1. OpenCV简介
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OpenCV是一个基于BSD许可（开源）发行的跨平台计算机视觉和机器学习软件库，可以运行在Linux、Windows、Android和Mac OS操作系统上(未来期待在Harmony OS上运行）.
它轻量级而且高效——由一系列 C 函数和少量 C++ 类构成，同时提供了Python、Ruby、MATLAB等语言的接口，实现了图像处理和计算机视觉方面的很多通用算法。
2. Opencv模块
| 模块 | 功能 |
|---|---|
| Core | 核心模块，包含最基础的操作 |
| Imgproc | 图像处理模块 |
| Objdectect | 目标检测模块 |
| Feature2D | 2D特征检测模块 |
| Video | 视频处理模块 |
| HighGUI | 高层图像用户界面 |
| Calib3d | 3D重建模块 |
| ML | 机器学习模块 |
| FLANN | 最近邻搜索模块 |
| Stitching | 图像拼接模块 |
| Photo | 计算图像学 |
| Superres | 超分辨率模块 |
| GPU | GPU并行加速模块 |
3. OpenCV总览
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OpenCV框架中的每一个模块都包含大量的计算机视觉方法，每一个模块都能独当一面，功能强大。
本篇文章将介绍OpenCV库中最重要的模块：Imgproc（图像处理模块）。
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图像处理模块包括：图像的读取、显示、保存；几何运算；灰度变换；几何变换；平滑、锐化；数学形态学；阈值分割；边缘检测；色彩空间；形状绘制等。
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图像读取、显示、保存
| 函数 | 功能 |
|---|---|
| cv2.imread( ) | 图像读取 |
| cv2.imshow( ) | 图像显示 |
| cv2.imwrite( ) | 图像保存 |
"""图像读取、显示、保存"""
img = cv2.imread('shiyuan.png')
cv2.imwrite('shi.png',img)
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
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几何运算
| 函数 | 功能 |
|---|---|
| img1+img2 | 图像加法 |
| cv2.addWeight( ) | 图像融合 |
"""几何运算"""
img1 = cv2.imread('shiyuan.png')
img2 = cv2.imread('lizi.png')
img3 = cv2.resize(img1,(300,300))+cv2.resize(img2,(300,300))
img4 = cv2.addWeighted(cv2.resize(img1,(300,300)),0.3,cv2.resize(img2,(300,300)),0.7,20)
cv2.imshow('img3',img3)
cv2.imshow('img4',img4)
cv2.waitKey(0)
cv2.destroyAllWindows()
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灰度变换
| 函数 | 功能 |
|---|---|
| 对数变换 | 变换图像灰度 |
| 伽马变换 | 变换图像灰度 |
| 直方图均衡化 | 变换图像灰度 |
| 直方图规定化 | 变换图像灰度 |
"""灰度变换"""
import cv2
import copy
img = cv2.imread('bai.png',1)
img1 = cv2.imread('bai.png',0)
img = cv2.resize(img,(400,300))
img1 = cv2.resize(img,(400,300))
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#伽马变换
gamma = copy.deepcopy(gray)
rows = img.shape[0]
cols = img.shape[1]
for i in range(rows):
for j in range(cols):
gamma[i][j]=3*pow(gamma[i][j],0.8)
cv2.imshow('img',img)
cv2.imshow('gray',img1)
cv2.imshow('gamma',gamma)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""灰度变换"""
import cv2
import copy
import math
img = cv2.imread('bai.png',1)
img1 = cv2.imread('bai.png',0)
img = cv2.resize(img,(400,300))
img1 = cv2.resize(img,(400,300))
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#对数变换
logc = copy.deepcopy(gray)
rows=img.shape[0]
cols=img.shape[1]
for i in range(rows):
for j in range(cols):
logc[i][j] = 3 * math.log(1 + logc[i][j])
cv2.imshow('img',img)
cv2.imshow('gray',img1)
cv2.imshow('logc',logc)
cv2.waitKey(0)
cv2.destroyAllWindows()
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"""灰度变换"""
import cv2
import copy
import math
img = cv2.imread('bai.png',1)
img1 = cv2.imread('bai.png',0)
img = cv2.resize(img,(400,300))
img1 = cv2.resize(img,(400,300))
