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opencv基本图像处理

作者：互联网

灰度图

import cv2 #opencv读取的格式是BGR
import numpy as np
import matplotlib.pyplot as plt#Matplotlib是RGB
%matplotlib inline 

img=cv2.imread('dog.jpg')
img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img_gray.shape
(1279, 1706)
cv2.imshow("img_gray", img_gray)
cv2.waitKey(0)    
cv2.destroyAllWindows()

 

HSV

  • H - 色调（主波长）。
  • S - 饱和度（纯度/颜色的阴影）。
  • V值（强度）
hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

cv2.imshow("hsv", hsv)
cv2.waitKey(0)    
cv2.destroyAllWindows()

 

图像阈值

ret, dst = cv2.threshold(src, thresh, maxval, type)

  • src： 输入图，只能输入单通道图像，通常来说为灰度图

  • dst： 输出图

  • thresh： 阈值

  • maxval： 当像素值超过了阈值（或者小于阈值，根据type来决定），所赋予的值

  • type：二值化操作的类型，包含以下5种类型： cv2.THRESH_BINARY； cv2.THRESH_BINARY_INV； cv2.THRESH_TRUNC； cv2.THRESH_TOZERO；cv2.THRESH_TOZERO_INV

  • cv2.THRESH_BINARY 超过阈值部分取maxval（最大值），否则取0

  • cv2.THRESH_BINARY_INV THRESH_BINARY的反转

  • cv2.THRESH_TRUNC 大于阈值部分设为阈值，否则不变

  • cv2.THRESH_TOZERO 大于阈值部分不改变，否则设为0

  • cv2.THRESH_TOZERO_INV THRESH_TOZERO的反转

ret, thresh1 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)#大于127的全部为白，<127的为黑 
ret, thresh2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)#对上面的方法进行反转
ret, thresh3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)#>127的像素值就等于127
ret, thresh4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)
ret, thresh5 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO_INV)

titles = ['Original Image', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]

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()

 图像平滑

                            

img = cv2.imread('lenaNoise.png')

cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

                   

# 均值滤波
# 简单的平均卷积操作
blur = cv2.blur(img, (3, 3))

cv2.imshow('blur', blur)
cv2.waitKey(0)
cv2.destroyAllWindows()

                 

# 方框滤波
# 基本和均值一样，可以选择归一化
box = cv2.boxFilter(img,-1,(3,3), normalize=True)  

cv2.imshow('box', box)
cv2.waitKey(0)
cv2.destroyAllWindows()

                 

# 方框滤波
# 基本和均值一样，可以选择归一化,容易越界 变成白色像素点
box = cv2.boxFilter(img,-1,(3,3), normalize=False)  

cv2.imshow('box', box)
cv2.waitKey(0)
cv2.destroyAllWindows()

                

# 高斯滤波
# 高斯模糊的卷积核里的数值是满足高斯分布，相当于更重视中间的
aussian = cv2.GaussianBlur(img, (5, 5), 1)  

cv2.imshow('aussian', aussian)
cv2.waitKey(0)
cv2.destroyAllWindows()

              

            

# 展示所有的
res = np.hstack((blur,aussian,median))
print (res)
cv2.imshow('median vs average', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
kernel = np.ones((3,3),np.uint8) 
erosion = cv2.erode(img,kernel,iterations = 1)

cv2.imshow('erosion', erosion)
cv2.waitKey(0)
cv2.destroyAllWindows()

 形态学-腐蚀操作

img = cv2.imread('AR.png')

cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

     

kernel = np.ones((3,3),np.uint8) 
erosion = cv2.erode(img,kernel,iterations = 1)

cv2.imshow('erosion', erosion)
cv2.waitKey(0)
cv2.destroyAllWindows()

 形态学-膨胀操作

img = cv2.imread('AR.png')
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

kernel = np.ones((3,3),np.uint8) 
dige_erosion = cv2.erode(img,kernel,iterations = 1)

cv2.imshow('erosion', erosion)
cv2.waitKey(0)
cv2.destroyAllWindows()

 

kernel = np.ones((3,3),np.uint8) 
dige_dilate = cv2.dilate(dige_erosion,kernel,iterations = 1)

cv2.imshow('dilate', dige_dilate)
cv2.waitKey(0)
cv2.destroyAllWindows()

