Depth Image Processing

processing depth images

"""
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)

Original RGB camera image:

Original depth camera image (hard to see!), which I name d_img_01:

d_img_02:

d_img_03: