mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-07 10:43:27 -04:00
* 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
347 lines
13 KiB
Python
347 lines
13 KiB
Python
#!/usr/bin/env python3
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""" Media items (Alignments, Faces, Frames)
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for alignments tool """
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import logging
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import os
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from tqdm import tqdm
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import cv2
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from lib.alignments import Alignments
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from lib.faces_detect import DetectedFace
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from lib.utils import _image_extensions, _video_extensions, hash_image_file, hash_encode_image
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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class AlignmentData(Alignments):
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""" Class to hold the alignment data """
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def __init__(self, alignments_file, destination_format):
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logger.debug("Initializing %s: (alignments file: '%s', destination_format: '%s')",
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self.__class__.__name__, alignments_file, destination_format)
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logger.info("[ALIGNMENT DATA]") # Tidy up cli output
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folder, filename = self.check_file_exists(alignments_file)
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if filename.lower() == "dfl":
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self.set_dfl(destination_format)
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return
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super().__init__(folder, filename=filename)
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self.set_destination_format(destination_format)
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logger.verbose("%s items loaded", self.frames_count)
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logger.debug("Initialized %s", self.__class__.__name__)
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@staticmethod
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def check_file_exists(alignments_file):
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""" Check the alignments file exists"""
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folder, filename = os.path.split(alignments_file)
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if filename.lower() == "dfl":
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folder = None
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filename = "dfl"
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logger.info("Using extracted pngs for alignments")
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elif not os.path.isfile(alignments_file):
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logger.error("ERROR: alignments file not found at: '%s'", alignments_file)
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exit(0)
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if folder:
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logger.verbose("Alignments file exists at '%s'", alignments_file)
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return folder, filename
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def set_dfl(self, destination_format):
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""" Set the alignments for dfl alignments """
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logger.debug("Alignments are DFL format")
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self.file = "dfl"
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self.set_destination_format(destination_format)
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def set_destination_format(self, destination_format):
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""" Standardize the destination format to the correct extension """
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extensions = {".json": "json",
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".p": "pickle",
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".yml": "yaml",
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".yaml": "yaml"}
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dst_fmt = None
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file_ext = os.path.splitext(self.file)[1].lower()
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logger.debug("File extension: '%s'", file_ext)
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if destination_format is not None:
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dst_fmt = destination_format
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elif self.file == "dfl":
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dst_fmt = "json"
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elif file_ext in extensions.keys():
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dst_fmt = extensions[file_ext]
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else:
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logger.error("'%s' is not a supported serializer. Exiting", file_ext)
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exit(0)
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logger.verbose("Destination format set to '%s'", dst_fmt)
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self.serializer = self.get_serializer("", dst_fmt)
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filename = os.path.splitext(self.file)[0]
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self.file = "{}.{}".format(filename, self.serializer.ext)
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logger.debug("Destination file: '%s'", self.file)
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def save(self):
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""" Backup copy of old alignments and save new alignments """
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self.backup()
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super().save()
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class MediaLoader():
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""" Class to load filenames from folder """
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def __init__(self, folder):
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logger.debug("Initializing %s: (folder: '%s')", self.__class__.__name__, folder)
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logger.info("[%s DATA]", self.__class__.__name__.upper())
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self.folder = folder
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self.vid_cap = self.check_input_folder()
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self.file_list_sorted = self.sorted_items()
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self.items = self.load_items()
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logger.verbose("%s items loaded", self.count)
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logger.debug("Initialized %s", self.__class__.__name__)
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@property
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def count(self):
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""" Number of faces or frames """
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if self.vid_cap:
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retval = int(self.vid_cap.get(cv2.CAP_PROP_FRAME_COUNT)) # pylint: disable=no-member
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else:
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retval = len(self.file_list_sorted)
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return retval
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def check_input_folder(self):
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""" makes sure that the frames or faces folder exists
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If frames folder contains a video file return video capture object """
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err = None
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loadtype = self.__class__.__name__
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if not self.folder:
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err = "ERROR: A {} folder must be specified".format(loadtype)
