def exr_channel_to_numpy(exr_file: OpenEXR.InputFile,
channel_name: str,
reshape: Optional[Tuple[int, int]],
dtype: ExrDtype = ExrDtype.FLOAT32) -> np.ndarray:
"""Extracts a channel in an EXR file into a numpy array
Args:
exr_file (OpenEXR.InputFile): The opened EXR file object
channel_name (str): The name of the channel to be converted to numpy
reshape (None or Tuple(height, width): If given, will reshape the 2D numpy array into (height, width)
dtype (ExrDtype): Whether the data in channel is of float32 or float16 type
Returns:
numpy.ndarray: The extracted channel in form of numpy array
"""
if dtype == ExrDtype.FLOAT32:
point_type = Imath.PixelType(Imath.PixelType.FLOAT)
np_type = np.float32
else:
point_type = Imath.PixelType(Imath.PixelType.HALF)
np_type = np.float16
channel_arr = np.frombuffer(exr_file.channel(channel_name, point_type),
dtype=np_type)
if reshape:
channel_arr = channel_arr.reshape(reshape)
return channel_arr
def get_pixels_data(self, exr: OpenEXR.InputFile) -> dict:
pixels_data = {}
for i, target_channel in enumerate(self.get_target_channels()):
label_index = i
if self.is_depth():
label_index = 0
pixels_data[target_channel] = exr.channel(self.get_labels()[label_index], self.get_pixel_type())
return pixels_data
def read_channels_from_exr(exr: OpenEXR.InputFile,
channel_names: Sequence[str]) -> np.ndarray:
"""Reads a single channel from an EXR file and returns it as a numpy array."""
channels_header = exr.header()['channels']
window = exr.header()['dataWindow']
width = window.max.x - window.min.x + 1
height = window.max.y - window.min.y + 1
outputs = []
for channel_name in channel_names:
channel_type = channels_header[channel_name].type.v
numpy_type = {
Imath.PixelType.HALF: np.float16,
Imath.PixelType.FLOAT: np.float32,
Imath.PixelType.UINT: np.uint32,
}[channel_type]
array = np.frombuffer(exr.channel(channel_name), numpy_type)
array = array.reshape([height, width])
outputs.append(array)
# TODO: verify that the types are all the same?
return np.stack(outputs, axis=-1)
def __getitem__(self, index):
if isinstance(index, str):
return super(ImageSequence, self).__getitem__(index)
if index == self.cache_index:
return self.cache * numpy.exp(self.exposure_offset)
if not isinstance(index, int) and index in self.frame_range:
raise IndexError
exrfile = InputFile(self.file_pattern % index)
channels = ('R', 'G', 'B')
output = numpy.ndarray(dtype=numpy.float, shape=(self.height, self.width, channels.__len__()))
for channel_index, channel in enumerate(channels):
output[:, :, channel_index] = numpy.fromstring(exrfile.channel(channel, Imath.PixelType(Imath.PixelType.HALF)), dtype=numpy.float16).astype(numpy.float).reshape([self.height, self.width])
self.cache = output
self.cache_index = index
return output * numpy.exp(self.exposure_offset)
