Image

Scalar Images

class huira.Image_f32

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: huira._huira.Image_f32) -> None

2. __init__(self: huira._huira.Image_f32, width: int, height: int) -> None

3. __init__(self: huira._huira.Image_f32, width: int, height: int, fill_value: float) -> None

4. __init__(self: huira._huira.Image_f32, array: numpy.ndarray[numpy.float32]) -> None

Construct from a numpy array. Single-channel: (H, W). Multi-channel: (H, W, C).

property channels

clear(self: huira._huira.Image_f32) → None

property empty

fill(self: huira._huira.Image_f32, value: float) → None

property height

property resolution

property sensor_bit_depth

property shape

to_numpy(self: huira._huira.Image_f32) → numpy.ndarray[numpy.float32]

Return image data as a numpy array (always copies).

property width

class huira.Image_f64

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: huira._huira.Image_f64) -> None

2. __init__(self: huira._huira.Image_f64, width: int, height: int) -> None

3. __init__(self: huira._huira.Image_f64, width: int, height: int, fill_value: float) -> None

4. __init__(self: huira._huira.Image_f64, array: numpy.ndarray[numpy.float64]) -> None

Construct from a numpy array. Single-channel: (H, W). Multi-channel: (H, W, C).

property channels

clear(self: huira._huira.Image_f64) → None

property empty

fill(self: huira._huira.Image_f64, value: float) → None

property height

property resolution

property sensor_bit_depth

property shape

to_numpy(self: huira._huira.Image_f64) → numpy.ndarray[numpy.float64]

Return image data as a numpy array (always copies).

property width

class huira.Image_u8

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: huira._huira.Image_u8) -> None

2. __init__(self: huira._huira.Image_u8, width: int, height: int) -> None

3. __init__(self: huira._huira.Image_u8, width: int, height: int, fill_value: int) -> None

4. __init__(self: huira._huira.Image_u8, array: numpy.ndarray[numpy.uint8]) -> None

Construct from a numpy array. Single-channel: (H, W). Multi-channel: (H, W, C).

property channels

clear(self: huira._huira.Image_u8) → None

property empty

fill(self: huira._huira.Image_u8, value: int) → None

property height

property resolution

property sensor_bit_depth

property shape

to_numpy(self: huira._huira.Image_u8) → numpy.ndarray[numpy.uint8]

Return image data as a numpy array (always copies).

property width

class huira.Image_u16

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: huira._huira.Image_u16) -> None

2. __init__(self: huira._huira.Image_u16, width: int, height: int) -> None

3. __init__(self: huira._huira.Image_u16, width: int, height: int, fill_value: int) -> None

4. __init__(self: huira._huira.Image_u16, array: numpy.ndarray[numpy.uint16]) -> None

Construct from a numpy array. Single-channel: (H, W). Multi-channel: (H, W, C).

property channels

clear(self: huira._huira.Image_u16) → None

property empty

fill(self: huira._huira.Image_u16, value: int) → None

property height

property resolution

property sensor_bit_depth

property shape

to_numpy(self: huira._huira.Image_u16) → numpy.ndarray[numpy.uint16]

Return image data as a numpy array (always copies).

property width

class huira.Image_u32

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: huira._huira.Image_u32) -> None

2. __init__(self: huira._huira.Image_u32, width: int, height: int) -> None

3. __init__(self: huira._huira.Image_u32, width: int, height: int, fill_value: int) -> None

4. __init__(self: huira._huira.Image_u32, array: numpy.ndarray[numpy.uint32]) -> None

Construct from a numpy array. Single-channel: (H, W). Multi-channel: (H, W, C).

property channels

clear(self: huira._huira.Image_u32) → None

property empty

fill(self: huira._huira.Image_u32, value: int) → None

property height

property resolution

property sensor_bit_depth

property shape

to_numpy(self: huira._huira.Image_u32) → numpy.ndarray[numpy.uint32]

Return image data as a numpy array (always copies).

