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faceswap
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v1.0.0
lib/convert.py
373 строки
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torzdf
Smart Masks to Convert (#957)
30 дек 2019, 02:13
Не верифицирован
30 дек 2019, 02:13
1bdc9da
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#!/usr/bin/env python3 """ Converter for Faceswap """ import logging import cv2 import numpy as np from plugins.plugin_loader import PluginLoader logger = logging.getLogger(__name__) # pylint: disable=invalid-name class Converter(): """ The converter is responsible for swapping the original face(s) in a frame with the output of a trained Faceswap model. Parameters ---------- output_size: int The size of the face, in pixels, that is output from the Faceswap model coverage_ratio: float The ratio of the training image that was used for training the Faceswap model draw_transparent: bool Whether the final output should be drawn onto a transparent layer rather than the original frame. Only available with certain writer plugins. pre_encode: python function Some writer plugins support the pre-encoding of images prior to saving out. As patching is done in multiple threads, but writing is done in a single thread, it can speed up the process to do any pre-encoding as part of the converter process. arguments: :class:`argparse.Namespace` The arguments that were passed to the convert process as generated from Faceswap's command line arguments configfile: str, optional Optional location of custom configuration ``ini`` file. If ``None`` then use the default config location. Default: ``None`` """ def __init__(self, output_size, coverage_ratio, draw_transparent, pre_encode, arguments, configfile=None): logger.debug("Initializing %s: (output_size: %s, coverage_ratio: %s, draw_transparent: " "%s, pre_encode: %s, arguments: %s, configfile: %s)", self.__class__.__name__, output_size, coverage_ratio, draw_transparent, pre_encode, arguments, configfile) self._output_size = output_size self._coverage_ratio = coverage_ratio self._draw_transparent = draw_transparent self._writer_pre_encode = pre_encode self._args = arguments self._configfile = configfile self._scale = arguments.output_scale / 100 self._adjustments = dict(box=None, mask=None, color=None, seamless=None, scaling=None) self._load_plugins() logger.debug("Initialized %s", self.__class__.__name__) @property def cli_arguments(self): """:class:`argparse.Namespace`: The command line arguments passed to the convert process """ return self._args def reinitialize(self, config): """ Reinitialize this :class:`Converter`. Called as part of the :mod:`~tools.preview` tool. Resets all adjustments then loads the plugins as specified in the given config. Parameters ---------- config: :class:`lib.config.FaceswapConfig` Pre-loaded :class:`lib.config.FaceswapConfig`. used over any configuration on disk. """ logger.debug("Reinitializing converter") self._adjustments = dict(box=None, mask=None, color=None, seamless=None, scaling=None) self._load_plugins(config=config, disable_logging=True) logger.debug("Reinitialized converter") def _load_plugins(self, config=None, disable_logging=False): """ Load the requested adjustment plugins. Loads the :mod:`plugins.converter` plugins that have been requested for this conversion session. Parameters ---------- config: :class:`lib.config.FaceswapConfig`, optional Optional pre-loaded :class:`lib.config.FaceswapConfig`. If passed, then this will be used over any configuration on disk. If ``None`` then it is ignored. Default: ``None`` disable_logging: bool, optional Plugin loader outputs logging info every time a plugin is loaded. Set to ``True`` to suppress these messages otherwise ``False``. Default: ``False`` """ logger.debug("Loading plugins. config: %s", config) self._adjustments["box"] = PluginLoader.get_converter( "mask", "box_blend", disable_logging=disable_logging)(self._output_size, configfile=self._configfile, config=config) self._adjustments["mask"] = PluginLoader.get_converter( "mask", "mask_blend", disable_logging=disable_logging)(self._args.mask_type, self._output_size, self._coverage_ratio, configfile=self._configfile, config=config) if self._args.color_adjustment != "none" and self._args.color_adjustment is not None: self._adjustments["color"] = PluginLoader.get_converter( "color", self._args.color_adjustment, disable_logging=disable_logging)(configfile=self._configfile, config=config) if