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# Author: Bingxin Ke
# Last modified: 2024-02-19
from pylab import count_nonzero, clip, np
# Adapted from https://github.com/apple/ml-hypersim/blob/main/code/python/tools/scene_generate_images_tonemap.py
def tone_map(rgb, entity_id_map):
assert (entity_id_map != 0).all()
gamma = 1.0 / 2.2 # standard gamma correction exponent
inv_gamma = 1.0 / gamma
percentile = (
90 # we want this percentile brightness value in the unmodified image...
)
brightness_nth_percentile_desired = 0.8 # ...to be this bright after scaling
valid_mask = entity_id_map != -1
if count_nonzero(valid_mask) == 0:
scale = 1.0 # if there are no valid pixels, then set scale to 1.0
else:
brightness = (
0.3 * rgb[:, :, 0] + 0.59 * rgb[:, :, 1] + 0.11 * rgb[:, :, 2]
) # "CCIR601 YIQ" method for computing brightness
brightness_valid = brightness[valid_mask]
eps = 0.0001 # if the kth percentile brightness value in the unmodified image is less than this, set the scale to 0.0 to avoid divide-by-zero
brightness_nth_percentile_current = np.percentile(brightness_valid, percentile)
if brightness_nth_percentile_current < eps:
scale = 0.0
else:
# Snavely uses the following expression in the code at https://github.com/snavely/pbrs_tonemapper/blob/master/tonemap_rgbe.py:
# scale = np.exp(np.log(brightness_nth_percentile_desired)*inv_gamma - np.log(brightness_nth_percentile_current))
#
# Our expression below is equivalent, but is more intuitive, because it follows more directly from the expression:
# (scale*brightness_nth_percentile_current)^gamma = brightness_nth_percentile_desired
scale = (
np.power(brightness_nth_percentile_desired, inv_gamma)
/ brightness_nth_percentile_current
)
rgb_color_tm = np.power(np.maximum(scale * rgb, 0), gamma)
rgb_color_tm = clip(rgb_color_tm, 0, 1)
return rgb_color_tm
# According to https://github.com/apple/ml-hypersim/issues/9
def dist_2_depth(width, height, flt_focal, distance):
img_plane_x = (
np.linspace((-0.5 * width) + 0.5, (0.5 * width) - 0.5, width)
.reshape(1, width)
.repeat(height, 0)
.astype(np.float32)[:, :, None]
)
img_plane_y = (
np.linspace((-0.5 * height) + 0.5, (0.5 * height) - 0.5, height)
.reshape(height, 1)
.repeat(width, 1)
.astype(np.float32)[:, :, None]
)
img_plane_z = np.full([height, width, 1], flt_focal, np.float32)
img_plane = np.concatenate([img_plane_x, img_plane_y, img_plane_z], 2)
depth = distance / np.linalg.norm(img_plane, 2, 2) * flt_focal
return depth
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