def load_depth(filePath):
''' Loads an depth OpenEXR image.'''
if oe.isOpenExrFile(filePath) is not True:
raise Exception("File ", filePath, " does not exist!")
try:
ifi = oe.InputFile(filePath)
# Compute size
header = ifi.header()
dw = header["dataWindow"]
w, h = dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1
# Read the three channels
ifiType = header["channels"]["Z"].type.v
if ifiType is not im.PixelType.FLOAT:
raise Exception("Only float32 supported! (file is type ", ifiType)
Z = ifi.channel("Z", im.PixelType(ifiType))
ifi.close()
image = np.zeros((h, w), dtype = np.float32) # order = "C"
image[:, :] = np.core.multiarray.fromstring(Z, dtype = np.float32).reshape(h, w)
except:
raise
return image
def matte_pass_channel_cleanup(in_path, out_path):
"""
@params:
in_path: Path to exr file.
out_path: Path to write out the new exr file.
"""
if os.path.exists(in_path) and OpenEXR.isOpenExrFile(in_path):
channels_to_merge = defaultdict(list)
base_channels = []
# Read the file and header
handle = OpenEXR.InputFile(in_path)
header = handle.header()
# Get the channels
channels = header['channels']
header['channels'] = {}
new_channels = {}
for channel_name, channel_data in channels.iteritems():
match = CHANNEL_MATCH.match(channel_name)
if match:
layer, channel = match.groups()
channels_to_merge[layer].append(channel)
elif not ALPHA_MATCH.match(channel_name):
base_channels.append(channel_name)
for layer in base_channels:
all_pixels = array.array( 'f', handle.channel(layer,
Imath.PixelType(Imath.PixelType.HALF))).tolist()
new_channels[layer] = array.array('f', all_pixels).tostring()
for layer, data in channels_to_merge.iteritems():
new_pixels = []
try:
new_layer_name = layer.split('_')[1]
except:
new_layer_name = layer
# new pixels
new_pixels = array.array('f',
handle.channel('{0}.{1}'.format(layer, data[0])))
new_channels['matte.{0}'.format(new_layer_name)] = array.array('f',
new_pixels).tostring()
# write out new exr
header['channels'] = dict([(c, HALF) for c in new_channels.keys()])
out = OpenEXR.OutputFile(out_path, header)
out.writePixels(new_channels)
def _load_exr(ctx, filename):
f = OpenEXR.InputFile(filename)
dw = f.header()['dataWindow']
shape = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1)
sz = shape[0] * shape[1]
buf = np.empty(4 * sz, np.float32)
buf[0::4] = _read_channel(f, 'R')
buf[1::4] = _read_channel(f, 'G')
buf[2::4] = _read_channel(f, 'B')
buf[3::4] = np.ones(sz, np.float32)
return cl.Image(ctx,
cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR,
cl.ImageFormat(cl.channel_order.RGBA,
cl.channel_type.FLOAT),
shape=shape,
hostbuf=buf)
def get_image(image):
latlong = OpenEXR.InputFile(image)
dw = latlong.header()['dataWindow']
width = dw.max.x - dw.min.x + 1
height = dw.max.y - dw.min.y + 1
(r_str, g_str, b_str) = latlong.channels("RGB", pt)
red = numpy.fromstring(r_str, dtype=numpy.float32)
red.shape = (height, width) # (row, col)
green = numpy.fromstring(g_str, dtype=numpy.float32)
green.shape = (height, width) # (row, col)
blue = numpy.fromstring(b_str, dtype=numpy.float32)
blue.shape = (height, width) # (row, col)
return red, green, blue, width, height
def exr_to_png(exrfile, pngfile):
File = OpenEXR.InputFile(exrfile)
PixType = Imath.PixelType(Imath.PixelType.FLOAT)
DW = File.header()['dataWindow']
Size = (DW.max.x - DW.min.x + 1, DW.max.y - DW.min.y + 1)
rgb = [
numpy.fromstring(File.channel(c, PixType), dtype=numpy.float32)
for c in 'RGB'
]
for i in range(3):
rgb[i] = numpy.where(rgb[i] <= 0.0031308, (rgb[i] * 12.92) * 255.0,
(1.055 * (rgb[i]**(1.0 / 2.2)) - 0.055) * 255.0)
rgb8 = [Image.frombytes("F", Size, c.tostring()).convert("L") for c in rgb]
Image.merge("RGB", rgb8).save(pngfile, "png", quality=100)
def test_reading_unknown_exr_type_fails(self):
image, channels = _MakeTestImage(3, np.float16)
with tempfile.NamedTemporaryFile() as temp:
exr.write_exr(temp.name, image, channels)
exr_file = OpenEXR.InputFile(temp.name)
