laplacian function.py

def laplacian(sigma,image):
    """
    sigma: Parameter controlling Gaussian blur
    image: input greyscale image n x m
    conv_laplacian: output image n x m to be returned, 
                    result of convolution of image with laplacian of Gaussian  
    """
    # 1 dimension gaussian kernels (Integration scale)
    K1,K2 = gauss_kernels(sigma)
    
    # The below code is adopted from the Lession 1 slide 11

    K1_derv_x = np.expand_dims(K1,axis=0) 
    K1_derv_y = np.transpose(K1_derv_x)

    K2_derv_x = np.expand_dims(K2,axis=0) 
    K2_derv_y = np.transpose(K2_derv_x)

    Gx = signal.convolve2d(image, K1_derv_x, mode='same')
    GxLx = signal.convolve2d(Gx, K2_derv_x, mode='same')

    Gy = signal.convolve2d(image, K1_derv_y, mode='same')
    GyLy = signal.convolve2d(Gy, K2_derv_y, mode='same')

    summed = GxLx+GyLy
    conv_laplacian = (summed - np.min(summed)) / (np.max(summed) - np.min(summed))
    
    return conv_laplacian