Upload folder using huggingface_hub (#1)

- ad12ac56454a9863a1f84a63e67f93672e88c7bc4f53fe03c54d3305d6ac72b2 (bab3d3dbc225dfeb967ae66261f34adf3e8ba327)
- 08f4ccb5459ba8d71ac7e1cb832d4f5e480338e9d9664a469baa1b1252917877 (d8e8a96d11e44ea9a06e58113e02a4b60bf1f548)

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+benchmark/target-1080p.mp4 filter=lfs diff=lfs merge=lfs -text
+rope/media/splash.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,11 @@
+**/__pycache__/**
+
+models/*.ckpt
+models/*.pth
+models/*.onnx
+saved_parameters.json
+data.json
+merged_embeddings.txt
+.vs
+*.sln
+*.pyproj

LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -0,0 +1,72 @@
+![image](https://github.com/Hillobar/Rope/assets/63615199/40f7397f-713c-4813-ac86-bab36f6bd5ba)
+
+
+Rope implements the insightface inswapper_128 model with a helpful GUI.
+### [Discord](https://discord.gg/EcdVAFJzqp)
+
+### [Donate](https://www.paypal.com/donate/?hosted_button_id=Y5SB9LSXFGRF2)
+
+### [Wiki with install instructions and usage](https://github.com/Hillobar/Rope/wiki)
+
+### [Demo Video (Rope-Ruby)](https://www.youtube.com/watch?v=4Y4U0TZ8cWY)
+
+### ${{\color{Goldenrod}{\textsf{Last Updated 2024-05-27}}}}$ ###
+### ${{\color{Goldenrod}{\textsf{Welcome to Rope-Pearl!}}}}$ ###
+
+![Screenshot 2024-02-10 104718](https://github.com/Hillobar/Rope/assets/63615199/4b2ee574-c91e-4db2-ad66-5b775a049a6b)
+
+### Updates for Rope-Pearl-00: ###
+### To update from Opal-03a, just need to replace the rope folder.
+* (feature) Selectable model swapping output resolution - 128, 256, 512
+* (feature) Better selection of input images (ctrl and shift modifiers work mostly like windows behavior)
+* (feature) Toggle between mean and median merging withou having to save to compare
+* (feature) Added back keyboard controls (q, w, a, s, d, space)
+* (feature) Gamma slider
+* 
+![image](https://github.com/Hillobar/Rope/assets/63615199/9d89fded-addb-46fe-b2d7-bfe6f1a88188)
+
+### Performance:  ###
+Machine: 3090Ti (24GB), i5-13600K
+
+<img src="https://github.com/Hillobar/Rope/assets/63615199/3e3505db-bc76-48df-b8ac-1e7e86c8d751" width="200">
+
+File: benchmark/target-1080p.mp4, 2048x1080, 269 frames, 25 fps, 10s
+
+Rendering time in seconds (5 threads):
+
+| Option | Crystal | Sapphire | Ruby | Opal | Pearl |
+| --- | --- | --- | --- | --- | --- |
+| Only Swap (128) | 7.3 | 7.5 | 4.4 | 4.3 | 4.4 |
+| Swap (256) | --- | --- | --- | --- | 8.6 |
+| Swap (512) | --- | --- | --- | --- | 28.6 |
+| Swap+GFPGAN | 10.7 | 11.0 | 9.0 | 9.8 | 9.3 |
+| Swap+Codeformer | 12.4 | 13.5 | 11.1 | 11.1 | 11.3 |
+| Swap+one word CLIP | 10.4 | 11.2 | 9.1 | 9.3 | 9.3 |
+| Swap+Occluder | 7.8 | 7.8 | 4.4 | 4.7 | 4.7 |
+| Swap+MouthParser | 13.9 | 12.1 | 5.0 | 4.9 | 5.1 |
+
+### Disclaimer: ###
+Rope is a personal project that I'm making available to the community as a thank you for all of the contributors ahead of me.
+I've copied the disclaimer from [Swap-Mukham](https://github.com/harisreedhar/Swap-Mukham) here since it is well-written and applies 100% to this repo.
+ 
+I would like to emphasize that our swapping software is intended for responsible and ethical use only. I must stress that users are solely responsible for their actions when using our software.
+
+Intended Usage: This software is designed to assist users in creating realistic and entertaining content, such as movies, visual effects, virtual reality experiences, and other creative applications. I encourage users to explore these possibilities within the boundaries of legality, ethical considerations, and respect for others' privacy.
+
+Ethical Guidelines: Users are expected to adhere to a set of ethical guidelines when using our software. These guidelines include, but are not limited to:
+
+Not creating or sharing content that could harm, defame, or harass individuals. Obtaining proper consent and permissions from individuals featured in the content before using their likeness. Avoiding the use of this technology for deceptive purposes, including misinformation or malicious intent. Respecting and abiding by applicable laws, regulations, and copyright restrictions.
+
+Privacy and Consent: Users are responsible for ensuring that they have the necessary permissions and consents from individuals whose likeness they intend to use in their creations. We strongly discourage the creation of content without explicit consent, particularly if it involves non-consensual or private content. It is essential to respect the privacy and dignity of all individuals involved.
+
+Legal Considerations: Users must understand and comply with all relevant local, regional, and international laws pertaining to this technology. This includes laws related to privacy, defamation, intellectual property rights, and other relevant legislation. Users should consult legal professionals if they have any doubts regarding the legal implications of their creations.
+
+Liability and Responsibility: We, as the creators and providers of the deep fake software, cannot be held responsible for the actions or consequences resulting from the usage of our software. Users assume full liability and responsibility for any misuse, unintended effects, or abusive behavior associated with the content they create.
+
+By using this software, users acknowledge that they have read, understood, and agreed to abide by the above guidelines and disclaimers. We strongly encourage users to approach this technology with caution, integrity, and respect for the well-being and rights of others.
+
+Remember, technology should be used to empower and inspire, not to harm or deceive. Let's strive for ethical and responsible use of deep fake technology for the betterment of society.
+
+
+
+  

Rope.bat

@@ -0,0 +1,2 @@
+call conda activate Rope && python Rope.py 
+pause

Rope.py

@@ -0,0 +1,5 @@
+#!/usr/bin/env python3
+
+from rope import Coordinator
+if __name__ == "__main__":
+    Coordinator.run()

benchmark/target-1080p.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a52edb2d7905ad57770e3d1953432573dc584b7fdee9367024773b0d4cf0de32
+size 3323493

models/meanshape_68.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39ffecf84ba73f0d0d7e49380833ba88713c9fcdec51df4f7ac45a48b8f4cc51
+size 974

models/place_model_files_here

@@ -0,0 +1 @@
+

requirements.txt

@@ -0,0 +1,17 @@
+--extra-index-url https://download.pytorch.org/whl/cu118
+
+numpy==1.23.5
+opencv-python==4.9.0.80
+scikit-image==0.21.0
+tk==0.1.0
+pillow==9.5.0
+onnx==1.14.0
+onnxruntime-gpu==1.16.2
+protobuf==4.23.2
+torch==2.0.1+cu118
+torchvision==0.15.2
+torchaudio==2.0.2
+tqdm
+ftfy
+regex
+customtkinter

rope/Coordinator.py

@@ -0,0 +1,177 @@
+# #!/usr/bin/env python3
+
+import time
+import torch
+from torchvision import transforms
+
+import rope.GUI as GUI
+import rope.VideoManager as VM
+import rope.Models as Models
+from rope.external.clipseg import CLIPDensePredT
+
+resize_delay = 1
+mem_delay = 1
+
+# @profile
+def coordinator():
+    global gui, vm, action, frame, r_frame, load_notice, resize_delay, mem_delay
+    # start = time.time()
+
+  
+    if gui.get_action_length() > 0:
+        action.append(gui.get_action())
+    if vm.get_action_length() > 0:
+        action.append(vm.get_action())
+##################    
+    if vm.get_frame_length() > 0:
+        frame.append(vm.get_frame())
+        
+    if len(frame) > 0:
+        gui.set_image(frame[0], False)
+        frame.pop(0)
+ ####################   
+    if vm.get_requested_frame_length() > 0:
+        r_frame.append(vm.get_requested_frame())    
+    if len(r_frame) > 0:
+        gui.set_image(r_frame[0], True)
+        r_frame=[]
+ ####################   
+    if len(action) > 0:
+        # print('Action:', action[0][0])
+        # print('Value:', action[0][1])
+        if action[0][0] == "load_target_video":
+            vm.load_target_video(action[0][1])
+            action.pop(0)
+        elif action[0][0] == "load_target_image":
+            vm.load_target_image(action[0][1])
+            action.pop(0)            
+        elif action[0][0] == "play_video":
+            vm.play_video(action[0][1])
+            action.pop(0)
+        elif action[0][0] == "get_requested_video_frame":
+            vm.get_requested_video_frame(action[0][1], marker=True)
+            action.pop(0)
+        elif action[0][0] == "get_requested_video_frame_without_markers":
+            vm.get_requested_video_frame(action[0][1], marker=False)
+            action.pop(0)
+        elif action[0][0] == "get_requested_image":
+            vm.get_requested_image()
+            action.pop(0)            
+        # elif action[0][0] == "swap":
+        #     vm.swap = action[0][1]
+        #     action.pop(0)
+        elif action[0][0] == "target_faces":
+            vm.assign_found_faces(action[0][1])
+            action.pop(0)
+        elif action [0][0] == "saved_video_path":
+            vm.saved_video_path = action[0][1]
+            action.pop(0) 
+        elif action [0][0] == "vid_qual":
+            vm.vid_qual = int(action[0][1])
+            action.pop(0) 
+        elif action [0][0] == "set_stop":
+            vm.stop_marker = action[0][1]
+            action.pop(0)  
+        elif action [0][0] == "perf_test":
+            vm.perf_test = action[0][1]
+            action.pop(0)               
+        elif action [0][0] == 'ui_vars':
+            vm.ui_data = action[0][1]
+            action.pop(0) 
+        elif action [0][0] == 'control':
+            vm.control = action[0][1]
+            action.pop(0) 
+        elif action [0][0] == "parameters":
+            if action[0][1]["CLIPSwitch"]:
+                if not vm.clip_session:
+                    vm.clip_session = load_clip_model()
+
+            vm.parameters = action[0][1]
+            action.pop(0) 
