docs(code): 优化代码结构和注释
- 调整代码格式，提高可读性- 增加详细注释，解释代码功能 - 优化变量命名，提高代码可理解性 - 简化部分代码逻辑，提高执行效率
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@ -1,55 +1,58 @@
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import cv2
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import numpy as np
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import time
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import os
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# 导入必要的库
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import cv2 # OpenCV库用于图像处理
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import numpy as np # NumPy库用于数值计算
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import time # 时间模块用于计时
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import os # 操作系统模块用于文件和目录操作
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def sift_feature_extraction(image_path):
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# 读取图像
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# 读取输入图像
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img = cv2.imread(image_path)
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# 检查图像是否加载成功
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if img is None:
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print("无法加载图像，请检查路径")
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return
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# 转换为灰度图
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# 将图像转换为灰度图
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 创建SIFT对象
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# 创建SIFT特征检测器对象
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sift = cv2.SIFT_create()
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# 记录开始时间
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# 记录特征提取开始时间
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start_time = time.time()
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# 检测关键点和描述符
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# 检测关键点并计算描述符
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keypoints, descriptors = sift.detectAndCompute(gray, None)
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# 计算耗时
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# 计算特征提取耗时
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end_time = time.time()
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processing_time = end_time - start_time
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# 绘制关键点
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# 绘制检测到的关键点
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img_with_keypoints = cv2.drawKeypoints(
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gray, keypoints, img,
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flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS
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)
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# 创建输出目录
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# 创建输出目录（如果不存在）
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output_dir = "output/sift_results"
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os.makedirs(output_dir, exist_ok=True)
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# 保存结果
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# 构建输出文件路径
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output_path = os.path.join(output_dir, "sift_result.jpg")
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# 保存结果图像
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cv2.imwrite(output_path, img_with_keypoints)
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# 输出结果信息
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# 输出特征提取结果信息
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print(f"检测到 {len(keypoints)} 个关键点")
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print(f"描述符形状: {descriptors.shape if descriptors is not None else 'None'}")
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print(f"特征提取耗时: {processing_time:.4f} 秒")
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print(f"结果已保存至: {output_path}")
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# 显示结果（可选）
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# 显示结果窗口（可选）
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cv2.imshow('SIFT Features', img_with_keypoints)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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cv2.waitKey(0) # 等待按键
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cv2.destroyAllWindows() # 关闭所有窗口
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if __name__ == "__main__":
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# 示例用法
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@ -1,38 +1,36 @@
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import os
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import torch
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from torch.utils.data import DataLoader
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from torchvision import transforms
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from PIL import Image
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import cv2
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import numpy as np
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# 导入必要的库
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import os # 操作系统模块
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import torch # PyTorch深度学习框架
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from torch.utils.data import DataLoader # 数据加载器
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from torchvision import transforms # 图像变换工具
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from PIL import Image # 图像处理库
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import cv2 # OpenCV库
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import numpy as np # NumPy库
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# 导入训练好的分割数据集类和模型定义
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from unet_coco_segmentation import CocoSegDataset
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import segmentation_models_pytorch as smp # 如使用smp模型
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# 导入自定义模块
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from unet_coco_segmentation import CocoSegDataset # COCO数据集类