# Deliberately break the R channel header info. A mock InputFile is
# required to override the header() method.
header_dict = exr_file.header()
header_dict['channels']['R'].type.v = -1 # Any bad value will do.
make_mock_exr = collections.namedtuple('MockExr',
['header', 'channel'])
mock_broken_exr = make_mock_exr(lambda: header_dict,
exr_file.channel)
with self.assertRaisesRegexp(RuntimeError,
'Unknown EXR channel type'):
_ = exr.channels_to_ndarray(mock_broken_exr, ['R', 'G', 'B'])
def readexr(path):
"""EXR read helper. Requires OpenEXR."""
import OpenEXR
fh = OpenEXR.InputFile(path)
dw = fh.header()['dataWindow']
w, h = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1)
rgb = [
np.ndarray([h, w], dtype="float32", buffer=fh.channel(c))
for c in ['R', 'G', 'B']
]
ret = np.zeros([h, w, 3], dtype='float32')
for i in [0, 1, 2]:
ret[:, :, i] = rgb[i]
return ret
def __init__(self, f):
self.file = OpenEXR.InputFile(f)
pixelType = Imath.PixelType(Imath.PixelType.FLOAT)
dw = self.file.header()['dataWindow']
self.width = dw.max.x - dw.min.x + 1
self.height = dw.max.y - dw.min.y + 1
self.size = (self.width, self.height)
redStr = self.file.channel('R', pixelType)
greenStr = self.file.channel('G', pixelType)
blueStr = self.file.channel('B', pixelType)
self.blue = array.array('f', blueStr)
self.red = array.array('f', redStr)
self.green = array.array('f', greenStr)
def readEXRImage(filepath, channelrange):
"""Helper function for reading .exr files from the KAIST dataset.
Returns an array with dimension ordering (C, H, W) as required by pytorch.
"""
file = exr.InputFile(filepath)
channels = ["w{}nm".format(wavelength) for wavelength in channelrange]
header = file.header()
ncols = header["displayWindow"].max.x + 1
nrows = header["displayWindow"].max.y + 1
pt = Imath.PixelType(Imath.PixelType.HALF)
imgstrs = file.channels(channels, pt)
full = np.zeros((len(channels), nrows, ncols), dtype=np.float16)
for i, imgstr in enumerate(imgstrs):
red = np.frombuffer(imgstr, dtype=np.float16)
full[i, :, :] = np.reshape(red, (nrows, ncols))
return full
def readExr(file_path):
exr_file = OpenEXR.InputFile(file_path)
exr_header = exr_file.header()
H, W = exr_header['dataWindow'].max.y + 1, exr_header[
'dataWindow'].max.x + 1
assert all(
(exr_header['channels'][channel].type == Imath.PixelType(OpenEXR.HALF)
for channel in ('R', 'G', 'B')))
R, G, B = exr_file.channels(['R', 'G', 'B'])
image_R = np.frombuffer(R, dtype=np.float16).reshape([H, W])
image_G = np.frombuffer(G, dtype=np.float16).reshape([H, W])
image_B = np.frombuffer(B, dtype=np.float16).reshape([H, W])
image = np.stack([image_R, image_G, image_B], axis=2)
return image
def main(exrfile, jpgfile):
file = OpenEXR.InputFile(exrfile)
pt = Imath.PixelType(Imath.PixelType.FLOAT)
dw = file.header()['dataWindow']
size = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1)
rgbf = [Image.fromstring("F", size, file.channel(c, pt)) for c in "RGB"]
extrema = [im.getextrema() for im in rgbf]
darkest = min([lo for (lo,hi) in extrema])
lighest = max([hi for (lo,hi) in extrema])
scale = 255 / (lighest - darkest)
def normalize_0_255(v):