开运算与闭运算

# 开：先腐蚀，再膨胀
img = cv2.imread('AR.png')

kernel = np.ones((5,5),np.uint8) 
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)

cv2.imshow('opening', opening)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 闭：先膨胀，再腐蚀
img = cv2.imread('AR.png')

kernel = np.ones((5,5),np.uint8) 
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)

cv2.imshow('closing', closing)
cv2.waitKey(0)
cv2.destroyAllWindows()

梯度运算

# 梯度=膨胀-腐蚀
pie = cv2.imread('pie.png')
kernel = np.ones((7,7),np.uint8) 
dilate = cv2.dilate(pie,kernel,iterations = 5)
erosion = cv2.erode(pie,kernel,iterations = 5)

res = np.hstack((dilate,erosion))

cv2.imshow('res', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

gradient = cv2.morphologyEx(pie, cv2.MORPH_GRADIENT, kernel)

cv2.imshow('gradient', gradient)
cv2.waitKey(0)
cv2.destroyAllWindows()

          

 图像梯度-Sobel算子

img = cv2.imread('pie.png',cv2.IMREAD_GRAYSCALE)
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()

 

dst = cv2.Sobel(src, ddepth, dx, dy, ksize)

  • ddepth:图像的深度
  • dx和dy分别表示水平和竖直方向
  • ksize是Sobel算子的大小
def cv_show(img,name):
    cv2.imshow(name,img)
    cv2.waitKey()
    cv2.destroyAllWindows()
sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)

cv_show(sobelx,'sobelx')

 白到黑是正数，黑到白就是负数了，所有的负数会被截断成0，所以要取绝对值

sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)
sobelx = cv2.convertScaleAbs(sobelx)
cv_show(sobelx,'sobelx')

sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)
sobely = cv2.convertScaleAbs(sobely)  
cv_show(sobely,'sobely')

 分别计算x和y，再求和

sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)
cv_show(sobelxy,'sobelxy')

img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)
cv_show(img,'img')

                       

 

img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)
sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)
sobelx = cv2.convertScaleAbs(sobelx)
sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)
sobely = cv2.convertScaleAbs(sobely)
sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)
cv_show(sobelxy,'sobelxy')

                 

 

img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)

sobelxy=cv2.Sobel(img,cv2.CV_64F,1,1,ksize=3) sobelxy = cv2.convertScaleAbs(sobelxy) cv_show(sobelxy,'sobelxy')

 图像梯度-Scharr算子

 图像梯度-laplacian算子

                               

#不同算子的差异
img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)
sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)
sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)
sobelx = cv2.convertScaleAbs(sobelx)   
sobely = cv2.convertScaleAbs(sobely)  
sobelxy =  cv2.addWeighted(sobelx,0.5,sobely,0.5,0)  

scharrx = cv2.Scharr(img,cv2.CV_64F,1,0)
scharry = cv2.Scharr(img,cv2.CV_64F,0,1)
scharrx = cv2.convertScaleAbs(scharrx)   
scharry = cv2.convertScaleAbs(scharry)  
scharrxy =  cv2.addWeighted(scharrx,0.5,scharry,0.5,0) 

laplacian = cv2.Laplacian(img,cv2.CV_64F)
laplacian = cv2.convertScaleAbs(laplacian)   

res = np.hstack((sobelxy,scharrxy,laplacian))
cv_show(res,'res')

Canny边缘检测

  • 1) 使用高斯滤波器，以平滑图像，滤除噪声。

  • 2) 计算图像中每个像素点的梯度强度和方向。

  • 3) 应用非极大值（Non-Maximum Suppression）抑制，以消除边缘检测带来的杂散响应。

  • 4) 应用双阈值（Double-Threshold）检测来确定真实的和潜在的边缘。

  • 5) 通过抑制孤立的弱边缘最终完成边缘检测。

1:高斯滤波器

 2:梯度和方向

 3：非极大值抑制

img=cv2.imread("lena.jpg",cv2.IMREAD_GRAYSCALE)

v1=cv2.Canny(img,80,150)#minVal=80 maxVal=150   
v2=cv2.Canny(img,50,100)

res = np.hstack((v1,v2))
cv_show(res,'res')

 4：双阈值检测

img=cv2.imread("lena.jpg",cv2.IMREAD_GRAYSCALE)

v1=cv2.Canny(img,80,150)#minVal=80 maxVal=150   
v2=cv2.Canny(img,50,100)

res = np.hstack((v1,v2))
cv_show(res,'res')

