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image_utils.py
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image_utils.py
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import importlib.util
import os
import sys
from os import PathLike
from typing import Tuple, Literal, Union, TYPE_CHECKING
import numpy as np
from numpy.typing import NDArray, DTypeLike
from .generator_process import RunInSubprocess
"""
This module allows for simple handling of image data in numpy ndarrays in some common formats.
Dimensions:
2: HW - L
3: HWC - L/LA/RGB/RGBA
4: NHWC - batched HWC
Channels:
1: L
2: LA
3: RGB
4: RGBA
"""
def version_str(version):
return ".".join(str(x) for x in version)
# find_spec("bpy") will never return None
has_bpy = sys.modules.get("bpy", None) is not None
has_ocio = importlib.util.find_spec("PyOpenColorIO") is not None
has_oiio = importlib.util.find_spec("OpenImageIO") is not None
has_pil = importlib.util.find_spec("PIL") is not None
if has_bpy:
# frontend
import bpy
BLENDER_VERSION = bpy.app.version
OCIO_CONFIG = os.path.join(bpy.utils.resource_path('LOCAL'), 'datafiles/colormanagement/config.ocio')
# Easier to share via environment variables than to enforce backends with subprocesses to use their own methods of sharing.
os.environ["BLENDER_VERSION"] = version_str(BLENDER_VERSION)
os.environ["BLENDER_OCIO_CONFIG"] = OCIO_CONFIG
else:
# backend
BLENDER_VERSION = tuple(int(x) for x in os.environ["BLENDER_VERSION"].split("."))
OCIO_CONFIG = os.environ["BLENDER_OCIO_CONFIG"]
if TYPE_CHECKING:
import bpy
import PIL.Image
def _bpy_version_error(required_version, feature, module):
if BLENDER_VERSION >= required_version:
return Exception(f"{module} is unexpectedly missing in Blender {version_str(BLENDER_VERSION)}")
return Exception(f"{feature} requires Blender {version_str(required_version)} or higher, you are using {version_str(BLENDER_VERSION)}")
def size(array: NDArray) -> Tuple[int, int]:
if array.ndim == 2:
return array.shape[1], array.shape[0]
if array.ndim in [3, 4]:
return array.shape[-2], array.shape[-3]
raise ValueError(f"Can't determine size from {array.ndim} dimensions")
def channels(array: NDArray) -> int:
if array.ndim == 2:
return 1
if array.ndim in [3, 4]:
return array.shape[-1]
raise ValueError(f"Can't determine channels from {array.ndim} dimensions")
def ensure_alpha(array: NDArray, alpha=None) -> NDArray:
"""
Args:
array: Image pixels values.
alpha: Default alpha value if an alpha channel will be made. Will be inferred from `array.dtype` if None.
Returns: The converted image or the original image if it already had alpha.
"""
c = channels(array)
if c in [2, 4]:
return array
if c not in [1, 3]:
raise ValueError(f"Can't ensure alpha from {c} channels")
if alpha is None:
alpha = 0
if np.issubdtype(array.dtype, np.floating):
alpha = 1
elif np.issubdtype(array.dtype, np.integer):
alpha = np.iinfo(array.dtype).max
array = ensure_channel_dim(array)
return np.pad(array, [*[(0, 0)]*(array.ndim-1), (0, 1)], constant_values=alpha)
def ensure_opaque(array: NDArray) -> NDArray:
"""
Removes the alpha channel if it exists.
"""
if channels(array) in [2, 4]:
return array[..., :-1]
return array
def ensure_channel_dim(array: NDArray) -> NDArray:
"""
Expands a HW grayscale image to HWC.
"""
if array.ndim == 2:
return array[..., np.newaxis]
return array
def rgb(array: NDArray) -> NDArray:
"""
Converts a grayscale image to RGB or removes the alpha channel from an RGBA image.
If the image was already RGB the original array will be returned.