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# 反色变换
cover=copy.deepcopy(gray)
rows=img.shape[0]
cols=img.shape[1]
for i in range(rows):
for j in range(cols):
cover[i][j]=255-cover[i][j]
#通过窗口展示图片 第一个参数为窗口名 第二个为读取的图片变量
cv2.imshow('img',img)
cv2.imshow('gray',img1)
cv2.imshow('cover',cover)
cv2.waitKey(0)
cv2.destroyAllWindows()
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#直方图规定化
import cv2
import numpy as np
import matplotlib.pyplot as plt
img0=cv2.imread('hua.png')#读取原图片
scr=cv2.imread('tu.png')#读取目标图片
#把两张图片转成真正的灰度图片，因为自己只会做灰度图片的规定化
img0=cv2.cvtColor(img0,cv2.COLOR_BGR2GRAY)
img=img0.copy()#用于之后做对比图
scr=cv2.cvtColor(scr,cv2.COLOR_BGR2GRAY)
mHist1=[]
mNum1=[]
inhist1=[]
mHist2=[]
mNum2=[]
inhist2=[]
#对原图像进行均衡化
for i in range(256):
mHist1.append(0)
row,col=img.shape#获取原图像像素点的宽度和高度
for i in range(row):
for j in range(col):
mHist1[img[i,j]]= mHist1[img[i,j]]+1#统计灰度值的个数
mNum1.append(mHist1[0]/img.size)
for i in range(0,255):
mNum1.append(mNum1[i]+mHist1[i+1]/img.size)
for i in range(256):
inhist1.append(round(255*mNum1[i]))
#对目标图像进行均衡化
for i in range(256):
mHist2.append(0)
rows,cols=scr.shape#获取目标图像像素点的宽度和高度
for i in range(rows):
for j in range(cols):
mHist2[scr[i,j]]= mHist2[scr[i,j]]+1#统计灰度值的个数
mNum2.append(mHist2[0]/scr.size)
for i in range(0,255):
mNum2.append(mNum2[i]+mHist2[i+1]/scr.size)
for i in range(256):
inhist2.append(round(255*mNum2[i]))
在这里插入图片描述
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几何变换
| 函数 | 功能 |
|---|---|
| cv2.resize( ) | 图像缩放 |
| cv2.warpAffine( ) | 图像平移 |
| cv2.getRotationMatrix2D( ) cv2.warpAffine( ) | 图像旋转 |
| cv2.getAffineTransform( ) cv2.warpAffine( ) | 仿射变换 |
| cv2.getPerspectiveTransform( ) cv2.warpPerspective( ) | 透射变换 |
| cv2.pyrUp( ) | 高斯金字塔上采样 |
| cv2.pyrDown( ) | 高斯金字塔下采样 |
| img-cv2.pyrUp(cv2.pyrDown(img)) | 拉普拉斯金字塔 |
"""几何变换"""
img = cv2.imread('shiyuan.png')
img1 = cv2.resize(img,(300,300))
M = np.float32([[1,0,30],[0,1,60]])
img2 = cv2.warpAffine(img1,M,(300,300))
img2 = cv2.putText(img2,'panning',(20,30),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
M = cv2.getRotationMatrix2D(((300-1)/2.0,(300-1)/2.0),45,1)
img3 = cv2.warpAffine(img1,M,(300,300))
img3 = cv2.putText(img3,'rotation',(20,30),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
matr1 = np.float32([[50,50],[200,50],[50,200]])
matr2 = np.float32([[10,100],[200,50],[100,250]])
M = cv2.getAffineTransform(matr1,matr2)
img4 = cv2.warpAffine(img1,M,(300,300))
img4 = cv2.putText(img4,'affine',(20,30),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
matr1 = np.float32([[56,65],[368,52],[28,387],[389,390]])
matr2 = np.float32([[0,0],[300,0],[0,300],[300,300]])
M = cv2.getPerspectiveTransform(matr1,matr2)
img5 = cv2.warpPerspective(img1,M,(300,300))
img5 = cv2.putText(img5,'perspective',(20,30),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
cv2.imshow('img1',img1)
cv2.imshow('img2',img2)
cv2.imshow('img3',img3)
cv2.imshow('img4',img4)
cv2.imshow('img5',img5)
cv2.waitKey(0)
