OpenCV—python 图像积分图

文章目录

积分图原理

第一个提出 Haar 特征快速计算方法的是 CVPR2001上 的那篇经典论文 [《Rapid object detection using a boosted cascade of simple features》] (http://www.cs.utexas.edu/~grauman/courses/spring2007/395T/papers/viola_cvpr2001.pdf), Viola 提出了一种利用积分图(integral image)快速计算 Haar 特征的方法, 这个方法使得图像的局部矩形求和运算的复杂度从 ​​O(MN)​​​ 下降到了 ​​O(4)​​ 。

void GetGrayIntegralImage(unsigned char *Src, int *Integral, int Width, int Height, int Stride)
{

  memset(Integral, 0, (Width + 1) * sizeof(int));          //  第一行都为0
  for (int Y = 0; Y < Height; Y++)
  {
    unsigned char *LinePS = Src + Y * Stride;
    int *LinePL = Integral + Y * (Width + 1) + 1;        //  上一行位置     
    int *LinePD = Integral + (Y + 1) * (Width + 1) + 1;      //  当前位置，注意每行的第一列的值都为0
    LinePD[-1] = 0;                        //  第一列的值为0
    for (int X = 0, Sum = 0; X < Width; X++)
    {
      Sum += LinePS[X];                    //  行方向累加
      LinePD[X] = LinePL[X] + Sum;              //  更新积分图
    }
  }
}

void GetRGBIntegralImage(unsigned char *Src, int *Integral, int Width, int Height, int Stride)
{
  const int Channel = 3;
  memset(Integral, 0, (Width + 1) * Channel * sizeof(int));
  for (int Y = 0; Y < Height; Y++)
  {
    unsigned char *LinePS = Src + Y * Stride;
    int *LinePL = Integral + Y * (Width + 1) * Channel + Channel;
    int *LinePD = Integral + (Y + 1) * (Width + 1) * Channel + Channel;
    LinePD[-3] = 0; LinePD[-2] = 0; LinePD[-1] = 0;
    for (int X = 0, SumB = 0, SumG = 0, SumR = 0; X < Width; X++)
    {
      SumB += LinePS[0];
      SumG += LinePS[1];
      SumR += LinePS[2];
      LinePD[0] = LinePL[0] + SumB;
      LinePD[1] = LinePL[1] + SumG;
      LinePD[2] = LinePL[2] + SumR;
      LinePS += Channel;
      LinePL += Channel;
      LinePD += Channel;
    }
  }
}



void BoxBlur(unsigned char *Src, unsigned char *Dest, int Width, int Height, int Stride, int Radius)
{
  int Channel = Stride / Width;
  int *Integral = (int *)malloc((Width + 1) * (Height + 1) * Channel * sizeof(int));
  if (Channel == 1)
  {
    GetGrayIntegralImage(Src, Integral, Width, Height, Stride);
    #pragma
    for (int Y = 0; Y < Height; Y++)
    {
      int Y1 = max(Y - Radius, 0);
      int Y2 = min(Y + Radius + 1, Height);
      //  int Y1 = Y - Radius;
      //  int Y2 = Y + Radius + 1;
      //  if (Y1 < 0) Y1 = 0;
      //  if (Y2 > Height) Y2 = Height;
      int *LineP1 = Integral + Y1 * (Width + 1);
      int *LineP2 = Integral + Y2 * (Width + 1);
      unsigned char *LinePD = Dest + Y * Stride;
      for (int X = 0; X < Width; X++)
      {
        int X1 = max(X - Radius, 0);
        int X2 = min(X + Radius + 1, Width);
        //  int X1 = X - Radius;
        //  if (X1 < 0) X1 = 0;
        //  int X2 = X + Radius + 1;
        //  if (X2 > Width) X2 = Width;
        int Sum = LineP2[X2] - LineP1[X2] - LineP2[X1] + LineP1[X1];
        int PixelCount = (X2 - X1) * (Y2 - Y1);          //  有效的像素数
        LinePD[X] = (Sum + (PixelCount >> 1)) / PixelCount;    //  四舍五入
      }
    }
  }
  else if (Channel == 3)
  {
    GetRGBIntegralImage(Src, Integral, Width, Height, Stride);
    #pragma
    for (int Y = 0; Y < Height; Y++)
    {
      int Y1 = max(Y - Radius, 0);
      int Y2 = min(Y + Radius + 1, Height);
      int *LineP1 = Integral + Y1 * (Width + 1) * 3;
      int *LineP2 = Integral + Y2 * (Width + 1) * 3;
      unsigned char *LinePD = Dest + Y * Stride;
      for (int X = 0; X < Width; X++)
      {
        int X1 = max(X - Radius, 0);
        int X2 = min(X + Radius + 1, Width);
        int Index1 = X1 * 3;
        int Index2 = X2 * 3;
        int SumB = LineP2[Index2 + 0] - LineP1[Index2 + 0] - LineP2[Index1 + 0] + LineP1[Index1 + 0];
        int SumG = LineP2[Index2 + 1] - LineP1[Index2 + 1] - LineP2[Index1 + 1] + LineP1[Index1 + 1];
        int SumR = LineP2[Index2 + 2] - LineP1[Index2 + 2] - LineP2[Index1 + 2] + LineP1[Index1 + 2];
        int PixelCount = (X2 - X1) * (Y2 - Y1);
        LinePD[0] = (SumB + (PixelCount >> 1)) / PixelCount;
        LinePD[1] = (SumG + (PixelCount >> 1)) / PixelCount;
        LinePD[2] = (SumR + (PixelCount >> 1)) / PixelCount;
        LinePD += 3;
      }
    }
  }
  free(Integral);
}

效果演示

python演示：

import cv2


def integral_rgb(img_path):
    image = cv2.imread(img_path)

    image_integral = cv2.integral(image, cv2.CV_32F)
    cv2.normalize(image_integral, image_integral, 0, 255, cv2.NORM_MINMAX, -1)
    image_integral_norm = cv2.convertScaleAbs(image_integral)

    cv2.imwrite("./image.png", image)
    cv2.imwrite("./image_integral_norm.png", image_integral_norm)
    cv2.imshow("image", image)
    cv2.imshow("image_integral_norm", image_integral_norm)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


def integral_gray(img_path):
    image = cv2.imread(img_path, 0)

    image_integral = cv2.integral(image, cv2.CV_32F)
    cv2.normalize(image_integral, image_integral, 0, 255, cv2.NORM_MINMAX, -1)
    image_integral_norm = cv2.convertScaleAbs(image_integral)

    cv2.imwrite("./gray_image.png", image)
    cv2.imwrite("./gray_image_integral_norm.png", image_integral_norm)
    cv2.imshow("gray_image", image)
    cv2.imshow("gray_image_integral_norm", image_integral_norm)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


if __name__ == '__main__':
    img_path = r'C:\Users\xxx\Desktop\test.png'
    integral_rgb(img_path)
    integral_gray(img_path)

RGB图：

灰度图：