def function_rgba(
red_data_and_metadata_in: _DataAndMetadataIndeterminateSizeLike,
green_data_and_metadata_in: _DataAndMetadataIndeterminateSizeLike,
blue_data_and_metadata_in: _DataAndMetadataIndeterminateSizeLike,
alpha_data_and_metadata_in: _DataAndMetadataIndeterminateSizeLike
) -> DataAndMetadata.DataAndMetadata:
red_data_and_metadata_c = DataAndMetadata.promote_indeterminate_array(
red_data_and_metadata_in)
green_data_and_metadata_c = DataAndMetadata.promote_indeterminate_array(
green_data_and_metadata_in)
blue_data_and_metadata_c = DataAndMetadata.promote_indeterminate_array(
blue_data_and_metadata_in)
alpha_data_and_metadata_c = DataAndMetadata.promote_indeterminate_array(
alpha_data_and_metadata_in)
shape = DataAndMetadata.determine_shape(red_data_and_metadata_c,
green_data_and_metadata_c,
blue_data_and_metadata_c)
if shape is None:
raise ValueError("RGBA: data shapes do not match or are indeterminate")
red_data_and_metadata = DataAndMetadata.promote_constant(
red_data_and_metadata_c, shape)
green_data_and_metadata = DataAndMetadata.promote_constant(
green_data_and_metadata_c, shape)
blue_data_and_metadata = DataAndMetadata.promote_constant(
blue_data_and_metadata_c, shape)
alpha_data_and_metadata = DataAndMetadata.promote_constant(
alpha_data_and_metadata_c, shape)
channels = (blue_data_and_metadata, green_data_and_metadata,
red_data_and_metadata, alpha_data_and_metadata)
if any([
not Image.is_data_valid(data_and_metadata.data)
for data_and_metadata in channels
]):
raise ValueError("RGB: invalid data")
rgba_image = numpy.empty(shape + (4, ), numpy.uint8)
for channel_index, channel in enumerate(channels):
data = channel._data_ex
if data.dtype.kind in 'iu':
rgba_image[..., channel_index] = numpy.clip(data, 0, 255)
elif data.dtype.kind in 'f':
rgba_image[...,
channel_index] = numpy.clip(numpy.multiply(data, 255),
0, 255)
return DataAndMetadata.new_data_and_metadata(
rgba_image,
intensity_calibration=red_data_and_metadata.intensity_calibration,
dimensional_calibrations=red_data_and_metadata.dimensional_calibrations
)
def function_rgb(
red_data_and_metadata: DataAndMetadata.DataAndMetadata,
green_data_and_metadata: DataAndMetadata.DataAndMetadata,
blue_data_and_metadata: DataAndMetadata.DataAndMetadata
) -> typing.Optional[DataAndMetadata.DataAndMetadata]:
red_data_and_metadata = DataAndMetadata.promote_ndarray(
red_data_and_metadata)
green_data_and_metadata = DataAndMetadata.promote_ndarray(
green_data_and_metadata)
blue_data_and_metadata = DataAndMetadata.promote_ndarray(
blue_data_and_metadata)
shape = tuple(
DataAndMetadata.determine_shape(red_data_and_metadata,
green_data_and_metadata,
blue_data_and_metadata))
red_data_and_metadata = DataAndMetadata.promote_constant(
red_data_and_metadata, shape)
green_data_and_metadata = DataAndMetadata.promote_constant(
green_data_and_metadata, shape)
blue_data_and_metadata = DataAndMetadata.promote_constant(
blue_data_and_metadata, shape)
def calculate_data():
rgb_image = numpy.empty(shape + (3, ), numpy.uint8)
channels = (blue_data_and_metadata, green_data_and_metadata,
red_data_and_metadata)
for channel_index, channel in enumerate(channels):
data = channel.data
if not Image.is_data_valid(data):
return None
if tuple(data.shape) != shape:
return None
if data.dtype.kind in 'iu':
rgb_image[..., channel_index] = numpy.clip(data, 0, 255)
elif data.dtype.kind in 'f':
rgb_image[..., channel_index] = numpy.clip(
numpy.multiply(data, 255), 0, 255)
else:
return None
return rgb_image
if green_data_and_metadata.data_shape != shape or blue_data_and_metadata.data_shape != shape:
return None
return DataAndMetadata.new_data_and_metadata(
calculate_data(),
intensity_calibration=red_data_and_metadata.intensity_calibration,
dimensional_calibrations=red_data_and_metadata.dimensional_calibrations
)
