下面列出的是api,单纯线性执行以下代码可能会跑不通

环境安装

• conda create -n XXX（新建一个conda环境）
• Activate XXX （打开conda的某一个环境）
• pip install opencv-python （安装opencv-python）

基本操作

import cv2
pic= cv2.imread('filename.png',0)  # 0为只读灰度，1为读BGR图像
cv2.imshow('xxx',pic) # 在xxx窗口显示pic图片
cv2.watKey(0)
cv2.destoryAllWindows()
cv2.imwrite('filename.png',pic)

视频

cap = cv2.VideoCapture('vtest.avi') 
while(cap.isOpened()):
    ret, frame = cap.read() # 读一帧
    if ret:
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)        
        cv2.imshow('frame',gray)
    if cv2.waitKey(1) & 0xFF == ord('q')  :
         break
cap.release()
cv2.destroyAllWindows()

# 获取摄像头
cap = cv2.VideoCapture(-1) #摄像头编号。
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID')#  注意编码器
out = cv2.VideoWriter('output.avi',fourcc, 20.0, (640,480))

绘图

# Draw a diagonal blue line with thickness of 5 px
cv2.line(img,(0,0),(511,511),(255,0,0),5)
cv2.rectangle(img,(384,0),(510,128),(0,255,0),3)
cv2.circle(img,(447,63), 63, (0,0,255), -1)
cv2.ellipse(img,(256,256),(100,50),0,0,180,255,-1)

# 多边形
pts=np.array([[10,5],[20,30],[70,20],[50,10]], np.int32)
pts=pts.reshape((-1,1,2))
cv2.polylines(img,[pts],True,(0,255,255))

# 文字
font=cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img,'OpenCV',(10,500), font, 4,(255,255,255),6) 

鼠标

# 对某个窗口应用鼠标事件函数
cv2.namedWindow('image')
cv2.setMouseCallback('image',draw_circle)

图像代数运算

cv2.add()
cv2.subtract(img,80)
cv2.multiply(img,1.5)
cv2.addWeighted(img1,0.7,img2,0.3,0)

img2gray = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY) # 图片img2转换为灰度值
ret, mask_front = cv2.threshold(img2gray, 175, 255, cv2.THRESH_BINARY) # 门限 灰度值大于175为1（白） 小于175为0
mask_inv = cv2.bitwise_not(mask_front) # 取反
img1_bg = cv2.bitwise_and(img1,img1,mask = mask_front) # 图片1扣背景
img2_fg = cv2.bitwise_and(img2,img2,mask = mask_inv) # 图片2扣内容（背景为白色，内容灰度值较低）
result = cv2.add(img1_bg,img2_fg) # 合并

几何变换

res=cv2.resize(img,None,fx=2,fy=2,interpolation=cv2.INTER_CUBIC)

M=cv2.getRotationMatrix2D((cols/2,rows/2),45,0.6) # 获得对应的旋转矩阵
dst=cv2.warpAffine(img,M,(cols,rows)) # 仿射

# 仿射
pts1=np.float32([[50,50],[200,50],[50,200]])
pts2=np.float32([[10,100],[200,50],[100,250]])
M=cv2.getAffineTransform(pts1,pts2)

# 透视
pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]])
pts2 = np.float32([[0,0],[300,0],[0,300],[300,300]])
M=cv2.getPerspectiveTransform(pts1,pts2)

直方图

cv2:calcHist(images; channels; mask; histSize; ranges[; hist[; accumulate]])
hist = cv2.calcHist([img],[0],None,[256],[0,256])
hist,bins = np.histogram(img.ravel(),256,[0,256])

# 均衡化灰度图
equ = cv2.equalizeHist(img)

卷积

# 计算卷积
dst = cv2.filter2D(img,-1,kernel)

blur3 = cv2.blur(img,(3,3)) # 均值
median = cv2.medianBlur(img,5) # 中值
blur = cv2.GaussianBlur(img,(5,5),0) # 高斯
laplacian=cv2.Laplacian(img,-1) # 拉普拉斯
sobelx=cv2.Sobel(img,-1,1,0,ksize=5) #sobel x方向1 y方向0

复变

f = np.fft.fft2(img)
fshift = np.fft.fftshift(f)

magnitude_spectrum = 20*np.log(np.abs(fshift)) # 进行对数处理，可视化

f_ishift = np.fft.ifftshift(fshift)
img_back = np.fft.ifft2(f_ishift)

形态学

kernel = np.ones((5,5),np.uint8) 
#kernel  = cv2.getStructuringElement(cv2.MORPH_RECT,(3, 3))
erosion = cv2.erode(img,kernel,iterations = 1) # 腐蚀
dilation = cv2.dilate(img,kernel,iterations = 1) # 膨胀
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) # 开
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel) # 闭
gradient = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel) # 膨胀-腐蚀 result = cv2.absdiff(dilate,erode)

形态学-阈值

ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
# 自适应阈值
th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)
th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)

cv2.cvtColor(input_image，flag) # 色彩转换
mask=cv2.inRange(hsv,lower_blue,upper_blue)  #构建掩模