mirror of
https://github.com/vladmandic/automatic
synced 2026-09-14 18:48:43 +02:00
Vladimir Mandic
119 lines
4.8 KiB
Python
119 lines
4.8 KiB
Python
from __future__ import annotations
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import logging
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import numpy as np
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MAX_DIMENSION = 16_384
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logging.getLogger().handlers.clear()
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logging.basicConfig(
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level=logging.DEBUG,
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filename='dlss.log',
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encoding='utf-8',
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filemode='a',
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format='%(asctime)s %(levelname)s %(message)s',
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# datefmt='%Y-%m-%d %H:%M:%S-%f',
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force=True,
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)
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log = logging.getLogger(__name__)
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log.debug('DLSSInit')
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class StandaloneError(RuntimeError):
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"""Base error with a stable machine-readable code."""
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def __init__(self, code: str, message: str) -> None:
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super().__init__(message)
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self.code = code
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self.message = message
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class InvalidArrayError(StandaloneError):
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def __init__(self, message: str) -> None:
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super().__init__("invalid_array", message)
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class VerificationError(StandaloneError):
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def __init__(self, message: str) -> None:
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super().__init__("verification_failed", message)
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class ProcessingError(StandaloneError):
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def __init__(self, message: str, *, code: str = "processing_failed") -> None:
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super().__init__(code, message)
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def validate_nchw(array: np.ndarray, *, name: str = "array") -> tuple[int, int, int, int]:
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if not isinstance(array, np.ndarray):
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raise InvalidArrayError(f"{name} must be a NumPy array.")
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if array.ndim != 4:
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raise InvalidArrayError(f"{name} must have shape (N, 3, H, W); got {array.shape}.")
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batch, channels, height, width = array.shape
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if batch < 1:
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raise InvalidArrayError(f"{name} must contain at least one image.")
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if channels != 3:
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raise InvalidArrayError(f"{name} must contain RGB data with C=3; got C={channels}.")
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if not 1 <= height <= MAX_DIMENSION or not 1 <= width <= MAX_DIMENSION:
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raise InvalidArrayError(
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f"{name} dimensions must be between 1 and {MAX_DIMENSION}; got {width}x{height}."
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)
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if array.dtype != np.uint8:
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raise InvalidArrayError(f"{name} must use dtype uint8; got {array.dtype}.")
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return batch, channels, height, width
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def copy_nchw(array: np.ndarray, *, name: str = "array") -> np.ndarray:
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validate_nchw(array, name=name)
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return np.ascontiguousarray(array.copy())
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def nchw_image_to_hwc(array: np.ndarray, index: int = 0, *, name: str = "array") -> np.ndarray:
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batch, _, _, _ = validate_nchw(array, name=name)
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if not 0 <= index < batch:
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raise InvalidArrayError(f"{name} image index {index} is outside batch size {batch}.")
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return np.ascontiguousarray(array[index].transpose(1, 2, 0))
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def hwc_to_nchw(array: np.ndarray, *, name: str = "image") -> np.ndarray:
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if not isinstance(array, np.ndarray) or array.ndim != 3 or array.shape[2] != 3:
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raise InvalidArrayError(f"{name} must have HWC RGB shape (H, W, 3); got {getattr(array, 'shape', None)}.")
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if array.dtype != np.uint8:
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raise InvalidArrayError(f"{name} must use dtype uint8; got {array.dtype}.")
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return np.ascontiguousarray(array.transpose(2, 0, 1)[None, ...])
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def rgb_to_rgba(array: np.ndarray) -> np.ndarray:
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"""Add opaque alpha only at the private native-worker boundary."""
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if array.ndim != 3 or array.shape[2] != 3 or array.dtype != np.uint8:
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raise InvalidArrayError("Native RGB input must have HWC uint8 shape with three channels.")
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result = np.empty((*array.shape[:2], 4), dtype=np.uint8)
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result[..., :3] = array
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result[..., 3] = 255
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return np.ascontiguousarray(result)
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def rgba_to_rgb_nchw(array: np.ndarray) -> np.ndarray:
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if array.ndim != 3 or array.shape[2] != 4 or array.dtype != np.uint8:
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raise InvalidArrayError("Native RGBA output must have HWC uint8 shape with four channels.")
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return hwc_to_nchw(np.ascontiguousarray(array[..., :3]), name="native RGB output")
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def srgb_to_worker(rgb: np.ndarray) -> np.ndarray:
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"""Convert HWC sRGB RGB data to the RTX Video worker's gamma-2.2 RGBA data."""
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if rgb.ndim != 3 or rgb.shape[2] != 3 or rgb.dtype != np.uint8:
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raise InvalidArrayError("RGB input must have HWC uint8 shape with three channels.")
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rgba = rgb_to_rgba(rgb)
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values = rgba[..., :3].astype(np.float32) / 255.0
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linear = np.where(values <= 0.04045, values / 12.92, ((values + 0.055) / 1.055) ** 2.4)
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rgba[..., :3] = np.rint(np.clip(linear, 0.0, 1.0) ** (1.0 / 2.2) * 255.0).astype(np.uint8) # pylint: disable=unsupported-assignment-operation
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return np.ascontiguousarray(rgba)
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def worker_to_srgb_rgb(data: bytes | bytearray | memoryview, width: int, height: int) -> np.ndarray:
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"""Convert packed worker RGBA output to an HWC sRGB RGB array."""
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rgba = np.frombuffer(data, dtype=np.uint8).reshape(height, width, 4).copy()
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values = (rgba[..., :3].astype(np.float32) / 255.0) ** 2.2
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rgb = np.where(values <= 0.0031308, values * 12.92, 1.055 * values ** (1.0 / 2.4) - 0.055)
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rgba[..., :3] = np.rint(np.clip(rgb, 0.0, 1.0) * 255.0).astype(np.uint8)
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return np.ascontiguousarray(rgba[..., :3])
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