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import numpy as np |
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import cv2 |
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def kitti_colormap(disparity, maxval=-1): |
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""" |
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A utility function to reproduce KITTI fake colormap |
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Arguments: |
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- disparity: numpy float32 array of dimension HxW |
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- maxval: maximum disparity value for normalization (if equal to -1, the maximum value in disparity will be used) |
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Returns a numpy uint8 array of shape HxWx3. |
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""" |
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if maxval < 0: |
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maxval = np.max(disparity) |
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colormap = np.asarray([[0,0,0,114],[0,0,1,185],[1,0,0,114],[1,0,1,174],[0,1,0,114],[0,1,1,185],[1,1,0,114],[1,1,1,0]]) |
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weights = np.asarray([8.771929824561404,5.405405405405405,8.771929824561404,5.747126436781609,8.771929824561404,5.405405405405405,8.771929824561404,0]) |
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cumsum = np.asarray([0,0.114,0.299,0.413,0.587,0.701,0.8859999999999999,0.9999999999999999]) |
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colored_disp = np.zeros([disparity.shape[0], disparity.shape[1], 3]) |
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values = np.expand_dims(np.minimum(np.maximum(disparity/maxval, 0.), 1.), -1) |
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bins = np.repeat(np.repeat(np.expand_dims(np.expand_dims(cumsum,axis=0),axis=0), disparity.shape[1], axis=1), disparity.shape[0], axis=0) |
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diffs = np.where((np.repeat(values, 8, axis=-1) - bins) > 0, -1000, (np.repeat(values, 8, axis=-1) - bins)) |
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index = np.argmax(diffs, axis=-1)-1 |
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w = 1-(values[:,:,0]-cumsum[index])*np.asarray(weights)[index] |
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colored_disp[:,:,2] = (w*colormap[index][:,:,0] + (1.-w)*colormap[index+1][:,:,0]) |
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colored_disp[:,:,1] = (w*colormap[index][:,:,1] + (1.-w)*colormap[index+1][:,:,1]) |
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colored_disp[:,:,0] = (w*colormap[index][:,:,2] + (1.-w)*colormap[index+1][:,:,2]) |
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return (colored_disp*np.expand_dims((disparity>0),-1)*255).astype(np.uint8) |
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def read_16bit_gt(path): |
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""" |
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A utility function to read KITTI 16bit gt |
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Arguments: |
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- path: filepath |
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Returns a numpy float32 array of shape HxW. |
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""" |
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gt = cv2.imread(path,-1).astype(np.float32)/256. |
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return gt |
