圖像中的簡單光照校正openCV C++

Question

我有一些彩色照片，照片在照片中不規則：圖像的一側比另一側明亮。

我想通過校正照度來解決這個問題。 我認爲局部對比度會幫助我，但我不知道怎麼:(

請你幫我用一塊代碼或管道？

Answer 1

轉換RGB圖像爲Lab色彩空間（例如，任何具有亮度通道的色彩空間都可以正常工作），然後將adaptive histogram equalization應用於L通道。最後將生成的Lab轉換回RGB。

您想要的是OpenCV的CLAHE（Contrast Limited Adaptive Histogram Equalization）對比度算法。然而，據我所知，這是沒有記錄。有an example in python。您可以閱讀關於CLAHE在Graphics Gems IV, pp474-485

這裏是CLAHE的動作的一個示例：

這裏是所產生的上述圖像，基於http://answers.opencv.org/question/12024/use-of-clahe/的C++，但延長的顏色。

#include <opencv2/core.hpp> 
#include <vector>  // std::vector 
int main(int argc, char** argv) 
{ 
    // READ RGB color image and convert it to Lab 
    cv::Mat bgr_image = cv::imread("image.png"); 
    cv::Mat lab_image; 
    cv::cvtColor(bgr_image, lab_image, CV_BGR2Lab); 

    // Extract the L channel 
    std::vector<cv::Mat> lab_planes(3); 
    cv::split(lab_image, lab_planes); // now we have the L image in lab_planes[0] 

    // apply the CLAHE algorithm to the L channel 
    cv::Ptr<cv::CLAHE> clahe = cv::createCLAHE(); 
    clahe->setClipLimit(4); 
    cv::Mat dst; 
    clahe->apply(lab_planes[0], dst); 

    // Merge the the color planes back into an Lab image 
    dst.copyTo(lab_planes[0]); 
    cv::merge(lab_planes, lab_image); 

    // convert back to RGB 
    cv::Mat image_clahe; 
    cv::cvtColor(lab_image, image_clahe, CV_Lab2BGR); 

    // display the results (you might also want to see lab_planes[0] before and after). 
    cv::imshow("image original", bgr_image); 
    cv::imshow("image CLAHE", image_clahe); 
    cv::waitKey(); 
} 

Answer 2

基於偉大的C++ example written by Bull，我能夠爲Android編寫此方法。

我用「Core.split」替換了「Core.extractChannel」。這避免了known memory leak issue。

public void applyCLAHE(Mat srcArry, Mat dstArry) { 
    //Function that applies the CLAHE algorithm to "dstArry". 

    if (srcArry.channels() >= 3) { 
     // READ RGB color image and convert it to Lab 
     Mat channel = new Mat(); 
     Imgproc.cvtColor(srcArry, dstArry, Imgproc.COLOR_BGR2Lab); 

     // Extract the L channel 
     Core.extractChannel(dstArry, channel, 0); 

     // apply the CLAHE algorithm to the L channel 
     CLAHE clahe = Imgproc.createCLAHE(); 
     clahe.setClipLimit(4); 
     clahe.apply(channel, channel); 

     // Merge the the color planes back into an Lab image 
     Core.insertChannel(channel, dstArry, 0); 

     // convert back to RGB 
     Imgproc.cvtColor(dstArry, dstArry, Imgproc.COLOR_Lab2BGR); 

     // Temporary Mat not reused, so release from memory. 
     channel.release(); 
    } 

} 

，並調用它像這樣：

public Mat onCameraFrame(CvCameraViewFrame inputFrame){ 
    Mat col = inputFrame.rgba(); 

    applyCLAHE(col, col);//Apply the CLAHE algorithm to input color image. 

    return col; 
} 

Answer 3

你有你的反應，但另一種soltuion是使用直方圖來校正圖像：http://docs.opencv.org/2.4/doc/tutorials/imgproc/histograms/histogram_equalization/histogram_equalization.html

Answer 4

由紅牛提供的答案是最好的到目前爲止我已經遇到過。我一直在使用它。 下面是相同的Python代碼：

import cv2 

#-----Reading the image----------------------------------------------------- 
img = cv2.imread('Dog.jpg', 1) 
cv2.imshow("img",img) 

#-----Converting image to LAB Color model----------------------------------- 
lab= cv2.cvtColor(img, cv2.COLOR_BGR2LAB) 
cv2.imshow("lab",lab) 

#-----Splitting the LAB image to different channels------------------------- 
l, a, b = cv2.split(lab) 
cv2.imshow('l_channel', l) 
cv2.imshow('a_channel', a) 
cv2.imshow('b_channel', b) 

#-----Applying CLAHE to L-channel------------------------------------------- 
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8)) 
cl = clahe.apply(l) 
cv2.imshow('CLAHE output', cl) 

#-----Merge the CLAHE enhanced L-channel with the a and b channel----------- 
limg = cv2.merge((cl,a,b)) 
cv2.imshow('limg', limg) 

#-----Converting image from LAB Color model to RGB model-------------------- 
final = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR) 
cv2.imshow('final', final) 

#_____END_____# 

Answer 5

您還可以使用自適應直方圖均衡，

from skimage import exposure 

img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)