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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 | |