👁️ 2025计算机视觉课程项目

项目时间: 2025年7月
项目类型: 课程项目
技术栈: Python, OpenCV, TensorFlow, PyTorch
GitHub: https://github.com/sjkncs/2025-cs-vision

📋 项目概述

本项目是2025年计算机视觉课程的综合实践项目，涵盖图像处理、目标检测、图像分割等多个计算机视觉核心技术。

项目链接:

🔗 GitHub仓库: https://github.com/sjkncs/2025-cs-vision
📚 项目文档: 完整的实验报告和代码注释

🎯 项目目标

核心任务

图像处理基础 - 滤波、边缘检测、形态学操作
特征提取 - SIFT、SURF、ORB等特征点检测
目标检测 - YOLO、Faster R-CNN等模型应用
图像分割 - 语义分割、实例分割
深度学习应用 - CNN、ResNet等网络架构

🛠️ 技术实现

1. 图像预处理

功能实现:

import cv2
import numpy as np

class ImageProcessor:
    def __init__(self):
        self.filters = {
            'gaussian': cv2.GaussianBlur,
            'median': cv2.medianBlur,
            'bilateral': cv2.bilateralFilter
        }
    
    def denoise(self, image, method='gaussian'):
        """图像去噪"""
        if method == 'gaussian':
            return cv2.GaussianBlur(image, (5, 5), 0)
        elif method == 'median':
            return cv2.medianBlur(image, 5)
        elif method == 'bilateral':
            return cv2.bilateralFilter(image, 9, 75, 75)
    
    def edge_detection(self, image, method='canny'):
        """边缘检测"""
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        
        if method == 'canny':
            edges = cv2.Canny(gray, 50, 150)
        elif method == 'sobel':
            sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=5)
            sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=5)
            edges = np.sqrt(sobelx**2 + sobely**2)
        
        return edges

2. 特征点检测与匹配

SIFT特征提取:

class FeatureDetector:
    def __init__(self):
        self.sift = cv2.SIFT_create()
        self.orb = cv2.ORB_create()
    
    def detect_sift(self, image):
        """SIFT特征检测"""
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        keypoints, descriptors = self.sift.detectAndCompute(gray, None)
        return keypoints, descriptors
    
    def match_features(self, desc1, desc2):
        """特征匹配"""
        bf = cv2.BFMatcher()
        matches = bf.knnMatch(desc1, desc2, k=2)
        
        # 应用比率测试
        good_matches = []
        for m, n in matches:
            if m.distance < 0.75 * n.distance:
                good_matches.append(m)
        
        return good_matches

3. 目标检测

YOLO模型应用:

import torch
from torchvision.models.detection import fasterrcnn_resnet50_fpn

class ObjectDetector:
    def __init__(self):
        self.model = fasterrcnn_resnet50_fpn(pretrained=True)
        self.model.eval()
    
    def detect(self, image):
        """目标检测"""
        # 预处理
        image_tensor = self.preprocess(image)
        
        # 推理
        with torch.no_grad():
            predictions = self.model([image_tensor])
        
        # 后处理
        boxes = predictions[0]['boxes'].cpu().numpy()
        labels = predictions[0]['labels'].cpu().numpy()
        scores = predictions[0]['scores'].cpu().numpy()
        
        return boxes, labels, scores
    
    def preprocess(self, image):
        """图像预处理"""
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = torch.from_numpy(image).permute(2, 0, 1).float() / 255.0
        return image

4. 图像分割

语义分割:

from torchvision.models.segmentation import deeplabv3_resnet50

class ImageSegmentation:
    def __init__(self):
        self.model = deeplabv3_resnet50(pretrained=True)
        self.model.eval()
    
    def segment(self, image):
        """语义分割"""
        input_tensor = self.preprocess(image)
        
        with torch.no_grad():
            output = self.model(input_tensor)['out'][0]
        
        output_predictions = output.argmax(0).byte().cpu().numpy()
        return output_predictions