绘制几何图形

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1’绘制直线

2‘绘制圆形

3’绘制矩形

4‘向图像中添加文字

5’效果展示

import cv2
import  numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
img=np.zeros((512,512,3),np.uint8)
cv.line(img,(0,0),(511,511),(255,0,0),5)
cv.circle(img,(256,256),60,(0,0,255),-1)
cv.putText(img,"hello",(100,150),cv.FONT_HERSHEY_COMPLEX,5,(255,255,255),3)
plt.imshow(img[:,:,::-1])
plt.show()

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获取并修改图像中的像素点

像素点的改变在图片上比较难以观察，则输出了改变点的像素点

# 更改（100，100）处的像素值
img[100,100]=(0,0,255)
plt.imshow(img[:,:,::-1])
#返回这一点的像素值
print(img[100,100]) #[  0   0 255]# ####获取像素的属性print(img.shape)#(512, 512, 3)行、列、通道数
print(img.dtype)#uint8数据类型
print(img.size)#786432图像大小#####通道的拆分与合并# b,g,r=cv.split(img)# img=cv.merge((b,g,r))# ####色彩空间的改变
test=cv.imread("opencv_1.png")
print(plt.imshow(test))
print(plt.imshow(test[:,:,::-1]))#AxesImage(80,52.8;496x369.6)#AxesImage(80,52.8;496x369.6)# cv.imshow('opencv_1.png',test)# img=cv.imread("opencv_1.png",1)
# cv.imshow('opencv_1.png',img)#灰色图形b,g,r=cv.split(test)
plt.imshow(b,cmap=plt.cm.gray)
img2=cv.merge((b,g,r))
plt.imshow(img[:,:,::-1])cv.imshow('opencv_1.png',test)

算术操作

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可以将两幅图相加，或者通过numpy操作添加两幅图像，
两幅图像应该具有相同的大小和类型，或者第二个图像可以是标量值
opencv加法是饱和操作，numpy中是模运算

import cv2
import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt# 读取图像
img1=cv.imread('opencv_1.png');img2=cv.imread("opencv_2.png");
plt.imshow(img1[:,:,::-1])
#加分运算
img3=cv.add(img2,img1)
img4=img1+img2
print(img3.shape)
img_200x200=cv.resize(img1,(200,200))
img_half=cv.resize(img1,(0,0),fx=0.5,fy=0.5,interpolation=cv.INTER_NEAREST)
cv.imshow("img_half",img_half)
cv.imshow("image",img_200x200)# 图像显示
cv.imshow('img3',img3)cv.imshow('img4',img4)# fig,axes=plt.subplot(nrows=1,ncols=2,figsize=(10.8),dpi=100)
# axes[0].imshow(img3[:,:,::-1])

图像的混合

img5=cv2.addWeighted(img1,0.7,img2,0.3,0)#表示所占权重不同
cv.imshow('img5',img5)
# cv.waitKey(0)# ####图像缩放photo1=cv.imread('opencv_2.png')
cv.imshow("123",photo1)#缩放变换
# rows,cols=photo1.shape[:2]
res=cv.resize(photo1,(0,0),fx=0.5,fy=0.5)cv.imshow("qw",res)

import numpy as np
import cv2img = cv2.imread('test.png')# 沿着横纵轴放大1.6倍，之后平移（-150，-240），最后沿原图大小截取，等效于剪裁并放大
M_crop_dog = np.array([[1.6, 0, -150],[0, 1.6, -240]
], dtype=np.float32)# cv2.warpAffine(原始图像, 变换矩阵,变换后的图像大小)
img_dog = cv2.warpAffine(img, M_crop_dog,(400, 400))
cv2.imshow("img_dog", img_dog)# x轴的剪切变换，逆时针旋转角度15°
theta=15*np.pi/180
M_shear=np.array([[1,np.tan(theta),0],[0,1,0]
],dtype=np.float32)img_sheared=cv2.warpAffine(img,M_shear,(400,600))
cv2.imshow("img_sheared",img_sheared)# 顺时针旋转，角度15°
M_rotate=np.array([[np.cos(theta),-np.sin(theta),0],[np.sin(theta),np.cos(theta),0]
],dtype=np.float32)im_rotate=cv2.warpAffine(img,M_rotate,(400,600))
cv2.imshow("im_rotate",im_rotate)# 旋转+缩放+旋转组合，可以通过SVD分解理解
M=np.array([[1,1.5,-400],[0.5,2,-100]
],dtype=np.float32)img_transformed=cv2.warpAffine(img,M,(400,600))
cv2.imshow("img_transformed",img_transformed)cv2.waitKey(0)