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Depth Image Processing
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""" | |
For the BAIR Blog post. | |
(c) 2018 by Daniel Seita (and Michael Laskey). | |
""" | |
import numpy as np | |
import cv2 | |
def depth_to_3ch(img, cutoff): | |
"""Useful to turn the background into black into the depth images. | |
""" | |
w,h = img.shape | |
new_img = np.zeros([w,h,3]) | |
img = img.flatten() | |
img[img>cutoff] = 0.0 | |
img = img.reshape([w,h]) | |
for i in range(3): | |
new_img[:,:,i] = img | |
return new_img | |
def depth_scaled_to_255(img): | |
assert np.max(img) > 0.0 | |
img = 255.0/np.max(img)*img | |
img = np.array(img,dtype=np.uint8) | |
for i in range(3): | |
img[:,:,i] = cv2.equalizeHist(img[:,:,i]) | |
return img | |
def depth_to_net_dim(img): | |
"""Careful if the cutoff is in meters or millimeters! | |
""" | |
cutoff = 1400 | |
img = depth_to_3ch(img, cutoff) | |
cv2.imwrite('d_img_02.png', img) # all values above 255 turned to white | |
img = depth_scaled_to_255(img) | |
cv2.imwrite('d_img_03.png', img) # correct scaling to be in [0,255) now | |
if __name__ == "__main__": | |
d_img_raw = np.load('d_img_01.npy') | |
cv2.imwrite('d_img_01.png', d_img_raw) | |
depth_to_net_dim(d_img_raw) |
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Original RGB camera image:
Original depth camera image (hard to see!), which I name
d_img_01
:d_img_02
:d_img_03
: