Visualize Optical Flow with color model

Question

I've implemented a dense optical flow algorithm and I want to visualize it with following color model

(color denotes direction of flow at some point, intensity denotes length of displacement vector)

I've implemented a dirty version of the visualization

def visualizeFlow(u, v):
    colorModel = cv2.imread('../colormodel.png')
    colorModelCenter = (colorModel.shape[0]/2, colorModel.shape[1]/2)
    res = np.zeros((u.shape[0], u.shape[1], 3), dtype=np.uint8)
    mag = np.max(np.sqrt(u**2 + v**2)) 
    if mag == 0:
        return res, colorModel
    for i in xrange(res.shape[0]):
        for j in xrange(res.shape[1]):
            res[i, j] = colorModel[
                        colorModelCenter[0] + (v[i, j]/mag*colorModelCenter[0]),
                        colorModelCenter[1] + (u[i, j]/mag*colorModelCenter[1])
                    ]
    return res, colorModel

It produce nice in general case pictures but it really slow

So my question is can anyone help me make this visualization faster? If somebody knows a better way to visualize dense flow it may be cool

Answer 1

Code from OpenCV's tutorial:

import cv2
import numpy as np

# Use Hue, Saturation, Value colour model 
hsv = np.zeros(im1.shape, dtype=np.uint8)
hsv[..., 1] = 255

mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
hsv[..., 0] = ang * 180 / np.pi / 2
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
cv2.imshow("colored flow", bgr)
cv2.waitKey(0)
cv2.destroyAllWindows()