def test03_put_values_basic(variant_scalar_rgb):
from mitsuba.core import srgb_to_xyz
from mitsuba.core.xml import load_string
from mitsuba.render import ImageBlock
# Recall that we must pass a reconstruction filter to use the `put` methods.
rfilter = load_string("""<rfilter version="2.0.0" type="box">
<float name="radius" value="0.4"/>
</rfilter>""")
im = ImageBlock([10, 8], 5, filter=rfilter)
im.clear()
# From a spectrum & alpha value
border = im.border_size()
ref = np.zeros(shape=(im.height() + 2 * border, im.width() + 2 * border,
3 + 1 + 1))
for i in range(border, im.height() + border):
for j in range(border, im.width() + border):
spectrum = np.random.uniform(size=(3, ))
ref[i, j, :3] = srgb_to_xyz(spectrum)
ref[i, j, 3] = 1 # Alpha
ref[i, j, 4] = 1 # Weight
# To avoid the effects of the reconstruction filter (simpler test),
# we'll just add one sample right in the center of each pixel.
im.put([j + 0.5, i + 0.5], [], spectrum, alpha=1.0)
check_value(im, ref, atol=1e-6)
def test04_put_packets_basic(variant_scalar_rgb):
from mitsuba.core import srgb_to_xyz
try:
mitsuba.set_variant("packet_rgb")
from mitsuba.core.xml import load_string
from mitsuba.render import ImageBlock
except ImportError:
pytest.skip("packet_rgb mode not enabled")
# Recall that we must pass a reconstruction filter to use the `put` methods.
rfilter = load_string("""<rfilter version="2.0.0" type="box">
<float name="radius" value="0.4"/>
</rfilter>""")
im = ImageBlock([10, 8], 5, filter=rfilter)
im.clear()
n = 29
positions = np.random.uniform(size=(n, 2))
positions[:,
0] = np.floor(positions[:, 0] * (im.width() - 1)).astype(np.int)
positions[:,
1] = np.floor(positions[:, 1] * (im.height() - 1)).astype(np.int)
# Repeat some of the coordinates to make sure that we test the case where
# the same pixel receives several values
positions[-3:, :] = positions[:3, :]
spectra = np.arange(n * 3).reshape((n, 3))
alphas = np.ones(shape=(n, ))
border = im.border_size()
ref = np.zeros(shape=(im.height() + 2 * border, im.width() + 2 * border,
3 + 1 + 1))
for i in range(n):
(x, y) = positions[i, :] + border
ref[int(y), int(x), :3] += srgb_to_xyz(spectra[i, :])
ref[int(y), int(x), 3] += 1 # Alpha
ref[int(y), int(x), 4] += 1 # Weight
# Vectorized `put`
im.put(positions + 0.5, [], spectra, alphas)
check_value(im, ref, atol=1e-6)
def test02_put_image_block(variant_scalar_rgb):
from mitsuba.core.xml import load_string
from mitsuba.render import ImageBlock
rfilter = load_string("""<rfilter version="2.0.0" type="box"/>""")
# TODO: test with varying `offset` values
im = ImageBlock([10, 5], 4, filter=rfilter)
im.clear()
check_value(im, 0)
im2 = ImageBlock(im.size(), 4, filter=rfilter)
im2.clear()
ref = 3.14 * np.arange(im.height() * im.width() * im.channel_count()) \
.reshape(im.height(), im.width(), im.channel_count())
for x in range(im.height()):
for y in range(im.width()):
im2.put([y + 0.5, x + 0.5], ref[x, y, :])
check_value(im2, ref)
for i in range(5):
im.put(im2)
check_value(im, (i + 1) * ref)
sample3=0)
# Intersect rays with the scene geometry
surface_interaction = scene.ray_intersect(rays)
# Given intersection, compute the final pixel values as the depth t
# of the sampled surface interaction
result = surface_interaction.t
# Set to zero if no intersection was found
result[~surface_interaction.is_valid()] = 0
block = ImageBlock(film.crop_size(),
channel_count=5,
filter=film.reconstruction_filter(),
border=False)
block.clear()
# ImageBlock expects RGB values (Array of size (n, 3))
block.put(position_sample, rays.wavelengths, Vector3f(result, result, result),
1)
# Write out the result from the ImageBlock
# Internally, ImageBlock stores values in XYZAW format
# (color XYZ, alpha value A and weight W)
xyzaw_np = block.data().reshape([film_size[1], film_size[0], 5])
# We then create a Bitmap from these values and save it out as EXR file
bmp = Bitmap(xyzaw_np, Bitmap.PixelFormat.XYZAW)
bmp = bmp.convert(Bitmap.PixelFormat.Y, Struct.Type.Float32, srgb_gamma=False)
bmp.write('depth.exr')
