• opencv学习笔记(2


    1.图像基础操作

    #1.视频读取,变成灰度视频,调节视频速度:
    import cv2 as cv
    video=cv.VideoCapture('lz.mp4')
    #检查是否打开正确
    if video.isOpened:
        open,frame=video.read()#读取第一帧
    else:
        open=False
    while open:#当视频可以正确打开时:
        open_2,frame=video.read()#读取照片
        if frame is None:#当图片读完时:
            break
        if open_2==True:#当图片没读完,且可以打开时:
            gray=cv.cvtColor(frame,cv.COLOR_BGR2GRAY)#把图片变成灰度图
            cv.imshow('result',gray)#展示图片
            if cv.waitKey(3)&0xFF==27:#视频在多少秒内结束,或者按esc键时,推出视频
                break
    video.release()
    cv.destroyAllWindows()
            
        
    
    #2.
    import cv2 as cv
    img=cv.imread('kst.jpg',1)
    #2.1颜色通道提取:
    b,g,r=cv.split(img)
    #print(b)
    #2.2颜色通道合并
    img=cv.merge((b,g,r))
    #print(img.shape)
    #2.3保留单通道
    cur_img=img.copy()
    cur_img[:,:,0]=0#蓝色全为0
    cur_img[:,:,1]=0#绿色通道全为0
    cv.imshow('r',cur_img)#红色通道像素值保留
    cv.waitKey(0)
    
    #3.边界填充
    top_size,bottom_size,left_size,right_size=(60,60,60,60)#边界要补多少
    replicate=cv.copyMakeBorder(img,top_size,bottom_size,left_size,right_size,borderType=cv.BORDER_REPLICATE)
    reflect=cv.copyMakeBorder(img,top_size,bottom_size,left_size,right_size,borderType=cv.BORDER_REFLECT)
    
    reflect101=cv.copyMakeBorder(img,top_size,bottom_size,left_size,right_size,borderType=cv.BORDER_REFLECT_101)
    
    wrap=cv.copyMakeBorder(img,top_size,bottom_size,left_size,right_size,borderType=cv.BORDER_WRAP)
    
    constant=cv.copyMakeBorder(img,top_size,bottom_size,left_size,right_size,borderType=cv.BORDER_CONSTANT,value=0)
    cv.imshow('a',constant)
    cv.waitKey(0)
    
    #4.图像用add
    img_1=img+100#+,溢出255时,取余数
    #print(img_1)
    cv.imshow('1',img_1)
    img_2=cv.add(img,100)#add,溢出255时,取255
    #print(img_2)
    cv.imshow('2',img_2)
    cv.waitKey(0)
    
    #5.阈值操作
    ret,dst1=cv.threshold(img,127,255,cv.THRESH_BINARY)#1原图,2阈值,3最大值,4类型
    ret,dst2=cv.threshold(img,127,255,cv.THRESH_BINARY_INV)
    ret,dst3=cv.threshold(img,127,255,cv.THRESH_TRUNC)
    ret,dst4=cv.threshold(img,127,255,cv.THRESH_TOZERO)
    ret,dst5=cv.threshold(img,127,255,cv.THRESH_TOZERO_INV)
    cv.imshow('DST',dst5)
    cv.waitKey(0)
    
    import matplotlib.pyplot as plt
    titles=['original image','binary','binary inv','trunc','tozero','tozero inv']
    images=[img,dst1,dst2,dst3,dst4,dst5]
    for i in range(6):
        plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
        plt.title(titles[i])
        plt.xticks([]),plt.yticks([])
    plt.show()
    