cv2.destroyAllWindows()
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"""图像金字塔"""
import cv2
#高斯金字塔
def pyramid_demo(image):
level = 2
temp = image.copy()
pyramid_images = []
for i in range(level):
dst = cv2.pyrDown(temp)
pyramid_images.append(dst)
cv2.imshow("pyramid"+str(i+1), dst)
temp = dst.copy()
return pyramid_images
#拉普拉斯金字塔
def lapalian_demo(image):
pyramid_images = pyramid_demo(image)
level = len(pyramid_images)
for i in range(level-1, -1, -1):
if (i-1) < 0:
expand = cv2.pyrUp(pyramid_images[i], dstsize = image.shape[:2])
lpls = cv2.subtract(image, expand)
cv2.imshow("lapalian_down_"+str(i+1), lpls)
else:
expand = cv2.pyrUp(pyramid_images[i], dstsize = pyramid_images[i-1].shape[:2])
lpls = cv2.subtract(pyramid_images[i-1], expand)
cv2.imshow("lapalian_down_"+str(i+1), lpls)
src = cv2.resize(cv2.imread('shiyuan.png'),(256,256))
cv2.namedWindow('input_image')
cv2.imshow('input_image', src)
lapalian_demo(src)
cv2.waitKey(0)
cv2.destroyAllWindows()
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"""直方图均衡化"""
import cv2
import numpy as np
img = cv2.imread('bai.png',0)
img = cv2.resize(img,(400,300))
equ = cv2.equalizeHist(img)
cv2.imshow('img',equ)
cv2.waitKey()
cv2.destroyAllWindows()
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平滑、锐化
| 函数 | 功能 |
|---|---|
| cv2.blur( ) | 均值滤波 |
| cv2.GaussianBlur( ) | 高斯滤波 |
| cv2.medianBlur( ) | 中值滤波 |
| cv2.bilateralFilter( ) | 双边滤波 |
"""平滑、锐化"""
import cv2
img = cv2.imread('shiyuan.png')
img = cv2.resize(img,(300,300))
img1 = cv2.blur(img,(11,11))
img2 = cv2.GaussianBlur(img,(11,11),0)
img3 = cv2.medianBlur(img,11)
img4 = cv2.bilateralFilter(img,9,75,75)
M = np.ones((5, 5), np.float32) / 25
img5 = cv.filter2D(img, -1, M)
cv2.imshow('img1',img1)
cv2.imshow('img2',img2)
cv2.imshow('img3',img3)
cv2.imshow('img4',img4)
cv2.imshow('img5',img5)
cv2.waitKey(0)
cv2.destroyAllWindows()
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数学形态学
| 函数 | 功能 |
|---|---|
| cv2.erode( ) | 腐蚀 |
| cv2.dilate( ) | 膨胀 |
| cv2.morphologyEx(,cv2.MORPH_OPEN) | 开运算 |
| cv2.morphologyEx(,cv2.MORPH_CLOSE) | 闭运算 |
| cv2.morphologyEx(,cv2.MORPH_TOPHAT) | 顶帽运算 |
| cv2.morphologyEx(,cv2.MORPH_BLACKHAT) | 底帽运算 |
| cv2.morphologyEx(,cv2.MORPH_GRADIENT) | 形态学梯度 |
"数学形态学"
import cv2
img = cv2.imread('shiyuan.png')
img = cv2.resize(img,(300,300))
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
img1 = cv2.dilate(img, kernel)
img2 = cv2.erode(img,kernel)
#设置结构元
kernel_rect=cv2.getStructuringElement(cv2.MORPH_RECT,(3,3))
kernel_cross=cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))
kernel_ellipse=cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
#图像开运算处理
open_rect=cv2.morphologyEx(img,cv2.MORPH_OPEN,kernel_rect)
open_cross=cv2.morphologyEx(img,cv2.MORPH_OPEN,kernel_cross)
open_ellipse=cv2.morphologyEx(img,cv2.MORPH_OPEN,kernel_ellipse)
#图像闭运算处理
close_rect=cv2.morphologyEx(img,cv2.MORPH_CLOSE,kernel_rect)
close_cross=cv2.morphologyEx(img,cv2.MORPH_CLOSE,kernel_cross)