return (v * scale) + darkest
rgb8 = [im.point(normalize_0_255).convert("L") for im in rgbf]
Image.merge("RGB", rgb8).save(jpgfile)
def load_hdr_multichannel(path):
"""
Loads an HDR file with RGB, normal (3 channels) and depth (1 channel)
:param path: file location
:return: HDR image
"""
pt = Imath.PixelType(Imath.PixelType.FLOAT)
rgb_img_openexr = OpenEXR.InputFile(path)
rgb_img = rgb_img_openexr.header()['dataWindow']
size_img = (rgb_img.max.x - rgb_img.min.x + 1, rgb_img.max.y - rgb_img.min.y + 1)
# color
redstr = rgb_img_openexr.channel('color.R', pt)
red = np.fromstring(redstr, dtype=np.float32)
red.shape = (size_img[1], size_img[0])
greenstr = rgb_img_openexr.channel('color.G', pt)
green = np.fromstring(greenstr, dtype=np.float32)
green.shape = (size_img[1], size_img[0])
bluestr = rgb_img_openexr.channel('color.B', pt)
blue = np.fromstring(bluestr, dtype=np.float32)
blue.shape = (size_img[1], size_img[0])
color = np.dstack((red, green, blue))
# normal
normal_x_str = rgb_img_openexr.channel('normal.R', pt)
normal_x = np.fromstring(normal_x_str, dtype=np.float32)
normal_x.shape = (size_img[1], size_img[0])
normal_y_str = rgb_img_openexr.channel('normal.G', pt)
normal_y = np.fromstring(normal_y_str, dtype=np.float32)
normal_y.shape = (size_img[1], size_img[0])
normal_z_str = rgb_img_openexr.channel('normal.B', pt)
normal_z = np.fromstring(normal_z_str, dtype=np.float32)
normal_z.shape = (size_img[1], size_img[0])
normal = np.dstack((normal_x, normal_y, normal_z))
# depth
depth_str = rgb_img_openexr.channel('distance.Y', pt)
depth = np.fromstring(depth_str, dtype=np.float32)
depth.shape = (size_img[1], size_img[0])
return color, depth, normal
def ext2hdr(exrpath):
File = OpenEXR.InputFile(exrpath)
PixType = Imath.PixelType(Imath.PixelType.FLOAT)
DW = File.header()['dataWindow']
Size = (DW.max.x - DW.min.x + 1, DW.max.y - DW.min.y + 1)
rgb = [
numpy.fromstring(File.channel(c, PixType), dtype=numpy.float32)
for c in 'RGB'
]
r = numpy.reshape(rgb[0], (Size[1], Size[0]))
g = numpy.reshape(rgb[1], (Size[1], Size[0]))
b = numpy.reshape(rgb[2], (Size[1], Size[0]))
hdr = numpy.zeros((Size[1], Size[0], 3), dtype=numpy.float32)
hdr[:, :, 0] = b
hdr[:, :, 1] = g
hdr[:, :, 2] = r
return hdr
def get_color_array(path):
pixel_type = Imath.PixelType(Imath.PixelType.FLOAT)
file = OpenEXR.InputFile(path)
data_window = file.header()['dataWindow']
size = (data_window.max.x - data_window.min.x + 1,
data_window.max.y - data_window.min.y + 1)
r_str = file.channel('R', pixel_type)
g_str = file.channel('G', pixel_type)
b_str = file.channel('B', pixel_type)
r = np.frombuffer(r_str, dtype=np.float32)
r.shape = (size[1], size[0], 1)
g = np.frombuffer(g_str, dtype=np.float32)
g.shape = (size[1], size[0], 1)
b = np.frombuffer(b_str, dtype=np.float32)
b.shape = (size[1], size[0], 1)
rgb = np.concatenate([r, g, b], axis=2)
return rgb
def readEXR(hdrfile):
pt = Imath.PixelType(Imath.PixelType.FLOAT)
hdr_t = OpenEXR.InputFile(hdrfile)
dw = hdr_t.header()['dataWindow']
size = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1)
rstr = hdr_t.channel('R', pt)
gstr = hdr_t.channel('G', pt)