    #6.滤波:
    #1.均值滤波
    blur=cv.blur(img,(3,3))
    #2.方框滤波
    box=cv.boxFilter(img,-1,(3,3),normalize=False)#false容易越界,true和均值滤波一样
    #3.高斯滤波
    gaussian=cv.GaussianBlur(img,(5,5),1)
    #4.中值滤波
    median=cv.medianBlur(img,5)
    #显示所有图片
    import numpy as np
    res=np.vstack((blur,gaussian,median))
    #print(res)
    cv.imshow('DST',res)
    cv.waitKey(0)
    
    import cv2 as cv
    
    #7.形态学操作
    #7.1腐蚀操作
    #kernel=np.ones((5,5),np.uint8)
    kernel=cv.getStructuringElement(cv.MORPH_RECT,(5,5))#kernel形状
    erosion=cv.erode(img,kernel,iterations=1)#iterations腐蚀次数
    #7.2膨胀操作
    kernel=np.ones((3,3),np.uint8)
    dilate=cv.dilate(img,kernel,iterations=1)
    #7.3开运算:先腐蚀再膨胀
    kernel=np.ones((5,5),np.uint8)
    opening=cv.morphologyEx(img,cv.MORPH_OPEN,kernel)
    #7.4闭运算:先膨胀再腐蚀
    closing=cv.morphologyEx(img,cv.MORPH_CLOSE,kernel)
    #7.5梯度运算:膨胀图片-腐蚀图片=边界信息
    gradient=cv.morphologyEx(img,cv.MORPH_GRADIENT,kernel)
    #7.6礼帽:原始输入-开运算结果=毛刺
    tophat=cv.morphologyEx(img,cv.MORPH_TOPHAT,kernel)
    #7.7黑帽:闭运算-原始输入==原始轮廓
    blackhat=cv.morphologyEx(img,cv.MORPH_BLACKHAT,kernel)
    #7.8图像梯度
    dst=cv.morphologyEx(img,cv.MORPH_GRADIENT,kernel)
    cv.imshow('DST',blackhat)
    cv.waitKey(0)
    
    #8.图像梯度
    #8.1sobel算子
    sobelx=cv.Sobel(img,cv.CV_64F,1,0,ksize=3)#1img,2图像深度(-1)(CV_64F保留负值,不让赋值=0),3dx(1算,0不算),4dy,5sobel算子的大小
    sobelx=cv.convertScaleAbs(sobelx)#将上一步的计算的负值取绝对值,得到边缘信息
    sobely=cv.Sobel(img,cv.CV_64F,0,1,ksize=3)
    sobely=cv.convertScaleAbs(sobely)
    #反别计算x和y,再求和
    sobelxy=cv.addWeighted(sobelx,0.5,sobely,0.5,0)#0是偏置项,一般不加偏置项
    #8.2scharr算子
    scharrx=cv.Scharr(img,cv.CV_64F,1,0)#ksize默认3
    scharrx=cv.convertScaleAbs(scharrx)
    scharry=cv.Scharr(img,cv.CV_64F,0,1)
    scharry=cv.convertScaleAbs(scharry)
    scharrxy=cv.addWeighted(scharrx,0.5,scharry,0.5,0)
    #8.3laplacian算子
    laplacian=cv.Laplacian(img,cv.CV_64F)
    laplacian=cv.convertScaleAbs(laplacian)
    
    res=np.hstack((sobelxy,scharrxy,laplacian))
    
    
    cv.imshow('DST',res)
    cv.waitKey(0)
    
    #9.canny边缘检测(含有零零散散的线段):高斯滤波,计算像素的梯度强度和方向,非极大值抑制,双阈值,
    gray=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    canny=cv.Canny(gray,80,150)#1灰度图片,2双阈值检测minvalue,3maxvalue
    
    cv.imshow('DST',canny)
    cv.waitKey(0)
    
    #10.图像金字塔
    #10.1高斯金字塔上采样:kernel
    print(img.shape)
    up=cv.pyrUp(img)
    print(up.shape)
    #金字塔下采样
    down=cv.pyrDown(img)
    print(down.shape)
    
