132 lines
4.2 KiB
Python
132 lines
4.2 KiB
Python
# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization
|
|
|
|
|
|
# MIT License
|
|
#
|
|
# Copyright (c) 2018 Tom Runia
|
|
#
|
|
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
|
# of this software and associated documentation files (the "Software"), to deal
|
|
# in the Software without restriction, including without limitation the rights
|
|
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
|
# copies of the Software, and to permit persons to whom the Software is
|
|
# furnished to do so, subject to conditions.
|
|
#
|
|
# Author: Tom Runia
|
|
# Date Created: 2018-08-03
|
|
|
|
import numpy as np
|
|
|
|
def make_colorwheel():
|
|
"""
|
|
Generates a color wheel for optical flow visualization as presented in:
|
|
Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
|
|
URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
|
|
|
|
Code follows the original C++ source code of Daniel Scharstein.
|
|
Code follows the the Matlab source code of Deqing Sun.
|
|
|
|
Returns:
|
|
np.ndarray: Color wheel
|
|
"""
|
|
|
|
RY = 15
|
|
YG = 6
|
|
GC = 4
|
|
CB = 11
|
|
BM = 13
|
|
MR = 6
|
|
|
|
ncols = RY + YG + GC + CB + BM + MR
|
|
colorwheel = np.zeros((ncols, 3))
|
|
col = 0
|
|
|
|
# RY
|
|
colorwheel[0:RY, 0] = 255
|
|
colorwheel[0:RY, 1] = np.floor(255*np.arange(0,RY)/RY)
|
|
col = col+RY
|
|
# YG
|
|
colorwheel[col:col+YG, 0] = 255 - np.floor(255*np.arange(0,YG)/YG)
|
|
colorwheel[col:col+YG, 1] = 255
|
|
col = col+YG
|
|
# GC
|
|
colorwheel[col:col+GC, 1] = 255
|
|
colorwheel[col:col+GC, 2] = np.floor(255*np.arange(0,GC)/GC)
|
|
col = col+GC
|
|
# CB
|
|
colorwheel[col:col+CB, 1] = 255 - np.floor(255*np.arange(CB)/CB)
|
|
colorwheel[col:col+CB, 2] = 255
|
|
col = col+CB
|
|
# BM
|
|
colorwheel[col:col+BM, 2] = 255
|
|
colorwheel[col:col+BM, 0] = np.floor(255*np.arange(0,BM)/BM)
|
|
col = col+BM
|
|
# MR
|
|
colorwheel[col:col+MR, 2] = 255 - np.floor(255*np.arange(MR)/MR)
|
|
colorwheel[col:col+MR, 0] = 255
|
|
return colorwheel
|
|
|
|
|
|
def flow_uv_to_colors(u, v, convert_to_bgr=False):
|
|
"""
|
|
Applies the flow color wheel to (possibly clipped) flow components u and v.
|
|
|
|
According to the C++ source code of Daniel Scharstein
|
|
According to the Matlab source code of Deqing Sun
|
|
|
|
Args:
|
|
u (np.ndarray): Input horizontal flow of shape [H,W]
|
|
v (np.ndarray): Input vertical flow of shape [H,W]
|
|
convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
|
|
|
|
Returns:
|
|
np.ndarray: Flow visualization image of shape [H,W,3]
|
|
"""
|
|
flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
|
|
colorwheel = make_colorwheel() # shape [55x3]
|
|
ncols = colorwheel.shape[0]
|
|
rad = np.sqrt(np.square(u) + np.square(v))
|
|
a = np.arctan2(-v, -u)/np.pi
|
|
fk = (a+1) / 2*(ncols-1)
|
|
k0 = np.floor(fk).astype(np.int32)
|
|
k1 = k0 + 1
|
|
k1[k1 == ncols] = 0
|
|
f = fk - k0
|
|
for i in range(colorwheel.shape[1]):
|
|
tmp = colorwheel[:,i]
|
|
col0 = tmp[k0] / 255.0
|
|
col1 = tmp[k1] / 255.0
|
|
col = (1-f)*col0 + f*col1
|
|
idx = (rad <= 1)
|
|
col[idx] = 1 - rad[idx] * (1-col[idx])
|
|
col[~idx] = col[~idx] * 0.75 # out of range
|
|
# Note the 2-i => BGR instead of RGB
|
|
ch_idx = 2-i if convert_to_bgr else i
|
|
flow_image[:,:,ch_idx] = np.floor(255 * col)
|
|
return flow_image
|
|
|
|
|
|
def flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False):
|
|
"""
|
|
Expects a two dimensional flow image of shape.
|
|
|
|
Args:
|
|
flow_uv (np.ndarray): Flow UV image of shape [H,W,2]
|
|
clip_flow (float, optional): Clip maximum of flow values. Defaults to None.
|
|
convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
|
|
|
|
Returns:
|
|
np.ndarray: Flow visualization image of shape [H,W,3]
|
|
"""
|
|
assert flow_uv.ndim == 3, 'input flow must have three dimensions'
|
|
assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'
|
|
if clip_flow is not None:
|
|
flow_uv = np.clip(flow_uv, 0, clip_flow)
|
|
u = flow_uv[:,:,0]
|
|
v = flow_uv[:,:,1]
|
|
rad = np.sqrt(np.square(u) + np.square(v))
|
|
rad_max = np.max(rad)
|
|
epsilon = 1e-5
|
|
u = u / (rad_max + epsilon)
|
|
v = v / (rad_max + epsilon)
|
|
return flow_uv_to_colors(u, v, convert_to_bgr) |