commit b1e90c4c9d38f154e009802f69f71746c70649d6 Author: fly6516 Date: Fri May 16 21:17:46 2025 +0800 feat(AI-exp-2): 实现图像分割与 SIFT 特征提取 - 添加 U-Net 模型实现图像分割功能- 实现 SIFT 特征提取算法 - 创建实验报告模板和环境配置指南 - 添加数据集下载脚本和目录结构设置脚本 - 实现模型训练和测试流程 diff --git a/README.md b/README.md new file mode 100644 index 0000000..aef60cd --- /dev/null +++ b/README.md @@ -0,0 +1,58 @@ +# AI-Exp-2 图像分割与SIFT特征提取 + +本项目实现了计算机视觉实验所需的U-Net图像分割和SIFT特征提取功能，包含完整的实验指导和报告模板。 + +## 项目结构 +``` +AI-exp-2/ +├── code/ # 核心代码（UNet/SIFT实现） +├── dataset/ # COCO格式数据集（包含train/valid/test子目录） +│ ├── train/ # 训练集图像 +│ ├── valid/ # 验证集图像 +│ ├── test/ # 测试集图像 +│ └── _annotations.coco.json # COCO标注文件 +├── docs/ # 实验报告模板和文档 +├── requirements.txt # 项目依赖库列表 +└── README.md # 项目说明文档 +``` + +## 快速开始 + +### 1. 环境配置 +```bash +# 创建虚拟环境 +python -m venv .venv +.\.venv\Scripts\activate + +# 安装依赖 +pip install -r requirements.txt +``` + +### 2. 数据准备 +1. 将COCO格式数据集组织为以下结构： +```bash +mkdir -p dataset/train dataset/valid dataset/test +``` +2. 在每个目录中放置对应的数据集图像和标注文件： + - `xxx_image.jpg` (原始图像) + - `_annotations.coco.json` (COCO格式标注文件) + +### 3. 运行实验 +```bash +# 图像分割实验（使用COCO格式数据集） +python code/unet_segmentation.py + +# SIFT特征提取 +python code/sift_features.py +``` + +## 文档资源 +1. [实验报告模板](docs/experiment_report.md) +2. [环境配置指南](setup_environment.md) +3. [实验执行手册](run_experiments.md) + +## 技术栈 +- PyTorch 1.13.1（深度学习框架） +- OpenCV 4.5.5（图像处理） +- Pillow（数据预处理） +- pycocotools 2.0.2（COCO数据集支持） \ No newline at end of file diff --git a/code/output/sift_results/sift_result.jpg b/code/output/sift_results/sift_result.jpg new file mode 100644 index 0000000..6d0ec3b Binary files /dev/null and b/code/output/sift_results/sift_result.jpg differ diff --git a/code/output/unet_results/debug_mask.png b/code/output/unet_results/debug_mask.png new file mode 100644 index 0000000..2eb2bca Binary files /dev/null and b/code/output/unet_results/debug_mask.png differ diff --git a/code/output/unet_results/example_mask.png b/code/output/unet_results/example_mask.png new file mode 100644 index 0000000..2eb2bca Binary files /dev/null and b/code/output/unet_results/example_mask.png differ diff --git a/code/sift_features.py b/code/sift_features.py new file mode 100644 index 0000000..721e820 --- /dev/null +++ b/code/sift_features.py @@ -0,0 +1,58 @@ +import cv2 +import numpy as np +import time +import os + +def sift_feature_extraction(image_path): + # 读取图像 + img = cv2.imread(image_path) + if img is None: + print("无法加载图像，请检查路径") + return + + # 转换为灰度图 + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + + # 创建SIFT对象 + sift = cv2.SIFT_create() + + # 记录开始时间 + start_time = time.time() + + # 检测关键点和描述符 + keypoints, descriptors = sift.detectAndCompute(gray, None) + + # 计算耗时 + end_time = time.time() + processing_time = end_time - start_time + + # 绘制关键点 + img_with_keypoints = cv2.drawKeypoints( + gray, keypoints, img, + flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS + ) + + # 创建输出目录 + output_dir = "output/sift_results" + os.makedirs(output_dir, exist_ok=True) + + # 保存结果 + output_path = os.path.join(output_dir, "sift_result.jpg") + cv2.imwrite(output_path, img_with_keypoints) + + # 输出结果信息 + print(f"检测到 {len(keypoints)} 个关键点") + print(f"描述符形状: {descriptors.shape if descriptors is not None else 'None'}") + print(f"特征提取耗时: {processing_time:.4f} 秒") + print(f"结果已保存至: {output_path}") + + # 显示结果（可选） + cv2.imshow('SIFT Features', img_with_keypoints) + cv2.waitKey(0) + cv2.destroyAllWindows() + +if __name__ == "__main__": + # 示例用法 + image_path = "../data/image.jpg" # 需要替换为实际图像路径 + print("开始SIFT特征提取...") + sift_feature_extraction(image_path) \ No newline at end of file diff --git a/code/test_unet.py b/code/test_unet.py new file mode 100644 index 0000000..edb695c --- /dev/null +++ b/code/test_unet.py @@ -0,0 +1,79 @@ +import os +import torch +from torch.utils.data import DataLoader +from torchvision import transforms +from PIL import Image +import cv2 +import numpy as np + +# 导入训练好的分割数据集类和模型定义 +from unet_coco_segmentation import CocoSegDataset +import segmentation_models_pytorch as smp # 如使用smp模型 + +# 配置 +TEST_DIR = '../data/test' # 测试集图像目录 +TEST_ANN = '../data/test/_annotations.coco.json' # 测试集COCO注释文件 +MODEL_PATH = 'unet_coco_segmentation.pth' # 预训练模型权重 +OUTPUT_DIR = 'output/unet_results' + +# 建立输出目录 +os.makedirs(OUTPUT_DIR, exist_ok=True) + +# 设备配置 +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +# 数据预处理（与训练时保持一致） +transform = transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Normalize(mean=(0.485,0.456,0.406), std=(0.229,0.224,0.225)), +]) +# 仅需同样的变换，不需要mask变换 + +# 加载测试集 +from unet_coco_segmentation import CocoSegDataset + +test_dataset = CocoSegDataset( + root_dir=TEST_DIR, + annotation_file=TEST_ANN, + transforms=None, # 在Dataset里单独处理 + mask_transforms=None +) +test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False) + +# 初始化模型（同训练时） +model = smp.Unet( + encoder_name='resnet34', + encoder_weights=None, + in_channels=3, + classes=1 +) +model.load_state_dict(torch.load(MODEL_PATH, map_location=device)) # 加载权重 +model.to(device) +model.eval() +print("成功加载模型权重并切换到评估模式") + +# 遍历测试集 +for img, mask_true in test_loader: + # img 保存为Tensor batch=1 + img = img.to(device) + img_id = test_dataset.image_ids[test_loader.dataset.image_ids.index(test_dataset.image_ids[0])] # 这里获取ID + # 预测 + with torch.no_grad(): + output = model(img) + output_prob = torch.sigmoid(output).squeeze().cpu().numpy() + # 恢复到原始尺寸 + # 获取原图尺寸 + # 重新加载原始图像获取尺寸 + img_info = next(item for item in test_dataset.coco['images'] if item['id']==test_dataset.image_ids[0]) + orig_w, orig_h = img_info['width'], img_info['height'] + output_prob = cv2.resize(output_prob, (orig_w, orig_h), interpolation=cv2.INTER_LINEAR) + + # 二值化 + threshold = 0.5 + output_mask = (output_prob > threshold).astype(np.uint8) * 255 + + # 保存结果 + output_path = os.path.join(OUTPUT_DIR, f"{img_id}_mask.png") + cv2.imwrite(output_path, output_mask) + print(f"Saved mask for image {img_id} to {output_path}") diff --git a/code/unet_segmentation.py b/code/unet_segmentation.py new file mode 100644 index 0000000..1f9c894 --- /dev/null +++ b/code/unet_segmentation.py @@ -0,0 +1,181 @@ +import os # 文件和路径管理 +import time # 计时 +import logging # 日志记录 +import json # 处理COCO注释JSON + +import numpy as np # 数值运算 +from PIL import Image # 图像处理 + +import torch # PyTorch核心 +import torch.nn as nn # 神经网络模块 +import torch.optim as optim # 优化器 +from torch.utils.data import Dataset, DataLoader # 数据集和加载器 +import torchvision.utils as vutils # 可视化工具 + +from pycocotools import mask as maskUtils # COCO掩码处理工具 +import albumentations as A # 数据增强 +from albumentations.pytorch import ToTensorV2 # Albumentations到Tensor的转换 + +torch.manual_seed(42) # 固定随机种子，确保可复现 +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # 配置日志格式 + +# ------------------ 定义 U-Net 模型（自定义，不依赖外部分割包） ------------------ +class UNet(nn.Module): + def __init__(self, in_channels=3, base_channels=64, out_channels=1): + super(UNet, self).__init__() + # 双卷积块 + def double_conv(in_c, out_c): + return nn.Sequential( + nn.Conv2d(in_c, out_c, kernel_size=3, padding=1), + nn.BatchNorm2d(out_c), + nn.ReLU(inplace=True), + nn.Conv2d(out_c, out_c, kernel_size=3, padding=1), + nn.BatchNorm2d(out_c), + nn.ReLU(inplace=True) + ) + # 下采样路径 + self.enc1 = double_conv(in_channels, base_channels) + self.enc2 = double_conv(base_channels, base_channels*2) + self.enc3 = double_conv(base_channels*2, base_channels*4) + self.enc4 = double_conv(base_channels*4, base_channels*8) + self.pool = nn.MaxPool2d(2) + # 中心 + self.center = double_conv(base_channels*8, base_channels*16) + # 上采样路径 + self.up4 = nn.ConvTranspose2d(base_channels*16, base_channels*8, kernel_size=2, stride=2) + self.dec4 = double_conv(base_channels*16, base_channels*8) + self.up3 = nn.ConvTranspose2d(base_channels*8, base_channels*4, kernel_size=2, stride=2) + self.dec3 = double_conv(base_channels*8, base_channels*4) + self.up2 = nn.ConvTranspose2d(base_channels*4, base_channels*2, kernel_size=2, stride=2) + self.dec2 = double_conv(base_channels*4, base_channels*2) + self.up1 = nn.ConvTranspose2d(base_channels*2, base_channels, kernel_size=2, stride=2) + self.dec1 = double_conv(base_channels*2, base_channels) + # 最终1x1卷积 + self.final = nn.Conv2d(base_channels, out_channels, kernel_size=1) + + def forward(self, x): + # 编码 + e1 = self.enc1(x) + e2 = self.enc2(self.pool(e1)) + e3 = self.enc3(self.pool(e2)) + e4 = self.enc4(self.pool(e3)) + # 中心 + c = self.center(self.pool(e4)) + # 解码 + d4 = self.up4(c) + d4 = torch.cat([d4, e4], dim=1) + d4 = self.dec4(d4) + d3 = self.up3(d4) + d3 = torch.cat([d3, e3], dim=1) + d3 = self.dec3(d3) + d2 = self.up2(d3) + d2 = torch.cat([d2, e2], dim=1) + d2 = self.dec2(d2) + d1 = self.up1(d2) + d1 = torch.cat([d1, e1], dim=1) + d1 = self.dec1(d1) + return self.final(d1) + +# ------------------ Dice Loss 与 IoU 指标 ------------------ +class DiceLoss(nn.Module): + def __init__(self, eps=1e-6): + super(DiceLoss, self).__init__() + self.eps = eps + def forward(self, logits, targets): + probs = torch.sigmoid(logits) + num = 2 * (probs * targets).sum(dim=(2,3)) + self.eps + den = probs.sum(dim=(2,3)) + targets.sum(dim=(2,3)) + self.eps + return 1 - (num/den).mean() + +def iou_score(preds, targets, eps=1e-6): + preds = (preds > 0.5).float() + inter = (preds * targets).sum(dim=(2,3)) + union = preds.sum(dim=(2,3)) + targets.sum(dim=(2,3)) - inter + return ((inter+eps)/(union+eps)).mean().item() + +# ------------------ COCO 分割数据集 ------------------ +class CocoSegDataset(Dataset): + def __init__(self, root_dir, annotation_file, transforms=None, mask_transforms=None): + self.root_dir = root_dir + self.transforms = transforms + self.mask_transforms = mask_transforms + with open(annotation_file, 'r') as f: + self.coco = json.load(f) + self.annotations = {} + for ann in self.coco['annotations']: + self.annotations.setdefault(ann['image_id'], []).append(ann) + self.image_ids = list(self.annotations.keys()) + def __len__(self): return len(self.image_ids) + def __getitem__(self, idx): + img_id = self.image_ids[idx] + info = next(x for x in self.coco['images'] if x['id']==img_id) + img = Image.open(os.path.join(self.root_dir, info['file_name'])).convert('RGB') + h, w = info['height'], info['width'] + mask = np.zeros((h,w), dtype=np.uint8) + for ann in self.annotations[img_id]: + seg = ann['segmentation'] + if isinstance(seg, list): rle = maskUtils.merge(maskUtils.frPyObjects(seg,h,w)) + else: rle = seg + mask += maskUtils.decode(rle) + mask = (mask>0).astype(np.float32) + mask = Image.fromarray(mask) + if self.transforms and self.mask_transforms: + aug = self.transforms(image=np.array(img), mask=np.array(mask)) + img_t = aug['image']; m_t = aug['mask'].unsqueeze(0) + else: + img_t = ToTensorV2()(image=np.array(img))['image'] + m_t = ToTensorV2()(image=np.array(mask))['image'] + return img_t, m_t + +# ------------------ 主训练流程 ------------------ +if __name__ == '__main__': + # 路径配置 + train_dir, val_dir = '../data/train', '../data/valid' + train_ann, val_ann = os.path.join(train_dir,'_annotations.coco.json'), os.path.join(val_dir,'_annotations.coco.json') + # 增强配置 + train_tf = A.Compose([A.Resize(256,256),A.HorizontalFlip(0.5),A.RandomBrightnessContrast(0.2),A.ShiftScaleRotate(0.5),A.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225)),ToTensorV2()]) + val_tf = A.Compose([A.Resize(256,256),A.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225)),ToTensorV2()]) + # 数据加载 + train_ds = CocoSegDataset(train_dir,train_ann,train_tf,train_tf) + val_ds = CocoSegDataset(val_dir, val_ann, val_tf, val_tf) + train_ld = DataLoader(train_ds,batch_size=8,shuffle=True,num_workers=4) + val_ld = DataLoader(val_ds, batch_size=8,shuffle=False,num_workers=4) + logging.info(f"Train samples: {len(train_ds)}, Val samples: {len(val_ds)}") + # 模型与训练配置 + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + model = UNet().to(device) + opt = optim.AdamW(model.parameters(),lr=1e-3,weight_decay=1e-4) + sched = optim.lr_scheduler.ReduceLROnPlateau(opt,'min',patience=3,factor=0.5) + bce = nn.BCEWithLogitsLoss(); dice = DiceLoss() + epochs=20; vis_dir='output/val_visuals'; os.makedirs(vis_dir,exist_ok=True) + # 训练循环 + for ep in range(1,epochs+1): + model.train(); run_loss=0 + for imgs,msks in train_ld: + imgs,msks=imgs.to(device),msks.to(device) + opt.zero_grad(); out=model(imgs) + l=(bce(out,msks)+dice(out,msks)); l.backward(); opt.step() + run_loss+=l.item()*imgs.size(0) + tr_loss=run_loss/len(train_ds) + # 验证 + model.eval(); v_loss=0; v_iou=0; v_dice=0 + with torch.no_grad(): + for imgs,msks in val_ld: + imgs,msks=imgs.to(device),msks.to(device) + out=model(imgs) + v_loss+=(bce(out,msks)+dice(out,msks)).item()*imgs.size(0) + pr=torch.sigmoid(out) + v_iou+=iou_score(pr,msks)*imgs.size(0) + v_dice+=(1 - dice(out,msks)).item()*imgs.size(0) + v_loss/=len(val_ds); v_iou/=len(val_ds); v_dice/=len(val_ds) + logging.info(f"Epoch {ep}/{epochs} - Tr:{tr_loss:.4f} Val:{v_loss:.4f} IoU:{v_iou:.4f} Dice:{v_dice:.4f}") + # 可视化 + si,sm=next(iter(val_ld)); si=si.to(device) + with torch.no_grad(): sp=torch.sigmoid(model(si)) + grid=vutils.make_grid(torch.cat([si.cpu(),sm.repeat(1,3,1,1).cpu(),sp.repeat(1,3,1,1).cpu()],0),nrow=si.size(0)) + vpth=os.path.join(vis_dir,f'ep{ep}.png'); vutils.save_image(grid,vpth) + logging.info(f"Saved visual: {vpth}") + sched.step(v_loss) + # 保存模型 + torch.save(model.state_dict(),'unet_coco_segmentation.pth') + logging.info('训练完成，模型已保存') diff --git a/data/README.txt b/data/README.txt new file mode 100644 index 0000000..251ff1f --- /dev/null +++ b/data/README.txt @@ -0,0 +1,20 @@ + +TumorSegmentation +=================================================================== + +This dataset was exported via roboflow.com on August 19, 2023 + +The dataset includes 2146 images. +Tumors are annotated in COCO Segmentation format. +==================================================================== + +The following pre-processing was applied to each image: +* Auto-orientation of pixel data (with EXIF-orientation stripping) +* Resize to 640x640 (Stretch) +* No image augmentation techniques 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\ No newline at end of file diff --git a/dataset-download.py b/dataset-download.py new file mode 100644 index 0000000..01458b3 --- /dev/null +++ b/dataset-download.py @@ -0,0 +1,6 @@ +import kagglehub + +# Download latest version +path = kagglehub.dataset_download("pkdarabi/brain-tumor-image-dataset-semantic-segmentation") + +print("Path to dataset files:", path) \ No newline at end of file diff --git a/docs/experiment_report.md b/docs/experiment_report.md new file mode 100644 index 0000000..e3ff797 --- /dev/null +++ b/docs/experiment_report.md @@ -0,0 +1,46 @@ +# 计算机视觉实验报告 - 图像分割与SIFT特征提取 + +## 一、实验目的 +1. 理解图像分割的基本原理 +2. 掌握U-Net网络架构和SIFT特征提取算法 +3. 实现端到端的图像分割和特征提取流程 + +## 二、实验环境 +- 操作系统：Windows 11 +- 开发环境：PyCharm 2025.1 +- 编程语言：Python 3.9 +- 依赖库： + - PyTorch 1.13.1 + - OpenCV 4.5.5 + - Pillow 9.2.0 + +## 三、实验原理 +### 3.1 U-Net网络结构 +- 编码器-解码器架构 +- 跳跃连接机制 +- 适用于医学图像分割 + +### 3.2 SIFT特征提取 +- 尺度不变性特征检测 +- 旋转不变性描述符 +- 关键点检测算法 + +## 四、核心代码展示 +```python +# UNet网络核心结构示例 +self.down1 = double_conv(in_channels, 64) +self.up3 = nn.ConvTranspose2d(512, 256, 2, stride=2) +``` + +## 五、算法分析 +| 算法 | 时间复杂度 | 空间复杂度 | 特点 | +|------|-----------|-----------|------| +| U-Net | O(n²) | O(n) | 适合高分辨率图像 | +| SIFT | O(n log n) | O(n) | 尺度/旋转不变性 | + +## 六、实验结果 +- 分割结果示例：`output/unet_results/` +- SIFT特征图：`output/sift_results/sift_result.jpg` + +## 七、实验结论 +（此处填写个人实验心得） \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..be906f9 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +torch>=1.13.1 +torchvision>=0.14.1 +opencv-python>=4.5.5.64 +Pillow>=9.2.0 +kagglehub>=0.2.2 +numpy~=2.2.5 \ No newline at end of file diff --git a/run_experiments.md b/run_experiments.md new file mode 100644 index 0000000..3a142ee --- /dev/null +++ b/run_experiments.md @@ -0,0 +1,69 @@ +# 激活虚拟环境 +.