OpenCV 图片美化

1、彩色直方图

def ImgHist(image,type):
    color = (255,255,255)
    windowName='gray'

    if type==31:
        color=(255,0,0)
        windowName='B'
    elif type==32:
        color=(0,255,0)
        windowName='G'
    elif type==33:
        color=(0,0,255)
        windowName='R'
    #[0]通道
    hist=cv2.calcHist([image],[0],None,[256],[0.0,255.0])
    minV,maxV,minL,maxL=cv2.minMaxLoc(hist)
    print('minV,maxV,minL,maxL',minV,maxV,minL,maxL)
    histImg=np.zeros([256,256,3],np.uint8)
    for i in range(256):
        intenNormal=int(hist[i]*256/maxV)
        print(hist[i],hist[i]*256/maxV)
        cv2.line(histImg,(i,256),(i,256-intenNormal),color)
    cv2.imshow(windowName,histImg)
    return histImg
img=cv2.imread('b.png',1)
channels=cv2.split(img)#RGB--->R  G   B
for i in range(3):
    ImgHist(channels[i],31+i)
cv2.waitKey(0)

结果：

2、灰度化

img = cv2.imread('b.png',1)
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#原图
dst=cv2.equalizeHist(gray)#均衡化
cv2.imshow('dst',dst)
cv2.imshow('gray',gray)
cv2.waitKey(0)

结果：；

3、彩色

分别将各个通道进行均衡化，然后组合

img=cv2.imread('b.png',1)
b,g,r=cv2.split(img)
bH=cv2.equalizeHist(b)
gH=cv2.equalizeHist(g)
rH=cv2.equalizeHist(r)
dst=cv2.merge((bH,gH,rH))
cv2.imshow('dst',dst)
cv2.waitKey(0)

结果：

4、YUV  亮度与色度分离

imgyuv=cv2.cvtColor(img,cv2.COLOR_BGR2YCrCb)
channels=cv2.split(imgyuv)
channels[0]=cv2.equalizeHist(channels[0])
channels[1]=cv2.equalizeHist(channels[1])
channels[2]=cv2.equalizeHist(channels[2])
dst=cv2.merge(channels)#融合通道
cv2.imshow('dst',dst)
cv2.waitKey(0)

结果：

5、滤波（双边滤波，高斯滤波）

# 2 双边滤波器
cv2.imshow('src',img)
dst=cv2.bilateralFilter(img,100,200,160)
cv2.imshow('shangbian',dst)
# cv2.waitKey(0)
# 高斯中值滤波
dst=np.zeros(img.shape,np.uint8)
height=img.shape[0]
width=img.shape[1]
for i in range(3,height-3):
    for j in range(3,width-3):
        sum_b=int(0)
        sum_g=int(0)
        sum_r=int(0)
        for m in range(-3,3):
            for n in range(-3,3):
                (b,g,r)=img[i+m,j+n]
                sum_b=sum_b+int(b)
                sum_g=sum_g+int(g)
                sum_r=sum_r+int(r)
        b=np.uint8(sum_b/36)
        g=np.uint8(sum_g/36)
        r=np.uint8(sum_r/36)
        dst[i,j]=(b,g,r)
cv2.imshow('gaosi',dst)
cv2.waitKey(0)

结果：