mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-07 10:43:27 -04:00
cd00859c40
* model_refactor (#571) * original model to new structure * IAE model to new structure * OriginalHiRes to new structure * Fix trainer for different resolutions * Initial config implementation * Configparse library added * improved training data loader * dfaker model working * Add logging to training functions * Non blocking input for cli training * Add error handling to threads. Add non-mp queues to queue_handler * Improved Model Building and NNMeta * refactor lib/models * training refactor. DFL H128 model Implementation * Dfaker - use hashes * Move timelapse. Remove perceptual loss arg * Update INSTALL.md. Add logger formatting. Update Dfaker training * DFL h128 partially ported * Add mask to dfaker (#573) * Remove old models. Add mask to dfaker * dfl mask. Make masks selectable in config (#575) * DFL H128 Mask. Mask type selectable in config. * remove gan_v2_2 * Creating Input Size config for models Creating Input Size config for models Will be used downstream in converters. Also name change of image_shape to input_shape to clarify ( for future models with potentially different output_shapes) * Add mask loss options to config * MTCNN options to config.ini. Remove GAN config. Update USAGE.md * Add sliders for numerical values in GUI * Add config plugins menu to gui. Validate config * Only backup model if loss has dropped. Get training working again * bugfixes * Standardise loss printing * GUI idle cpu fixes. Graph loss fix. * mutli-gpu logging bugfix * Merge branch 'staging' into train_refactor * backup state file * Crash protection: Only backup if both total losses have dropped * Port OriginalHiRes_RC4 to train_refactor (OriginalHiRes) * Load and save model structure with weights * Slight code update * Improve config loader. Add subpixel opt to all models. Config to state * Show samples... wrong input * Remove AE topology. Add input/output shapes to State * Port original_villain (birb/VillainGuy) model to faceswap * Add plugin info to GUI config pages * Load input shape from state. IAE Config options. * Fix transform_kwargs. Coverage to ratio. Bugfix mask detection * Suppress keras userwarnings. Automate zoom. Coverage_ratio to model def. * Consolidation of converters & refactor (#574) * Consolidation of converters & refactor Initial Upload of alpha Items - consolidate convert_mased & convert_adjust into one converter -add average color adjust to convert_masked -allow mask transition blur size to be a fixed integer of pixels and a fraction of the facial mask size -allow erosion/dilation size to be a fixed integer of pixels and a fraction of the facial mask size -eliminate redundant type conversions to avoid multiple round-off errors -refactor loops for vectorization/speed -reorganize for clarity & style changes TODO - bug/issues with warping the new face onto a transparent old image...use a cleanup mask for now - issues with mask border giving black ring at zero erosion .. investigate - remove GAN ?? - test enlargment factors of umeyama standard face .. match to coverage factor - make enlargment factor a model parameter - remove convert_adjusted and referencing code when finished * Update Convert_Masked.py default blur size of 2 to match original... description of enlargement tests breakout matrxi scaling into def * Enlargment scale as a cli parameter * Update cli.py * dynamic interpolation algorithm Compute x & y scale factors from the affine matrix on the fly by QR decomp. Choose interpolation alogrithm for the affine warp based on an upsample or downsample for each image * input size input size from config * fix issues with <1.0 erosion * Update convert.py * Update Convert_Adjust.py more work on the way to merginf * Clean up help note on sharpen * cleanup seamless * Delete Convert_Adjust.py * Update umeyama.py * Update training_data.py * swapping * segmentation stub * changes to convert.str * Update masked.py * Backwards compatibility fix for models Get converter running * Convert: Move masks to class. bugfix blur_size some linting * mask fix * convert fixes - missing facehull_rect re-added - coverage to % - corrected coverage logic - cleanup of gui option ordering * Update cli.py * default for blur * Update masked.py * added preliminary low_mem version of OriginalHighRes model plugin * Code cleanup, minor fixes * Update masked.py * Update masked.py * Add dfl mask to convert * histogram fix & seamless location * update * revert * bugfix: Load actual configuration in gui * Standardize nn_blocks * Update cli.py * Minor code amends * Fix Original HiRes model * Add masks to preview output for mask trainers refactor trainer.__base.py * Masked trainers converter support * convert bugfix * Bugfix: Converter for masked (dfl/dfaker) trainers * Additional Losses (#592) * initial upload * Delete blur.py * default initializer = He instead of Glorot (#588) * Allow kernel_initializer to be overridable * Add ICNR Initializer option for upscale on all models. * Hopefully fixes RSoDs with original-highres model plugin * remove debug line * Original-HighRes model plugin Red Screen of Death fix, take #2 * Move global options to _base. Rename Villain model * clipnorm and res block biases * scale the end of res block * res block * dfaker pre-activation res * OHRES pre-activation * villain pre-activation * tabs/space in nn_blocks * fix for histogram with mask all set to zero * fix to prevent two networks with same name * GUI: Wider tooltips. Improve TQDM capture * Fix regex bug * Convert padding=48 to ratio of image size * Add size option to alignments tool extract * Pass through training image size to convert from model * Convert: Pull training coverage from model * convert: coverage, blur and erode to percent * simplify matrix scaling * ordering of sliders in train * Add matrix scaling to utils. Use interpolation in lib.aligner transform * masked.py Import get_matrix_scaling from utils * fix circular import * Update masked.py * quick fix for matrix scaling * testing thus for now * tqdm regex capture bugfix * Minor ammends * blur size cleanup * Remove coverage option from convert (Now cascades from model) * Implement convert for all model types * Add mask option and coverage option to all existing models * bugfix for model loading on convert * debug print removal * Bugfix for masks in dfl_h128 and iae * Update preview display. Add preview scaling to cli * mask notes * Delete training_data_v2.py errant file * training data variables * Fix timelapse function * Add new config items to state file for legacy purposes * Slight GUI tweak * Raise exception if problem with loaded model * Add Tensorboard support (Logs stored in model directory) * ICNR fix * loss bugfix * convert bugfix * Move ini files to config folder. Make TensorBoard optional * Fix training data for unbalanced inputs/outputs * Fix config "none" test * Keep helptext in .ini files when saving config from GUI * Remove frame_dims from alignments * Add no-flip and warp-to-landmarks cli options * Revert OHR to RC4_fix version * Fix lowmem mode on OHR model * padding to variable * Save models in parallel threads * Speed-up of res_block stability * Automated Reflection Padding * Reflect Padding as a training option Includes auto-calculation of proper padding shapes, input_shapes, output_shapes Flag included in config now * rest of reflect padding * Move TB logging to cli. Session info to state file * Add session iterations to state file * Add recent files to menu. GUI code tidy up * [GUI] Fix recent file list update issue * Add correct loss names to TensorBoard logs * Update live graph to use TensorBoard and remove animation * Fix analysis tab. GUI optimizations * Analysis Graph popup to Tensorboard Logs * [GUI] Bug fix for graphing for models with hypens in name * [GUI] Correctly split loss to tabs during training * [GUI] Add loss type selection to analysis graph * Fix store command name in recent files. Switch to correct tab on open * [GUI] Disable training graph when 'no-logs' is selected * Fix graphing race condition * rename original_hires model to unbalanced
