IplImage *input = cvLoadImage(path, 0)，
CvMat* M = cvCreateMat(4,4,CV_32FC1); //或是8UC1， 因为C1表示nChannel = 1,也就是单通道

void colorFilter(CvMat *inputImage, CvMat *&outputImage)
{
 int i, j;
 IplImage* image = cvCreateImage(cvGetSize(inputImage), 8, 3);
 cvGetImage(inputImage, image); 
 IplImage* hsv = cvCreateImage( cvGetSize(image), 8, 3 ); 
 
 cvCvtColor(image,hsv,CV_BGR2HSV);
 int width = hsv->width;
 int height = hsv->height;
 for (i = 0; i < height; i++)
 for (j = 0; j < width; j++)
 {
 CvScalar s_hsv = cvGet2D(hsv, i, j);//获取像素点为（j, i）点的HSV的值 
 /*
 opencv 的H范围是0~180，红色的H范围大概是(0~8)∪(160,180) 
 S是饱和度，一般是大于一个值,S过低就是灰色（参考值S>80)，
 V是亮度，过低就是黑色，过高就是白色(参考值220>V>50)。
 */
 CvScalar s;
 if (!(((s_hsv.val[0]>0)&&(s_hsv.val[0]<8)) || (s_hsv.val[0]>120)&&(s_hsv.val[0]<180)))
 {
 s.val[0] =0;
 s.val[1]=0;
 s.val[2]=0;
 cvSet2D(hsv, i ,j, s);
 }
 
 }
 outputImage = cvCreateMat( hsv->height, hsv->width, CV_8UC3 );
 cvConvert(hsv, outputImage);
 cvNamedWindow("filter");
 cvShowImage("filter", hsv);
 waitKey(0);
 cvReleaseImage(&hsv);
}

#coding=utf-8
import cv2 as cv
bg_color = [197, 102, 6]
threshold = 3000

def calc_diff(pixel):
'''
计算pixel与背景的离差平方和，作为当前像素点与背景相似程度的度量
'''
  return (pixel[0]-bg_color[0])**2 + (pixel[1]-bg_color[1])**2 + (pixel[2]-bg_color[2])**2

def remove_bg():
  image_path = './logo.png'
  logo = cv.imread(image_path)
  logo = cv.cvtColor(logo, cv.COLOR_BGR2BGRA) #将图像转成带透明通道的BGRA格式
  h, w = logo.shape[0:2]
  for i in range(h):
    for j in range(w):
      if calc_diff(logo[i][j]) < threshold:
      #若果logo[i][j]为背景，将其颜色设为白色，且完全透明
        logo[i][j][0] = 255
        logo[i][j][1] = 255
        logo[i][j][2] = 255
        logo[i][j][3] = 0
 
  cv.imwrite("./logo_rmbg.png", logo)
        
if __name__ == '__main__':
  remove_bg()