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elif not os.path.exists(self.folder):
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err = ("ERROR: The {} location {} could not be "
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"found".format(loadtype, self.folder))
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if err:
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logger.error(err)
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exit(0)
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if (loadtype == "Frames" and
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os.path.isfile(self.folder) and
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os.path.splitext(self.folder)[1] in _video_extensions):
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logger.verbose("Video exists at : '%s'", self.folder)
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retval = cv2.VideoCapture(self.folder) # pylint: disable=no-member
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else:
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logger.verbose("Folder exists at '%s'", self.folder)
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retval = None
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return retval
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@staticmethod
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def valid_extension(filename):
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""" Check whether passed in file has a valid extension """
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extension = os.path.splitext(filename)[1]
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retval = extension in _image_extensions
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logger.trace("Filename has valid extension: '%s': %s", filename, retval)
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return retval
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@staticmethod
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def sorted_items():
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""" Override for specific folder processing """
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return list()
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@staticmethod
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def process_folder():
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""" Override for specific folder processing """
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return list()
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@staticmethod
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def load_items():
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""" Override for specific item loading """
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return dict()
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def load_image(self, filename):
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""" Load an image """
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if self.vid_cap:
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image = self.load_video_frame(filename)
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else:
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src = os.path.join(self.folder, filename)
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logger.trace("Loading image: '%s'", src)
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image = cv2.imread(src) # pylint: disable=no-member
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return image
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def load_video_frame(self, filename):
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""" Load a requested frame from video """
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frame = os.path.splitext(filename)[0]
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logger.trace("Loading video frame: '%s'", frame)
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frame_no = int(frame[frame.rfind("_") + 1:])
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self.vid_cap.set(cv2.CAP_PROP_POS_FRAMES, frame_no) # pylint: disable=no-member
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_, image = self.vid_cap.read()
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return image
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@staticmethod
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def save_image(output_folder, filename, image):
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""" Save an image """
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output_file = os.path.join(output_folder, filename)
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logger.trace("Saving image: '%s'", output_file)
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cv2.imwrite(output_file, image) # pylint: disable=no-member
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class Faces(MediaLoader):
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""" Object to hold the faces that are to be swapped out """
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def process_folder(self):
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""" Iterate through the faces dir pulling out various information """
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logger.info("Loading file list from %s", self.folder)
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for face in tqdm(os.listdir(self.folder), desc="Reading Face Hashes"):
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if not self.valid_extension(face):
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continue
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filename = os.path.splitext(face)[0]
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file_extension = os.path.splitext(face)[1]
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face_hash = hash_image_file(os.path.join(self.folder, face))
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retval = {"face_fullname": face,
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"face_name": filename,
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"face_extension": file_extension,
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"face_hash": face_hash}
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logger.trace(retval)
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yield retval
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def load_items(self):
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""" Load the face names into dictionary """
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faces = dict()
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for face in self.file_list_sorted:
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faces.setdefault(face["face_hash"], list()).append((face["face_name"],
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face["face_extension"]))
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logger.trace(faces)
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return faces
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def sorted_items(self):
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""" Return the items sorted by face name """
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items = sorted([item for item in self.process_folder()],
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key=lambda x: (x["face_name"]))
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logger.trace(items)
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return items
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class Frames(MediaLoader):
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""" Object to hold the frames that are to be checked against """
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def process_folder(self):
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""" Iterate through the frames dir pulling the base filename """
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iterator = self.process_video if self.vid_cap else self.process_frames
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for item in iterator():
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yield item
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def process_frames(self):
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""" Process exported Frames """
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logger.info("Loading file list from %s", self.folder)
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for frame in os.listdir(self.folder):
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if not self.valid_extension(frame):
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continue
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filename = os.path.splitext(frame)[0]
