            image_path = os.path.join(dataset_dir, filename)
            image = skimage.io.imread(image_path)
            height, width = image.shape[:2]
            self.add_image(
                "mydataset",
                image_id=filename,
                path=image_path,
                width=width, height=height,
                annotations=annotations)

    def load_mask(self, image_id):
        # 读取对应图像的标注文件
        info = self.image_info[image_id]
        path = info['path']
        mask_path = path[:-4] + "_mask.png"
        mask = skimage.io.imread(mask_path)
        # 创建一个包含所有物体的掩码
        obj_mask = np.zeros([info["height"], info["width"], len(info["annotations"])],
                            dtype=np.uint8)
        # 为每个物体创建一个单独的掩码
        class_ids = []
        for i, anno in enumerate(info["annotations"]):
            class_id = self.class_names.index(anno['class'])
            class_ids.append(class_id)
            obj_mask[:, :, i:i + 1] = mask == i + 1
        return obj_mask, np.array(class_ids, dtype=np.int32)

# 加载数据集
dataset_dir = "/path/to/dataset"
dataset = MyDataset()
dataset.load_mydataset(dataset_dir, "train")
dataset.prepare()

# 加载模型
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
model = modellib.MaskRCNN(mode="training", config=MyConfig(),
                          model_dir=MODEL_DIR)

# 开始训练模型
model.train(dataset, dataset_val,
            learning_rate=config.LEARNING_RATE,
            epochs=30,
            layers='heads')

这段代码使用`MyDataset`类来读取数据集，其中`load_mydataset`函数用于读取图像和标注，`load_mask`函数用于读取标注并创建一个掩码。通过这些函数，数据集可以被加载到模型中进行训练和验证。