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faceswap/tools/lib_alignments/media.py
torzdf cd00859c40
model_refactor (#571) (#572)
* 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
2019-02-09 18:35:12 +00:00

347 lines
13 KiB
Python

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