Canny边缘检测算法原理及OpenCV实现详解

# Python + OpenCV 示例代码
import cv2
import numpy as np

# 读取图像
image = cv2.imread('example.jpg')

# 显示图像信息
print(f"图像形状: {image.shape}")
print(f"图像类型: {image.dtype}")
print(f"图像大小: {image.size} bytes")

# 显示图像
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np

class ImageProcessor:
    """图像处理示例类"""
    
    def __init__(self, image_path):
        """
        初始化图像处理器
        
        Args:
            image_path: 图像文件路径
        """
        self.image = cv2.imread(image_path)
        if self.image is None:
            raise ValueError(f"无法读取图像: {image_path}")
        
        self.height, self.width = self.image.shape[:2]
        print(f"图像尺寸: {self.width} x {self.height}")
    
    def to_grayscale(self):
        """转换为灰度图"""
        return cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)
    
    def resize(self, scale_percent):
        """按比例缩放图像"""
        width = int(self.width * scale_percent / 100)
        height = int(self.height * scale_percent / 100)
        return cv2.resize(self.image, (width, height))
    
    def apply_gaussian_blur(self, kernel_size=(5, 5)):
        """应用高斯模糊"""
        return cv2.GaussianBlur(self.image, kernel_size, 0)
    
    def detect_edges(self, threshold1=100, threshold2=200):
        """边缘检测"""
        gray = self.to_grayscale()
        return cv2.Canny(gray, threshold1, threshold2)

# 使用示例
if __name__ == "__main__":
    processor = ImageProcessor("example.jpg")
    
    # 灰度转换
    gray = processor.to_grayscale()
    cv2.imwrite("gray.jpg", gray)
    
    # 边缘检测
    edges = processor.detect_edges()
    cv2.imwrite("edges.jpg", edges)

# 创建虚拟环境
python -m venv cv_env
source cv_env/bin/activate  # Linux/Mac
# 或 cv_env\Scripts\activate  # Windows
# 安装OpenCV
pip install opencv-python
pip install opencv-contrib-python  # 包含额外模块
# 安装其他常用库
pip install numpy matplotlib pillow
# 验证安装
python -c "import cv2; print(cv2.__version__)"

# 检查环境配置
import cv2
import numpy as np
import matplotlib.pyplot as plt

print(f"OpenCV版本: {cv2.__version__}")
print(f"NumPy版本: {np.__version__}")

# 检查是否支持GPU
print(f"CUDA支持: {cv2.cuda.getCudaEnabledDeviceCount()}")

import cv2
import numpy as np

# 读取图像
image = cv2.imread('image.jpg')

# 检查是否成功读取
if image is None:
    print("错误：无法读取图像")
else:
    # 显示图像信息
    print(f"图像尺寸: {image.shape}")
    print(f"数据类型: {image.dtype}")
    
    # 显示图像
    cv2.imshow('Original Image', image)
    
    # 转换为灰度图
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    cv2.imshow('Gray Image', gray)
    
    # 等待按键
    cv2.waitKey(0)
    cv2.destroyAllWindows()

import cv2
import numpy as np

def process_image(image_path):
    """完整的图像处理流程"""
    
    # 1. 读取图像
    image = cv2.imread(image_path)
    if image is None:
        raise ValueError("无法读取图像")
    
    # 2. 转换为灰度图
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # 3. 高斯模糊去噪
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    
    # 4. 边缘检测
    edges = cv2.Canny(blurred, 50, 150)
    
    # 5. 查找轮廓
    contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    # 6. 绘制轮廓
    result = image.copy()
    cv2.drawContours(result, contours, -1, (0, 255, 0), 2)
    
    print(f"检测到 {len(contours)} 个轮廓")
    
    return result

# 使用示例
result = process_image('objects.jpg')
cv2.imshow('Result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np

class FeatureDetector:
    """特征检测器类"""
    
    def __init__(self):
        # 初始化ORB检测器
        self.orb = cv2.ORB_create()
        # 初始化SIFT检测器（需要opencv-contrib-python）
        # self.sift = cv2.SIFT_create()
    
    def detect_and_compute(self, image):
        """检测关键点并计算描述子"""
        keypoints, descriptors = self.orb.detectAndCompute(image, None)
        return keypoints, descriptors
    
    def match_features(self, img1, img2):
        """特征匹配"""
        # 检测特征点
        kp1, des1 = self.detect_and_compute(img1)
        kp2, des2 = self.detect_and_compute(img2)
        