    #10.2拉普拉斯金字塔:原图-(上采样(下采样))
    down=cv.pyrDown(img)
    down_up=cv.pyrUp(down)
    laplaus=img-down_up
    cv.imshow('dst',laplaus)
    cv.waitKey(0)
    
    img=cv.imread('figure.jpg',1)
    gray=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    
    #11.轮廓检测(不含零散的线段):
    #mode:RETR_EXTERNAL只检测最外面轮廓,RETR_LIST检测所有轮廓,RETR_CCOMP分两层,RETR_TREE嵌套(最常用)
    #method:CHAIN_APPROX_NONE,CHAIN_APPROX_SIMPLE
    #-1二值图像,轮廓信息
    ret,thresh=cv.threshold(gray,0,255,cv.THRESH_BINARY)#图像二值
    contours,hierarchy=cv.findContours(thresh,cv.RETR_EXTERNAL,cv.CHAIN_APPROX_NONE)
    #contours轮廓信息,hierarchy层级信息
    #-2绘制轮廓
    draw_img=img.copy()#复制原图像
    res=cv.drawContours(draw_img,contours,-1,(0,0,255),-1)#1在哪个图片上画,2画的轮廓信息,3画几条轮廓,4画笔颜色,5画笔粗细
    
    #-3分析轮廓特征
    cnt=contours[0]#拿出第0条轮廓信息
    res=cv.drawContours(draw_img,cnt,-1,(0,255,0),2)
    area=cv.contourArea(cnt)#计算第0条轮廓的面积
    perimeter=cv.arcLength(cnt,True)#计算周长,True表示闭合
    #-4轮廓近似
    epsilon=0.1*cv.arcLength(cnt,True)#周长的0.1倍作为轮廓近似的阈值
    approx=cv.approxPolyDP(cnt,epsilon,True)
    draw=img.copy()
    res=cv.drawContours(draw,[approx],-1,(255,0,0),2)
    print(area,perimeter)
    #-5.轮廓的外接矩形
    x,y,w,h=cv.boundingRect(cnt)#第0条轮廓的外接矩形参数
    res=cv.rectangle(img,(x,y),(x+w,y+h),(255,0,0),3)#画
    #-6.轮廓外接圆
    (x,y),radius=cv.minEnclosingCircle(cnt)
    center=(int(x),int(y))
    radius=int(radius)
    res=cv.circle(img,center,radius,(0,0,255),3)
    cv.imshow('dst',res)
    cv.waitKey(0)
    
    #12.模板匹配
    import matplotlib.pyplot as plt
    img=cv.imread('kst.jpg',0)
    head=cv.imread('head.jpg',0)
    h=head.shape[0]
    w=head.shape[1]
    print(img.shape,head.shape)
    #TM_SQDIFF:计算平方不同,计算出来的值越小,越相关
    #TM_CCORR:计算相关性,值越大,越相关
    #TM_CCOEFF:计算相关系数,值越大,越相关
    #TM_SQDIFF_NORMED:计算归一化平方不同,值越接近0,越相关
    #TM_CCORR_NORMED:计算归一化,越接近1,越相关
    #TM_CCOEFF_NORMED:计算归一化相关系数,越接近1,越相关
    res=cv.matchTemplate(img,head,cv.TM_SQDIFF)#匹配返回的是每次计算完的结果
    print(res.shape)#(A-a+1)*(B-b+1)
    min_val,max_val,min_loc,max_loc=cv.minMaxLoc(res)#找出这个矩阵里最值和位置
    methods=['cv.TM_SQDIFF','cv.TM_CCORR','cv.TM_CCOEFF','cv.TM_SQDIFF_NORMED','cv.TM_CCORR_NORMED','cv.TM_CCOEFF_NORMED']
    for meth in methods:
        img2=img.copy()
        method=eval(meth)#字符串形式化成专有名词
        res=cv.matchTemplate(img,head,method)
        min_val,max_val,min_loc,max_loc=cv.minMaxLoc(res)
        if method in [cv.TM_SQDIFF,cv.TM_SQDIFF_NORMED]:
            top_left=min_loc
        else:
            top_left=max_loc
        bottom_right=(top_left[0]+w,top_left[1]+h)
        #画矩形
        cv.rectangle(img2,top_left,bottom_right,255,2)
        plt.subplot(121),plt.imshow(res,cmap='gray')
        plt.xticks([]),plt.yticks([])#隐藏坐标img
        plt.subplot(122),plt.imshow(img2,cmap='gray')
        plt.xticks([]),plt.yticks([])#隐藏坐标
        plt.suptitle(meth)
        plt.show()
    