bstr = hdr_t.channel('B', pt)
r = np.fromstring(rstr, dtype=np.float32)
r.shape = (size[1], size[0])
g = np.fromstring(gstr, dtype=np.float32)
g.shape = (size[1], size[0])
b = np.fromstring(bstr, dtype=np.float32)
b.shape = (size[1], size[0])
res = np.stack([r, g, b], axis=-1)
imhdr = np.asarray(res)
return imhdr
def getPixels(path):
image = OpenEXR.InputFile(path)
header = image.header()
pt = Imath.PixelType(Imath.PixelType.FLOAT)
dw = header['dataWindow']
x, y = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1)
im = numpy.zeros((x, y, 3))
im[:, :, 0] = numpy.frombuffer(image.channel('R', pt),
dtype=numpy.float32).reshape((x, y))
im[:, :, 1] = numpy.frombuffer(image.channel('G', pt),
dtype=numpy.float32).reshape((x, y))
im[:, :, 2] = numpy.frombuffer(image.channel('B', pt),
dtype=numpy.float32).reshape((x, y))
im = numpy.clip(im, 0, 1)
return srgb.to(im), x, y
def __init__(self, image_path):
exrfile = exr.InputFile(image_path)
dw = exrfile.header()['dataWindow']
channels = ['R', 'G', 'B', 'A']
self.header = exrfile.header()
self.channels = []
self.size = (dw.max.y - dw.min.y + 1, dw.max.x - dw.min.x + 1)
self.channelData = dict()
for c in self.header['channels']:
self.channels.append(c)
C = exrfile.channel(c, Imath.PixelType(Imath.PixelType.FLOAT))
C = np.fromstring(C, dtype=np.float32)
C = np.reshape(C, self.size)
self.channelData[c] = C
def process_depth(exr_path):
depth_exr = OpenEXR.InputFile(exr_path)
dw = depth_exr.header()['dataWindow']
sz = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1)
depth = depth_exr.channel('Image.V', Imath.PixelType(Imath.PixelType.FLOAT))
depth = np.fromstring(depth, dtype='float32')
depth.shape = (sz[1], sz[0])
depth = depth.astype('float32') * 10000
depth = depth.astype(np.uint16)
mask = 255 * np.ones(shape=depth.shape, dtype='uint8')
mask[depth == 0] = 0
os.remove(exr_path)
return depth, mask
def exr2arr(exrfile):
file = OpenEXR.InputFile(exrfile)
dw = file.header()['dataWindow']
channels = file.header()['channels'].keys()
channels_list = list()
for c in ('R', 'G', 'B', 'A'):
if c in channels:
channels_list.append(c)
# the shape had incorrect order
#size = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1)
size = (dw.max.y - dw.min.y + 1, dw.max.x - dw.min.x + 1)
color_channels = file.channels(channels_list, Imath.PixelType(Imath.PixelType.FLOAT))
channels_tuple = [np.fromstring(channel, dtype='f') for channel in color_channels]
res = np.dstack(channels_tuple)
return res.reshape(size + (len(channels_tuple),))
def read_exr_image(exr_filename, channels=['R', 'G', 'B']):
exrfile = exr.InputFile(exr_filename)
dw = exrfile.header()['dataWindow']
isize = (dw.max.y - dw.min.y + 1, dw.max.x - dw.min.x + 1)
channelData_arr = dict()
for c in channels:
C = exrfile.channel(c, Imath.PixelType(Imath.PixelType.FLOAT))
C = np.fromstring(C, dtype=np.float32)
C = np.reshape(C, isize)
channelData_arr[c] = C
# get the whole array
img_arr = np.concatenate(
[channelData_arr[c][..., np.newaxis] for c in ['R', 'G', 'B']],
axis=2) # (res, res, 3)
return img_arr
def loadexr(filename):
"""Open an exr image and return a numpy array."""