        # 创建匹配器
        bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
        
        # 匹配特征点
        matches = bf.match(des1, des2)
        
        # 按距离排序
        matches = sorted(matches, key=lambda x: x.distance)
        
        # 绘制匹配结果
        result = cv2.drawMatches(img1, kp1, img2, kp2, matches[:20], None, flags=2)
        
        return result, len(matches)
    
    def find_homography(self, img1, img2):
        """计算单应性矩阵"""
        kp1, des1 = self.detect_and_compute(img1)
        kp2, des2 = self.detect_and_compute(img2)
        
        bf = cv2.BFMatcher(cv2.NORM_HAMMING)
        matches = bf.knnMatch(des1, des2, k=2)
        
        # 应用比率测试
        good = []
        for m, n in matches:
            if m.distance < 0.75 * n.distance:
                good.append(m)
        
        if len(good) > 10:
            src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
            dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
            
            H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
            return H
        
        return None

# 使用示例
detector = FeatureDetector()
img1 = cv2.imread('image1.jpg', 0)
img2 = cv2.imread('image2.jpg', 0)

result, num_matches = detector.match_features(img1, img2)
print(f"匹配点数量: {num_matches}")

cv2.imshow('Matches', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 更新pip
python -m pip install --upgrade pip
# 使用国内镜像
pip install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple
# 如果还是失败，尝试安装特定版本
pip install opencv-python==4.5.5.64

# Ubuntu/Debian
sudo apt-get install libgl1-mesa-glx
sudo apt-get install libglib2.0-0
# 或安装headless版本
pip install opencv-python-headless

import cv2
import os

# 检查文件是否存在
image_path = "image.jpg"
if not os.path.exists(image_path):
    print(f"文件不存在: {image_path}")
else:
    image = cv2.imread(image_path)
    if image is None:
        print("文件存在但无法读取，可能是格式问题")
    else:
        print("读取成功")

# 处理中文路径问题
def cv_imread(file_path):
    """支持中文路径的图像读取"""
    cv_img = cv2.imdecode(np.fromfile(file_path, dtype=np.uint8), -1)
    return cv_img

import cv2

# 分块处理大图像
def process_large_image(image_path, block_size=1000):
    """分块处理大图像"""
    image = cv2.imread(image_path)
    h, w = image.shape[:2]
    
    results = []
    for y in range(0, h, block_size):
        for x in range(0, w, block_size):
            # 提取图像块
            block = image[y:y+block_size, x:x+block_size]
            # 处理图像块
            processed = process_block(block)
            results.append(processed)
    
    return results

def process_block(block):
    """处理单个图像块"""
    # 这里添加具体的处理逻辑
    return cv2.GaussianBlur(block, (5, 5), 0)

# 1. 使用有意义的变量名
image_height, image_width = image.shape[:2]  # ✅ 好
h, w = image.shape[:2]  # ❌ 不够清晰

# 2. 添加文档字符串
def detect_faces(image, scale_factor=1.1, min_neighbors=5):
    """
    检测图像中的人脸
    
    Args:
        image: 输入图像（BGR格式）
        scale_factor: 图像缩放因子
        min_neighbors: 候选框邻居数量
    
    Returns:
        faces: 人脸边界框列表 [(x, y, w, h), ...]
    """
    pass

# 3. 使用类型注解
def resize_image(image: np.ndarray, scale: float) -> np.ndarray:
    h, w = image.shape[:2]
    new_size = (int(w * scale), int(h * scale))
    return cv2.resize(image, new_size)

# 4. 异常处理
try:
    image = cv2.imread('image.jpg')
    if image is None:
        raise ValueError("无法读取图像")
    # 处理图像...
except Exception as e:
    print(f"错误: {e}")