"""
c = channels(array)
match channels(array):
case 1:
return np.concatenate([ensure_channel_dim(array)] * 3, axis=-1)
case 2:
return np.concatenate([array[..., :1]] * 3, axis=-1)
case 3:
return array
case 4:
return array[..., :3]
raise ValueError(f"Can't make {c} channels RGB")
def rgba(array: NDArray, alpha=None) -> NDArray:
"""
Args:
array: Image pixels values.
alpha: Default alpha value if an alpha channel will be made. Will be inferred from `array.dtype` if None.
Returns: The converted image or the original image if it already was RGBA.
"""
c = channels(array)
if c == 4:
return array
if c == 2:
l, a = np.split(array, 2, axis=-1)
return np.concatenate([l, l, l, a], axis=-1)
return ensure_alpha(rgb(array), alpha)
def grayscale(array: NDArray) -> NDArray:
"""
Converts `array` into HW or NHWC grayscale. This is intended for converting an
RGB image that is already visibly grayscale, such as a depth map. It will not
make a good approximation of perceived lightness of an otherwise colored image.
"""
if array.ndim == 2:
return array
c = channels(array)
if array.ndim == 3:
if c in [1, 2]:
return array[..., 0]
elif c in [3, 4]:
return np.max(array[..., :3], axis=-1)
raise ValueError(f"Can't make {c} channels grayscale")
elif array.ndim == 4:
if c in [1, 2]:
return array[..., :1]
elif c in [3, 4]:
return np.max(array[..., :3], axis=-1, keepdims=True)
raise ValueError(f"Can't make {c} channels grayscale")
raise ValueError(f"Can't make {array.ndim} dimensions grayscale")
def _passthrough_alpha(from_array, to_array):
if channels(from_array) not in [2, 4]:
return to_array
to_array = np.concatenate([ensure_channel_dim(to_array), from_array[..., -1:]], axis=-1)
return to_array
def linear_to_srgb(array: NDArray, clamp=True) -> NDArray:
"""
Args:
array: Image to convert from linear to sRGB color space. Will be converted to float32 if it isn't already a float dtype.
clamp: whether to restrict the result between 0..1
"""
if not np.issubdtype(array.dtype, np.floating):
array = to_dtype(array, np.float32)
srgb = ensure_opaque(array)
srgb = np.where(
srgb <= 0.0031308,
srgb * 12.92,
(np.abs(srgb) ** (1/2.4) * 1.055) - 0.055
# abs() to suppress `RuntimeWarning: invalid value encountered in power` for negative values
)
if clamp:
# conversion may produce values outside standard range, usually >1
srgb = np.clip(srgb, 0, 1)
srgb = _passthrough_alpha(array, srgb)
return srgb
def srgb_to_linear(array: NDArray) -> NDArray:
"""
Converts from sRGB to linear color space. Will be converted to float32 if it isn't already a float dtype.
"""
if not np.issubdtype(array.dtype, np.floating):
array = to_dtype(array, np.float32)
linear = ensure_opaque(array)
linear = np.where(
linear <= 0.04045,
linear / 12.92,
((linear + 0.055) / 1.055) ** 2.4
)
linear = _passthrough_alpha(array, linear)
return linear
@RunInSubprocess.when_raised
def color_transform(array: NDArray, from_color_space: str, to_color_space: str, *, clamp_srgb=True) -> NDArray:
"""
Args:
array: Pixel values in `from_color_space`
from_color_space: Color space of `array`
to_color_space: Desired color space
clamp_srgb: Restrict values inside the standard range when converting to sRGB.
Returns: Pixel values in `to_color_space`. The image will be converted to RGB/RGBA float32 for most transforms.
Transforms between linear and sRGB may remain grayscale and keep the original DType if it was floating point.
