1
0
Fork 0
mirror of https://github.com/deepfakes/faceswap synced 2025-06-07 10:43:27 -04:00
faceswap/lib/gui/tooltip.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

165 lines
5.1 KiB
Python
Executable file

#!/usr/bin python3
""" Tooltip. Pops up help messages for the GUI """
import platform
import tkinter as tk
class Tooltip:
"""
Create a tooltip for a given widget as the mouse goes on it.
Adapted from StackOverflow:
http://stackoverflow.com/questions/3221956/
what-is-the-simplest-way-to-make-tooltips-
in-tkinter/36221216#36221216
http://www.daniweb.com/programming/software-development/
code/484591/a-tooltip-class-for-tkinter
- Originally written by vegaseat on 2014.09.09.
- Modified to include a delay time by Victor Zaccardo on 2016.03.25.
- Modified
- to correct extreme right and extreme bottom behavior,
- to stay inside the screen whenever the tooltip might go out on
the top but still the screen is higher than the tooltip,
- to use the more flexible mouse positioning,
- to add customizable background color, padding, waittime and
wraplength on creation
by Alberto Vassena on 2016.11.05.
Tested on Ubuntu 16.04/16.10, running Python 3.5.2
"""
def __init__(self, widget,
*,
background="#FFFFEA",
pad=(5, 3, 5, 3),
text="widget info",
waittime=400,
wraplength=250):
self.waittime = waittime # in milliseconds, originally 500
self.wraplength = wraplength # in pixels, originally 180
self.widget = widget
self.text = text
self.widget.bind("<Enter>", self.on_enter)
self.widget.bind("<Leave>", self.on_leave)
self.widget.bind("<ButtonPress>", self.on_leave)
self.background = background
self.pad = pad
self.ident = None
self.topwidget = None
def on_enter(self, event=None):
""" Schedule on an enter event """
self.schedule()
def on_leave(self, event=None):
""" Unschedule on a leave event """
self.unschedule()
self.hide()
def schedule(self):
""" Show the tooltip after wait period """
self.unschedule()
self.ident = self.widget.after(self.waittime, self.show)
def unschedule(self):
""" Hide the tooltip """
id_ = self.ident
self.ident = None
if id_:
self.widget.after_cancel(id_)
def show(self):
""" Show the tooltip """
def tip_pos_calculator(widget, label,
*,
tip_delta=(10, 5), pad=(5, 3, 5, 3)):
""" Calculate the tooltip position """
s_width, s_height = widget.winfo_screenwidth(), widget.winfo_screenheight()
width, height = (pad[0] + label.winfo_reqwidth() + pad[2],
pad[1] + label.winfo_reqheight() + pad[3])
mouse_x, mouse_y = widget.winfo_pointerxy()
x_1, y_1 = mouse_x + tip_delta[0], mouse_y + tip_delta[1]
x_2, y_2 = x_1 + width, y_1 + height
x_delta = x_2 - s_width
if x_delta < 0:
x_delta = 0
y_delta = y_2 - s_height
if y_delta < 0:
y_delta = 0
offscreen = (x_delta, y_delta) != (0, 0)
if offscreen:
if x_delta:
x_1 = mouse_x - tip_delta[0] - width
if y_delta:
y_1 = mouse_y - tip_delta[1] - height
offscreen_again = y_1 < 0 # out on the top
if offscreen_again:
# No further checks will be done.
# TIP:
# A further mod might auto-magically augment the
# wraplength when the tooltip is too high to be
# kept inside the screen.
y_1 = 0
return x_1, y_1
background = self.background
pad = self.pad
widget = self.widget
# creates a toplevel window
self.topwidget = tk.Toplevel(widget)
if platform.system() == "Darwin":
# For Mac OS
self.topwidget.tk.call("::tk::unsupported::MacWindowStyle",
"style", self.topwidget._w,
"help", "none")
# Leaves only the label and removes the app window
self.topwidget.wm_overrideredirect(True)
win = tk.Frame(self.topwidget,
background=background,
borderwidth=0)
label = tk.Label(win,
text=self.text,
justify=tk.LEFT,
background=background,
relief=tk.SOLID,
borderwidth=0,
wraplength=self.wraplength)
label.grid(padx=(pad[0], pad[2]),
pady=(pad[1], pad[3]),
sticky=tk.NSEW)
win.grid()
xpos, ypos = tip_pos_calculator(widget, label)
self.topwidget.wm_geometry("+%d+%d" % (xpos, ypos))
def hide(self):
""" Hide the tooltip """
topwidget = self.topwidget
if topwidget:
topwidget.destroy()
self.topwidget = None