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