def invalidate_temporal(self):
if self.temporal.is_prepared:
self.contrast = numpy.zeros(self.temporal.key.shape)
self.brightness = numpy.zeros(self.temporal.brightness.shape)
for i in range(self.temporal.key.shape[1]):
self.brightness[:, i] = wlsfilter(self.temporal.brightness[:, i], self.temporal.lambda_, self.temporal.alpha)
self.contrast[:, i] = (1 - self.tmo.contrast) + wlsfilter(self.tmo.contrast * self.temporal.key[:, i], self.temporal.lambda_, self.temporal.alpha)
def __getitem__(self, index):
abs_index = Configuration.sequence.frame_range.absolutize(index)
hdr = Configuration.sequence[index]
hdr = remove_specials(hdr)
if self.tmo.drawback_details:
hdr_coarse = numpy.log(hdr + EPSILON)
for i in range(hdr_coarse.shape[2]):
hdr_coarse[..., i] = wlsfilter(hdr_coarse[..., i], self.tmo.lambda_, self.tmo.alpha)
hdr_coarse = (hdr * (1 - self.tmo.detail_drawback) + (numpy.exp(hdr_coarse) - EPSILON) * self.tmo.detail_drawback)
hdr = hdr * numpy.exp((hdr - hdr_coarse) * self.tmo.detail_highlight)
else:
hdr_coarse = hdr
transfered = numpy.zeros(hdr.shape)
hdr_mean = numpy.mean(hdr, axis=2)
for channel_index in range(hdr.shape[2]):
brightness_map = self.brightness[abs_index, channel_index] + hdr_mean
ia = hdr_coarse[..., channel_index] / brightness_map
ib = (hdr_coarse[..., channel_index] + brightness_map) * (brightness_map * numpy.sinh(ia))
transfered[..., channel_index] = numpy.exp(-self.tmo.brightness_offset) * remove_specials(ib) ** self.contrast[abs_index, channel_index]
ldr = hdr ** self.tmo.gamma / (hdr_coarse ** self.tmo.gamma + transfered)
# ldr **= (1.0 / (self.tmo.gamma + numpy.spacing(0.0)))
return ldr
import numpy
from gt_io import Configuration
from tonemapping.leonhardt_tmo import wlsfilter
if __name__ == "__main__":
Configuration.open('/Volumes/Daten/EXR/Untitled.vtmo')
brightness = numpy.array(Configuration['_temporal']['_brightness'])
for i in range(brightness.shape[1]):
brightness[:, i] = wlsfilter(brightness[:, i], 1.0, 1.2)
print wlsfilter(numpy.array([1 ,2, 3]), 1.0, 1.2)
