def compose(imInput,
object_filename,
output_filename,
global_modifier_factor=1):
# read exr
imRender = hdrio.imread(object_filename).astype('float32')
imInputResized = zoom(imInput, (imRender.shape[0] / imInput.shape[0],
imRender.shape[1] / imInput.shape[1], 1.0),
order=2)
imInputResized = imInputResized**2.2 # undo gamma on crop
alphaRender = imRender[..., 3:4]
imRender = (imRender[..., :3] * global_modifier_factor)**(1 / 2.2)
imCompose = (1 - alphaRender) * imInputResized + alphaRender * imRender
imCompose = imCompose**(1 / 2.2) # apply gamma on compose
imComposeUint8 = (np.clip(imCompose, 0.0, 1.0) * 255).astype('uint8')
imageio.imsave(output_filename, imComposeUint8)
print("\tCompositing done...")
def process(im_path):
im_filename = "{}-{}.jpg".format(os.path.basename(im_path), 0)
subdir = im_filename.replace('pano_a', '')[:2]
os.makedirs(os.path.join(output_dir, subdir), exist_ok=True)
out_path = os.path.join(output_dir, subdir, im_filename)
metadata_path = os.path.join(
output_dir, subdir, '{}-meta.json'.format(os.path.basename(im_path)))
if os.path.isfile(metadata_path):
return
im = imread(im_path, 'native')
if len(im.shape) == 2:
im = np.stack((im, ) * 3, axis=-1)
else:
im = im[:, :, :3]
data = []
for random_id in range(7):
datum = makeAndSaveImg(im_path, im, random_id)
json_path = os.path.splitext(out_path)[0][:-2] + "-%d.json" % random_id
with open(json_path, 'w') as fhdl_datum:
json.dump(datum, fhdl_datum)
data.append(datum)
with open(metadata_path, 'w') as fhdl:
json.dump(data, fhdl)
def generateLDRfromHDR(im_path, out_prefix):
"""Convert an HDR image into a clipped 0-255 value ("simulating" a camera)"""
print('Processing: ', im_path)
im = imread(im_path)
h, w, c = im.shape
im = im[:, w/2 - h/2:w/2 + h/2]
envmap = EnvironmentMap(im, 'SkyAngular').convertTo('LatLong', TARGET_SIZE[0])
im = envmap.data
valid = (im > 0) & (~np.isnan(im))
im_median = np.median(im[valid])
im_low = np.percentile(im[valid], 3)
im_high = np.percentile(im[valid], 95)
#scales = (TARGET_SIZE[0]/im.shape[0], TARGET_SIZE[1]/im.shape[1])
#im = zoom(im, [scales[0], scales[1], 1])
with open(out_prefix + "_hdr.pkl", 'wb') as fhdl:
pickle.dump(im, fhdl, pickle.HIGHEST_PROTOCOL)
imsave(out_prefix + '_hdr.exr', im)
# 20th percentile -> value 5
# 80th percentile -> value 250
#print("Ratio:", (im_high - im_low))
ratio = im_high - im_low
if ratio < 0.1:
ratio = 0.1
im_ldr = (im - im_low) * 250. / ratio + 5
im_ldr = np.clip(im_ldr, 0, 255).astype('uint8')
imsave(out_prefix + '_ldr.jpg', im_ldr)
plt.figure()
plt.subplot(1,2,1); plt.hist(im.ravel()[im.ravel()<im_high], 50)
plt.subplot(1,2,2); plt.hist(im_ldr.ravel()[im_ldr.ravel()>0], 50)
plt.savefig(out_prefix + 'debug.png')
plt.close()
def __init__(self, im, format_):
"""
Creates an EnvironmentMap.
:param im: Image to be converted to an EnvironmentMap
:param format_: EnvironmentMap format. Can be `Angular`, ...
:type im: float, numpy array
:type format_: string
"""
assert format_.lower() in SUPPORTED_FORMATS, (
"Unknown format: {}".format(format_))
self.format_ = format_.lower()
self.backgroundColor = np.array([0, 0, 0])
if isinstance(im, str):
# We received the filename
self.data = imread(im)
elif isinstance(im, int):
# We received a single scalar
if self.format_ == 'latlong':
self.data = np.zeros((im, im*2))
elif self.format_ == 'cube':
self.data = np.zeros((im, round(3/4*im)))
else:
self.data = np.zeros((im, im))
elif type(im).__module__ == np.__name__:
# We received a numpy array
self.data = np.asarray(im, dtype='double')
else:
raise Exception('Could not understand input. Please prove a '
'filename, a size or an image.')
# Ensure size is valid
if self.format_ in ['sphere', 'angular', 'skysphere', 'skyangular']:
assert self.data.shape[0] == self.data.shape[1], (
"Sphere/Angular formats must have the same width/height")
import sys
import json
import numpy as np
import scipy.ndimage
from hdrio import imread
# Usage : gethdr.py source dest
image = imread(sys.argv[1])
imresized = scipy.ndimage.interpolation.zoom(
image, (580. / image.shape[0], 580 / image.shape[1], 1), order=1)
alpha = np.zeros(imresized.shape[0:2]) + 255.
imresizeda = np.dstack((imresized, alpha))
out = imresizeda
with open(sys.argv[2], 'w') as fhdl:
fhdl.write("var targetData = ")
json.dump(out.flatten().tolist(), fhdl)
fhdl.write(";")
def __init__(self, im, format_=None, copy=True, color=True):
"""
Creates an EnvironmentMap.