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import segmentation_models_pytorch as smp # 分割模型库
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# 配置
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TEST_DIR = '../data/test' # 测试集图像目录
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TEST_ANN = '../data/test/_annotations.coco.json' # 测试集COCO注释文件
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MODEL_PATH = 'unet_coco_segmentation.pth' # 预训练模型权重
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OUTPUT_DIR = 'output/unet_results'
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# 配置参数
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TEST_DIR = '../data/test' # 测试集目录
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TEST_ANN = '../data/test/_annotations.coco.json' # 测试集标注文件
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MODEL_PATH = 'unet_coco_segmentation.pth' # 模型权重路径
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OUTPUT_DIR = 'output/unet_results' # 输出目录
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# 建立输出目录
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# 创建输出目录（如果不存在）
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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# 设备配置
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# 设备配置（GPU或CPU）
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# 数据预处理（与训练时保持一致）
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# 数据预处理配置（与训练时保持一致）
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transform = transforms.Compose([
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transforms.Resize((256, 256)),
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transforms.ToTensor(),
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transforms.Normalize(mean=(0.485,0.456,0.406), std=(0.229,0.224,0.225)),
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transforms.Resize((256, 256)), # 调整尺寸
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transforms.ToTensor(), # 转换为张量
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transforms.Normalize(mean=(0.485,0.456,0.406), std=(0.229,0.224,0.225)), # 归一化
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])
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# 仅需同样的变换，不需要mask变换
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# 加载测试集
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from unet_coco_segmentation import CocoSegDataset
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test_dataset = CocoSegDataset(
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root_dir=TEST_DIR,
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annotation_file=TEST_ANN,
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@ -41,35 +39,36 @@ test_dataset = CocoSegDataset(
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)
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test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False)
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# 初始化模型（同训练时）
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# 初始化模型（与训练时相同）
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model = smp.Unet(
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encoder_name='resnet34',
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encoder_weights=None,
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in_channels=3,
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classes=1
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encoder_name='resnet34', # 编码器名称
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encoder_weights=None, # 不使用预训练权重
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in_channels=3, # 输入通道数
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classes=1 # 输出类别数
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)
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model.load_state_dict(torch.load(MODEL_PATH, map_location=device)) # 加载权重
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model.to(device)
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model.eval()
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# 加载预训练模型权重
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model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
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model.to(device) # 将模型移动到指定设备
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model.eval() # 设置为评估模式
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print("成功加载模型权重并切换到评估模式")
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# 遍历测试集
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for img, mask_true in test_loader:
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# img 保存为Tensor batch=1
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# 图像保存为Tensor，batch=1
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img = img.to(device)
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img_id = test_dataset.image_ids[test_loader.dataset.image_ids.index(test_dataset.image_ids[0])] # 这里获取ID
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# 预测
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# 获取图像ID
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img_id = test_dataset.image_ids[test_loader.dataset.image_ids.index(test_dataset.image_ids[0])]
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# 进行预测
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with torch.no_grad():
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output = model(img)
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output_prob = torch.sigmoid(output).squeeze().cpu().numpy()
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# 恢复到原始尺寸
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# 获取原图尺寸
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# 重新加载原始图像获取尺寸
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# 获取原始图像尺寸
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img_info = next(item for item in test_dataset.coco['images'] if item['id']==test_dataset.image_ids[0])
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orig_w, orig_h = img_info['width'], img_info['height']
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# 调整输出尺寸到原始图像大小
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output_prob = cv2.resize(output_prob, (orig_w, orig_h), interpolation=cv2.INTER_LINEAR)
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# 二值化
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# 二值化处理
