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add_noise.py
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add_noise.py
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import numpy as np
import cv2
from scipy.interpolate import griddata
from scipy import ndimage
def add_gaussian_shifts(depth, std=1/2.0):
rows, cols = depth.shape
gaussian_shifts = np.random.normal(0, std, size=(rows, cols, 2))
gaussian_shifts = gaussian_shifts.astype(np.float32)
# creating evenly spaced coordinates
xx = np.linspace(0, cols-1, cols)
yy = np.linspace(0, rows-1, rows)
# get xpixels and ypixels
xp, yp = np.meshgrid(xx, yy)
xp = xp.astype(np.float32)
yp = yp.astype(np.float32)
xp_interp = np.minimum(np.maximum(xp + gaussian_shifts[:, :, 0], 0.0), cols)
yp_interp = np.minimum(np.maximum(yp + gaussian_shifts[:, :, 1], 0.0), rows)
depth_interp = cv2.remap(depth, xp_interp, yp_interp, cv2.INTER_LINEAR)
return depth_interp
def filterDisp(disp, dot_pattern_, invalid_disp_):
size_filt_ = 9
xx = np.linspace(0, size_filt_-1, size_filt_)
yy = np.linspace(0, size_filt_-1, size_filt_)
xf, yf = np.meshgrid(xx, yy)
xf = xf - int(size_filt_ / 2.0)
yf = yf - int(size_filt_ / 2.0)
sqr_radius = (xf**2 + yf**2)
vals = sqr_radius * 1.2**2
vals[vals==0] = 1
weights_ = 1 /vals
fill_weights = 1 / ( 1 + sqr_radius)
fill_weights[sqr_radius > 9] = -1.0
disp_rows, disp_cols = disp.shape
dot_pattern_rows, dot_pattern_cols = dot_pattern_.shape
lim_rows = np.minimum(disp_rows - size_filt_, dot_pattern_rows - size_filt_)
lim_cols = np.minimum(disp_cols - size_filt_, dot_pattern_cols - size_filt_)
center = int(size_filt_ / 2.0)
window_inlier_distance_ = 0.1
out_disp = np.ones_like(disp) * invalid_disp_
interpolation_map = np.zeros_like(disp)
for r in range(0, lim_rows):
for c in range(0, lim_cols):
if dot_pattern_[r+center, c+center] > 0:
# c and r are the top left corner
window = disp[r:r+size_filt_, c:c+size_filt_]
dot_win = dot_pattern_[r:r+size_filt_, c:c+size_filt_]
valid_dots = dot_win[window < invalid_disp_]
n_valids = np.sum(valid_dots) / 255.0
n_thresh = np.sum(dot_win) / 255.0
if n_valids > n_thresh / 1.2:
mean = np.mean(window[window < invalid_disp_])
diffs = np.abs(window - mean)
diffs = np.multiply(diffs, weights_)
cur_valid_dots = np.multiply(np.where(window<invalid_disp_, dot_win, 0),
np.where(diffs < window_inlier_distance_, 1, 0))
n_valids = np.sum(cur_valid_dots) / 255.0
if n_valids > n_thresh / 1.2:
accu = window[center, center]
assert(accu < invalid_disp_)
out_disp[r+center, c + center] = round((accu)*8.0) / 8.0
interpolation_window = interpolation_map[r:r+size_filt_, c:c+size_filt_]
disp_data_window = out_disp[r:r+size_filt_, c:c+size_filt_]
substitutes = np.where(interpolation_window < fill_weights, 1, 0)
interpolation_window[substitutes==1] = fill_weights[substitutes ==1 ]
disp_data_window[substitutes==1] = out_disp[r+center, c+center]
return out_disp
if __name__ == "__main__":
# reading the image directly in gray with 0 as input
dot_pattern_ = cv2.imread("./data/kinect-pattern_3x3.png", 0)
count = 181
# various variables to handle the noise modelling
scale_factor = 100 # converting depth from m to cm
focal_length = 480.0 # focal length of the camera used
baseline_m = 0.075 # baseline in m
invalid_disp_ = 99999999.9
while count < 190:
depth_uint16 = cv2.imread("depth/{}.png".format(count), cv2.IMREAD_UNCHANGED)
h, w = depth_uint16.shape
# Our depth images were scaled by 5000 to store in png format so dividing to get
# depth in meters
depth = depth_uint16.astype('float') / 5000.0
depth_interp = add_gaussian_shifts(depth)
disp_= focal_length * baseline_m / (depth_interp + 1e-10)
depth_f = np.round(disp_ * 8.0)/8.0
out_disp = filterDisp(depth_f, dot_pattern_, invalid_disp_)
depth = focal_length * baseline_m / out_disp
depth[out_disp == invalid_disp_] = 0
# The depth here needs to converted to cms so scale factor is introduced
# though often this can be tuned from [100, 200] to get the desired banding / quantisation effects
noisy_depth = (35130/np.round((35130/np.round(depth*scale_factor)) + np.random.normal(size=(h, w))*(1.0/6.0) + 0.5))/scale_factor
noisy_depth = noisy_depth * 5000.0
noisy_depth = noisy_depth.astype('uint16')
# Displaying side by side the orignal depth map and the noisy depth map with barron noise cvpr 2013 model
cv2.namedWindow('Adding Kinect Noise', cv2.WINDOW_AUTOSIZE)
# cv2.imshow('Adding Kinect Noise', np.hstack((depth_uint16, noisy_depth)))
cv2.imshow('Adding Kinect Noise', np.hstack((depth_uint16, noisy_depth)))
key = cv2.waitKey(1)
# Press esc or 'q' to close the image window
if key & 0xFF == ord('q') or key == 27:
cv2.destroyAllWindows()
break
depth2save = np.hstack((depth_uint16, noisy_depth))
cv2.imwrite('depth_noised_{}.png'.format(count), depth2save)
print(count)
count = count + 1