property width

class huira.Image_u64

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: huira._huira.Image_u64) -> None

2. __init__(self: huira._huira.Image_u64, width: int, height: int) -> None

3. __init__(self: huira._huira.Image_u64, width: int, height: int, fill_value: int) -> None

4. __init__(self: huira._huira.Image_u64, array: numpy.ndarray[numpy.uint64]) -> None

Construct from a numpy array. Single-channel: (H, W). Multi-channel: (H, W, C).

property channels

clear(self: huira._huira.Image_u64) → None

property empty

fill(self: huira._huira.Image_u64, value: int) → None

property height

property resolution

property sensor_bit_depth

property shape

to_numpy(self: huira._huira.Image_u64) → numpy.ndarray[numpy.uint64]

Return image data as a numpy array (always copies).

property width

RGB Images

class huira.Image_rgb_f32

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: huira._huira.Image_rgb_f32) -> None

2. __init__(self: huira._huira.Image_rgb_f32, width: int, height: int) -> None

3. __init__(self: huira._huira.Image_rgb_f32, width: int, height: int, fill_value: glm::vec<3, float, (glm::qualifier)0>) -> None

4. __init__(self: huira._huira.Image_rgb_f32, array: numpy.ndarray[numpy.float32]) -> None

Construct from a numpy array. Single-channel: (H, W). Multi-channel: (H, W, C).

property channels

clear(self: huira._huira.Image_rgb_f32) → None

property empty

fill(self: huira._huira.Image_rgb_f32, value: glm::vec<3, float, (glm::qualifier)0>) → None

property height

property resolution

property sensor_bit_depth

property shape

to_numpy(self: huira._huira.Image_rgb_f32) → numpy.ndarray[numpy.float32]

Return image data as a numpy array (always copies).

property width

class huira.Image_rgb_f64

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: huira._huira.Image_rgb_f64) -> None

2. __init__(self: huira._huira.Image_rgb_f64, width: int, height: int) -> None

3. __init__(self: huira._huira.Image_rgb_f64, width: int, height: int, fill_value: huira._huira.Vec3) -> None

4. __init__(self: huira._huira.Image_rgb_f64, array: numpy.ndarray[numpy.float64]) -> None

Construct from a numpy array. Single-channel: (H, W). Multi-channel: (H, W, C).

property channels

clear(self: huira._huira.Image_rgb_f64) → None

property empty

fill(self: huira._huira.Image_rgb_f64, value: huira._huira.Vec3) → None

property height

property resolution

property sensor_bit_depth

property shape

to_numpy(self: huira._huira.Image_rgb_f64) → numpy.ndarray[numpy.float64]

Return image data as a numpy array (always copies).

property width

Image I/O

huira.read_png_f32(filepath: str) → tuple

Read a PNG into (Image_f32, Image_f32 alpha).

huira.read_png_u8(filepath: str) → tuple

Read a PNG into (Image_u8, Image_f32 alpha).

huira.read_png_u16(filepath: str) → tuple

Read a PNG into (Image_u16, Image_f32 alpha).

huira.write_png(*args, **kwargs)

Overloaded function.

1. write_png(filepath: str, image: huira._huira.Image_f32, bit_depth: int = 8) -> None

2. write_png(filepath: str, image: huira._huira.Image_u8, bit_depth: int = 8) -> None

3. write_png(filepath: str, image: huira._huira.Image_u16, bit_depth: int = 16) -> None

4. write_png(filepath: str, image: huira._huira.Image_rgb_f32, bit_depth: int = 8) -> None

5. write_png(filepath: str, image: huira._huira.Image_f32, alpha: huira._huira.Image_f32, bit_depth: int = 8) -> None

huira.read_fits(filepath: str) → tuple

Read a FITS file into (Image_f32, FitsMetadata).

huira.write_fits(filepath: str, image: huira._huira.Image_f32, bit_depth: int = -32, metadata: huira._huira.FitsMetadata = FitsMetadata(custom_keywords=0)) → None

Write an Image_f32 to a FITS file.