mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-09 04:36:50 -04:00
236c35f11a
* Initial prediction pipeline and display * Patch faces from convert pipeline. Load configs * Add tkinter canvas for image display * Radio buttons to config gui. Standardise config gui. Remove dssim loss from realface * Add action frame * Live update cli options * Live update config changes * Quicker frame loading. Refresh faces button callback * remove debug code * Add saving and reloading config buttons * Reduce lag with threading * Busy indicator + geometry updates * Fix options canvas resizing * Add global config saving and resetting * tools to own tab * Rename tool to 'preview' * geometry tweak * pep8 fixes
224 lines
10 KiB
Python
224 lines
10 KiB
Python
#!/usr/bin/env python3
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""" Converter for faceswap.py
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Based on: https://gist.github.com/anonymous/d3815aba83a8f79779451262599b0955
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found on https://www.reddit.com/r/deepfakes/ """
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import logging
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import cv2
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import numpy as np
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from lib.model import masks as model_masks
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from plugins.plugin_loader import PluginLoader
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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class Converter():
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""" Swap a source face with a target """
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def __init__(self, output_dir, output_size, output_has_mask,
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draw_transparent, pre_encode, arguments):
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logger.debug("Initializing %s: (output_dir: '%s', output_size: %s, output_has_mask: %s, "
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"draw_transparent: %s, pre_encode: %s, arguments: %s)",
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self.__class__.__name__, output_dir, output_size, output_has_mask,
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draw_transparent, pre_encode, arguments)
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self.output_dir = output_dir
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self.draw_transparent = draw_transparent
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self.writer_pre_encode = pre_encode
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self.scale = arguments.output_scale / 100
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self.output_size = output_size
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self.output_has_mask = output_has_mask
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self.args = arguments
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self.adjustments = dict(box=None, mask=None, color=None, seamless=None, scaling=None)
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self.load_plugins()
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logger.debug("Initialized %s", self.__class__.__name__)
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def reinitialize(self, config):
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""" reinitialize converter """
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logger.debug("Reinitializing converter")
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self.adjustments = dict(box=None, mask=None, color=None, seamless=None, scaling=None)
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self.load_plugins(config=config, disable_logging=True)
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logger.debug("Reinitialized converter")
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def load_plugins(self, config=None, disable_logging=False):
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""" Load the requested adjustment plugins """
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logger.debug("Loading plugins. config: %s", config)
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self.adjustments["box"] = PluginLoader.get_converter(
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"mask",
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"box_blend",
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disable_logging=disable_logging)("none",
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self.output_size,
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config=config)
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self.adjustments["mask"] = PluginLoader.get_converter(
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"mask",
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"mask_blend",
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disable_logging=disable_logging)(self.args.mask_type,
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self.output_size,
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self.output_has_mask,
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config=config)
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if self.args.color_adjustment != "none" and self.args.color_adjustment is not None:
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self.adjustments["color"] = PluginLoader.get_converter(
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"color",
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self.args.color_adjustment,
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disable_logging=disable_logging)(config=config)
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if self.args.scaling != "none" and self.args.scaling is not None:
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self.adjustments["scaling"] = PluginLoader.get_converter(
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"scaling",
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self.args.scaling,
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disable_logging=disable_logging)(config=config)
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logger.debug("Loaded plugins: %s", self.adjustments)
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def process(self, in_queue, out_queue):
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""" Process items from the queue """
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logger.debug("Starting convert process. (in_queue: %s, out_queue: %s)",
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in_queue, out_queue)
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while True:
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item = in_queue.get()
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if item == "EOF":
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logger.debug("EOF Received")
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logger.debug("Patch queue finished")
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# Signal EOF to other processes in pool
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logger.debug("Putting EOF back to in_queue")
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in_queue.put(item)
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break
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logger.trace("Patch queue got: '%s'", item["filename"])
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try:
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image = self.patch_image(item)
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except Exception as err: # pylint: disable=broad-except
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# Log error and output original frame
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logger.error("Failed to convert image: '%s'. Reason: %s",
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item["filename"], str(err))
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image = item["image"]
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# UNCOMMENT THIS CODE BLOCK TO PRINT TRACEBACK ERRORS
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# import sys
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# import traceback
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# exc_info = sys.exc_info()
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# traceback.print_exception(*exc_info)
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logger.trace("Out queue put: %s", item["filename"])
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out_queue.put((item["filename"], image))
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logger.debug("Completed convert process")
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def patch_image(self, predicted):
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""" Patch the image """
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logger.trace("Patching image: '%s'", predicted["filename"])
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frame_size = (predicted["image"].shape[1], predicted["image"].shape[0])
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new_image = self.get_new_image(predicted, frame_size)
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patched_face = self.post_warp_adjustments(predicted, new_image)
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patched_face = self.scale_image(patched_face)
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patched_face = np.rint(patched_face * 255.0).astype("uint8")