    #12.2匹配多个对象
    import cv2 as cv
    import numpy as np
    img=cv.imread('AA.jpg',1)
    imh=cv.imread('AA.jpg',0)
    head=cv.imread('BB.jpg',0)
    h,w=head.shape[:2]
    res=cv.matchTemplate(imh,head,cv.TM_CCOEFF_NORMED)#匹配计算
    threshold=0.7#阈值
    loc=np.where(res>=threshold)#大于阈值的挑出来
    for pt in zip(*loc[::-1]):#*号表示可选参数:
        bottom_right=(pt[0]+w,pt[1]+h)
        cv.rectangle(img,pt,bottom_right,(0,0,225),3)
    cv.imshow('img',img)
    cv.waitKey(0)
    
    
    import cv2 as cv
    import numpy as np
    img=cv.imread(r'D:papervgg_segnetdataset2jpgC_0001_1.RIGHT_MLO.jpg',0)
    img=cv.resize(img,(260,250))
    #.createCLAHE函数原型:createCLAHE([, clipLimit[, tileGridSize]]) -> retval
    #clipLimit参数表示对比度的大小。
    #tileGridSize参数表示每次处理块的大小 
    equ=cv.equalizeHist(img)
    clahe=cv.createCLAHE(clipLimit=9.0,tileGridSize=(3,3))
    res_clahe=clahe.apply(img)
    res=np.hstack((equ,img,res_clahe))
    cv.imshow('aaa',res)
    cv.waitKey(0)
    #plt.show()
    
    #13.直方图
    import cv2 as cv
    import matplotlib.pyplot as plt
    import numpy as np
    #灰度直方图
    gray=cv.imread('kst.jpg',0)#读取灰度图
    hist=cv.calcHist([gray],[0],None,[256],[0,256])
    #1【图像】,2第几通道【0,1,2】,3掩膜(自己选的范围内),4bin数,分成几段,5从0到255
    
    #彩色直方图
    img=cv.imread('kst.jpg')
    color=['b','g','r']
    for i,col in enumerate(color):#i是坐标,col是内容
        histr=cv.calcHist([img],[i],None,[256],[0,256])
        #plt.plot(histr,color=col)
        #plt.xlim([0,256])
    #plt.hist(gray.ravel(),256)
    
    
    #直方图均衡化
    equ=cv.equalizeHist(gray)
    #plt.hist(equ.ravel(),256)
    
    
    #自适应直方图均衡化
    clahe=cv.createCLAHE(clipLimit=2.0,tileGridSize=(8,8))
    res_clahe=clahe.apply(gray)
    res=np.hstack((gray,equ,res_clahe))
    
    cv.imshow('res',res)
    cv.waitKey(0)
    #plt.show()
    
    #14.傅里叶变换,输入图像要先转换成np.float32格式,dft,idft
    
    import numpy as np
    import cv2 as cv
    import matplotlib.pyplot as plt
    gray=cv.imread('kst.jpg',0)
    gray_float32=np.float32(gray)#灰度图像先转换成float32位的
    dft=cv.dft(gray_float32,flags=cv.DFT_COMPLEX_OUTPUT)#傅里叶变换
    dft_shift=np.fft.fftshift(dft)#将低频信息转换到中间位置
    magnitude_spectrum=20*np.log(cv.magnitude(dft_shift[:,:,0],dft_shift[:,:,1]))
    #利用公式转换成可以以图片格式显示
    plt.subplot(121),plt.imshow(gray,cmap='gray')
    plt.title('input image'),plt.xticks([]),plt.yticks([])
    plt.subplot(122),plt.imshow(magnitude_spectrum,cmap='gray')
    plt.title('magnitude_spectrum'),plt.xticks([]),plt.yticks([])
    plt.show()
    #14-2低通滤波
    