f = OpenEXR.InputFile(filename)
dw = f.header()['dataWindow']
sz = (dw.max.y - dw.min.y + 1, dw.max.x - dw.min.x + 1)
# Read the three color channels as 32-bit floats
FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)
image = np.empty((sz[0], sz[1], 3), dtype=np.float32)
image[..., 0] = np.fromstring(f.channel("R", FLOAT),
dtype=np.float32).reshape(sz)
image[..., 1] = np.fromstring(f.channel("G", FLOAT),
dtype=np.float32).reshape(sz)
image[..., 2] = np.fromstring(f.channel("B", FLOAT),
dtype=np.float32).reshape(sz)
return image
def readEXR(filename):
"""Read color + depth data from EXR image file.
Parameters
----------
filename : str
File path.
Returns
-------
img : Float matrix
Z : Depth buffer in float32 format or None if the EXR file has no Z channel.
"""
exrfile = exr.InputFile(filename)
header = exrfile.header()
dw = header["dataWindow"]
isize = (dw.max.y - dw.min.y + 1, dw.max.x - dw.min.x + 1)
channelData = dict()
# convert all channels in the image to numpy arrays
for c in header["channels"]:
C = exrfile.channel(c, Imath.PixelType(Imath.PixelType.FLOAT))
C = np.frombuffer(C, dtype=np.float32)
C = np.reshape(C, isize)
channelData[c] = C
colorChannels = (["R", "G", "B", "A"]
if "A" in header["channels"] else ["R", "G", "B"])
img = np.concatenate(
[channelData[c][..., np.newaxis] for c in colorChannels], axis=2)
# linear to standard RGB
# img[..., :3] = np.where(img[..., :3] <= 0.0031308,
# 12.92 * img[..., :3],
# 1.055 * np.power(img[..., :3], 1 / 2.4) - 0.055)
# sanitize image to be in range [0, 1]
# img = np.where(img < 0.0, 0.0, np.where(img > 1.0, 1, img))
Z = None if "Z" not in header["channels"] else channelData["Z"]
return img, Z
def readEXR(filenameEXR):
pt = Imath.PixelType(
Imath.PixelType.FLOAT
) #only works hard-coding this and np.float32, instead of using the value from: exr.header()['channels'][c].type
exr = OpenEXR.InputFile(filenameEXR)
dw = exr.header()['dataWindow']
size = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1)
#this needs to be fixed. 2 of the following 3 lines swap axes.
rgb = np.swapaxes(
np.array([
np.fromstring(exr.channel(c, pt), dtype=np.float32) for c in "RGB"
]), 0, 1) #I guess if we used HALF we would need to use np.float16
rgb.shape = (size[1], size[0], 3) #numpy array are (row, col) = (y, x)
rgb = np.swapaxes(rgb, 0,
1) #(y,x,c) -> (x,y,c) #probably (x,y,c) -> (y,x,c)
exr.close()
return rgb
def read_openexr(fname):
file = OpenEXR.InputFile(fname)
header = file.header()
dw = header['dataWindow']
pt = Imath.PixelType(Imath.PixelType.HALF)
shape = (dw.max.y - dw.min.y + 1, dw.max.x - dw.min.x + 1)
r = np.frombuffer(file.channel('Color.R', pt),
dtype=np.float16).reshape(shape + (1, ))
g = np.frombuffer(file.channel('Color.G', pt),
dtype=np.float16).reshape(shape + (1, ))
b = np.frombuffer(file.channel('Color.B', pt),
dtype=np.float16).reshape(shape + (1, ))
a = np.frombuffer(file.channel('Color.A', pt),
dtype=np.float16).reshape(shape + (1, ))
img = np.concatenate((r, g, b, a), -1).astype(np.float32)
file.close()
return img
def loadEXR(fname, channelNames):
# Open the input file
f = OpenEXR.InputFile(fname)