"""
# Blender handles Raw and Non-Color images as if they were in Linear color space.
if from_color_space in ["Raw", "Non-Color"]:
from_color_space = "Linear"
if to_color_space in ["Raw", "Non-Color"]:
to_color_space = "Linear"
if from_color_space == to_color_space:
return array
elif from_color_space == "Linear" and to_color_space == "sRGB":
return linear_to_srgb(array, clamp_srgb)
elif from_color_space == "sRGB" and to_color_space == "Linear":
return srgb_to_linear(array)
if not has_ocio:
raise RunInSubprocess
import PyOpenColorIO as OCIO
config = OCIO.Config.CreateFromFile(OCIO_CONFIG)
proc = config.getProcessor(from_color_space, to_color_space).getDefaultCPUProcessor()
# OCIO requires RGB/RGBA float32.
# There is a channel agnostic apply(), but I can't seem to get it to work.
# getOptimizedCPUProcessor() can handle different precisions, but I doubt it would have meaningful use.
array = to_dtype(array, np.float32)
c = channels(array)
if c in [1, 3]:
array = rgb(array)
proc.applyRGB(array)
if clamp_srgb and to_color_space == "sRGB":
array = np.clip(array, 0, 1)
return array
elif c in [2, 4]:
array = rgba(array)
proc.applyRGBA(array)
if clamp_srgb and to_color_space == "sRGB":
array = np.clip(array, 0, 1)
return array
raise ValueError(f"Can't color transform {c} channels")
# inverse=True is often crashing from EXCEPTION_ACCESS_VIOLATION while on frontend.
# Normally this is caused by not running on the main thread or accessing a deleted
# object, neither seem to be the issue here. Doesn't matter if the backend imports
# its own OCIO or the one packaged with Blender.
# Stack trace:
# OpenColorIO_2_2.dll :0x00007FFDE8961160 OpenColorIO_v2_2::GradingTone::validate
# OpenColorIO_2_2.dll :0x00007FFDE8A2BD40 OpenColorIO_v2_2::Processor::isNoOp
# OpenColorIO_2_2.dll :0x00007FFDE882EA00 OpenColorIO_v2_2::CPUProcessor::apply
# PyOpenColorIO.pyd :0x00007FFDEB0F0E40 pybind11::error_already_set::what
# PyOpenColorIO.pyd :0x00007FFDEB0F0E40 pybind11::error_already_set::what
# PyOpenColorIO.pyd :0x00007FFDEB0F0E40 pybind11::error_already_set::what
# PyOpenColorIO.pyd :0x00007FFDEB0E7510 pybind11::error_already_set::discard_as_unraisable
@RunInSubprocess.when(lambda *_, inverse=False, **__: inverse or not has_ocio)
def render_color_transform(
array: NDArray,
exposure: float,
gamma: float,
view_transform: str,
display_device: str,
look: str,
*,
inverse: bool = False,
color_space: str | None = None,
clamp_srgb: bool = True,
) -> NDArray:
import PyOpenColorIO as OCIO
ocio_config = OCIO.Config.CreateFromFile(OCIO_CONFIG)
# A reimplementation of `OCIOImpl::createDisplayProcessor` from the Blender source.
# https://github.com/blender/blender/blob/3816fcd8611bc2836ee8b2a5225b378a02141ce4/intern/opencolorio/ocio_impl.cc#L666
# Modified to support a final color space transform.
def create_display_processor(
config,
input_colorspace,
view,
display,
look,
scale, # Exposure
exponent, # Gamma
inverse,
color_space
):
group = OCIO.GroupTransform()
# Exposure
if scale != 1:
# Always apply exposure in scene linear.
color_space_transform = OCIO.ColorSpaceTransform()
color_space_transform.setSrc(input_colorspace)
color_space_transform.setDst(OCIO.ROLE_SCENE_LINEAR)
group.appendTransform(color_space_transform)
# Make further transforms aware of the color space change
input_colorspace = OCIO.ROLE_SCENE_LINEAR
# Apply scale
matrix_transform = OCIO.MatrixTransform(
[scale, 0.0, 0.0, 0.0, 0.0, scale, 0.0, 0.0, 0.0, 0.0, scale, 0.0, 0.0, 0.0, 0.0, 1.0])
group.appendTransform(matrix_transform)
# Add look transform
use_look = look is not None and len(look) > 0
if use_look:
look_output = config.getLook(look).getProcessSpace()
if look_output is not None and len(look_output) > 0:
look_transform = OCIO.LookTransform()
look_transform.setSrc(input_colorspace)
look_transform.setDst(look_output)
look_transform.setLooks(look)
group.appendTransform(look_transform)