self._args.scaling != "none" and self._args.scaling is not None: self._adjustments["scaling"] = PluginLoader.get_converter( "scaling", self._args.scaling, disable_logging=disable_logging)(configfile=self._configfile, config=config) logger.debug("Loaded plugins: %s", self._adjustments) def process(self, in_queue, out_queue): """ Main convert process. Takes items from the in queue, runs the relevant adjustments, patches faces to final frame and outputs patched frame to the out queue. Parameters ---------- in_queue: :class:`queue.Queue` The output from :class:`scripts.convert.Predictor`. Contains detected faces from the Faceswap model as well as the frame to be patched. out_queue: :class:`queue.Queue` The queue to place patched frames into for writing by one of Faceswap's :mod:`plugins.convert.writer` plugins. """ logger.debug("Starting convert process. (in_queue: %s, out_queue: %s)", in_queue, out_queue) while True: items = in_queue.get() if items == "EOF": logger.debug("EOF Received") logger.debug("Patch queue finished") # Signal EOF to other processes in pool logger.debug("Putting EOF back to in_queue") in_queue.put(items) break if isinstance(items, dict): items = [items] for item in items: logger.trace("Patch queue got: '%s'", item["filename"]) try: image = self._patch_image(item) except Exception as err: # pylint: disable=broad-except # Log error and output original frame logger.error("Failed to convert image: '%s'. Reason: %s", item["filename"], str(err)) image = item["image"] # UNCOMMENT THIS CODE BLOCK TO PRINT TRACEBACK ERRORS # import sys ; import traceback # exc_info = sys.exc_info() ; traceback.print_exception(*exc_info) logger.trace("Out queue put: %s", item["filename"]) out_queue.put((item["filename"], image)) logger.debug("Completed convert process") def _patch_image(self, predicted): """ Patch a swapped face onto a frame. Run selected adjustments and swap the faces in a frame. Parameters ---------- predicted: dict The output from :class:`scripts.convert.Predictor`. Returns ------- :class: `numpy.ndarray` or pre-encoded image output The final frame ready for writing by a :mod:`plugins.convert.writer` plugin. Frame is either an array, or the pre-encoded output from the writer's pre-encode function (if it has one) """ logger.trace("Patching image: '%s'", predicted["filename"]) frame_size = (predicted["image"].shape[1], predicted["image"].shape[0]) new_image, background = self._get_new_image(predicted, frame_size) patched_face = self._post_warp_adjustments(background, new_image) patched_face = self._scale_image(patched_face) patched_face *= 255.0 patched_face = np.rint(patched_face, out=np.empty(patched_face.shape, dtype="uint8"), casting='unsafe') if self._writer_pre_encode is not None: patched_face = self._writer_pre_encode(patched_face) logger.trace("Patched image: '%s'", predicted["filename"]) return patched_face def _get_new_image(self, predicted, frame_size): """ Get the new face from the predictor and apply pre-warp manipulations. Applies any requested adjustments to the raw output of the Faceswap model before transforming the image into the target frame. Parameters ---------- predicted: dict The output from :class:`scripts.convert.Predictor`. frame_size: tuple The (`width`, `height`) of the final frame in pixels Returns ------- placeholder: :class: `numpy.ndarray` The original frame with the swapped faces patched onto it background: :class: `numpy.ndarray` The original frame """ logger.trace("Getting: (filename: '%s', faces: %s)", predicted["filename"], len(predicted["swapped_faces"])) placeholder = np.zeros((frame_size[1], frame_size[0], 4), dtype="float32") background = predicted["image"] / np.array(255.0, dtype="float32") placeholder[:, :, :3] = background for new_face, detected_face in zip(predicted["swapped_faces"], predicted["detected_faces"]): predicted_mask = new_face[:, :, -1] if new_face.shape[2] == 4 else None new_face = new_face[:, :, :3] interpolator = detected_face.reference_interpolators[1] new_face = self._pre_warp_adjustments(new_face, detected_face, predicted_mask) # Warp face with the mask cv2.warpAffine(new_face, detected_face.reference_matrix, frame_size, placeholder, flags=cv2.WARP_INVERSE_MAP | interpolator, borderMode=cv2.BORDER_TRANSPARENT) np.clip(placeholder, 0.0, 1.0, out=placeholder) logger.trace("Got filename: '%s'. (placeholders: %s)", predicted["filename"], placeholder.shape) return placeholder, background def _pre_warp_adjustments(self, new_face, detected_face, predicted_mask): """ Run any requested adjustments that can be performed on the raw output from the Faceswap model. Any adjustments that can be performed before warping the face into the final frame are performed here. Parameters ---------- new_face: :class:`numpy.ndarray` The swapped face received from the faceswap model. detected_face: :class:`~lib.faces_detect.DetectedFace` The detected_face object as defined in :class:`scripts.convert.Predictor` predicted_mask: :class:`numpy.ndarray` or ``None`` The predicted mask output from the Faceswap model. ``None`` if the model did not learn a mask Returns ------- :class:`numpy.ndarray` The face output from the Faceswap Model with any requested pre-warp adjustments performed. """ logger.trace("new_face shape: %s, predicted_mask shape: %s", new_face.shape, predicted_mask.shape if predicted_mask is not None else None) old_face = detected_face.reference_face[..., :3] / 255.0 new_face = self._adjustments["box"].run(new_face) new_face, raw_mask = self._get_image_mask(new_face, detected_face, predicted_mask) if self._adjustments["color"] is not None: new_face = self._adjustments["color"].run(old_face, new_face, raw_mask) if self._adjustments["seamless"] is not None: new_face = self._adjustments["seamless"].run(old_face, new_face, raw_mask) logger.trace("returning: new_face shape %s", new_face.shape) return new_face def _get_image_mask(self, new_face, detected_face, predicted_mask): """ Return any selected image mask and intersect with any box mask. Places the requested mask into the new face's Alpha channel, intersecting with any box mask that has already been applied. Parameters ---------- new_face: :class:`numpy.ndarray` The swapped face received from the faceswap model, with any box mask applied detected_face: :class:`~lib.faces_detect.DetectedFace` The detected_face object as defined in :class:`scripts.convert.Predictor` predicted_mask: :class:`numpy.ndarray` or ``None`` The predicted mask output from the Faceswap model. ``None`` if the model did not learn a mask Returns :class:`numpy.ndarray` The swapped face with the requested mask added to the Alpha channel """ logger.trace("Getting mask. Image shape: %s", new_face.shape) mask, raw_mask = self._adjustments["mask"].run(detected_face, predicted_mask) if new_face.shape[2] == 4: logger.trace("Combining mask with alpha channel box mask") new_face[:, :, -1] = np.minimum(new_face[:, :, -1], mask.squeeze()) else: logger.trace("Adding mask to alpha channel") new_face = np.concatenate((new_face, mask), -1) logger.trace("Got mask. Image shape: %s", new_face.shape) return new_face, raw_mask def _post_warp_adjustments(self, background, new_image): """ Perform any requested adjustments to the swapped faces after they have been transformed into the final frame. Parameters ---------- background: :class:`numpy.ndarray` The original frame new_image: :class:`numpy.ndarray` A blank frame of original frame size with the faces warped onto it Returns ------- :class:`numpy.ndarray` The final merged and swapped frame with any requested post-warp adjustments applied """ if self._adjustments["scaling"] is not None: new_image = self._adjustments["scaling"].run(new_image) if self._draw_transparent: frame = new_image else: foreground, mask = np.split(new_image, # pylint:disable=unbalanced-tuple-unpacking (3, ), axis=-1) foreground *= mask background *= (1.0 - mask) background += foreground frame = background np.clip(frame, 0.0, 1.0, out=frame) return frame def _scale_image(self, frame): """ Scale the final image if requested. If output scale has been requested in command line arguments, scale the output otherwise return the final frame. Parameters ---------- frame: :class:`numpy.ndarray` The final frame with faces swapped Returns ------- :class:`numpy.ndarray` The final frame scaled by the requested scaling factor """ if self._scale == 1: return frame logger.trace("source frame: %s", frame.shape) interp = cv2.INTER_CUBIC if self._scale > 1 else cv2.INTER_AREA dims = (round((frame.shape[1] / 2 * self._scale) * 2), round((frame.shape[0] / 2 * self._scale) * 2)) frame = cv2.resize(frame, dims, interpolation=interp) logger.trace("resized frame: %s", frame.shape) np.clip(frame, 0.0, 1.0, out=frame) return frame