import cv2
import numpy as np
cv2.nameWindow("gray")
img = np.zeros((512, 512), np.uint8) #生成一张空的灰度图像
cv2.line(img, (0, 0), (511, 511), 255, 5) #绘制一条白色直线
cv2.imshow("gray", img) #显示图像
#循环等待，按q键退出
while True:
key = cv2.waitKey(1)
if key == ord("q"):
break
cv2.destoryWindow("gray")
# -*- coding: UTF-8 -*-
import cv2
import numpy as np
img = np.zeros((512, 512), np.uint8) #生成一张空的灰度图像
cv2.line(img, (0, 0), (511, 511), 255, 5) #绘制一条白色直线
cv2.imshow("gray", img) #显示图像
#循环等待，按q键退出
while True:
key = cv2.waitKey(1)
if key == ord("q"):
break
cv2.destoryWindow("white")
import cv2
import numpy as np
img_gray=cv2.imread('chidalu.jpg',cv2.IMREAD_GRAYSCALE); #image read in grayscale
ft=cv2.dft(np.float32(img_gray),flags=cv2.DFT_COMPLEX_OUTPUT); #frequency transform in image
ft_shift=np.fft.fftshift(ft); #frequency transform shift
row,col=img_gray.shape; #get shape of image
mask=np.zeros((row,col,2),np.uint8); #create a mask of image
mrow,mcol=int(row/2),int(col/2);
mask[mrow-30:mrow+30,mcol-30:mcol+30]=1;
ft_shift=mask*ft_shift; #new frequency shift
i_ft_shift=np.fft.ifftshift(ft_shift); #find inverse of frequency transform shift
i_ft=cv2.idft(i_ft_shift); #find inverse of frequency shift
lpf=cv2.magnitude(i_ft[:,:,0],i_ft[:,:,1]); #lowpassfilter
cv2.imshow('LowPassFilter',lpf); #Image show
if cv2.waitKey(0)&0xFF==ord('q'):
cv2.destoryWindow(); #destroy window
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import cv2
image1 = cv2.imread(
'test.png',
0) # 0:grayscale 1:color image(default) -1:unchanged(alpha channel)
cv2.imshow('窗口名', image1) #创建窗口并显示图像
cv2.waitKey(0) #暂停，直到按任意键
cv2.destoryAllWindows() #关闭所有窗口
cv2.destoryWindow('窗口名') #关闭指定窗口
cv2.namedWindow(
'窗口名', cv2.WINDOW_NORMAL
) #创建一个窗口 cv2.WINDOW_NORMAL:可以缩放 CV2.WINDOW_AUTOSIZE(DEFAULT):不可缩放
cv2.imshow('窗口名', image1) #显示图像
cv2.imwrite('文件名.png', iamge1) #在工作目录保存图片 png格式
##实例
import numpy as np
import cv2
img = cv2.imread('1.png', 0) #打开为灰度图
cv2.imshow('image', img)
k = cv2.waitKey(0) #64位系统使用 k = cv2.waitKey(0) & 0xFF
if k == 27: #读入ESC后退出
cv2.destoryAllWindows()
elif k == ord('s'): #读入s后保存并退出
cv2.imwrite('2.png', img)
cv2.destoryAllWindows()
return img
def addweighted_img_mask(img, mask, img_value, mask_value, r):
print(mask.shape[0])
mask_low = mask.shape[0]
mask_row = mask.shape[1]
dst = cv2.addWeighted(img[0:mask_low, 0:mask_row], img_value, mask,
mask_value, r)
#dst = cv2.add(img,mask)
img[0:mask_low, 0:mask_row] = dst
return img
if __name__ == "__main__":
img1 = cv2.imread("/home/ly/opencvtest/opencvlearn/image/4n.jpg")
img2 = cv2.imread("/home/ly/opencvtest/opencvlearn/image/n4.png")
logo = cv2.imread("/home/ly/opencvtest/opencvlearn/image/opencvlogo.png")
mianju = cv2.imread("/home/ly/opencvtest/opencvlearn/image/mianju.jpg")
mianju = cv2.resize(mianju, (200, 200))
#addimage(img1,img2)
dst_mask = add_img_mask(img1, logo)
#dst_mianju =add_img_mask(img1,mianju)
#cv2.imshow('dst_mianju',dst_mianju)
cv2.imshow('dst_mask', dst_mask)
dst_addweighted = addweighted_img_mask(img1, logo, 0.3, 0.7, 0)
cv2.imshow('dst_addweighted', dst_addweighted)
if cv2.waitKey(0) == 27:
cv2.destoryWindow(dst_mask)
cv2.destoryWindow(dst_addweighted)
# -*- coding:utf-8 -*-'
import cv2
face_cascade = cv2.CascadeClassifier(
'haarcascades/haarcascade_frontalface_alt.xml')
eye_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_eye.xml')
img1 = cv2.imread('33.png')
img = cv2.resize(img1, (240, 320), interpolation=cv2.INTER_LINEAR)
faces = face_cascade.detectMultiScale(img, 1.2, 2)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, x + h), (255, 0, 0),
2) #用颜色为BGR（255,0,0）粗度为2的线条在img画出识别出的矩型
face_re = img[y:y + h, x:x + w] #抽取出框出的脸部部分，注意顺序y在前
eyes = eye_cascade.detectMultiScale(face_re) #在框出的脸部部分识别眼睛
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(face_re, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)
cv2.imshow('img', img)
key = cv2.waitKey(0)
if key == 27:
cv2.destoryWindow('img')
