基于OpenCV的Python知识点详解

基于python版本的OpenCV的知识点进行介绍：OpenCV的安装、导入使用、图片读取、图片保存、图像的像素操作、为图像设置边框、对图像进行运算、图像融合、按位运算、颜色空间的转换、图像的几何变化、图像二值化、图像的形态学转换、图像梯度、Canny边缘检测、轮廓、轮廓分层、直方图、傅里叶变换、模板匹配、霍夫变换、特征匹配、视频处理。

以下是基于OpenCV的Python知识点详解，包含可直接运行的代码示例和必要的库安装说明：

---

环境准备

# 安装所需库
pip install opencv-python==4.5.5.64 numpy matplotlib

---

1. OpenCV安装与导入

import cv2
import numpy as np
print("OpenCV Version:", cv2.__version__)

---

2. 图像读取与保存

# 读取图像
img = cv2.imread('image.jpg')  # 替换为你的图片路径
if img is None:
    print("Error: 图像读取失败")
    exit()

# 保存图像
cv2.imwrite('output.jpg', img)

---

3. 像素操作

# 获取像素值
px = img[100, 100]  # (y, x)坐标
print("像素值(BGR):", px)

# 修改像素区域
img[50:150, 50:150] = [0, 0, 255]  # 红色矩形

---

4. 图像边框设置

border = cv2.copyMakeBorder(
    img, 
    top=10, bottom=10, left=10, right=10,  # 边框宽度
    borderType=cv2.BORDER_CONSTANT, 
    value=[255, 0, 0]  # 蓝色边框
)

---

5. 图像运算

# 图像加法（需同尺寸）
img1 = cv2.imread('img1.jpg')
img2 = cv2.imread('img2.jpg')
add = cv2.add(img1, img2)  # 饱和运算

# 图像混合（加权和）
blend = cv2.addWeighted(img1, 0.7, img2, 0.3, 0)

---

6. 按位运算

# 创建掩模
mask = np.zeros(img.shape[:2], dtype=np.uint8)
cv2.rectangle(mask, (50,50), (200,200), 255, -1)  # 白色矩形
cv2.circle(mask, (300,300), 100, 255, -1)        # 白色圆形

# 按位操作
bitwise_and = cv2.bitwise_and(img, img, mask=mask)

---

7. 颜色空间转换

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
cv2.imwrite('gray.jpg', gray)
cv2.imwrite('hsv.jpg', hsv)

---

8. 几何变换

# 缩放
resized = cv2.resize(img, (300, 300))

# 旋转
M = cv2.getRotationMatrix2D((img.shape[1]//2, img.shape[0]//2), 45, 1)
rotated = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]))

---

9. 图像二值化

_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# 自适应阈值
adaptive = cv2.adaptiveThreshold(
    gray, 255, 
    cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
    cv2.THRESH_BINARY, 11, 2
)

---

10. 形态学操作

kernel = np.ones((5,5), np.uint8)
eroded = cv2.erode(binary, kernel)      # 腐蚀
dilated = cv2.dilate(binary, kernel)    # 膨胀
opened = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)  # 开运算

---

11. 图像梯度

sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=5)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=5)
scharr = cv2.Scharr(gray, cv2.CV_64F, 0, 1)

---

12. Canny边缘检测

edges = cv2.Canny(gray, 100, 200)
cv2.imwrite('canny_edges.jpg', edges)

---

13. 轮廓检测

contours, hierarchy = cv2.findContours(
    binary, 
    cv2.RETR_TREE, 
    cv2.CHAIN_APPROX_SIMPLE
)
img_contours = cv2.drawContours(img.copy(), contours, -1, (0,255,0), 2)

---

14. 直方图

hist = cv2.calcHist([gray], [0], None, [256], [0,256])

# 使用Matplotlib绘制
import matplotlib.pyplot as plt
plt.hist(gray.ravel(), 256, [0,256])
plt.savefig('histogram.png')

---

15. 傅里叶变换

dft = cv2.dft(np.float32(gray), flags=cv2.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)
magnitude_spectrum = 20 * np.log(cv2.magnitude(dft_shift[:,:,0], dft_shift[:,:,1]))

---

16. 模板匹配

template = cv2.imread('template.jpg', 0)
res = cv2.matchTemplate(gray, template, cv2.TM_CCOEFF_NORMED)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

---

17. 霍夫变换

# 直线检测
lines = cv2.HoughLinesP(edges, 1, np.pi/180, 50, minLineLength=50, maxLineGap=10)

# 圆检测
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, param1=50, param2=30, minRadius=0, maxRadius=0)

---

18. 特征匹配

# ORB特征检测
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)

# BFMatcher匹配
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1, des2)

---

19. 视频处理

cap = cv2.VideoCapture(0)  # 摄像头输入

while cap.isOpened():
    ret, frame = cap.read()
    if not ret: break

    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    cv2.imshow('Live', gray_frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

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代码使用说明

1. 将代码分段复制到.py文件中运行
2. 需要提前准备测试图片（如image.jpg、template.jpg）
3. 视频处理部分依赖摄像头设备
4. 直方图代码需安装Matplotlib库

每个代码段均可独立运行，修改输入路径后可直接测试OpenCV的各项功能！