# Make further transforms aware of the color space change.
input_colorspace = look_output
else:
# For empty looks, no output color space is returned.
use_look = False
# Add view and display transform
display_view_transform = OCIO.DisplayViewTransform()
display_view_transform.setSrc(input_colorspace)
display_view_transform.setLooksBypass(use_look)
display_view_transform.setView(view)
display_view_transform.setDisplay(display)
group.appendTransform(display_view_transform)
if color_space is not None:
group.appendTransform(OCIO.ColorSpaceTransform(input_colorspace if display == "None" else display, color_space))
# Gamma
if exponent != 1:
exponent_transform = OCIO.ExponentTransform([exponent, exponent, exponent, 1.0])
group.appendTransform(exponent_transform)
if inverse:
group.setDirection(OCIO.TransformDirection.TRANSFORM_DIR_INVERSE)
# Create processor from transform. This is the moment were OCIO validates
# the entire transform, no need to check for the validity of inputs above.
return config.getProcessor(group)
# Exposure and gamma transformations derived from Blender source:
# https://github.com/blender/blender/blob/3816fcd8611bc2836ee8b2a5225b378a02141ce4/source/blender/imbuf/intern/colormanagement.cc#L867
scale = 2 ** exposure
exponent = 1 / max(gamma, np.finfo(np.float32).eps)
processor = create_display_processor(ocio_config, OCIO.ROLE_SCENE_LINEAR, view_transform, display_device, look if look != 'None' else None, scale, exponent, inverse, color_space)
array = to_dtype(array, np.float32)
c = channels(array)
if c in [1, 3]:
array = rgb(array)
processor.getDefaultCPUProcessor().applyRGB(array)
elif c in [2, 4]:
array = rgba(array)
processor.getDefaultCPUProcessor().applyRGBA(array)
else:
raise ValueError(f"Can't color transform {c} channels")
if clamp_srgb and (color_space == "sRGB" or (display_device == "sRGB" and color_space is None)) and not inverse:
array = np.clip(array, 0, 1)
return array
def scene_color_transform(array: NDArray, scene: Union["bpy.types.Scene", None] = None, *, inverse: bool = False, color_space: str | None = None, clamp_srgb=True) -> NDArray:
if scene is None:
import bpy
scene = bpy.context.scene
view = scene.view_settings
display = scene.display_settings.display_device
return render_color_transform(
array,
view.exposure,
view.gamma,
view.view_transform,
display,
view.look,
inverse=inverse,
clamp_srgb=clamp_srgb,
color_space=color_space
)
def _unsigned(dtype: DTypeLike) -> DTypeLike:
match bits := np.iinfo(dtype).bits:
case 8:
return np.uint8
case 16:
return np.uint16
case 32:
return np.uint32
case 64:
return np.uint64
raise ValueError(f"unexpected bit depth {bits} from {repr(dtype)}")
def to_dtype(array: NDArray, dtype: DTypeLike) -> NDArray:
"""
Remaps values with respect to ranges rather than simply casting for integer DTypes.