    
    #14-3高通滤波
    
    #14-2低通滤波
    import numpy as np
    import cv2 as cv
    import matplotlib.pyplot as plt
    img=cv.imread('kst.jpg',1)
    gray=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    gray_float=np.float32(gray)
    dft=cv.dft(gray_float,flags=cv.DFT_COMPLEX_OUTPUT)
    dft_shift=np.fft.fftshift(dft)
    rows,cols=gray.shape#总高,宽
    crow,ccol=int(rows/2),int(cols/2)#中心位置
    #低通滤波
    mask=np.zeros((rows,cols,2),np.uint8)#黑色
    mask[crow-30:crow+30,ccol-30:ccol+30]=1#白色
    #IDFT
    fshift=dft_shift*mask
    f_ishift=np.fft.ifftshift(fshift)
    img_back=cv.idft(f_ishift)
    img_back=cv.magnitude(img_back[:,:,0],img_back[:,:,1])
    #plot
    plt.subplot(121),plt.imshow(gray,cmap='gray')
    plt.title('input image'),plt.xticks([]),plt.yticks([])
    plt.subplot(122),plt.imshow(img_back,cmap='gray')
    plt.title('result image'),plt.xticks([]),plt.yticks([])
    
    
    plt.show()
    
    
    
    
    #14-3高通滤波
    import numpy as np
    import cv2 as cv
    import matplotlib.pyplot as plt
    img=cv.imread('kst.jpg',1)
    gray=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    gray_float=np.float32(gray)
    dft=cv.dft(gray_float,flags=cv.DFT_COMPLEX_OUTPUT)
    dft_shift=np.fft.fftshift(dft)
    rows,cols=gray.shape#总高,宽
    crow,ccol=int(rows/2),int(cols/2)#中心位置
    #低通滤波
    mask=np.ones((rows,cols,2),np.uint8)#
    mask[crow-30:crow+30,ccol-30:ccol+30]=0#
    #IDFT
    fshift=dft_shift*mask
    f_ishift=np.fft.ifftshift(fshift)
    img_back=cv.idft(f_ishift)
    img_back=cv.magnitude(img_back[:,:,0],img_back[:,:,1])
    #plot
    plt.subplot(121),plt.imshow(gray,cmap='gray')
    plt.title('input image'),plt.xticks([]),plt.yticks([])
    plt.subplot(122),plt.imshow(img_back,cmap='gray')
    plt.title('result image'),plt.xticks([]),plt.yticks([])
    
    
    plt.show()
    #1.harris
    import cv2 as cv
    import numpy as np
    img=cv.imread('kst.jpg')
    gray=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    dst=cv.cornerHarris(gray,2,3,0.04)#1data,2角点检测中指定区域大小,3sobel求导使用的窗口大小,4k参数取值【0.04,0.06
    img[dst>0.01*dst.max()]=[0,0,255]
    cv.imshow('dst',img)
    cv.waitKey(0)
    
    #2.sift
    import cv2 as cv
    import numpy as np
    img=cv.imread('kst.jpg')
    gray=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    
    sift=cv.xfeatures2d.SIFT_create()#实例化
    kp=sift.detect(gray,None)#得到特征点
    img=cv.drawKeypoints(gray,kp,img)
    cv.imshow('dst',img)
    cv.waitKey(0)
    #计算特征个数,属性
    kp,des=sift.compute(gray,kp)
    print(np.array(kp).shape)#特征点个数
    print(des.shape)#关键点特征值
    

      

     2. 

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  • 原文地址:https://www.cnblogs.com/Turing-dz/p/13196178.html
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