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if self.writer_pre_encode is not None:
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patched_face = self.writer_pre_encode(patched_face)
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logger.trace("Patched image: '%s'", predicted["filename"])
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return patched_face
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def get_new_image(self, predicted, frame_size):
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""" Get the new face from the predictor and apply box manipulations """
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logger.trace("Getting: (filename: '%s', faces: %s)",
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predicted["filename"], len(predicted["swapped_faces"]))
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placeholder = predicted["image"] / 255.0
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placeholder = np.concatenate((placeholder,
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np.zeros((frame_size[1], frame_size[0], 1))),
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axis=-1).astype("float32")
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for new_face, detected_face in zip(predicted["swapped_faces"],
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predicted["detected_faces"]):
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predicted_mask = new_face[:, :, -1] if new_face.shape[2] == 4 else None
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new_face = new_face[:, :, :3]
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src_face = detected_face.reference_face
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interpolator = detected_face.reference_interpolators[1]
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new_face = self.pre_warp_adjustments(src_face, new_face, detected_face, predicted_mask)
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# Warp face with the mask
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placeholder = cv2.warpAffine( # pylint: disable=no-member
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new_face,
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detected_face.reference_matrix,
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frame_size,
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placeholder,
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flags=cv2.WARP_INVERSE_MAP | interpolator, # pylint: disable=no-member
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borderMode=cv2.BORDER_TRANSPARENT) # pylint: disable=no-member
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placeholder = np.clip(placeholder, 0.0, 1.0)
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logger.trace("Got filename: '%s'. (placeholders: %s)",
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predicted["filename"], placeholder.shape)
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return placeholder
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def pre_warp_adjustments(self, old_face, new_face, detected_face, predicted_mask):
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""" Run the pre-warp adjustments """
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logger.trace("old_face shape: %s, new_face shape: %s, predicted_mask shape: %s",
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old_face.shape, new_face.shape,
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predicted_mask.shape if predicted_mask is not None else None)
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new_face = self.adjustments["box"].run(new_face)
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new_face, raw_mask = self.get_image_mask(new_face, detected_face, predicted_mask)
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if self.adjustments["color"] is not None:
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new_face = self.adjustments["color"].run(old_face, new_face, raw_mask)
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if self.adjustments["seamless"] is not None:
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new_face = self.adjustments["seamless"].run(old_face, new_face, raw_mask)
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logger.trace("returning: new_face shape %s", new_face.shape)
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return new_face
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def get_image_mask(self, new_face, detected_face, predicted_mask):
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""" Get the image mask """
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logger.trace("Getting mask. Image shape: %s", new_face.shape)
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mask, raw_mask = self.adjustments["mask"].run(detected_face, predicted_mask)
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if new_face.shape[2] == 4:
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logger.trace("Combining mask with alpha channel box mask")
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new_face[:, :, -1] = np.minimum(new_face[:, :, -1], mask.squeeze())
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else:
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logger.trace("Adding mask to alpha channel")
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new_face = np.concatenate((new_face, mask), -1)
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new_face = np.clip(new_face, 0.0, 1.0)
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logger.trace("Got mask. Image shape: %s", new_face.shape)
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return new_face, raw_mask
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def post_warp_adjustments(self, predicted, new_image):
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""" Apply fixes to the image after warping """
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if self.adjustments["scaling"] is not None:
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new_image = self.adjustments["scaling"].run(new_image)
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mask = np.repeat(new_image[:, :, -1][:, :, np.newaxis], 3, axis=-1)
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foreground = new_image[:, :, :3]
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background = (predicted["image"][:, :, :3] / 255.0) * (1.0 - mask)
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foreground *= mask
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frame = foreground + background
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frame = self.add_alpha_mask(frame, predicted)
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np.clip(frame, 0.0, 1.0, out=frame)
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return frame
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def add_alpha_mask(self, frame, predicted):
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""" Adding a 4th channel should happen after all other channel operations
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Add the default mask as 4th channel for saving as image with alpha channel """
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if not self.draw_transparent:
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return frame
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logger.trace("Creating transparent image: '%s'", predicted["filename"])
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mask_type = getattr(model_masks, model_masks.get_default_mask())
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final_mask = np.zeros(frame.shape[:2] + (1, ), dtype="float32")
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for detected_face in predicted["detected_faces"]:
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landmarks = detected_face.landmarks_as_xy
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final_mask = cv2.bitwise_or(final_mask, # pylint: disable=no-member
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mask_type(landmarks, frame, channels=1).mask)
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final_mask = np.expand_dims(final_mask, axis=-1) if final_mask.ndim == 2 else final_mask
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frame = np.concatenate((frame, final_mask), axis=-1)
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logger.trace("Created transparent image: '%s'", predicted["filename"])
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return frame
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def scale_image(self, frame):
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""" Scale the image if requested """
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if self.scale == 1:
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return frame
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logger.trace("source frame: %s", frame.shape)
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interp = cv2.INTER_CUBIC if self.scale > 1 else cv2.INTER_AREA # pylint: disable=no-member
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dims = (round((frame.shape[1] / 2 * self.scale) * 2),
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round((frame.shape[0] / 2 * self.scale) * 2))
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frame = cv2.resize(frame, dims, interpolation=interp) # pylint: disable=no-member
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logger.trace("resized frame: %s", frame.shape)
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return frame
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