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threshold = 0.5
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output_mask = (output_prob > threshold).astype(np.uint8) * 255
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@ -1,29 +1,34 @@
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# 导入必要的库
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import os # 文件和路径管理
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import time # 计时
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import time # 计时功能
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import logging # 日志记录
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import json # 处理COCO注释JSON
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import json # 处理JSON数据
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import numpy as np # 数值运算
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from PIL import Image # 图像处理
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import torch # PyTorch核心
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import torch # PyTorch深度学习框架
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import torch.nn as nn # 神经网络模块
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import torch.optim as optim # 优化器
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from torch.utils.data import Dataset, DataLoader # 数据集和加载器
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import torchvision.utils as vutils # 可视化工具
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# COCO数据集相关库
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from pycocotools import mask as maskUtils # COCO掩码处理工具
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import albumentations as A # 数据增强
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import albumentations as A # 数据增强库
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from albumentations.pytorch import ToTensorV2 # Albumentations到Tensor的转换
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torch.manual_seed(42) # 固定随机种子，确保可复现
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # 配置日志格式
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torch.backends.cudnn.benchmark = True
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# 固定随机种子，确保实验可复现
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torch.manual_seed(42)
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# 配置日志格式
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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# ------------------ 定义 U-Net 模型（自定义，不依赖外部分割包） ------------------
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# ------------------ 定义 U-Net 模型（自定义） ------------------
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class UNet(nn.Module):
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def __init__(self, in_channels=3, base_channels=64, out_channels=1):
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super(UNet, self).__init__()
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# 双卷积块
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# 双卷积块定义
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def double_conv(in_c, out_c):
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return nn.Sequential(
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nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
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@ -38,8 +43,8 @@ class UNet(nn.Module):
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self.enc2 = double_conv(base_channels, base_channels*2)
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self.enc3 = double_conv(base_channels*2, base_channels*4)
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self.enc4 = double_conv(base_channels*4, base_channels*8)
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self.pool = nn.MaxPool2d(2)
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# 中心
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self.pool = nn.MaxPool2d(2) # 最大池化层
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# 中心层
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self.center = double_conv(base_channels*8, base_channels*16)
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# 上采样路径
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self.up4 = nn.ConvTranspose2d(base_channels*16, base_channels*8, kernel_size=2, stride=2)
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self.dec2 = double_conv(base_channels*4, base_channels*2)
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self.up1 = nn.ConvTranspose2d(base_channels*2, base_channels, kernel_size=2, stride=2)
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self.dec1 = double_conv(base_channels*2, base_channels)
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# 最终1x1卷积
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# 最终1x1卷积层
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self.final = nn.Conv2d(base_channels, out_channels, kernel_size=1)
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def forward(self, x):
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# 编码
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# 编码路径
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e1 = self.enc1(x)
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e2 = self.enc2(self.pool(e1))
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e3 = self.enc3(self.pool(e2))
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e4 = self.enc4(self.pool(e3))
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# 中心
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# 中心层
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c = self.center(self.pool(e4))
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# 解码
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# 解码路径
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d4 = self.up4(c)
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d4 = torch.cat([d4, e4], dim=1)
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d4 = torch.cat([d4, e4], dim=1) # 特征拼接
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d4 = self.dec4(d4)
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d3 = self.up3(d4)
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d3 = torch.cat([d3, e3], dim=1)
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@ -74,108 +79,121 @@ class UNet(nn.Module):
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d1 = self.up1(d2)