`integer(0)=float(0)`, `integer.MAX=float(1)`, and signed `integer.MIN+1=float(-1)`
"""
dtype = np.dtype(dtype)
from_dtype = array.dtype
if dtype == from_dtype:
return array
from_floating = np.issubdtype(from_dtype, np.floating)
from_integer = np.issubdtype(from_dtype, np.integer)
to_floating = np.issubdtype(dtype, np.floating)
to_integer = np.issubdtype(dtype, np.integer)
if from_floating and to_floating:
array = array.astype(dtype)
if np.finfo(from_dtype).bits > np.finfo(dtype).bits:
# prevent inf when lowering precision
array = np.nan_to_num(array)
elif from_floating and to_integer:
iinfo = np.iinfo(dtype)
array = (array.clip(-1 if iinfo.min < 0 else 0, 1) * iinfo.max).round().astype(dtype)
elif from_integer and to_floating:
iinfo = np.iinfo(from_dtype)
array = (array / iinfo.max).astype(dtype)
elif from_integer and to_integer:
from_signed = np.issubdtype(from_dtype, np.signedinteger)
to_signed = np.issubdtype(dtype, np.signedinteger)
from_bits = np.iinfo(from_dtype).bits
to_bits = np.iinfo(dtype).bits
if from_signed:
from_bits -= 1
if to_signed:
to_bits -= 1
bit_diff = to_bits - from_bits
if from_signed and not to_signed:
# unsigned output does not support negative
array = np.maximum(array, 0)
if from_signed and to_signed:
# simpler to handle bit manipulation in unsigned
sign = np.sign(array)
array = np.abs(array)
if bit_diff > 0:
# Repeat bits rather than using a single left shift
# so that from_iinfo.max turns into to_iinfo.max
# and all values remain equally spaced.
# Example 8 to 16 bits:
# (incorrect) 0x00FF << 8 = 0xFF00
# (correct) 0x00FF << 8 | 0x00FF = 0xFFFF
# Implementation uses multiplication instead of potentially multiple left shifts and ors:
# 0x00FF * 0x0101 = 0xFFFF
base = array.astype(_unsigned(dtype))
m = 0
for i in range(bit_diff, -1, -from_bits):
m += 2 ** i
array = base * m
remaining_bits = bit_diff % from_bits
if remaining_bits > 0:
# when changing between signed and unsigned bit_diff is not a multiple of from_bits
array |= base >> (from_bits-remaining_bits)
elif bit_diff < 0:
array = array.astype(_unsigned(from_dtype), copy=False) >> -bit_diff
if from_signed and to_signed:
array = np.multiply(array, sign, dtype=dtype)
array = array.astype(dtype, copy=False)
else:
raise TypeError(f"Unable to convert from {array.dtype} to {dtype}")
return array
@RunInSubprocess.when(not has_oiio)
def resize(array: NDArray, size: Tuple[int, int], clamp=True):
no_channels = array.ndim == 2
if no_channels:
array = array[..., np.newaxis]
no_batch = array.ndim < 4
if no_batch:
array = array[np.newaxis, ...]
if clamp:
c_min = np.min(array, axis=(1, 2), keepdims=True)
c_max = np.max(array, axis=(1, 2), keepdims=True)
if has_oiio:
import OpenImageIO as oiio
resized = []
for unbatched in array:
# OpenImageIO can have batched images, but doesn't support resizing them
image_in = oiio.ImageBuf(unbatched)
image_out = oiio.ImageBufAlgo.resize(image_in, roi=oiio.ROI(0, int(size[0]), 0, int(size[1])))
if image_out.has_error:
raise Exception(image_out.geterror())
resized.append(image_out.get_pixels(image_in.spec().format))
array = np.stack(resized)
else:
original_dtype = array.dtype
if np.issubdtype(original_dtype, np.floating):
if original_dtype == np.float16:
# interpolation not implemented for float16 on CPU
array = to_dtype(array, np.float32)
elif np.issubdtype(original_dtype, np.integer):
# integer interpolation only supported for uint8 nearest, nearest-exact or bilinear
bits = np.iinfo(original_dtype).bits
array = to_dtype(array, np.float64 if bits >= 32 else np.float32)
import torch
array = torch.from_numpy(np.transpose(array, (0, 3, 1, 2)))
array = torch.nn.functional.interpolate(array, size=(size[1], size[0]), mode="bilinear")
array = np.transpose(array, (0, 2, 3, 1)).numpy()
array = to_dtype(array, original_dtype)
if clamp:
array = np.clip(array, c_min, c_max)
if no_batch:
array = np.squeeze(array, 0)
if no_channels:
array = np.squeeze(array, -1)
return array
def bpy_to_np(image: "bpy.types.Image", *, color_space: str | None = "sRGB", clamp_srgb=True, top_to_bottom=True) -> NDArray:
"""
Args:
image: Image to extract pixels values from.
color_space: The color space to convert to. `None` will apply no color transform.
