def getGaussKernel(sigma, H, W):
  r, c = np.mgrid[0:H:1, 0:W:1]
  r -= (H - 1) / 2
  c -= (W - 1) / 2
  gaussMatrix = np.exp(-0.5 * (np.power(r) + np.power(c)) / math.pow(sigma, 2))
  # 计算高斯矩阵的和
  sunGM = np.sum(gaussMatrix)
  # 归一化
  gaussKernel = gaussMatrix / sunGM
  return gaussKernel

def gaussBlur(image,sigma,H,W,_boundary = 'fill', _fillvalue = 0):
  #水平方向上的高斯卷积核
  gaussKenrnel_x = cv2.getGaussianKernel(sigma,W,cv2.CV_64F)
  #进行转置
  gaussKenrnel_x = np.transpose(gaussKenrnel_x)
  #图像矩阵与水平高斯核卷积
  gaussBlur_x = signal.convolve2d(image,gaussKenrnel_x,mode='same',boundary=_boundary,fillvalue=_fillvalue)
  #构建垂直方向上的卷积核
  gaussKenrnel_y = cv2.getGaussianKernel(sigma,H,cv2.CV_64F)
  #图像与垂直方向上的高斯核卷积核
  gaussBlur_xy = signal.convolve2d(gaussBlur_x,gaussKenrnel_y,mode='same',boundary= _boundary,fillvalue=_fillvalue)
  return gaussBlur_xy
if __name__ == "__main__":
  image = cv2.imread("../images/timg.jpg", cv2.IMREAD_GRAYSCALE)
  cv2.imshow("image",image)
  #高斯平滑
  blurImage = gaussBlur(image, 5, 400, 400, 'symm')
  #对bIurImage进行灰度级显示
  blurImage = np.round(blurImage)
  blurImage = blurImage.astype(np.uint8)
  cv2.imshow("GaussBlur", blurImage)
  cv2.waitKey(0)
  cv2.destroyAllWindows()