# Compute the size
dw = f.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)
channels = [
array.array('f', f.channel(Chan, FLOAT)).tolist()
for Chan in channelNames
]
channels = [np.array(channel).reshape(sz) for channel in channels]
return np.array(channels)
def load_exr(self, prefix_dir, prefix, file_kind, expected_height,
expected_width):
exr_file = OpenEXR.InputFile(
os.path.join(prefix_dir, prefix + "-" + file_kind + ".exr"))
cm_dw = exr_file.header()['dataWindow']
exr_data = np.fromstring(exr_file.channel(
'R', Imath.PixelType(Imath.PixelType.HALF)),
dtype=np.float16)
exr_data.shape = (cm_dw.max.y - cm_dw.min.y + 1,
cm_dw.max.x - cm_dw.min.x + 1) # rows, cols
if exr_data.shape[0] != expected_height:
print("[ERROR] ", prefix, file_kind, " != expected image height",
exr_data.shape[0], expected_height)
if exr_data.shape[1] != expected_width:
print("[ERROR] ", prefix, file_kind, " width != image width",
exr_data.shape[1], expected_width)
return exr_data
def convert_to_numpy(img_list, intens=None):
imgs = []
for idx, img_name in enumerate(img_list):
if idx % 10 == 0:
print('Current idx %d' % (idx))
img = exr_to_array(OpenEXR.InputFile(img_name), 'color')
if intens is not None:
img = np.dot(img, intens[idx])
save_suffix = '_calib.npy'
else:
save_suffix = '_uncalib.npy'
imgs.append(img)
img_array = np.concatenate(imgs, 2)
save_name = os.path.dirname(img_name) + save_suffix
print('Saving numpy array to %s' % (save_name))
np.save(save_name, img_array)
return img_array
def ConvertEXRToJPG(exrfile, jpgfile):
File = OpenEXR.InputFile(exrfile)
PixType = Imath.PixelType(Imath.PixelType.FLOAT)
DW = File.header()['dataWindow']
Size = (DW.max.x - DW.min.x + 1, DW.max.y - DW.min.y + 1)
rgb = [
np.fromstring(File.channel(c, PixType), dtype=np.float32)
for c in 'RGB'
]
for i in range(3):
rgb[i] = np.where(rgb[i] <= 0.0031308, (rgb[i] * 12.92) * 255.0,
(1.055 * (rgb[i]**(1.0 / 2.4)) - 0.055) * 255.0)
rgb8 = [Image.frombytes("F", Size, c.tostring()).convert("L") for c in rgb]
# rgb8 = [Image.fromarray(c.astype(int)) for c in rgb]
Image.merge("RGB", rgb8).save(jpgfile, "JPEG", quality=95)
def get_point_cloud_from_exr(path, pos, ori, intrinsic):
exr_file = OpenEXR.InputFile(path)
PixType = Imath.PixelType(Imath.PixelType.FLOAT)
DW = exr_file.header()['dataWindow']
Size = (DW.max.x - DW.min.x + 1, DW.max.y - DW.min.y + 1)
rgb = [
np.frombuffer(exr_file.channel(c, PixType), dtype=np.float32)
for c in 'RGB'
]
img = np.reshape(rgb[0], (Size[1], Size[0]))
depth = cv2.resize(
img, (224, 224),
interpolation=cv2.INTER_NEAREST) # all float number indicate distance
pc = Depth2PointCloud(depth, intrinsic, ori, pos,
org_size=img.shape).transpose()
inf_idx = (pc != pc) | (np.abs(pc) > 100)
pc[inf_idx] = 0.0
pc_index = np.nonzero(pc[:, 2] > 0.002)[0]
pc = pc[pc_index]
pc_x = pc[:, 0]
pc_y = pc[:, 1]
pc_z = pc[:, 2]
del_idx = (pc_x < -0.22 / 2) | (pc_x > 0.22 / 2) | (pc_y < -0.22 / 2) | (
pc_y > 0.22 / 2) | (pc_z > 0.22)
pc = pc[np.logical_not(del_idx)]
xyz_max = pc.max(0)
xyz_min = pc.min(0)
xyz_diff = xyz_max - xyz_min
xyz_idx = np.where(xyz_diff < 0.22 / 10)[0]
if xyz_idx.shape[0] > 0:
xyz_max[xyz_idx] += (0.22 / 10)
