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图像智能识别算法记录

news 2025/7/28 8:30:59

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()}")