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d1 = torch.cat([d1, e1], dim=1)
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d1 = self.dec1(d1)
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return self.final(d1)
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return self.final(d1) # 最终输出
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# ------------------ Dice Loss 与 IoU 指标 ------------------
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class DiceLoss(nn.Module):
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def __init__(self, eps=1e-6):
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super(DiceLoss, self).__init__()
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self.eps = eps
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self.eps = eps # 平滑项
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def forward(self, logits, targets):
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probs = torch.sigmoid(logits)
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num = 2 * (probs * targets).sum(dim=(2,3)) + self.eps
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den = probs.sum(dim=(2,3)) + targets.sum(dim=(2,3)) + self.eps
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return 1 - (num/den).mean()
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probs = torch.sigmoid(logits) # 转换为概率
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num = 2 * (probs * targets).sum(dim=(2,3)) + self.eps # 分子
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den = probs.sum(dim=(2,3)) + targets.sum(dim=(2,3)) + self.eps # 分母
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return 1 - (num/den).mean() # 返回Dice损失
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# 计算IoU评分
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def iou_score(preds, targets, eps=1e-6):
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preds = (preds > 0.5).float()
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inter = (preds * targets).sum(dim=(2,3))
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union = preds.sum(dim=(2,3)) + targets.sum(dim=(2,3)) - inter
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return ((inter+eps)/(union+eps)).mean().item()
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preds = (preds > 0.5).float() # 二值化预测结果
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inter = (preds * targets).sum(dim=(2,3)) # 交集
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union = preds.sum(dim=(2,3)) + targets.sum(dim=(2,3)) - inter # 并集
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return ((inter+eps)/(union+eps)).mean().item() # 返回IoU评分
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# ------------------ COCO 分割数据集 ------------------
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class CocoSegDataset(Dataset):
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def __init__(self, root_dir, annotation_file, transforms=None, mask_transforms=None):
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self.root_dir = root_dir
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self.transforms = transforms
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self.mask_transforms = mask_transforms
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self.root_dir = root_dir # 数据集根目录
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self.transforms = transforms # 数据变换
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self.mask_transforms = mask_transforms # 掩码变换
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with open(annotation_file, 'r') as f:
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self.coco = json.load(f)
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self.coco = json.load(f) # 加载COCO标注文件
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self.annotations = {}
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for ann in self.coco['annotations']:
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self.annotations.setdefault(ann['image_id'], []).append(ann)
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self.image_ids = list(self.annotations.keys())
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def __len__(self): return len(self.image_ids)
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self.annotations.setdefault(ann['image_id'], []).append(ann) # 建立图像ID到标注的映射
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self.image_ids = list(self.annotations.keys()) # 所有图像ID列表
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def __len__(self): return len(self.image_ids) # 返回数据集大小
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def __getitem__(self, idx):
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img_id = self.image_ids[idx]
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info = next(x for x in self.coco['images'] if x['id']==img_id)
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img = Image.open(os.path.join(self.root_dir, info['file_name'])).convert('RGB')
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h, w = info['height'], info['width']
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mask = np.zeros((h,w), dtype=np.uint8)
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for ann in self.annotations[img_id]:
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img_id = self.image_ids[idx] # 获取图像ID
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info = next(x for x in self.coco['images'] if x['id']==img_id) # 获取图像信息
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img = Image.open(os.path.join(self.root_dir, info['file_name'])).convert('RGB') # 读取图像
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h, w = info['height'], info['width'] # 获取图像尺寸
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mask = np.zeros((h,w), dtype=np.uint8) # 初始化掩码
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for ann in self.annotations[img_id]: # 生成掩码
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seg = ann['segmentation']
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if isinstance(seg, list): rle = maskUtils.merge(maskUtils.frPyObjects(seg,h,w))
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else: rle = seg
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mask += maskUtils.decode(rle)
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mask = (mask>0).astype(np.float32)
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mask = Image.fromarray(mask)
