Convert from RGB to SUV color space

rgb_2_suv.py

import numpy as np
from scipy.signal import convolve
from numpy import linalg

# implementation of RGB2SUV http://vision.ucsd.edu/~spmallick/research/suv/index.html


def get_rot_mat(rot_v, unit=None):
    '''
    takes a vector and returns the rotation matrix required to align the unit vector(2nd arg) to it
    '''
    if unit is None:
        unit = [1.0, 0.0, 0.0]

    rot_v = rot_v/np.linalg.norm(rot_v)
    uvw = np.cross(rot_v, unit) #axis of rotation

    rcos = np.dot(rot_v, unit) #cos by dot product
    rsin = np.linalg.norm(uvw) #sin by magnitude of cross product

    #normalize and unpack axis
    if not np.isclose(rsin, 0):
        uvw = uvw/rsin
    u, v, w = uvw

    # Compute rotation matrix
    # Artsiom: I haven't checked that this is correct
    R = (
        rcos * np.eye(3) +
        rsin * np.array([
            [ 0, -w,  v],
            [ w,  0, -u],
            [-v,  u,  0]
        ]) +
        (1.0 - rcos) * uvw[:,None] * uvw[None,:]
    )

    return R

def RGBToSUV(I_rgb,rot_vec):
    '''
    your implementation which takes an RGB image and a vector encoding the orientation of S channel wrt to RGB
    '''
    I_suv=np.zeros(I_rgb.shape)
    R=get_rot_mat(rot_vec,unit=None)
    for i in range(I_rgb.shape[0]):
        for j in range(I_rgb.shape[1]):
            I_suv[i,j,:]=np.matmul(R,I_rgb[i,j,:])

    S=I_suv[:,:,0]
    G=np.sqrt(I_suv[:,:,1]**2 + I_suv[:,:,2]**2)
    return (S,G)