\.venv\Scripts\activate + +# 确保数据集已正确放置在data/目录下 +# 验证数据集结构： +# - data/images/ (原始图像) +# - data/masks/ (对应掩码) + +# 运行UNet训练 +python code/unet_segmentation.py +``` + +预期输出： +``` +Epoch [1/10], Loss: 0.4567 +Epoch [2/10], Loss: 0.3214 +... +训练完成，总耗时: 125.36 秒 +模型已保存为 unet_brain_tumor.pth +``` + +## 运行图像分割实验（COCO格式数据集） +```bash +# 激活虚拟环境 +.\.venv\Scripts\activate + +# 确保数据集已正确放置在dataset/目录下 +# 验证数据集结构： +# - dataset/train/ (训练集图像) +# - dataset/valid/ (验证集图像) +# - dataset/test/ (测试集图像) +# - _annotations.coco.json (标注文件) + +# 运行UNet训练 +python code/unet_segmentation.py +``` + +预期输出： +``` +INFO - 成功加载 N 个训练样本 +INFO - Epoch [1/10], Loss: 0.4567 +INFO - Epoch [2/10], Loss: 0.3214 +... +INFO - 训练完成，总耗时: 125.36 秒 +INFO - 模型已保存为 unet_coco.pth``` + +## 运行SIFT特征提取实验 +```bash +# 激活虚拟环境 +.\.venv\Scripts\activate + +# 运行SIFT特征提取 +python code/sift_features.py +``` + +预期输出： +``` +开始SIFT特征提取... +检测到 128 个关键点 +描述符形状: (128, 128) +特征提取耗时: 0.0456 秒 +结果已保存至: output/sift_results/sift_result.jpg``` +``` + +``` + +``` + +``` diff --git a/setup_directories.py b/setup_directories.py new file mode 100644 index 0000000..6e6c7d9 --- /dev/null +++ b/setup_directories.py @@ -0,0 +1,15 @@ +import os + +def create_directories(): + directories = [ + "data/images", + "data/masks", + "output/sift_results" + ] + + for dir_path in directories: + os.makedirs(dir_path, exist_ok=True) + print(f"已创建目录: {dir_path}") + +if __name__ == "__main__": + create_directories() \ No newline at end of file diff --git a/setup_environment.md b/setup_environment.md new file mode 100644 index 0000000..e052535 --- /dev/null +++ b/setup_environment.md @@ -0,0 +1,71 @@ +# 环境配置指南 + +## 安装依赖库 +```bash +# 激活虚拟环境（如果使用） +.\.venv\Scripts\activate + +# 安装核心依赖 +pip install -r requirements.txt +``` + +## COCO数据集准备 +> 重要：请将COCO格式数据集组织为以下结构： + +``` +dataset/ +├── train/ # 存放训练集图像 +├── valid/ # 存放验证集图像 +├── test/ # 存放测试集图像 +└── _annotations.coco.json # COCO格式标注文件（需在每个子目录下） +``` + +### 数据集获取建议 +- 可从COCO官方数据集下载：https://cocodataset.org/#download +- 或使用自定义标注数据（需符合COCO格式规范） + +## 数据集准备 +> 重要：请确保已将脑肿瘤数据集组织为以下结构： + +``` +data/ +├── images/ # 存放原始图像（文件名需包含"_image"后缀） +└── masks/ # 存放对应掩码图像（文件名需包含"_mask"后缀） +``` + +### 数据集命名规范 +1. 原始图像：`xxx_image.jpg` +2. 对应掩码：`xxx_mask.jpg` +3. 确保图像和掩码文件名除后缀外完全相同 + +### 数据集获取建议 +- 可从Kaggle脑肿瘤数据集下载：https://www.kaggle.com/datasets/pkdarabi/brain-tumor-image-dataset-semantic-segmentation +- 或使用自定义标注数据 + +## 数据准备指南 + +### 图像分割数据集 +1. **Oxford-IIIT Pet Dataset** (推荐) + - 下载地址: https://www.robots.ox.ac.uk/~vgg/data/pets/ + - 解压后包含两个主要目录： + - `images/` - 原始图像 + - `annotations/trimaps/` - 对应的掩码图像 + - 将图像复制到项目目录： + ```bash + cp -r images/ ../AI-exp-2/data/images/ + cp -r annotations/trimaps/ ../AI-exp-2/data/masks/ + ``` + +2. **自定义数据集** + - 准备至少20对图像和对应的二值掩码图像 + - 图像命名需保持一致（如：image001.jpg 和 image001_mask.jpg） + +### SIFT测试图像 +1. 准备一张彩色测试图像（JPG格式） +2. 重命名为 `image.jpg` +3. 放置到 `data/` 目录下 + +## 目录结构验证 +```bash +# 验证目录结构是否正确 +python setup_directories.py \ No newline at end of file