打开C V从开始到放弃--2。图像处理(Open C V=Open+C(ctrl C)+V(ctrl V)),Opencv,入门,OpenCVopen,开源,cctrlcvctrlv


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('data/cat.jpg')
img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img_gray.shape

image-20200509170026099

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

image-20200509172110064

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()

image-20200509172205480

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)
ret, thresh2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)
ret, thresh3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)
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()

image-20200509172403342

图像平滑

image-20200509172622853

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

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

image-20200509202448634

# 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()

image-20200509213204256

# 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()

image-20200509213241693

# 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()

image-20200509213336581

# 中值滤波
# 相当于用中值代替
median = cv2.medianBlur(img, 5)  # 中值滤波

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

image-20200509213402696

# 展示所有的
res = np.hstack((blur,aussian,median))
#print (res)
cv2.imshow('median vs average', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

image-20200509215803311

形态学-腐蚀操作
img = cv2.imread('data/dige.png')

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

image-20200509214504831

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

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

image-20200509214538186

pie = cv2.imread('data/pie.png')

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

image-20200509214707766

kernel = np.ones((30,30),np.uint8) 
erosion_1 = cv2.erode(pie,kernel,iterations = 1)
erosion_2 = cv2.erode(pie,kernel,iterations = 2)
erosion_3 = cv2.erode(pie,kernel,iterations = 3)
res = np.hstack((erosion_1,erosion_2,erosion_3))
cv2.imshow('res', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

image-20200509215624175

形态学-膨胀操作
img = cv2.imread('data/dige.png')
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

image-20200509220236687

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()

image-20200509215904989

pie = cv2.imread('data/pie.png')

kernel = np.ones((30,30),np.uint8) 
dilate_1 = cv2.dilate(pie,kernel,iterations = 1)
dilate_2 = cv2.dilate(pie,kernel,iterations = 2)
dilate_3 = cv2.dilate(pie,kernel,iterations = 3)
res = np.hstack((dilate_1,dilate_2,dilate_3))
cv2.imshow('res', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

image-20200509215954906

开运算与闭运算
# 开:先腐蚀,再膨胀
img = cv2.imread('data/dige.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()

image-20200509224357784

# 闭:先膨胀,再腐蚀
img = cv2.imread('data/dige.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()

image-20200509225223511

梯度运算
# 梯度=膨胀-腐蚀
pie = cv2.imread('data/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()

image-20200509225511478

gradient = cv2.morphologyEx(pie, cv2.MORPH_GRADIENT, kernel)
cv2.imshow('gradient', gradient)
cv2.waitKey(0)
cv2.destroyAllWindows()

image-20200509225557466

礼帽与黑帽
  • 礼帽 = 原始输入-开运算结果
  • 黑帽 = 闭运算-原始输入
#礼帽
img = cv2.imread('data/dige.png')
tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)
cv2.imshow('tophat', tophat)
cv2.waitKey(0)
cv2.destroyAllWindows()

image-20200509225710838

#黑帽
img = cv2.imread('data/dige.png')
blackhat  = cv2.morphologyEx(img,cv2.MORPH_BLACKHAT, kernel)
cv2.imshow('blackhat ', blackhat )
cv2.waitKey(0)
cv2.destroyAllWindows()

image-20200509225739688