close_ellipse=cv2.morphologyEx(img,cv2.MORPH_CLOSE,kernel_ellipse)
gradient_rect = cv2.morphologyEx(img,cv2.MORPH_GRADIENT,kernel_rect)
gradient_cross = cv2.morphologyEx(img,cv2.MORPH_GRADIENT,kernel_cross)
gradient_ellipse = cv2.morphologyEx(img,cv2.MORPH_GRADIENT,kernel_ellipse)
#顶帽变换
tophat_rect=cv2.morphologyEx(img,cv2.MORPH_TOPHAT,kernel_rect)
tophat_cross=cv2.morphologyEx(img,cv2.MORPH_TOPHAT,kernel_cross)
tophat_ellipse=cv2.morphologyEx(img,cv2.MORPH_TOPHAT,kernel_ellipse)
#顶帽变换
blackhat_rect=cv2.morphologyEx(img,cv2.MORPH_BLACKHAT,kernel_rect)
blackhat_cross=cv2.morphologyEx(img,cv2.MORPH_BLACKHAT,kernel_cross)
blackhat_ellipse=cv2.morphologyEx(img,cv2.MORPH_BLACKHAT,kernel_ellipse)
cv2.imshow('blackhat_rect',blackhat_rect)
cv2.imshow('blackhat_cross',blackhat_cross)
cv2.imshow('blackhat_ellipse',blackhat_ellipse)
cv2.imshow('tophat_rect',tophat_rect)
cv2.imshow('tophat_cross',tophat_cross)
cv2.imshow('tophat_ellipse',tophat_ellipse)
cv2.imshow('img1',img1)
cv2.imshow('img2',img2)
cv2.imshow('open_rect',open_rect)
cv2.imshow('open_cross',open_cross)
cv2.imshow('open_ellipse',open_ellipse)
cv2.imshow('close_rect',close_rect)
cv2.imshow('close_cross',close_cross)
cv2.imshow('close_ellipse',close_ellipse)
cv2.imshow('gradient_rect',gradient_rect)
cv2.imshow('gradient_cross',gradient_cross)
cv2.imshow('gradient_ellipse',gradient_ellipse)
cv2.waitKey(0)
cv2.destroyAllWindows()
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阈值分割
| 函数 | 功能 |
|---|---|
| cv2.threshold(,cv2.THRESH_BINARY) | 二值化阈值 |
| cv2.threshold(,cv2.THRESH_BINARY_INV) | 反二值化阈值 |
| cv2.threshold(,cv2.THRESH_TOZERO) | 低阈值零处理 |
| cv2.threshold(,cv2.THRESH_TOZERO_INV) | 超阈值零处理 |
| cv2.threshold(,cv2.THRESH_OSTU) | 大津算法 |
| cv2.threshold(,cv2.THRESH_TRIANGLE) | 截断阈值化处理 |
| cv2.adaptiveThreshold(,,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,) | 自适应阈值处理 |
| cv2.adaptiveThreshold(,,cv2.ADAPTIVE_THRESH_MEAN_C,) | 自适应阈值处理 |
"阈值分割"
import cv2
img = cv2.imread('shiyuan.png')
img = cv2.resize(img,(400,300))
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,img1 = cv2.threshold(img,110,255,cv2.THRESH_BINARY)
ret,img2 = cv2.threshold(img,110,255,cv2.THRESH_BINARY_INV)
ret,img3 = cv2.threshold(img,110,255,cv2.THRESH_TOZERO)
ret,img4 = cv2.threshold(img,110,255,cv2.THRESH_TOZERO_INV)
ret,img5 = cv2.threshold(img,110,255,cv2.THRESH_TRUNC)
ret,img6 = cv2.threshold(img,110,255,cv2.THRESH_TRIANGLE)
ret,img7 = cv2.threshold(img,110,255,cv2.THRESH_OTSU)
ret,img8 = cv2.threshold(cv2.GaussianBlur(img,(7,7),0),110,255,cv2.THRESH_OTSU)
img9 = cv2.adaptiveThreshold(img,127, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 9, 11)
img10 = cv2.adaptiveThreshold(img,127,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,9,11)
cv2.imshow('img',img)
cv2.imshow('img1',img1)
cv2.imshow('img2',img2)
cv2.imshow('img3',img3)
cv2.imshow('img4',img4)
cv2.imshow('img5',img5)
cv2.imshow('img6',img6)
cv2.imshow('img7',img7)
cv2.imshow('img8',img8)