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file_extension = os.path.splitext(frame)[1]
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retval = {"frame_fullname": frame,
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"frame_name": filename,
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"frame_extension": file_extension}
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logger.trace(retval)
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yield retval
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def process_video(self):
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"""Dummy in frames for video """
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logger.info("Loading video frames from %s", self.folder)
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vidname = os.path.splitext(os.path.basename(self.folder))[0]
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for i in range(self.count):
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idx = i + 1
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# Keep filename format for outputted face
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filename = "{}_{:06d}".format(vidname, idx)
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retval = {"frame_fullname": "{}.png".format(filename),
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"frame_name": filename,
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"frame_extension": ".png"}
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logger.trace(retval)
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yield retval
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def load_items(self):
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""" Load the frame info into dictionary """
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frames = dict()
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for frame in self.file_list_sorted:
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frames[frame["frame_fullname"]] = (frame["frame_name"],
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frame["frame_extension"])
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logger.trace(frames)
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return frames
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def sorted_items(self):
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""" Return the items sorted by filename """
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items = sorted([item for item in self.process_folder()],
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key=lambda x: (x["frame_name"]))
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logger.trace(items)
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return items
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class ExtractedFaces():
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""" Holds the extracted faces and matrix for
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alignments """
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def __init__(self, frames, alignments, size=256, align_eyes=False):
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logger.trace("Initializing %s: (size: %s, padding: %s, align_eyes: %s)",
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self.__class__.__name__, size, align_eyes)
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self.size = size
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self.padding = int(size * 0.1875)
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self.align_eyes = align_eyes
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self.alignments = alignments
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self.frames = frames
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self.current_frame = None
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self.faces = list()
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logger.trace("Initialized %s", self.__class__.__name__)
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def get_faces(self, frame):
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""" Return faces and transformed landmarks
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for each face in a given frame with it's alignments"""
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logger.trace("Getting faces for frame: '%s'", frame)
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self.current_frame = None
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alignments = self.alignments.get_faces_in_frame(frame)
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logger.trace("Alignments for frame: (frame: '%s', alignments: %s)", frame, alignments)
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if not alignments:
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self.faces = list()
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return
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image = self.frames.load_image(frame)
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self.faces = [self.extract_one_face(alignment, image.copy())
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for alignment in alignments]
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self.current_frame = frame
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def extract_one_face(self, alignment, image):
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""" Extract one face from image """
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logger.trace("Extracting one face: (frame: '%s', alignment: %s)",
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self.current_frame, alignment)
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face = DetectedFace()
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face.from_alignment(alignment, image=image)
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face.load_aligned(image, size=self.size, align_eyes=self.align_eyes)
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return face
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def get_faces_in_frame(self, frame, update=False):
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""" Return the faces for the selected frame """
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logger.trace("frame: '%s', update: %s", frame, update)
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if self.current_frame != frame or update:
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self.get_faces(frame)
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return self.faces
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def get_roi_size_for_frame(self, frame):
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""" Return the size of the original extract box for
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the selected frame """
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logger.trace("frame: '%s'", frame)
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if self.current_frame != frame:
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self.get_faces(frame)
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sizes = list()
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for face in self.faces:
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top_left, top_right = face.original_roi[0], face.original_roi[3]
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len_x = top_right[0] - top_left[0]
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len_y = top_right[1] - top_left[1]
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if top_left[1] == top_right[1]:
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length = len_y
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else:
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length = int(((len_x ** 2) + (len_y ** 2)) ** 0.5)
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sizes.append(length)
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logger.trace("sizes: '%s'", sizes)
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return sizes
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@staticmethod
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def save_face_with_hash(filename, extension, face):
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""" Save a face and return it's hash """
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f_hash, img = hash_encode_image(face, extension)
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logger.trace("Saving face: '%s'", filename)
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with open(filename, "wb") as out_file:
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out_file.write(img)
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return f_hash
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