xyz_min[xyz_idx] -= (0.22 / 10)
grids = generate_grid(0.22, 10)
grid_choose = (xyz_min.reshape(1, -1) <= grids) * (grids <=
xyz_max.reshape(1, -1))
grid_choose = (grid_choose.sum(1) == 3)
grids = grids[grid_choose]
if grids.shape[0] > 400:
idx = np.random.choice(np.arange(grids.shape[0]), 400, replace=False)
grids = grids[idx]
contact_index = np.arange(pc.shape[0])
pc = pc / np.array([0.22 / 2, 0.22 / 2, 0.22])
return pc, grids, contact_index
def loadEXR(self, filename):
import OpenEXR
import Imath
pt = Imath.PixelType(Imath.PixelType.HALF)
image = OpenEXR.InputFile(filename)
header = image.header()
dw = header['dataWindow']
channels = header['channels']
size = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1)
self.source_width = size[0]
self.source_height = size[1]
if self.parm("width").eval() != 0:
self.width = self.parm("width").eval()
else:
self.width = self.source_width
if self.parm("height").eval() != 0:
self.height = self.parm("height").eval()
else:
self.height = self.source_height
redstr = image.channel('R', pt)
host_buff_r = numpy.fromstring(redstr, dtype = numpy.float16)
host_buff_r.shape = (size[1], size[0]) # Numpy arrays are (row, col)
self.devInBufferR = cl.Image(self.engine.ctx, self.engine.mf.READ_ONLY | self.engine.mf.COPY_HOST_PTR, cl.ImageFormat(cl.channel_order.INTENSITY, cl.channel_type.HALF_FLOAT), shape=(self.source_width, self.source_height,), pitches=(self.source_width * 2,), hostbuf=host_buff_r)
greenstr = image.channel('G', pt)
host_buff_g = numpy.fromstring(greenstr, dtype = numpy.float16)
host_buff_g.shape = (size[1], size[0]) # Numpy arrays are (row, col)
self.devInBufferG = cl.Image(self.engine.ctx, self.engine.mf.READ_ONLY | self.engine.mf.COPY_HOST_PTR, cl.ImageFormat(cl.channel_order.INTENSITY, cl.channel_type.HALF_FLOAT), shape=(self.source_width, self.source_height,), pitches=(self.source_width * 2,), hostbuf=host_buff_g)
bluestr = image.channel('B', pt)
host_buff_b = numpy.fromstring(bluestr, dtype = numpy.float16)
host_buff_b.shape = (size[1], size[0]) # Numpy arrays are (row, col)
self.devInBufferB = cl.Image(self.engine.ctx, self.engine.mf.READ_ONLY | self.engine.mf.COPY_HOST_PTR, cl.ImageFormat(cl.channel_order.INTENSITY, cl.channel_type.HALF_FLOAT), shape=(self.source_width, self.source_height,), pitches=(self.source_width * 2,), hostbuf=host_buff_b)
if(channels.get('A') is not None):
alphastr = image.channel('A', pt)
host_buff_a = numpy.fromstring(alphastr, dtype = numpy.float16)
host_buff_a.shape = (size[1], size[0]) # Numpy arrays are (row, col)
self.devInBufferA = cl.Image(self.engine.ctx, self.engine.mf.READ_ONLY | self.engine.mf.COPY_HOST_PTR, cl.ImageFormat(cl.channel_order.INTENSITY, cl.channel_type.HALF_FLOAT), shape=(self.source_width, self.source_height,), pitches=(self.source_width * 2,), hostbuf=host_buff_a)
else:
self.devInBufferA = cl.Image(self.engine.ctx, self.engine.mf.READ_ONLY | self.engine.mf.COPY_HOST_PTR, cl.ImageFormat(cl.channel_order.INTENSITY, cl.channel_type.HALF_FLOAT), shape=(self.source_width, self.source_height,), pitches=(self.source_width * 2,), hostbuf=numpy.ones(self.source_width * self.source_height, dtype = numpy.float16))