:param im: Image path or data to be converted to an EnvironmentMap, or
the height of an empty EnvironmentMap.
:param format_: EnvironmentMap format. Can be `Angular`, ...
:param copy: When a numpy array is given, should it be copied.
:param color: When providing an integer, create an empty color or
grayscale EnvironmentMap.
:type im: float, numpy array
:type format_: string
:type copy: bool
"""
if not format_ and isinstance(im, str):
filename = os.path.splitext(im)[0]
metadata = EnvmapXMLParser("{}.meta.xml".format(filename))
format_ = metadata.getFormat()
assert format_ is not None, (
"Please provide format (metadata file not found).")
assert format_.lower() in SUPPORTED_FORMATS, (
"Unknown format: {}".format(format_))
self.format_ = format_.lower()
self.backgroundColor = np.array([0, 0, 0])
if isinstance(im, str):
# We received the filename
self.data = imread(im)
elif isinstance(im, int):
# We received a single scalar
if self.format_ == 'latlong':
if color:
self.data = np.zeros((im, im * 2, 3))
else:
self.data = np.zeros((im, im * 2))
elif self.format_ == 'skylatlong':
if color:
self.data = np.zeros((im, im * 4, 3))
else:
self.data = np.zeros((im, im * 4))
elif self.format_ == 'cube':
if color:
self.data = np.zeros((im, round(3 / 4 * im), 3))
else:
self.data = np.zeros((im, round(3 / 4 * im)))
else:
if color:
self.data = np.zeros((im, im, 3))
else:
self.data = np.zeros((im, im))
elif type(im).__module__ == np.__name__:
# We received a numpy array
self.data = np.asarray(im, dtype='double')
if copy:
self.data = self.data.copy()
else:
raise Exception('Could not understand input. Please provide a '
'filename, a single size (height) or an image.')
# Ensure size is valid
if self.format_ in ['sphere', 'angular', 'skysphere', 'skyangular']:
assert self.data.shape[0] == self.data.shape[1], (
"Sphere/Angular formats must have the same width/height")
elif self.format_ == 'latlong':
assert 2 * self.data.shape[0] == self.data.shape[1], (
"LatLong format width should be twice the height")
elif self.format_ == 'skylatlong':
assert 4 * self.data.shape[0] == self.data.shape[1], (
"SkyLatLong format width should be four times the height")
xyz = np.array(xyzp)
xyzn = normalize(xyz)
# get correspondency between 3d and pixel colors
uv_S = np.array(world2latlong(xyzn[:, 0], xyzn[:, 1], xyzn[:, 2]))
assert not np.isnan(np.sum(uv_S))
# create xml with area light sources
gen_mitsuba_xml(xyz, np.array([0, 0, 0]), uv_S, texture,
'area_light_scene.xml')
command = 'mitsuba -o non_parametric.exr area_light_scene.xml'
print(command)
os.system(command)
warped_texture = hdrio.imread('non_parametric.exr')
# for j in range(3):
# warped_texture[:,:,j]/=(warped_texture[:,:,j].max()/ambients[j])
warped_texture[:, :, 0:3] = warped_texture[:, :, 0:3]**2 / 2
# combine them
final_prediction = warped_texture[:, :, 0:3] + iblN
# final_prediction = iblN
hdrio.imwrite(final_prediction, "combined_envmap.exr")
# render
command = 'mitsuba -o final_render.exr ' + ' -Dobjx=' + str(
obj_pos[0]) + ' -Dobjy=' + str(obj_pos[1]) + ' -Dobjz=' + str(
obj_pos[2]) + ' -Denvmap=combined_envmap.exr render_scene.xml'
print(command)
os.system(command)
ctr = 0.5 - 0.5 * np.tan(pitch) / np.tan(vfov / 2)
return ctr
if __name__ == '__main__':
from hdrio import imread, imsave
import matplotlib
matplotlib.use('qt5agg')
from matplotlib import pyplot as plt
import dataset
fn, fp, data = dataset.getParameters()
valid_idx = dataset.getValidIdx(data)
fn = fn[valid_idx]
fp = fp[valid_idx]
data = data[valid_idx, :]
pitch = data[:, 0]
roll = data[:, 2]
vfov = data[:, 3]
for i in range(100):
print(i)
print(fp[i], pitch[i], roll[i], vfov[i])
im = imread(fp[i], 'native')[:, :, :3]
imout = showHorizonLine(im, vfov[i], pitch[i], roll[i], debug=True)
imsave('{0:03d}.png'.format(i), imout)
#plt.clf()
#plt.imshow(imout)
#plt.show(block=False)
#plt.pause(1)
import sys
import json
import numpy as np
import scipy.ndimage
from hdrio import imread
# Usage : gethdr.py source dest
image = imread(sys.argv[1])
imresized = scipy.ndimage.interpolation.zoom(image, (580./image.shape[0], 580/image.shape[1], 1), order=1)
alpha = np.zeros(imresized.shape[0:2]) + 255.
imresizeda = np.dstack((imresized, alpha))
out = imresizeda
with open(sys.argv[2], 'w') as fhdl:
fhdl.write("var targetData = ")
json.dump(out.flatten().tolist(), fhdl)
fhdl.write(";")