cv2.imshow('img9',img9)
cv2.imshow('img10',img10)
cv2.waitKey(0)
cv2.destroyAllWindows()
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边缘检测
| 函数 | 功能 |
|---|---|
| cv2.Canny( ) | Canny算子 |
| cv2.findContours( ) | 轮廓检测 |
| cv2.filter2D( ) | 边缘提取 |
"边缘检测"
import cv2
img = cv2.imread('bai.png')
img = cv2.resize(img,(400,300))
img1 = cv2.Canny(img,123,5)
cv2.imshow('img1',img1)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""边缘检测"""
import cv2
img = cv2.imread('bai.png')
img = cv2.resize(img,(400,300))
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, binary = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img,contours,-1,(0,0,255),1)
cv2.imshow("img", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""边缘检测"""
import cv2
import numpy as np
def find_contours(kernel):
img = cv2.imread('bai.png')
img = cv2.resize(img,(400,300))
img1 = cv2.filter2D(img,-1,kernel)
cv2.imshow('img1',img1)
cv2.waitKey(0)
cv2.destroyAllWindows()
kernel1 = np.array((
[0.0625, 0.125, 0.0625],
[0.125, 0.25, 0.125],
[0.0625, 0.125, 0.0625]), dtype="float32")
#Sobel算子
kernel2 = np.array(([-1,-2,-1],
[0,0,0],
[1,2,1]))
kernel3 = np.array(([-2,-1,0],
[-1,1,1],
[0,-1,-2]))
kernel4 = np.array([[-1,-1,-1],
[-1,8,-1],
[-1,-1,-1]])
kernel5 = np.array([[0,-1,0],
[-1,5,-1],
[0,-1,0]])
kernel6 = np.array([[0,1,0],
[1,-4,1],
[0,1,0]])
find_contours(kernel1)
find_contours(kernel2)
find_contours(kernel3)
find_contours(kernel4)
find_contours(kernel5)
find_contours(kernel6)
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色彩空间
| 函数 | 功能 |
|---|---|
| cv2.cvtColor(,cv2.COLOR_BGR2GRAY) | 图像灰度化 |
| cv2.cvtColor(,cv2.COLOR_BGR2HSV) | RGB转HSV |
"""色彩空间"""
import cv2
img = cv2.imread('bai.png')
img = cv2.resize(img,(400,300))
img1 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img2 = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
cv2.imshow('img1',img1)
cv2.imshow('img2',img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
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形状绘制
| 函数 | 功能 |
|---|---|
| cv2.line( ) | 绘制直线 |
| cv2.circle( ) | 绘制圆圈 |
| cv2.ellipse( ) | 绘制椭圆 |
| cv2.rectangle( ) | 绘制矩形 |
| cv2.arrowedLine( ) | 绘制箭头 |
| cv2.putText( ) | 绘制文本 |
"""形状绘制"""
import cv2
img = cv2.imread('bai.png')
img = cv2.resize(img,(400,300))
imgx = img.copy()
imgy = img.copy()
imgz = img.copy()
imgw = img.copy()
img = cv2.resize(img,(400,300))
img1 = cv2.line(img,(10,10),(200,300),(0,0,255),2)
img2 = cv2.circle(imgx,(60,60),30,(0,0,213),-1)
img3 = cv2.rectangle(imgy,(10,10),(100,80),(0,0,200),2)
img4 = cv2.ellipse(imgz,(256,256),(50,40),0,5,360,(20,213,79),-1)
font=cv2.FONT_HERSHEY_SIMPLEX
img5 = cv2.putText(imgw,'opencv',(80,90), font, 2,(255,255,255),3)
cv2.imshow('img1',img1)
cv2.imshow('img2',img2)
cv2.imshow('img3',img3)
cv2.imshow('img4',img4)
cv2.imshow('img5',img5)
cv2.waitKey(0)
cv2.destroyAllWindows()
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写在最后
下一期将扒拉sklearn库，该库是做机器学习的不二之选，欢迎大家搬好小板凳呀！
