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torzdf
837bc2d51d
* FaceSwap 3 (#1515) * Update extract pipeline * Update requirements + setup for nvidia * Remove allow-growth option * tf.keras to keras updates * lib.model.losses - Port + fix all loss functions for Keras3 * lib.model - port initializers, layers. normalization to Keras3 * lib.model.autoclip to Keras 3 * Update mixed precision layer storage * model file to .keras format * Restructure nn_blocks to initialize layers in __init__ * Tensorboard - Trainer: Add Torch compatible Tensorboard callbacks - GUI event reader remove TF dependency * Loss logging - Flush TB logs on save - Replace TB live iterator for GUI * Backup models on total loss drop rather than per side * Update all models to Keras3 Compat * Remove lib.model.session * Update clip ViT to Keras 3 * plugins.extract.mask.unet-dfl - Fix for Keras3/Torch backend * Port AdaBelief to Keras 3 * setup.py: - Add --dev flag for dev tool install * Fix Keras 3 syntax * Fix LR Finder for Keras 3 * Fix mixed precision switching for Keras 3 * Add more optimizers + open up config setting * train: Remove updating FS1 weights to FS2 models * Alignments: Remove support for legacy .json files * tools.model: - Remove TF Saved Format saving - Fix Backup/Restore + Nan-Scan * Fix inference model creation for Keras 3 * Preview tool: Fix for Keras3 * setup.py: Configure keras backend * train: Migration of FS2 models to FS3 * Training: Default coverage to 100% * Remove DirectML backend * Update setup for MacOS * GUI: Force line reading to UTF-8 * Remove redundant Tensorflow references * Remove redundant code * Legacy model loading: Fix TFLamdaOp scalar ops and DepthwiseConv2D * Add vertical offset option for training * Github actions: Add more python versions * Add python version to workflow names * Github workflow: Exclude Python 3.12 for macOS * Implement custom training loop * Fs3 - Add RTX5xxx and ROCm 6.1-6.4 support (#1511) * setup.py: Add Cuda/ROCm version select options * bump minimum python version to 3.11 * Switch from setup.cgf to pyproject.toml * Documentation: Update all docs to use automodapi * Allow sysinfo to run with missing packages + correctly install tk under Linux * Bugfix: dot naming convention in clip models * lib.config: Centralise globally rather than passing as object - Add torch DataParallel for multi-gpu training - GUI: Group switches together when generating cli args - CLI: Remove deprecated multi-character argparse args - Refactor: - Centralise tensorboard reading/writing + unit tests - Create trainer plugin interfaces + add original + distributed * Update installers
86 lines
3.0 KiB
Python
86 lines
3.0 KiB
Python
#!/usr/bin/env python3
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""" Handles the generation of thumbnail jpgs for storing inside an alignments file/png header """
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from __future__ import annotations
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import logging
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import typing as T
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import numpy as np
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from lib.logger import parse_class_init
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from lib.utils import get_module_objects
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if T.TYPE_CHECKING:
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from lib import align
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logger = logging.getLogger(__name__)
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class Thumbnails():
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""" Thumbnail images stored in the alignments file.
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The thumbnails are stored as low resolution (64px), low quality jpg in the alignments file
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and are used for the Manual Alignments tool.
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Parameters
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----------
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alignments: :class:'~lib.align.alignments.Alignments`
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The parent alignments class that these thumbs belong to
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"""
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def __init__(self, alignments: align.alignments.Alignments) -> None:
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logger.debug(parse_class_init(locals()))
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self._alignments_dict = alignments.data
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self._frame_list = list(sorted(self._alignments_dict))
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logger.debug("Initialized %s", self.__class__.__name__)
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@property
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def has_thumbnails(self) -> bool:
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""" bool: ``True`` if all faces in the alignments file contain thumbnail images
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otherwise ``False``. """
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retval = all(np.any(T.cast(np.ndarray, face.get("thumb")))
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for frame in self._alignments_dict.values()
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for face in frame["faces"])
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logger.trace(retval) # type:ignore[attr-defined]
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return retval
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def get_thumbnail_by_index(self, frame_index: int, face_index: int) -> np.ndarray:
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""" Obtain a jpg thumbnail from the given frame index for the given face index
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Parameters
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----------
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frame_index: int
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The frame index that contains the thumbnail
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face_index: int
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The face index within the frame to retrieve the thumbnail for
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Returns
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-------
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:class:`numpy.ndarray`
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The encoded jpg thumbnail
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"""
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retval = self._alignments_dict[self._frame_list[frame_index]]["faces"][face_index]["thumb"]
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assert retval is not None
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logger.trace( # type:ignore[attr-defined]
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"frame index: %s, face_index: %s, thumb shape: %s",
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frame_index, face_index, retval.shape)
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return retval
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def add_thumbnail(self, frame: str, face_index: int, thumb: np.ndarray) -> None:
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""" Add a thumbnail for the given face index for the given frame.
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Parameters
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----------
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frame: str
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The name of the frame to add the thumbnail for
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face_index: int
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The face index within the given frame to add the thumbnail for
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thumb: :class:`numpy.ndarray`
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The encoded jpg thumbnail at 64px to add to the alignments file
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"""
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logger.debug("frame: %s, face_index: %s, thumb shape: %s thumb dtype: %s",
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frame, face_index, thumb.shape, thumb.dtype)
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self._alignments_dict[frame]["faces"][face_index]["thumb"] = thumb
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__all__ = get_module_objects(__name__)
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