def _render_helper(scene, spp=None, sensor_index=0):
"""
Internally used function: render the specified Mitsuba scene and return a
floating point array containing RGB values and AOVs, if applicable
"""
from mitsuba.core import (Float, UInt32, UInt64, Vector2f,
is_monochromatic, is_rgb, is_polarized)
from mitsuba.render import ImageBlock
sensor = scene.sensors()[sensor_index]
film = sensor.film()
sampler = sensor.sampler()
film_size = film.crop_size()
if spp is None:
spp = sampler.sample_count()
total_sample_count = ek.hprod(film_size) * spp
if sampler.wavefront_size() != total_sample_count:
sampler.seed(ek.arange(UInt64, total_sample_count))
pos = ek.arange(UInt32, total_sample_count)
pos //= spp
scale = Vector2f(1.0 / film_size[0], 1.0 / film_size[1])
pos = Vector2f(Float(pos % int(film_size[0])),
Float(pos // int(film_size[0])))
pos += sampler.next_2d()
rays, weights = sensor.sample_ray_differential(
time=0,
sample1=sampler.next_1d(),
sample2=pos * scale,
sample3=0
)
spec, mask, aovs = scene.integrator().sample(scene, sampler, rays)
spec *= weights
del mask
if is_polarized:
from mitsuba.core import depolarize
spec = depolarize(spec)
if is_monochromatic:
rgb = [spec[0]]
elif is_rgb:
rgb = spec
else:
from mitsuba.core import spectrum_to_xyz, xyz_to_srgb
xyz = spectrum_to_xyz(spec, rays.wavelengths)
rgb = xyz_to_srgb(xyz)
del xyz
aovs.insert(0, Float(1.0))
for i in range(len(rgb)):
aovs.insert(i + 1, rgb[i])
del rgb, spec, weights, rays
block = ImageBlock(
size=film.crop_size(),
channel_count=len(aovs),
filter=film.reconstruction_filter(),
warn_negative=False,
warn_invalid=False,
border=False
)
block.clear()
block.put(pos, aovs)
del pos
del aovs
data = block.data()
ch = block.channel_count()
i = UInt32.arange(ek.hprod(block.size()) * (ch - 1))
weight_idx = i // (ch - 1) * ch
values_idx = (i * ch) // (ch - 1) + 1
weight = ek.gather(data, weight_idx)
values = ek.gather(data, values_idx)
return values / (weight + 1e-8)
dragon_c = scene.shapes()[6].bbox().center()
c = Vector3f(dragon_c)
d = c - surface_interaction.p
result = ek.norm(d)
max_dist = ek.hmax(result)
result /= max_dist
result = 0.3 * (1.0 - result**(2.0))
result[~surface_interaction.is_valid()] = 0
block = ImageBlock(film.crop_size(),
channel_count=5,
filter=film.reconstruction_filter(),
border=False)
block.clear()
# ImageBlock expects RGB values (Array of size (n, 3))
block.put(pos, rays.wavelengths, Vector3f(result, result, result), 1)
# Write out the result from the ImageBlock
# Internally, ImageBlock stores values in XYZAW format
# (color XYZ, alpha value A and weight W)
xyzaw_np = np.array(block.data()).reshape([film_size[1], film_size[0], 5])
# We then create a Bitmap from these values and save it out as EXR file
bmp = Bitmap(xyzaw_np, Bitmap.PixelFormat.XYZAW)
bmp = bmp.convert(Bitmap.PixelFormat.RGBA,
Struct.Type.Float32,
srgb_gamma=False)
bmp.write('distance.exr')
def test03_develop(variant_scalar_rgb, file_format, tmpdir):
from mitsuba.core.xml import load_string
from mitsuba.core import Bitmap, Struct, ReconstructionFilter, float_dtype
from mitsuba.render import ImageBlock
import numpy as np
"""Create a test image. Develop it to a few file format, each time reading
it back and checking that contents are unchanged."""
np.random.seed(12345 + ord(file_format[0]))
# Note: depending on the file format, the alpha channel may be automatically removed.
film = load_string("""<film version="2.0.0" type="hdrfilm">
<integer name="width" value="41"/>
<integer name="height" value="37"/>
<string name="file_format" value="{}"/>
<string name="pixel_format" value="rgba"/>
<string name="component_format" value="float32"/>
<rfilter type="box"/>
</film>""".format(file_format))
# Regardless of the output file format, values are stored as XYZAW (5 channels).
contents = np.random.uniform(size=(film.size()[1], film.size()[0], 5))
# RGBE and will only reconstruct well images that have similar scales on
# all channel (because exponent is shared between channels).
if file_format is "rgbe":