Keep in mind that Raw/Non-Color images are handled as if they were in Linear color space.
clamp_srgb: Restrict values inside the standard range when converting to sRGB.
top_to_bottom: The y-axis is flipped to a more common standard of `top=0` to `bottom=height-1`.
Returns: A ndarray copy of `image.pixels` in RGBA float32 format.
"""
if image.type == "RENDER_RESULT":
# can't get pixels automatically without rendering again and freezing Blender until it finishes, or saving to disk
raise ValueError(f"{image.name} image can't be used directly, alternatively use a compositor viewer node")
array = np.empty((image.size[1], image.size[0], image.channels), dtype=np.float32)
# foreach_get/set is extremely fast to read/write an entire image compared to alternatives
# see https://projects.blender.org/blender/blender/commit/9075ec8269e7cb029f4fab6c1289eb2f1ae2858a
image.pixels.foreach_get(array.ravel())
if color_space is not None:
if image.type == "COMPOSITING":
# Viewer Node
array = scene_color_transform(array, color_space=color_space, clamp_srgb=clamp_srgb)
else:
array = color_transform(array, image.colorspace_settings.name, color_space, clamp_srgb=clamp_srgb)
if top_to_bottom:
array = np.flipud(array)
return rgba(array)
def np_to_bpy(array: NDArray, name=None, existing_image=None, float_buffer=None, color_space: str = "sRGB", top_to_bottom=True) -> "bpy.types.Image":
"""
Args:
array: Image pixel values. The y-axis is expected to be ordered `top=0` to `bottom=height-1`.
name: Name of the image data-block. If None it will be `existing_image.name` or "Untitled".
existing_image: Image data-block to overwrite.
float_buffer:
Make Blender keep data in (`True`) 32-bit float values, or (`False`) 8-bit integer values.
`None` won't invalidate `existing_image`, but if a new image is created it will be `False`.
color_space: Color space of `array`.
Returns: A new Blender image or `existing_image` if it didn't require replacement.
"""
if array.ndim == 4 and array.shape[0] > 1:
raise ValueError(f"Can't convert a batched array of {array.shape[0]} images to a Blender image")
# create or replace image
import bpy
width, height = size(array)
if name is None:
name = "Untitled" if existing_image is None else existing_image.name
if existing_image is not None and existing_image.type in ["RENDER_RESULT", "COMPOSITING"]:
existing_image = None
elif existing_image is not None and (
existing_image.size[0] != width
or existing_image.size[1] != height
or (existing_image.channels != channels(array) and existing_image.channels != 4)
or (existing_image.is_float != float_buffer and float_buffer is not None)
):
bpy.data.images.remove(existing_image)
existing_image = None
if existing_image is None:
image = bpy.data.images.new(
name,
width=width,
height=height,
alpha=channels(array) == 4,
float_buffer=False if float_buffer is None else float_buffer
)
else:
image = existing_image
image.name = name
image.colorspace_settings.name = color_space
# adjust array pixels to fit into image
if array.ndim == 4:
array = array[0]
if top_to_bottom:
array = np.flipud(array)
array = to_dtype(array, np.float32)
if image.channels == 4:
array = rgba(array)
elif image.channels == 3:
