__email__ = '*****@*****.**'
__date__ = '2020-05-20 09:30:16'
"""
"""
import sys
import array
import OpenEXR
import Imath
# Open the input file
file = OpenEXR.InputFile(br"C:\Users\timmyliang\Desktop\FX\new\Male_rig_1__0002_head_ctrl_follow_plane.exr")
print(file.header())
file = OpenEXR.InputFile(br"C:\Users\timmyliang\Desktop\FX\red_eye001_UV1(1).EXR")
print(file.header())
# # Compute the size
# dw = file.header()['dataWindow']
# sz = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1)
# # Read the three color channels as 32-bit floats
# FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)
# (R,G,B) = [array.array('f', file.channel(Chan, FLOAT)).tolist() for Chan in ("R", "G", "B") ]
fbegin = ishape * stepsize * stride
fend = min(ishape * stepsize * stride + stepsize * clipsize,
len(data['poses']))
# LOOP OVER FRAMES
for seq_frame, (pose, trans) in enumerate(
zip(data['poses'][fbegin:fend:stepsize],
data['trans'][fbegin:fend:stepsize])):
iframe = seq_frame
log_message("Processing frame %d" % iframe)
for k, folder in res_paths.items():
if not k == 'vblur' and not k == 'fg':
path = join(folder,
'Image%04d.exr' % get_real_frame(seq_frame))
exr_file = OpenEXR.InputFile(path)
if k == 'normal':
mat = np.transpose(np.reshape([array.array('f',
exr_file.channel(Chan, FLOAT)).tolist() for Chan in \
("R", "G", "B")], (3, resx, resy)), (1, 2, 0))
# +1 for the 1-indexing
dict_normal['normal_%d' % (iframe + 1)] = \
mat.astype(np.float32, copy=False)
elif k == 'gtflow':
mat = np.transpose(np.reshape([array.array('f',
exr_file.channel(Chan, FLOAT)).tolist() for Chan in \
("R", "G")], (2, resx, resy)), (1, 2, 0))
dict_gtflow['gtflow_%d' % (iframe + 1)] = \
mat.astype(np.float32, copy=False)
def load_exr(filename):
loaded_exr = OpenEXR.InputFile(filename)
print(loaded_exr)
