nvidia.dali.fn.experimental.debayer¶
- nvidia.dali.fn.experimental.debayer(*inputs, **kwargs)¶
Performs image demosaicing/debayering.
Converts single-channel image to RGB using specified color filter array.
The supported input types are
uint8_t
anduint16_t
. The input images must be 2D tensors (HW
) or 3D tensors (HWC
) where the number of channels is 1. The operator supports sequence of images/video-like inputs (layoutFHW
).For example, the following snippet presents debayering of batch of image sequences:
def bayered_sequence(sample_info): # some actual source of video inputs with corresponding pattern # as opencv-style string video, bayer_pattern = get_sequence(sample_info) if bayer_pattern == "bggr": blue_position = [0, 0] elif bayer_pattern == "gbrg": blue_position = [0, 1] elif bayer_pattern == "grbg": blue_position = [1, 0] else: assert bayer_pattern == "rggb" blue_position = [1, 1] return video, np.array(blue_position, dtype=np.int32) @pipeline_def def debayer_pipeline(): bayered_sequences, blue_positions = fn.external_source( source=bayered_sequence, batch=False, num_outputs=2, layout=["FHW", None]) # note the "FHW" layout, for plain images it would be "HW" debayered_sequences = fn.experimental.debayer( bayered_sequences.gpu(), blue_position=blue_positions) return debayered_sequences
This operator allows sequence inputs.
- Supported backends
‘gpu’
- Parameters:
input (TensorList ('HW', 'HWC', 'FHW', 'FHWC')) – Input to the operator.
- Keyword Arguments:
blue_position (int or list of int or TensorList of int) –
The layout of color filter array/bayer tile.
A position of the blue value in the 2x2 bayer tile. The supported values correspond to the following OpenCV bayer layouts:
(0, 0)
-BG
/BGGR
(0, 1)
-GB
/GBRG
(1, 0)
-GR
/GRBG
(1, 1)
-RG
/RGGB
The argument follows OpenCV’s convention of referring to a 2x2 tile that starts in the second row and column of the sensors’ matrix.
For example, the
(0, 0)
/BG
/BGGR
corresponds to the following matrix of sensors:R
G
R
G
R
G
B
G
B
G
R
G
R
G
R
G
B
G
B
G
Supports
per-frame
inputs.algorithm (str, optional, default = ‘bilinear_npp’) –
The algorithm to be used when inferring missing colours for any given pixel. Currently only
bilinear_npp
is supported.The
bilinear_npp
algorithm uses bilinear interpolation to infer red and blue values. For green values a bilinear interpolation with chroma correlation is used as explained in NPP documentation.
bytes_per_sample_hint (int or list of int, optional, default = [0]) –
Output size hint, in bytes per sample.
If specified, the operator’s outputs residing in GPU or page-locked host memory will be preallocated to accommodate a batch of samples of this size.
preserve (bool, optional, default = False) – Prevents the operator from being removed from the graph even if its outputs are not used.
seed (int, optional, default = -1) –
Random seed.
If not provided, it will be populated based on the global seed of the pipeline.