contents = 1 + 0.1 * contents
# Use unit weights.
contents[:, :, 4] = 1.0
block = ImageBlock(film.size(), 5, film.reconstruction_filter())
block.clear()
for x in range(film.size()[1]):
for y in range(film.size()[0]):
block.put([y + 0.5, x + 0.5], contents[x, y, :])
film.prepare(['X', 'Y', 'Z', 'A', 'W'])
film.put(block)
with pytest.raises(RuntimeError):
# Should raise when the destination file hasn't been specified.
film.develop()
filename = str(tmpdir.join('test_image.' + file_format))
film.set_destination_file(filename)
film.develop()
# Read back and check contents
other = Bitmap(filename).convert(Bitmap.PixelFormat.XYZAW,
Struct.Type.Float32,
srgb_gamma=False)
img = np.array(other, copy=False)
if False:
import matplotlib.pyplot as plt
plt.figure()
plt.subplot(1, 3, 1)
plt.imshow(contents[:, :, :3])
plt.subplot(1, 3, 2)
plt.imshow(img[:, :, :3])
plt.subplot(1, 3, 3)
plt.imshow(ek.sum(ek.abs(img[:, :, :3] - contents[:, :, :3]), axis=2),
cmap='coolwarm')
plt.colorbar()
plt.show()
if file_format == "exr":
assert ek.allclose(img, contents, atol=1e-5)
else:
if file_format == "rgbe":
assert ek.allclose(img[:, :, :3], contents[:, :, :3], atol=1e-2), \
'\n{}\nvs\n{}\n'.format(img[:4, :4, :3], contents[:4, :4, :3])
else:
assert ek.allclose(img[:, :, :3], contents[:, :, :3], atol=1e-5)
# Alpha channel was ignored, alpha and weights should default to 1.0.
assert ek.allclose(img[:, :, 3:5], 1.0, atol=1e-6)
def test05_put_with_filter(variant_scalar_rgb):
from mitsuba.core import srgb_to_xyz
from mitsuba.core.xml import load_string
from mitsuba.render import ImageBlock
"""The previous tests used a very simple box filter, parametrized so that
it essentially had no effect. In this test, we use a more realistic
Gaussian reconstruction filter, with non-zero radius."""
try:
mitsuba.set_variant("packet_rgb")
from mitsuba.core.xml import load_string as load_string_packet
from mitsuba.render import ImageBlock as ImageBlockP
except ImportError:
pytest.skip("packet_rgb mode not enabled")
rfilter = load_string("""<rfilter version="2.0.0" type="gaussian">
<float name="stddev" value="0.5"/>
</rfilter>""")
rfilter_p = load_string_packet("""<rfilter version="2.0.0" type="gaussian">
<float name="stddev" value="0.5"/>
</rfilter>""")
size = [12, 12]
im = ImageBlockP(size, 5, filter=rfilter_p)
im.clear()
im2 = ImageBlock(size, 5, filter=rfilter)
im2.clear()
positions = np.array([[5, 6], [0, 1], [5, 6], [1, 11], [11, 11], [0, 1],
[2, 5], [4, 1], [0, 11], [5, 4]],
dtype=np.float)
n = positions.shape[0]
positions += np.random.uniform(size=positions.shape, low=0, high=0.95)
spectra = np.arange(n * 3).reshape((n, 3))
alphas = np.ones(shape=(n, ))
radius = int(math.ceil(rfilter.radius()))
border = im.border_size()
ref = np.zeros(shape=(im.height() + 2 * border, im.width() + 2 * border,
3 + 1 + 1))
for i in range(n):
# -- Scalar `put`
im2.put(positions[i, :], [], spectra[i, :], alpha=1.0)
# Fractional part of the position
offset = positions[i, :] - positions[i, :].astype(np.int)
# -- Reference
# Reconstruction window around the pixel position
pos = positions[i, :] - 0.5 + border
lo = np.ceil(pos - radius).astype(np.int)
hi = np.floor(pos + radius).astype(np.int)
for dy in range(lo[1], hi[1] + 1):
for dx in range(lo[0], hi[0] + 1):
r_pos = np.array([dx, dy])
w_pos = r_pos - pos
if (np.any(r_pos < 0) or np.any(r_pos >= ref.shape[:2])):
continue
weight = rfilter.eval_discretized(
w_pos[0]) * rfilter.eval_discretized(w_pos[1])
xyz = srgb_to_xyz(spectra[i, :])
ref[r_pos[1], r_pos[0], :3] += weight * xyz
ref[r_pos[1], r_pos[0], 3] += weight * 1 # Alpha
ref[r_pos[1], r_pos[0], 4] += weight * 1 # Weight
# -- Vectorized `put`
im.put(positions, [], spectra, alphas)
check_value(im, ref, atol=1e-6)
check_value(im2, ref, atol=1e-6)
