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常用算法概念

1. 传统机器学习算法

SIFT (Scale-Invariant Feature Transform)

• 概念：尺度不变特征变换，提取图像中的关键点和描述符
• 特点：对尺度、旋转、光照变化具有不变性

HOG (Histogram of Oriented Gradients)

• 概念：方向梯度直方图，统计图像局部区域的梯度方向分布
• 特点：对光照变化和阴影有良好鲁棒性，常用于行人检测

SVM (Support Vector Machine)

• 概念：支持向量机，用于分类和回归分析
• 特点：在高维空间中有效，内存使用效率高

2. 深度学习算法

CNN (Convolutional Neural Networks)

• 概念：卷积神经网络，专为图像处理设计的深度学习模型
• 特点：自动学习图像特征，层次化特征提取

ResNet (Residual Networks)

• 概念：残差网络，通过跳跃连接解决深层网络训练问题
• 特点：可训练非常深的网络（超过100层）

MobileNet

• 概念：轻量级CNN架构，使用深度可分离卷积
• 特点：适合移动和嵌入式设备，计算效率高

简单示例

使用OpenCV和SVM进行简单图像分类

import cv2
import numpy as np
from sklearn import svm
from sklearn.model_selection import train_test_split# 1. 特征提取 - 使用HOG
def extract_hog_features(image):win_size = (64, 64)block_size = (16, 16)block_stride = (8, 8)cell_size = (8, 8)nbins = 9hog = cv2.HOGDescriptor(win_size, block_size, block_stride, cell_size, nbins)features = hog.compute(image)return features.flatten()# 2. 准备训练数据（示例）
# 假设有两类图像数据：正样本和负样本
positive_samples = []  # 正样本图像列表
negative_samples = []  # 负样本图像列表# 提取特征
positive_features = [extract_hog_features(img) for img in positive_samples]
negative_features = [extract_hog_features(img) for img in negative_samples]# 构建训练数据
X = np.vstack([positive_features, negative_features])
y = np.hstack([np.ones(len(positive_features)), np.zeros(len(negative_features))])# 3. 训练SVM分类器
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
classifier = svm.SVC(kernel='rbf')
classifier.fit(X_train, y_train)# 4. 预测新图像
def predict_image(image):features = extract_hog_features(image)prediction = classifier.predict([features])return prediction[0]

使用PyTorch实现简单CNN图像分类

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader# 1. 定义简单的CNN模型
class SimpleCNN(nn.Module):def __init__(self, num_classes=10):super(SimpleCNN, self).__init__()self.features = nn.Sequential(nn.Conv2d(3, 32, kernel_size=3, padding=1),nn.ReLU(),nn.MaxPool2d(2, 2),nn.Conv2d(32, 64, kernel_size=3, padding=1),nn.ReLU(),nn.MaxPool2d(2, 2),nn.Conv2d(64, 128, kernel_size=3, padding=1),nn.ReLU(),nn.AdaptiveAvgPool2d((4, 4)))self.classifier = nn.Sequential(nn.Linear(128 * 4 * 4, 512),nn.ReLU(),nn.Dropout(0.5),nn.Linear(512, num_classes))def forward(self, x):x = self.features(x)x = x.view(x.size(0), -1)x = self.classifier(x)return x# 2. 模型训练示例
def train_model(model, train_loader, criterion, optimizer, num_epochs=10):model.train()for epoch in range(num_epochs):running_loss = 0.0for i, (inputs, labels) in enumerate(train_loader):optimizer.zero_grad()outputs = model(inputs)loss = criterion(outputs, labels)loss.backward()optimizer.step()running_loss += loss.item()print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader):.4f}')# 3. 使用模型进行预测
def predict(model, image):model.eval()with torch.no_grad():output = model(image.unsqueeze(0))_, predicted = torch.max(output, 1)return predicted.item()

使用预训练模型进行图像分类

import torch
from torchvision import models, transforms
from PIL import Image# 1. 加载预训练模型
model = models.resnet18(pretrained=True)
model.eval()# 2. 图像预处理
preprocess = transforms.Compose([transforms.Resize(256),transforms.CenterCrop(224),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])# 3. 加载并处理图像
image = Image.open('example.jpg')
input_tensor = preprocess(image)
input_batch = input_tensor.unsqueeze(0)# 4. 进行预测
with torch.no_grad():output = model(input_batch)# 5. 解析结果
probabilities = torch.nn.functional.softmax(output[0], dim=0)
_, predicted_class = torch.max(output, 1)print(f"Predicted class index: {predicted_class.item()}")