 

 

img=cv2.imread("car.png",cv2.IMREAD_GRAYSCALE)

v1=cv2.Canny(img,120,250)
v2=cv2.Canny(img,50,100)

res = np.hstack((v1,v2))
cv_show(res,'res')

 图像金字塔

  • 高斯金字塔
  • 拉普拉斯金字塔

 高斯金字塔：向下采样方法（缩小）

 高斯金字塔：向上采样方法（放大）

img=cv2.imread("ting.jpg")
cv_show(img,'img')
print (img.shape)

up=cv2.pyrUp(img)
cv_show(up,'up')
print (up.shape)
down=cv2.pyrDown(img)
cv_show(down,'down')
print (down.shape)

 

up2=cv2.pyrUp(up)
cv_show(up2,'up2')
print (up2.shape)
up=cv2.pyrUp(img)
up_down=cv2.pyrDown(up)
cv_show(up_down,'up_down')
cv_show(np.hstack((img,up_down)),'up_down')
up=cv2.pyrUp(img)
up_down=cv2.pyrDown(up)
cv_show(img-up_down,'img-up_down')

 拉普拉斯金字塔

 图像轮廓

cv2.findContours(img,mode,method)

mode:轮廓检索模式

  • RETR_EXTERNAL ：只检索最外面的轮廓；
  • RETR_LIST：检索所有的轮廓，并将其保存到一条链表当中；
  • RETR_CCOMP：检索所有的轮廓，并将他们组织为两层：顶层是各部分的外部边界，第二层是空洞的边界;
  • RETR_TREE：检索所有的轮廓，并重构嵌套轮廓的整个层次;

method:轮廓逼近方法

  • CHAIN_APPROX_NONE：以Freeman链码的方式输出轮廓，所有其他方法输出多边形（顶点的序列）。
  • CHAIN_APPROX_SIMPLE:压缩水平的、垂直的和斜的部分，也就是，函数只保留他们的终点部分。

 为了更高的准确率，使用二值图像。

img = cv2.imread('contours.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
cv_show(thresh,'thresh')

binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
#传入绘制图像，轮廓，轮廓索引，颜色模式，线条厚度
# 注意需要copy,要不原图会变。。。
draw_img = img.copy()
res = cv2.drawContours(draw_img, contours, -1, (0, 0, 255), 2)
cv_show(res,'res')

draw_img = img.copy()
res = cv2.drawContours(draw_img, contours, 0, (0, 0, 255), 2)
cv_show(res,'res')

轮廓特征

cnt = contours[0]
#面积
cv2.contourArea(cnt)
8500.5
#周长，True表示闭合的
cv2.arcLength(cnt,True)
437.9482651948929

轮廓近似

img = cv2.imread('contours2.png')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
cnt = contours[0]

draw_img = img.copy()
res = cv2.drawContours(draw_img, [cnt], -1, (0, 0, 255), 2)
cv_show(res,'res')

 

img = cv2.imread('contours.png')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
cnt = contours[0]

x,y,w,h = cv2.boundingRect(cnt)
img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
cv_show(img,'img')
epsilon = 0.15*cv2.arcLength(cnt,True) 
approx = cv2.approxPolyDP(cnt,epsilon,True)

draw_img = img.copy()
res = cv2.drawContours(draw_img, [approx], -1, (0, 0, 255), 2)
cv_show(res,'res')

 边界矩形

img = cv2.imread('contours.png')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
cnt = contours[0]

x,y,w,h = cv2.boundingRect(cnt)
img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
cv_show(img,'img')

area = cv2.contourArea(cnt)
x, y, w, h = cv2.boundingRect(cnt)
rect_area = w * h
extent = float(area) / rect_area
print ('轮廓面积与边界矩形比',extent)
轮廓面积与边界矩形比 0.5154317244724715
(x,y),radius = cv2.minEnclosingCircle(cnt) 
center = (int(x),int(y)) 
radius = int(radius) 
img = cv2.circle(img,center,radius,(0,255,0),2)
cv_show(img,'img')

标签：基本,img,show,res,cv2,opencv,图像处理,np,cv
来源： https://blog.csdn.net/weixin_53660567/article/details/123642967