用OpenCV从图像中去除水印

Question

首先，我有这个图像，我想做一个应用程序，可以检测像它这样的图像，并从其中删除圆圈(水印)。

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int main(){
    Mat im1,im2,im3,gray,gray2,result;

    im2=imread(" (2).jpg");
    namedWindow("x",CV_WINDOW_FREERATIO);
    imshow("x",im2);

    //converting it to gray
    cvtColor(im2,gray,CV_BGR2GRAY);
    // creating a new image that will have the cropped ellipse
    Mat ElipseImg(im2.rows,im2.cols,CV_8UC1,Scalar(0,0,0));

    //detecting the largest circle
    GaussianBlur(gray,gray,Size(5,5),0);
    vector<Vec3f> circles;
    HoughCircles(gray,circles,CV_HOUGH_GRADIENT,1,gray.rows/8,100,100,100,0);

    uchar x;
    int measure=0;int id=0;
    for(int i=0;i<circles.size();i++){
        if(cvRound(circles[i][2])>measure && cvRound(circles[i][2])<1000){
            measure=cvRound(circles[i][2]);
            id=i;
        }
    }


    Point center(cvRound(circles[id][0]),cvRound(circles[id][1]));
    int radius=cvRound(circles[id][2]);
    circle(im2,center,3,Scalar(0,255,0),-1,8,0);
    circle(im2,center,radius,Scalar(0,255,0),2,8,0);
    ellipse(ElipseImg,center,Size(radius,radius),0,0,360,Scalar(255,255,255),-1,8);
    cout<<"center: "<<center<<" radius: "<<radius<<endl;



    Mat res;
    bitwise_and(gray,ElipseImg,result);
    namedWindow("bitwise and",CV_WINDOW_FREERATIO);
    imshow("bitwise and",result);

    // trying to estimate the Intensity  of the circle for the thresholding
    x=result.at<uchar>(cvRound(circles[id][0]+30),cvRound(circles[id][1]));
    cout<<(int)x;

    //thresholding the  output image
    threshold(ElipseImg,ElipseImg,(int)x-10,250,CV_THRESH_BINARY);
    namedWindow("threshold",CV_WINDOW_FREERATIO);
    imshow("threshold",ElipseImg);

    // making bitwise_or
    bitwise_or(gray,ElipseImg,res);
    namedWindow("bitwise or",CV_WINDOW_FREERATIO);
    imshow("bitwise or",res);

    waitKey(0);
}

到目前为止我做的是：

1. 我把它转换成灰度
2. 我用霍夫圆检测出最大的圆，然后在新的图像中形成一个半径相同的圆。
3. 这个新的带灰度的圆圈(bitwise_and)给了我一个只有那个圆的图像。
4. 新图像的阈值
5. bitwise_or阈值的结果

我的问题是，这个圆圈内弯曲的白线上的任何黑色文本都没有出现。我试图通过使用像素值而不是阈值来删除颜色，但问题是一样的。那么有什么解决办法或建议吗？

以下是研究结果：

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Answer 1

我不确定在您的情况下，下面的解决方案是否可以接受。但我认为它的性能稍好，不关心水印的形状。

• 使用形态过滤删除笔画。这应该给你一个背景图像。

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• 计算差分图像:差分=背景-初始值，阈值它:二进制=阈值(差)

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• 对背景图像进行阈值化处理，提取水印覆盖的黑暗区域。

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• 从初始图像中提取水印区域内的像素并对这些像素进行阈值化，然后将它们粘贴到先前的二值图像中。

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以上是一个粗略的描述。下面的代码应该能更好地解释它。

Mat im = [load the color image here];

Mat gr, bg, bw, dark;

cvtColor(im, gr, CV_BGR2GRAY);

// approximate the background
bg = gr.clone();
for (int r = 1; r < 5; r++)
{
    Mat kernel2 = getStructuringElement(MORPH_ELLIPSE, Size(2*r+1, 2*r+1));
    morphologyEx(bg, bg, CV_MOP_CLOSE, kernel2);
    morphologyEx(bg, bg, CV_MOP_OPEN, kernel2);
}

// difference = background - initial
Mat dif = bg - gr;
// threshold the difference image so we get dark letters
threshold(dif, bw, 0, 255, CV_THRESH_BINARY_INV | CV_THRESH_OTSU);
// threshold the background image so we get dark region
threshold(bg, dark, 0, 255, CV_THRESH_BINARY_INV | CV_THRESH_OTSU);

// extract pixels in the dark region
vector<unsigned char> darkpix(countNonZero(dark));
int index = 0;
for (int r = 0; r < dark.rows; r++)
{
    for (int c = 0; c < dark.cols; c++)
    {
        if (dark.at<unsigned char>(r, c))
        {
            darkpix[index++] = gr.at<unsigned char>(r, c);
        }
    }
}
// threshold the dark region so we get the darker pixels inside it
threshold(darkpix, darkpix, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);

// paste the extracted darker pixels
index = 0;
for (int r = 0; r < dark.rows; r++)
{
    for (int c = 0; c < dark.cols; c++)
    {
        if (dark.at<unsigned char>(r, c))
        {
            bw.at<unsigned char>(r, c) = darkpix[index++];
        }
    }
}

Answer 2

dhanushka's answer的Python版本

# Import the necessary packages
import cv2
import numpy as np


def back_rm(filename):
    # Load the image
    img = cv2.imread(filename)

    # Convert the image to grayscale
    gr = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Make a copy of the grayscale image
    bg = gr.copy()

    # Apply morphological transformations
    for i in range(5):
        kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,
                                            (2 * i + 1, 2 * i + 1))
        bg = cv2.morphologyEx(bg, cv2.MORPH_CLOSE, kernel2)
        bg = cv2.morphologyEx(bg, cv2.MORPH_OPEN, kernel2)

    # Subtract the grayscale image from its processed copy
    dif = cv2.subtract(bg, gr)

    # Apply thresholding
    bw = cv2.threshold(dif, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
    dark = cv2.threshold(bg, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

    # Extract pixels in the dark region
    darkpix = gr[np.where(dark > 0)]

    # Threshold the dark region to get the darker pixels inside it
    darkpix = cv2.threshold(darkpix, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

    # Paste the extracted darker pixels in the watermark region
    bw[np.where(dark > 0)] = darkpix.T

    cv2.imwrite('final.jpg', bw)


back_rm('watermark.jpg')

最后的结果如下：

使用numpy处理时间非常短。

time python back_rm.py 

real    0m0.391s
user    0m0.518s
sys     0m0.185s

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