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faceswap/lib/utils.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

214 lines
7.7 KiB
Python

#!/usr/bin python3
""" Utilities available across all scripts """
import logging
import os
import warnings
from hashlib import sha1
from pathlib import Path
from re import finditer
import cv2
import numpy as np
import dlib
from lib.faces_detect import DetectedFace
from lib.logger import get_loglevel
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
# Global variables
_image_extensions = [ # pylint: disable=invalid-name
".bmp", ".jpeg", ".jpg", ".png", ".tif", ".tiff"]
_video_extensions = [ # pylint: disable=invalid-name
".avi", ".flv", ".mkv", ".mov", ".mp4", ".mpeg", ".webm"]
def get_folder(path):
""" Return a path to a folder, creating it if it doesn't exist """
logger.debug("Requested path: '%s'", path)
output_dir = Path(path)
output_dir.mkdir(parents=True, exist_ok=True)
logger.debug("Returning: '%s'", output_dir)
return output_dir
def get_image_paths(directory):
""" Return a list of images that reside in a folder """
image_extensions = _image_extensions
dir_contents = list()
if not os.path.exists(directory):
logger.debug("Creating folder: '%s'", directory)
directory = get_folder(directory)
dir_scanned = sorted(os.scandir(directory), key=lambda x: x.name)
logger.debug("Scanned Folder contains %s files", len(dir_scanned))
logger.trace("Scanned Folder Contents: %s", dir_scanned)
for chkfile in dir_scanned:
if any([chkfile.name.lower().endswith(ext)
for ext in image_extensions]):
logger.trace("Adding '%s' to image list", chkfile.path)
dir_contents.append(chkfile.path)
logger.debug("Returning %s images", len(dir_contents))
return dir_contents
def hash_image_file(filename):
""" Return an image file's sha1 hash """
img = cv2.imread(filename) # pylint: disable=no-member
img_hash = sha1(img).hexdigest()
logger.trace("filename: '%s', hash: %s", filename, img_hash)
return img_hash
def hash_encode_image(image, extension):
""" Encode the image, get the hash and return the hash with
encoded image """
img = cv2.imencode(extension, image)[1] # pylint: disable=no-member
f_hash = sha1(
cv2.imdecode(img, cv2.IMREAD_UNCHANGED)).hexdigest() # pylint: disable=no-member
return f_hash, img
def backup_file(directory, filename):
""" Backup a given file by appending .bk to the end """
logger.trace("Backing up: '%s'", filename)
origfile = os.path.join(directory, filename)
backupfile = origfile + '.bk'
if os.path.exists(backupfile):
logger.trace("Removing existing file: '%s'", backup_file)
os.remove(backupfile)
if os.path.exists(origfile):
logger.trace("Renaming: '%s' to '%s'", origfile, backup_file)
os.rename(origfile, backupfile)
def set_system_verbosity(loglevel):
""" Set the verbosity level of tensorflow and suppresses
future and deprecation warnings from any modules
From:
https://stackoverflow.com/questions/35911252/disable-tensorflow-debugging-information
Can be set to:
0 - all logs shown
1 - filter out INFO logs
2 - filter out WARNING logs
3 - filter out ERROR logs """
numeric_level = get_loglevel(loglevel)
loglevel = "2" if numeric_level > 15 else "0"
logger.debug("System Verbosity level: %s", loglevel)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = loglevel
if loglevel != '0':
for warncat in (FutureWarning, DeprecationWarning, UserWarning):
warnings.simplefilter(action='ignore', category=warncat)
def rotate_landmarks(face, rotation_matrix):
# pylint: disable=c-extension-no-member
""" Rotate the landmarks and bounding box for faces
found in rotated images.
Pass in a DetectedFace object, Alignments dict or DLib rectangle"""
logger.trace("Rotating landmarks: (rotation_matrix: %s, type(face): %s",
rotation_matrix, type(face))
if isinstance(face, DetectedFace):
bounding_box = [[face.x, face.y],
[face.x + face.w, face.y],
[face.x + face.w, face.y + face.h],
[face.x, face.y + face.h]]
landmarks = face.landmarksXY
elif isinstance(face, dict):
bounding_box = [[face.get("x", 0), face.get("y", 0)],
[face.get("x", 0) + face.get("w", 0),
face.get("y", 0)],
[face.get("x", 0) + face.get("w", 0),
face.get("y", 0) + face.get("h", 0)],
[face.get("x", 0),
face.get("y", 0) + face.get("h", 0)]]
landmarks = face.get("landmarksXY", list())
elif isinstance(face,
dlib.rectangle): # pylint: disable=c-extension-no-member
bounding_box = [[face.left(), face.top()],
[face.right(), face.top()],
[face.right(), face.bottom()],
[face.left(), face.bottom()]]
landmarks = list()
else:
raise ValueError("Unsupported face type")
logger.trace("Original landmarks: %s", landmarks)
rotation_matrix = cv2.invertAffineTransform( # pylint: disable=no-member
rotation_matrix)
rotated = list()
for item in (bounding_box, landmarks):
if not item:
continue
points = np.array(item, np.int32)
points = np.expand_dims(points, axis=0)
transformed = cv2.transform(points, # pylint: disable=no-member
rotation_matrix).astype(np.int32)
rotated.append(transformed.squeeze())
# Bounding box should follow x, y planes, so get min/max
# for non-90 degree rotations
pt_x = min([pnt[0] for pnt in rotated[0]])
pt_y = min([pnt[1] for pnt in rotated[0]])
pt_x1 = max([pnt[0] for pnt in rotated[0]])
pt_y1 = max([pnt[1] for pnt in rotated[0]])
if isinstance(face, DetectedFace):
face.x = int(pt_x)
face.y = int(pt_y)
face.w = int(pt_x1 - pt_x)
face.h = int(pt_y1 - pt_y)
face.r = 0
if len(rotated) > 1:
rotated_landmarks = [tuple(point) for point in rotated[1].tolist()]
face.landmarksXY = rotated_landmarks
elif isinstance(face, dict):
face["x"] = int(pt_x)
face["y"] = int(pt_y)
face["w"] = int(pt_x1 - pt_x)
face["h"] = int(pt_y1 - pt_y)
face["r"] = 0
if len(rotated) > 1:
rotated_landmarks = [tuple(point) for point in rotated[1].tolist()]
face["landmarksXY"] = rotated_landmarks
else:
rotated_landmarks = dlib.rectangle( # pylint: disable=c-extension-no-member
int(pt_x), int(pt_y), int(pt_x1), int(pt_y1))
face = rotated_landmarks
logger.trace("Rotated landmarks: %s", rotated_landmarks)
return face
def camel_case_split(identifier):
""" Split a camel case name
from: https://stackoverflow.com/questions/29916065 """
matches = finditer(
".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)",
identifier)
return [m.group(0) for m in matches]
def safe_shutdown():
""" Close queues, threads and processes in event of crash """
logger.debug("Safely shutting down")
from lib.queue_manager import queue_manager
from lib.multithreading import terminate_processes
queue_manager.terminate_queues()
terminate_processes()
logger.debug("Cleanup complete. Shutting down queue manager and exiting")
queue_manager._log_queue.put(None) # pylint: disable=protected-access
while not queue_manager._log_queue.empty(): # pylint: disable=protected-access
continue
queue_manager.manager.shutdown()