# I believe image.channels only exists for backwards compatibility and modern versions of Blender
# will always handle images as RGBA. I can't manage to make or import an image and end up with
# anything but 4 channels. Support for images with 3 channels will be kept just in case.
array = rgb(array)
else:
raise NotImplementedError(f"Blender image unexpectedly has {image.channels} channels")
# apply pixels to image
image.pixels.foreach_set(array.ravel())
image.pack()
image.update()
return image
def render_pass_to_np(
render_pass: "bpy.types.RenderPass",
size: Tuple[int, int],
*,
color_management: bool = False,
color_space: str | None = None,
clamp_srgb: bool = True,
top_to_bottom: bool = True
):
array = np.empty((*reversed(size), render_pass.channels), dtype=np.float32)
render_pass.rect.foreach_get(array.reshape((-1, render_pass.channels)))
if color_management:
array = scene_color_transform(array, color_space=color_space, clamp_srgb=clamp_srgb)
elif color_space is not None:
array = color_transform(array, "Linear", color_space, clamp_srgb=clamp_srgb)
if top_to_bottom:
array = np.flipud(array)
return array
def np_to_render_pass(
array: NDArray,
render_pass: "bpy.types.RenderPass",
*,
inverse_color_management: bool = False,
color_space: str | None = None,
dtype: DTypeLike = np.float32,
top_to_bottom: bool = True
):
if inverse_color_management:
array = scene_color_transform(array, inverse=True, color_space=color_space)
elif color_space is not None:
array = color_transform(color_space, "Linear")
if channels(array) != render_pass.channels:
match render_pass.channels:
case 1:
array = grayscale(array)
case 3:
array = rgb(array)
case 4:
array = rgba(array)
case _:
raise NotImplementedError(f"Render pass {render_pass.name} unexpectedly requires {render_pass.channels} channels")
if dtype is not None:
array = to_dtype(array, dtype)
if top_to_bottom:
array = np.flipud(array)
render_pass.rect.foreach_set(array.reshape(-1, render_pass.channels))
def _mode(array, mode):
if mode is None:
return array
elif mode == "RGBA":
return rgba(array)
elif mode == "RGB":
return rgb(array)
elif mode == "L":
return grayscale(array)
elif mode == "LA":
return ensure_alpha(_passthrough_alpha(array, grayscale(array)))
raise ValueError(f"mode expected one of {['RGB', 'RGBA', 'L', 'LA', None]}, got {repr(mode)}")
def pil_to_np(image, *, dtype: DTypeLike | None = np.float32, mode: Literal["RGB", "RGBA", "L", "LA"] | None = None) -> NDArray:
# some modes don't require being converted to RGBA for proper handling in other module functions
# see for other modes https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes
if image.mode not in ["RGB", "RGBA", "L", "LA", "I", "F", "I;16"]:
image = image.convert("RGBA")
array = np.array(image)
if dtype is not None:
array = to_dtype(array, dtype)
array = _mode(array, mode)
return array
def np_to_pil(array: NDArray, *, mode: Literal["RGB", "RGBA", "L", "LA"] | None = None):
from PIL import Image
array = to_dtype(array, np.uint8)
if mode is None:
if channels(array) == 1 and array.ndim == 3:
# PIL L mode can't have a channel dimension
array = array[..., 1]
else:
array = _mode(array, mode)
# PIL does support higher precision modes for a single channel, but I don't see a need for supporting them yet.
# uint16="I;16", int32="I", float32="F"
return Image.fromarray(array, mode=mode)
def _dtype_to_type_desc(dtype):
import OpenImageIO as oiio
dtype = np.dtype(dtype)
match dtype:
case np.uint8:
return oiio.TypeUInt8
case np.uint16:
return oiio.TypeUInt16
case np.uint32:
return oiio.TypeUInt32
case np.uint64:
return oiio.TypeUInt64
case np.int8:
return oiio.TypeInt8
case np.int16:
return oiio.TypeInt16
case np.int32:
return oiio.TypeInt32
case np.int64:
return oiio.TypeInt64
case np.float16:
return oiio.TypeHalf
case np.float32:
return oiio.TypeFloat
case np.float64:
# no oiio.TypeDouble
return oiio.TypeDesc(oiio.BASETYPE.DOUBLE)
raise TypeError(f"can't convert {dtype} to OpenImageIO.TypeDesc")
@RunInSubprocess.when(not has_oiio)
def path_to_np(
path: str | PathLike,
*,
dtype: DTypeLike | None = np.float32,
default_color_space: str | None = None,
to_color_space: str | None = "sRGB"
) -> NDArray:
"""
Args:
path: Path to an image file.
