交叉处理算法（图像处理）

Question

我正在使用Javascript开发一个图像处理库，并想知道实现“跨进程”效果的算法是什么

有点像这样

Answer 1

我的脚本基于http://photographypla.net/cross-processed-lightroom/

我使用根据segmoid（对于红色和绿色通道）重新映射颜色并使用蓝色通道的双指数进行基本通道校正。我从http://www.flong.com/texts/code/shapers_exp/获得的那些功能。

基本修正后的图像如下所示：

您可以通过更改参数sFactor1和sFactor2来使用此结果。

之后我降低了总对比度并做了一些局部直方图增强，但我建议你不要使用这部分并搜索高光阴影和白色和黑色调整的良好实现。

最终结果：

代码：

import cv2
import numpy as np
import math

# Define an S shape segmoid that with controlled shape. Based on http://www.flong.com/texts/code/shapers_exp/

# Function for sigmoid creation with s shape facor
def doubleExponentialSigmoid(x, a):

    epsilon = 0.00001
    min_param_a = 0.0 + epsilon
    max_param_a = 1.0 - epsilon
    a = min(max_param_a, max(min_param_a, a))
    a = 1.0 - a # for sensible results
    y = 0
    if x <= 0.5:
        y = (math.pow(2.0 * x, 1.0 / a)) / 2.0
    else:
        y = 1.0 - (pow(2.0 * (1.0-x), 1.0 / a)) / 2.0
    return y

# Function for reverse sigmoid creation with reverse s shape facor
def doubleExponentialSeat(x,a):

    epsilon = 0.00001
    min_param_a = 0.0 + epsilon
    max_param_a = 1.0 - epsilon
    a = min(max_param_a, max(min_param_a, a))
    y = 0
    if x <= 0.5:
        y = (math.pow(2.0*x, 1-a))/2.0;
    else:
        y = 1.0 - (math.pow(2.0*(1.0-x), 1-a))/2.0
    return y

# Function for s shape function creation
def getSigmoidLut(sFactor,reverseShape=False):
    rangeOfValues = np.arange(0, 1+(float(1) / float(255)), float(1) / float(255))
    index = 0
    sigmoidLUT = np.zeros_like(rangeOfValues)
    if reverseShape:
        for v in rangeOfValues:
            sigmoidLUT[index] = doubleExponentialSeat(v, sFactor)
            index = index + 1
    else:
        for v in rangeOfValues:
            sigmoidLUT[index] = doubleExponentialSigmoid(v, sFactor)
            index = index + 1

    return sigmoidLUT

# A function to map one range to another
def RangeMapping(currentMin,currentMax,newMin,newMax):

    newRange = np.zeros((256,1))
    for v in range(256):
        newRange[v] = (((v - currentMin) * (newMax - newMin)) / (currentMax - currentMin)) + newMin

    return newRange

# Function to lower contrast by a factor
def LowerContrast(intensityChannel, factor):

    # Second chane the contrast by the factor
    mappingLUT = RangeMapping(np.min(intensityChannel),np.max(intensityChannel),np.round(np.min(intensityChannel)*factor),np.round(np.max(intensityChannel)/factor))
    newIntensity = cv2.LUT(intensityChannel,mappingLUT)

    return newIntensity

# This cross processing is based on the tutorial in http://photographypla.net/cross-processed-lightroom/

# Params
sFactor1 = 0.7
sFactor2 = 0.3
lowContrastFactor = 1.05

# Read image
I = cv2.imread('im.jpg')

# Step 1: Separate to the three channels
R,G,B = cv2.split(I)

# Step 2: Map to a S curve each channel

# Get a S shaped segmoid
redChannelLUT = np.round(getSigmoidLut(sFactor1,False)*255).astype(np.uint8)
greenChannelLUT = redChannelLUT
blueChannelLUT =np.round(getSigmoidLut(sFactor2,True)*255).astype(np.uint8)

# Apply correction
redChannelCorrection = cv2.LUT(R, redChannelLUT)
greenChannelCorrection = cv2.LUT(G, greenChannelLUT)
blueChannelCorrection = cv2.LUT(B, blueChannelLUT)

# Step 3: Merge corrected channels
ICorrection = cv2.merge((redChannelCorrection,greenChannelCorrection,blueChannelCorrection))

# From here you can do whatever you want to the colors shadows highlights etc...
# Separate color and intensity
Iycr = cv2.cvtColor(ICorrection,cv2.COLOR_RGB2YCR_CB)
intensityCh,C,R = cv2.split(Iycr)

# Step 4: lower contrast
newLowerIntensityContrast = LowerContrast(intensityCh,lowContrastFactor)

# Step 5: Local contrast enhacment
clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(8,8))
ICorrectedShadows = clahe.apply(newLowerIntensityContrast.astype(np.uint8))

# Final step re construct image
IycrLowContrast = cv2.merge((ICorrectedShadows,C,R))
finalImage = cv2.cvtColor(IycrLowContrast,cv2.COLOR_YCrCb2RGB)

cv2.imshow('Original',I)
cv2.imshow('ColorCorrection',ICorrection)
cv2.imshow('LowContrast',newLowerIntensityContrast.astype(np.uint8))
cv2.imshow('Final',finalImage)