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mask = (mask>0).astype(np.float32) # 二值化掩码
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mask = Image.fromarray(mask) # 转换为PIL图像
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# 应用数据增强
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if self.transforms and self.mask_transforms:
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aug = self.transforms(image=np.array(img), mask=np.array(mask))
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img_t = aug['image']; m_t = aug['mask'].unsqueeze(0)
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else:
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img_t = ToTensorV2()(image=np.array(img))['image']
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m_t = ToTensorV2()(image=np.array(mask))['image']
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return img_t, m_t
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return img_t, m_t # 返回处理后的图像和掩码
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# ------------------ 主训练流程 ------------------
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if __name__ == '__main__':
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# 路径配置
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train_dir, val_dir = '../data/train', '../data/valid'
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train_ann, val_ann = os.path.join(train_dir,'_annotations.coco.json'), os.path.join(val_dir,'_annotations.coco.json')
|
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# 增强配置
|
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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()])
|
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val_tf = A.Compose([A.Resize(256,256),A.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225)),ToTensorV2()])
|
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train_dir, val_dir = '../data/train', '../data/valid' # 训练集和验证集目录
|
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train_ann, val_ann = os.path.join(train_dir,'_annotations.coco.json'), os.path.join(val_dir,'_annotations.coco.json') # 标注文件路径
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# 数据增强配置
|
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train_tf = A.Compose([
|
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A.Resize(256,256), # 调整尺寸
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A.HorizontalFlip(0.5), # 水平翻转
|
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A.RandomBrightnessContrast(0.2), # 随机亮度对比度
|
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A.ShiftScaleRotate(0.5), # 随机位移缩放旋转
|
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A.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225)), # 归一化
|
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ToTensorV2()]) # 转换为Tensor
|
||||
val_tf = A.Compose([
|
||||
A.Resize(256,256), # 调整尺寸
|
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A.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225)), # 归一化
|
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ToTensorV2()]) # 转换为Tensor
|
||||
# 数据加载
|
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train_ds = CocoSegDataset(train_dir,train_ann,train_tf,train_tf)
|
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val_ds = CocoSegDataset(val_dir, val_ann, val_tf, val_tf)
|
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train_ld = DataLoader(train_ds,batch_size=8,shuffle=True,num_workers=4)
|
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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)}")
|
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train_ds = CocoSegDataset(train_dir,train_ann,train_tf,train_tf) # 训练数据集
|
||||
val_ds = CocoSegDataset(val_dir, val_ann, val_tf, val_tf) # 验证数据集
|
||||
train_ld = DataLoader(train_ds,batch_size=8,shuffle=True,num_workers=4) # 训练数据加载器
|
||||
val_ld = DataLoader(val_ds, batch_size=8,shuffle=False,num_workers=4) # 验证数据加载器
|
||||
logging.info(f"Train samples: {len(train_ds)}, Val samples: {len(val_ds)}") # 输出数据集信息
|
||||
# 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)
|
||||
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # 设备选择
|
||||
print(f"当前设备：{device}") # 输出当前设备信息
|
||||
model = UNet().to(device) # 初始化模型并移动到指定设备
|
||||
opt = optim.AdamW(model.parameters(),lr=1e-3,weight_decay=1e-4) # 优化器
|
||||
sched = optim.lr_scheduler.ReduceLROnPlateau(opt,'min',patience=3,factor=0.5) # 学习率调度器
|
||||
bce = nn.BCEWithLogitsLoss(); dice = DiceLoss() # 损失函数
|
||||
epochs=20; vis_dir='output/val_visuals'; os.makedirs(vis_dir,exist_ok=True) # 训练参数和可视化目录
|
||||
# 训练循环
|
||||
for ep in range(1,epochs+1):
|
||||
model.train(); run_loss=0
|
||||
model.train(); run_loss=0 # 设置为训练模式
|
||||
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
|
||||
imgs,msks=imgs.to(device),msks.to(device) # 将数据移动到指定设备
|
||||
opt.zero_grad(); out=model(imgs) # 前向传播
|
||||
l=(bce(out,msks)+dice(out,msks)); l.backward(); opt.step() # 计算损失、反向传播
|
||||
run_loss+=l.item()*imgs.size(0) # 累计损失
|
||||
tr_loss=run_loss/len(train_ds) # 计算平均训练损失
|
||||
# 验证阶段
|
||||
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)
|
||||
imgs,msks=imgs.to(device),msks.to(device) # 将数据移动到指定设备
|
||||
out=model(imgs) # 前向传播
|
||||
v_loss+=(bce(out,msks)+dice(out,msks)).item()*imgs.size(0) # 累计验证损失
|
||||
pr=torch.sigmoid(out) # 转换为概率
|
||||
v_iou+=iou_score(pr,msks)*imgs.size(0) # 累计IoU评分
|
||||
v_dice+=(1 - dice(out,msks)).item()*imgs.size(0) # 累计Dice评分
|
||||
v_loss/=len(val_ds); v_iou/=len(val_ds); v_dice/=len(val_ds) # 计算平均验证指标
|
||||
# 输出训练信息
|
||||
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
|
||||
si,sm=next(iter(val_ld)); si=si.to(device) # 获取示例图像
|
||||
with torch.no_grad(): sp=torch.sigmoid(model(si)) # 进行预测
|
||||
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)) # 创建网格
|
||||
vpth=os.path.join(vis_dir,f'ep{ep}.png'); vutils.save_image(grid,vpth) # 保存可视化结果
|
||||
logging.info(f"Saved visual: {vpth}") # 输出保存信息
|
||||
sched.step(v_loss) # 更新学习率
|
||||
# 保存模型权重
|
||||
torch.save(model.state_dict(),'unet_coco_segmentation.pth')
|
||||
logging.info('训练完成，模型已保存')
|
||||
@ -1,6 +1,8 @@
|
||||
torch>=1.13.1
|
||||
torchvision>=0.14.1
|
||||
torch
|
||||
torchvision
|
||||
opencv-python>=4.5.5.64
|
||||
Pillow>=9.2.0
|
||||
kagglehub>=0.2.2
|
||||
numpy~=2.2.5
|
||||
pycocotools
|
||||
albumentations
|
||||
Loading…
Reference in New Issue
Block a user