dtype: Data type of the returned array. `None` won't change the data type. The data type may still change if a color transform occurs.
default_color_space: The color space that `image_or_path` will be handled as when it can't be determined automatically.
to_color_space: Color space of the returned array. `None` won't apply a color transform.
"""
if has_oiio:
import OpenImageIO as oiio
image = oiio.ImageInput.open(str(path))
if image is None:
raise IOError(oiio.geterror())
type_desc = image.spec().format
if dtype is not None:
type_desc = _dtype_to_type_desc(dtype)
array = image.read_image(type_desc)
from_color_space = image.spec().get_string_attribute("oiio:ColorSpace", default_color_space)
image.close()
else:
from PIL import Image
array = pil_to_np(Image.open(path))
if dtype is not None:
array = to_dtype(array, dtype)
from_color_space = "sRGB"
if from_color_space is not None and to_color_space is not None:
array = color_transform(array, from_color_space, to_color_space)
return array
ImageOrPath = Union[NDArray, "PIL.Image.Image", str, PathLike]
"""Backend compatible image types"""
def image_to_np(
image_or_path: ImageOrPath | "bpy.types.Image" | None,
*,
dtype: DTypeLike | None = np.float32,
mode: Literal["RGB", "RGBA", "L", "LA"] | None = "RGBA",
default_color_space: str | None = None,
to_color_space: str | None = "sRGB",
size: Tuple[int, int] | None = None,
top_to_bottom: bool = True
) -> NDArray:
"""
Opens an image from disk or takes an image object and converts it to `numpy.ndarray`.
Usable for image argument sanitization when the source can vary in type or format.
Args:
image_or_path: Either a file path or an instance of `bpy.types.Image`, `PIL.Image.Image`, or `numpy.ndarray`. `None` will return `None`.
dtype: Data type of the returned array. `None` won't change the data type. The data type may still change if a color transform occurs.
mode: Channel mode of the returned array. `None` won't change the mode. The mode may still change if a color transform occurs.
default_color_space: The color space that `image_or_path` will be handled as when it can't be determined automatically.
to_color_space: Color space of the returned array. `None` won't apply a color transform.
size: Resize to specific dimensions. `None` won't change the size.
top_to_bottom: Flips the image like `bpy_to_np(top_to_bottom=True)` does when `True` and `image_or_path` is a Blender image. Other image sources will only be flipped when `False`.
"""
if image_or_path is None:
return None
# convert image_or_path to numpy.ndarray
match image_or_path:
case PathLike() | str():
array = path_to_np(image_or_path, dtype=dtype, default_color_space=default_color_space, to_color_space=to_color_space)
from_color_space = None
case object(__module__="PIL.Image", __class__=type(__name__="Image")):
# abnormal class check because PIL cannot be imported on frontend
array = pil_to_np(image_or_path)
from_color_space = "sRGB"
case object(__module__="bpy.types", __class__=type(__name__="Image")):
# abnormal class check because bpy cannot be imported on backend
array = bpy_to_np(image_or_path, color_space=to_color_space)
from_color_space = None
case np.ndarray():
array = image_or_path
from_color_space = default_color_space
case _:
raise TypeError(f"not an image or path {repr(type(image_or_path))}")
# apply image requirements
if not top_to_bottom:
array = np.flipud(array)
if from_color_space is not None and to_color_space is not None:
array = color_transform(array, from_color_space, to_color_space)
if dtype is not None:
array = to_dtype(array, dtype)
array = _mode(array, mode)
if size is not None:
array = resize(array, size)
return array