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