将图片处理为灰度图
import os
import re
import cv2
from PIL import Image
import numpy as np
import matplotlib.pylab as pylab
img=cv2.imread("./111.jpg",0);
cv2.imshow('image',img)
cv2.waitKey(10000);
cv2.destroyAllWindows()
创建一个矩阵,来模拟黑色图像
import os
import re
import cv2
from PIL import Image
import numpy as np
import matplotlib.pylab as pylab
img=np.zeros((100,100),dtype=np.uint8); #创建一个8x8的全零数组
print("img=\n",img)
cv2.imshow('image',img)
cv2.waitKey(10000);
cv2.destroyAllWindows();
某一个区域赋值为255,也就是白色
import os
import re
import cv2
from PIL import Image
import numpy as np
import matplotlib.pylab as pylab
img=np.zeros((100,100),dtype=np.uint8); #创建一个8x8的全零数组
print("img=\n",img)
cv2.imshow('image',img)
cv2.waitKey(1000);
cv2.destroyAllWindows()
for i in range(30,60):
for j in range(30,60):
img[i][j] = 255
cv2.imshow('image',img)
cv2.waitKey(10000);
cv2.destroyAllWindows()
输出蓝色照片
import os
import re
import cv2
from PIL import Image
import numpy as np
import matplotlib.pylab as pylab
blue=np.zeros((300,300,3),dtype=np.uint8)
blue[:,:,0]=255
print("blue=",blue)
cv2.imshow('image',blue)
cv2.waitKey(10000)
cv2.destroyAllWindows();
蓝色转灰度
import os
import re
import cv2
from PIL import Image
import numpy as np
import matplotlib.pylab as pylab
blue=np.zeros((300,300,3),dtype=np.uint8)
blue[:,:,0]=255
print("blue=",blue)
cv2.imshow('image',blue)
cv2.waitKey(10000)
gray=cv2.cvtColor(blue,cv2.COLOR_BGR2GRAY) #将BGR转换成RGB
cv2.imshow('image',gray)
cv2.waitKey(10000)
cv2.destroyAllWindows();
RGB2BGR
import os
import re
import cv2
from PIL import Image
import numpy as np
import matplotlib.pylab as pylab
blue=np.zeros((300,300,3),dtype=np.uint8)
blue[:,:,0]=255
print("blue=",blue)
cv2.imshow('image',blue)
cv2.waitKey(10000)
gray=cv2.cvtColor(blue,cv2.COLOR_RGB2BGR) #将BGR转换成RGB
cv2.imshow('image',gray)
cv2.waitKey(10000)
cv2.destroyAllWindows();
截取部分图像
import os
import re
import cv2
from PIL import Image
import numpy as np
import matplotlib.pylab as pylab
img=cv2.imread('./111.jpg')
jpg=img[0:200,0:200]
cv2.imshow('image',jpg)
cv2.waitKey(10000)
cv2.destroyAllWindows();
颜色通道提取
import os
import re
import cv2
from PIL import Image
import numpy as np
import matplotlib.pylab as pylab
img=cv2.imread('./111.jpg')
b,g,r=cv2.split(img)
print("r=",r)
print("r.shapr=",r.shape)
print("g=",g)
print("g.shapr=",g.shape)
print("b=",b)
print("b.shapr=",b.shape)
通道合并
import os
import re
import cv2
from PIL import Image
import numpy as np
import matplotlib.pylab as pylab
img=cv2.imread('./111.jpg')
b,g,r=cv2.split(img)
img=cv2.merge((b,g,r))
print("img.shape=",img.shape);
按照索引拆分
import os
import re
import cv2
from PIL import Image
import numpy as np
import matplotlib.pylab as pylab
img=cv2.imread('./111.jpg')
b=img[:,:,0]
g=img[:,:,1]
r=img[:,:,2]
print("r=",r)
print("r.shapr=",r.shape)
print("g=",g)
print("g.shapr=",g.shape)
print("b=",b)
print("b.shapr=",b.shape)
分别只保留r,g,b通道
import os
import re
import cv2
from PIL import Image
import numpy as np
import matplotlib.pylab as pylab
img=cv2.imread('./111.jpg')
copy_img = img.copy()
# 0,1,2
copy_img[:,:,0] = 0;
copy_img[:,:,1] = 0;
cv2.imshow('image',copy_img)
cv2.waitKey(10000)
cv2.destroyAllWindows();
颜色通道合成对比
import os
import re
import cv2
from PIL import Image
import numpy as np
import matplotlib.pylab as pylab
img=cv2.imread('./111.jpg')
b,g,r=cv2.split(img)
img=cv2.merge((b,g,r)) # b,g,r
print("img.shape=",img.shape);
cv2.imshow('image',img)
cv2.waitKey(10000)
cv2.destroyAllWindows();
img=cv2.merge((r,g,b)) #r,g,b
print("img.shape=",img.shape);
cv2.imshow('image',img)
cv2.waitKey(10000)
cv2.destroyAllWindows();
b,g,r
r,g,b
获取图像属性
import os
import re
import cv2
from PIL import Image
import numpy as np
import matplotlib.pylab as pylab
gray_lena=cv2.imread("./111.jpg",0) #获取灰色lena
color_lena=cv2.imread("./111.jpg",-1) #获取BGR彩色lena
print("灰度图像属性")
print("gray_lena.shape={}".format(gray_lena.shape))
print("gray_lena.size={}".format(gray_lena.size))
print("gray_lena.dtype={}".format(gray_lena.dtype))
print("彩色图像lena属性:")
color_lena=cv2.cvtColor(color_lena,cv2.COLOR_BGR2RGB)
pylab.imshow(color_lena)
print("color_lena.shape={}".format(color_lena.shape))
print("color_lena.size={}".format(color_lena.size))
print("color_lena.dtype={}".format(color_lena.dtype))