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
)
def function_rgb_linear_combine(
data_and_metadata_in: _DataAndMetadataLike, red_weight: float,
green_weight: float,
blue_weight: float) -> DataAndMetadata.DataAndMetadata:
data_and_metadata = DataAndMetadata.promote_ndarray(data_and_metadata_in)
data = data_and_metadata.data
if not Image.is_data_valid(data):
raise ValueError("RGB linear combine: invalid data.")
if not data_and_metadata.is_data_rgb_type:
raise ValueError("RGB linear combine: data is not RGB type.")
assert data is not None
combined_data: _ImageDataType
if Image.is_shape_and_dtype_rgb(data.shape, data.dtype):
combined_data = numpy.sum(
data[..., :] * (blue_weight, green_weight, red_weight), 2)
elif Image.is_shape_and_dtype_rgba(data.shape, data.dtype):
combined_data = numpy.sum(
data[..., :] * (blue_weight, green_weight, red_weight, 0.0), 2)
else:
raise ValueError("RGB channel: unable to extract channel.")
return DataAndMetadata.new_data_and_metadata(
combined_data,
intensity_calibration=data_and_metadata.intensity_calibration,
dimensional_calibrations=data_and_metadata.dimensional_calibrations)
def function_rgb_channel(data_and_metadata_in: _DataAndMetadataLike,
channel: int) -> DataAndMetadata.DataAndMetadata:
data_and_metadata = DataAndMetadata.promote_ndarray(data_and_metadata_in)
if channel < 0 or channel > 3:
raise ValueError("RGB channel: invalid channel.")
data = data_and_metadata.data
if not Image.is_data_valid(data):
raise ValueError("RGB channel: invalid data.")
if not data_and_metadata.is_data_rgb_type:
raise ValueError("RGB channel: data is not RGB type.")
assert data is not None
channel_data: _ImageDataType
if Image.is_shape_and_dtype_rgb(data.shape, data.dtype):
if channel == 3:
channel_data = numpy.ones(data.shape, int)
else:
channel_data = data[..., channel].astype(int)
elif Image.is_shape_and_dtype_rgba(data.shape, data.dtype):
channel_data = data[..., channel].astype(int)
else:
raise ValueError("RGB channel: unable to extract channel.")
return DataAndMetadata.new_data_and_metadata(
channel_data,
intensity_calibration=data_and_metadata.intensity_calibration,
dimensional_calibrations=data_and_metadata.dimensional_calibrations)
def function_rgb_linear_combine(
data_and_metadata: DataAndMetadata.DataAndMetadata, red_weight: float,
green_weight: float, blue_weight: float
) -> typing.Optional[DataAndMetadata.DataAndMetadata]:
data_and_metadata = DataAndMetadata.promote_ndarray(data_and_metadata)
def calculate_data():
data = data_and_metadata.data
if not Image.is_data_valid(data):
return None
if Image.is_shape_and_dtype_rgb(data.shape, data.dtype):
return numpy.sum(
data[..., :] * (blue_weight, green_weight, red_weight), 2)
elif Image.is_shape_and_dtype_rgba(data.shape, data.dtype):
return numpy.sum(
data[..., :] * (blue_weight, green_weight, red_weight, 0.0), 2)
else:
return None
if not data_and_metadata.is_data_rgb_type:
return None
return DataAndMetadata.new_data_and_metadata(
calculate_data(),
intensity_calibration=data_and_metadata.intensity_calibration,
dimensional_calibrations=data_and_metadata.dimensional_calibrations)
def function_rgb_channel(
data_and_metadata: DataAndMetadata.DataAndMetadata,
channel: int) -> typing.Optional[DataAndMetadata.DataAndMetadata]:
data_and_metadata = DataAndMetadata.promote_ndarray(data_and_metadata)
def calculate_data():
data = data_and_metadata.data
if channel < 0 or channel > 3:
return None
if not Image.is_data_valid(data):
return None
if Image.is_shape_and_dtype_rgb(data.shape, data.dtype):
if channel == 3:
return numpy.ones(data.shape, int)
return data[..., channel].astype(int)
elif Image.is_shape_and_dtype_rgba(data.shape, data.dtype):
return data[..., channel].astype(int)
else:
return None
if not data_and_metadata.is_data_rgb_type:
return None
return DataAndMetadata.new_data_and_metadata(
calculate_data(),
intensity_calibration=data_and_metadata.intensity_calibration,
dimensional_calibrations=data_and_metadata.dimensional_calibrations)
