图像美画

Posted 2021-01-03 stling

彩色图片直方图

#三色通道分离
import cv2
import numpy as np
　　# 1 image 2 [0] 3 mask None 4 256 5 0-255
　　hist = cv2.calcHist([image],[0],None,[256],[0.0,255.0])
　　cv2.calcHist(images, channels, mask, histSize, ranges, hist=None, accumulate=None)
　　# minMaxLoc(src[, mask]) -> minVal, maxVal, minLoc, maxLoc
　　minV,maxV,minL,maxL = cv2.minMaxLoc(hist)　
　　for h in range(256):
　　　　intenNormal = int(hist[h]*256/maxV)
　　　　cv2.line(histImg,(h,256),(h,256-intenNormal),color)
# 源码实现
# 统计每一个色阶出现的概率 使用matplotlib作图

均衡化

#灰度
cv2.equalizeHist()
#equalizeHist(src[, dst]) -> dst
#彩色  三通道分离
#分别均衡化
cv2.equalizeHist()
# 源码实现
# 本质：统计每个像素灰度 出现的概率 0-255  　　count[i] = count[i]/(height*width)
# 计算累计概率　　　　sum = sum+count[i]　　count[i] = sum1
# 计算映射表　　　　map1[i] = np.uint16(count[i]*255)

import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread(‘image0.jpg‘,1)


imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow(‘src‘,gray)
count = np.zeros(256,np.float)
for i in range(0,height):
    for j in range(0,width):
        pixel = gray[i,j]
        index = int(pixel)
        count[index] = count[index]+1
for i in range(0,255):
    count[i] = count[i]/(height*width)
#计算累计概率
sum1 = float(0)
for i in range(0,256):
    sum1 = sum1+count[i]
    count[i] = sum1
#print(count)
# 计算映射表
map1 = np.zeros(256,np.uint16)
for i in range(0,256):
    map1[i] = np.uint16(count[i]*255)
# 映射
for i in range(0,height):
    for j in range(0,width):
        pixel = gray[i,j]
        gray[i,j] = map1[pixel]
cv2.imshow(‘dst‘,gray)
cv2.waitKey(0)

 

YUV 直方图均衡化

cv2.cvtColor(img,cv2.COLOR_BGR2YCrCb)
channelYUV = cv2.split(imgYUV)
channelYUV[0] = cv2.equalizeHist(channelYUV[0])
channels = cv2.merge(channelYUV)
result = cv2.cvtColor(channels,cv2.COLOR_YCrCb2BGR)

修补

cv2.inpaint(img,paint,3,cv2.INPAINT_TELEA)
# inpaint(src, inpaintMask, inpaintRadius, flags[, dst]) -> dst

亮度增强

加像素值
超过255按255计

磨皮美白

#双边滤波
cv2.bilateralFilter(img,15,35,35)

高斯滤波

cv2.GaussianBlur(img,(5,5),1.5)

# 源码
import cv2
import numpy as np
img = cv2.imread(‘image11.jpg‘,1)
cv2.imshow(‘src‘,img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
dst = np.zeros((height,width,3),np.uint8)
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):#-3 -2 -1 0 1 2
            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(‘dst‘,dst)
cv2.waitKey(0)

中值滤波

import cv2
import numpy as np
img = cv2.imread(‘image11.jpg‘,1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
img = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
cv2.imshow(‘src‘,img)
dst = np.zeros((height,width,3),np.uint8)
collect = np.zeros(9,np.uint8)
for i in range(1,height-1):
    for j in range(1,width-1):
        k = 0
        for m in range(-1,2):
            for n in range(-1,2):
                gray = img[i+m,j+n]
                collect[k] = gray
                k = k+1
        # 0 1 2 3 4 5 6 7 8
        #   1 
        for k in range(0,9):
            p1 = collect[k]
            for t in range(k+1,9):
                if p1<collect[t]:
                    mid = collect[t]
                    collect[t] = p1
                    p1 = mid
        dst[i,j] = collect[4]
cv2.imshow(‘dst‘,dst)
cv2.waitKey(0)