101 lines
4 KiB
Python
101 lines
4 KiB
Python
#!/usr/bin/env python3
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""" Masks functions for faceswap.py
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Masks from:
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dfaker: https://github.com/dfaker/df"""
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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.umeyama import umeyama
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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def dfaker(landmarks, face, channels=4):
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""" Dfaker model mask
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Embeds the mask into the face alpha channel
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channels: 1, 3 or 4:
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1 - Return a single channel mask
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3 - Return a 3 channel mask
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4 - Return the original image with the mask in the alpha channel
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"""
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padding = int(face.shape[0] * 0.1875)
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coverage = face.shape[0] - (padding * 2)
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logger.trace("face_shape: %s, coverage: %s, landmarks: %s", face.shape, coverage, landmarks)
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mat = umeyama(landmarks[17:], True)[0:2]
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mat = np.array(mat.ravel()).reshape(2, 3)
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mat = mat * coverage
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mat[:, 2] += padding
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points = np.array(landmarks).reshape((-1, 2))
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facepoints = np.array(points).reshape((-1, 2))
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mask = np.zeros_like(face, dtype=np.uint8)
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hull = cv2.convexHull(facepoints.astype(int)) # pylint: disable=no-member
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hull = cv2.transform(hull.reshape(1, -1, 2), # pylint: disable=no-member
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mat).reshape(-1, 2).astype(int)
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cv2.fillConvexPoly(mask, hull, (255, 255, 255)) # pylint: disable=no-member
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15, 15)) # pylint: disable=no-member
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mask = cv2.dilate(mask, # pylint: disable=no-member
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kernel,
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iterations=1,
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borderType=cv2.BORDER_REFLECT) # pylint: disable=no-member
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mask = mask[:, :, :1]
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return merge_mask(face, mask, channels)
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def dfl_full(landmarks, face, channels=4):
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""" DFL Face Full Mask
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channels: 1, 3 or 4:
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1 - Return a single channel mask
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3 - Return a 3 channel mask
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4 - Return the original image with the mask in the alpha channel
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"""
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logger.trace("face_shape: %s, landmarks: %s", face.shape, landmarks)
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mask = np.zeros(face.shape[0:2] + (1, ), dtype=np.float32)
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jaw = cv2.convexHull(np.concatenate(( # pylint: disable=no-member
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landmarks[0:17], # jawline
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landmarks[48:68], # mouth
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[landmarks[0]], # temple
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[landmarks[8]], # chin
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[landmarks[16]]))) # temple
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nose_ridge = cv2.convexHull(np.concatenate(( # pylint: disable=no-member
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landmarks[27:31], # nose line
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[landmarks[33]]))) # nose point
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eyes = cv2.convexHull(np.concatenate(( # pylint: disable=no-member
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landmarks[17:27], # eyebrows
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[landmarks[0]], # temple
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[landmarks[27]], # nose top
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[landmarks[16]], # temple
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[landmarks[33]]))) # nose point
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cv2.fillConvexPoly(mask, jaw, (255, 255, 255)) # pylint: disable=no-member
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cv2.fillConvexPoly(mask, nose_ridge, (255, 255, 255)) # pylint: disable=no-member
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cv2.fillConvexPoly(mask, eyes, (255, 255, 255)) # pylint: disable=no-member
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return merge_mask(face, mask, channels)
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def merge_mask(image, mask, channels):
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""" Return the mask in requested shape """
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logger.trace("image_shape: %s, mask_shape: %s, channels: %s",
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image.shape, mask.shape, channels)
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assert channels in (1, 3, 4), "Channels should be 1, 3 or 4"
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assert mask.shape[2] == 1 and mask.ndim == 3, "Input mask be 3 dimensions with 1 channel"
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if channels == 3:
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retval = np.tile(mask, 3)
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elif channels == 4:
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retval = np.concatenate((image, mask), -1)
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else:
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retval = mask
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logger.trace("Final mask shape: %s", retval.shape)
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return retval
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