def colorDeptImage(colorFile, depthFile):
pt = Imath.PixelType(Imath.PixelType.FLOAT)
depthFile_ = OpenEXR.InputFile(depthFile)
dw = depthFile_.header()['dataWindow']
size = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1)
depthstr = depthFile_.channel('Y', pt)
depth = np.fromstring(depthstr, dtype=np.float32)
depth.shape = (size[1], size[0])
img = cv2.imread(colorFile)
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
res, thresh = cv2.threshold(imgray, 120, 255, 0)
img0, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
contours = list(
filter(
lambda x: cv2.contourArea(x) > 1000 and cv2.contourArea(x) < 5000,
contours))
def loadImageWithOpenEXR(exrfile):
# Taken from http://discourse.techart.online/t/exr-files-into-pyqt-interface/3325/9
file = OpenEXR.InputFile(exrfile)
pt = Imath.PixelType(Imath.PixelType.FLOAT)
dw = file.header()['dataWindow']
size = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1)
rgbf = [Image.frombytes("F", size, file.channel(c, pt)) for c in "RGB"]
extrema = [im.getextrema() for im in rgbf]
darkest = min([lo for (lo,hi) in extrema])
lighest = max([hi for (lo,hi) in extrema])
scale = 255 / (lighest - darkest)
def normalize_0_255(v):
return (v * scale) + darkest
rgb8 = [im.point(normalize_0_255).convert("L") for im in rgbf]
myjpg = Image.merge("RGB", rgb8)
return myjpg
def exrHeaderInfo(imagePath):
'''
check if the EXR is valid, read the header and return a key, value dict of its values
'''
if not 'OpenEXR' in sys.modules.keys():
print "No openEXR module"
return
#check it is a valid EXR
if OpenEXR.isOpenExrFile(imagePath):
infile = OpenEXR.InputFile(imagePath)
if not infile.isComplete():
print "Invalid EXR: %s" %imagePath
#return True here as we still want the directory scan to continue
return True
EXRheader = infile.header()
rDict = {}
for k,v in EXRheader.iteritems():
rDict[k]=v
#print k,v
return rDict
def open(filename):
# Check if the file is an EXR file
if not OpenEXR.isOpenExrFile(filename):
raise Exception("File '%s' is not an EXR file." % filename)
# Return an `InputFile`
return InputFile(OpenEXR.InputFile(filename), filename)
def load_rgb(filePath):
''' Loads an rgb OpenEXR image.'''
if oe.isOpenExrFile(filePath) is not True:
raise Exception("File ", filePath, " does not exist!")
try:
ifi = oe.InputFile(filePath)
# Compute size
header = ifi.header()
dw = header["dataWindow"]
w, h = dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1
# Read the three channels
ifiType = header["channels"]["R"].type.v
if ifiType is not im.PixelType.UINT:
raise Exception("Only uint32 supported! (file is type ", ifiType)
R = ifi.channel("R", im.PixelType(ifiType))
G = ifi.channel("G", im.PixelType(ifiType))
B = ifi.channel("B", im.PixelType(ifiType))
ifi.close()
image = np.zeros((h, w, 3), dtype = np.uint32) # order = "C"
image[:, :, 0] = np.core.multiarray.fromstring(R, dtype = np.uint32).reshape(h, w)
image[:, :, 1] = np.core.multiarray.fromstring(G, dtype = np.uint32).reshape(h, w)
image[:, :, 2] = np.core.multiarray.fromstring(B, dtype = np.uint32).reshape(h, w)
except:
raise
return image