+        elif action [0][0] == "markers":
+            vm.markers = action[0][1]
+            action.pop(0)            
+
+
+        elif action[0][0] == "function":
+            eval(action[0][1])
+            action.pop(0)
+        elif action [0][0] == "clear_mem":
+            vm.clear_mem()
+            action.pop(0)    
+
+            
+        # From VM    
+        elif action[0][0] == "stop_play":
+            gui.set_player_buttons_to_inactive()
+            action.pop(0)        
+        
+        elif action[0][0] == "set_slider_length":
+            gui.set_video_slider_length(action[0][1])
+            action.pop(0)
+
+        elif action[0][0] == "update_markers_canvas":
+            gui.update_markers_canvas()
+            action.pop(0)        
+          
+            
+        else:
+            print("Action not found: "+action[0][0]+" "+str(action[0][1]))
+            action.pop(0)
+
+
+  
+
+    if resize_delay > 100:
+        gui.check_for_video_resize()
+        resize_delay = 0
+    else:
+        resize_delay +=1
+        
+    if mem_delay > 1000:
+        gui.update_vram_indicator()
+        mem_delay = 0
+    else:
+        mem_delay +=1
+        
+    vm.process()
+    gui.after(1, coordinator)
+    # print(time.time() - start)    
+    
+
+
+
+    
+def load_clip_model():
+    # https://github.com/timojl/clipseg
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    clip_session = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, complex_trans_conv=True)
+    # clip_session = CLIPDensePredTMasked(version='ViT-B/16', reduce_dim=64)
+    clip_session.eval();
+    clip_session.load_state_dict(torch.load('./models/rd64-uni-refined.pth'), strict=False) 
+    clip_session.to(device)    
+    return clip_session 
+
+
+    
+    
+def run():
+    global gui, vm, action, frame, r_frame, resize_delay, mem_delay
+
+    models = Models.Models()
+    gui = GUI.GUI(models)
+    vm = VM.VideoManager(models)
+    
+
+    action = []
+    frame = []
+    r_frame = []
+
+    gui.initialize_gui() 
+    
+
+    coordinator()    
+    
+    gui.mainloop()
+
+

rope/Dicts.py

@@ -0,0 +1,441 @@
+DEFAULT_DATA = {
+# Buttons
+'AddMarkerButtonDisplay':           'icon',
+'AddMarkerButtonIconHover':            './rope/media/add_marker_hover.png',    
+'AddMarkerButtonIconOff':              './rope/media/add_marker_off.png',
+'AddMarkerButtonIconOn':               './rope/media/add_marker_off.png',
+'AddMarkerButtonInfoText':             'ADD MARKER:\nAttaches a parameter marker to the current frame. Markers copy all parameter settings and apply them to all future frames, or until another marker is encountered.',
+'AddMarkerButtonState':           False,
+
+'SaveMarkerButtonDisplay':           'icon',
+'SaveMarkerButtonIconHover':            './rope/media/marker_save.png',    
+'SaveMarkerButtonIconOff':              './rope/media/marker_save.png',
+'SaveMarkerButtonIconOn':               './rope/media/marker_save.png',
+'SaveMarkerButtonInfoText':             'SAVE MARKERS:\nSave markers for this source video. The markers will be saved as a json file in the same folder as your source video.',
+'SaveMarkerButtonState':           False,
+
+'AudioDisplay':             'text', 
+'AudioInfoText':             'ENABLE REAL-TIME AUDIO:\nAdds audio from the input video during preview playback. If you are unable to maintain the input video frame rate, the audio will lag.',   
+'AudioState':               False,
+'AudioText':                'Enable Audio',      
+'AutoSwapState':            False,
+'ClearFacesDisplay':        'text', 
+'ClearFacesIcon':            './rope/media/tarfacedel.png',
+'ClearFacesIconHover':      './rope/media/rec.png',
+'ClearFacesIconOff':        './rope/media/rec.png',
+'ClearFacesIconOn':         './rope/media/rec.png',
+'ClearFacesInfoText':             'REMOVE FACES:\nRemove all currently found faces.',
+'ClearFacesState':          False,      
+'ClearFacesText':           'Clear Faces',   
+'ClearmemState':            False,
+'DefaultParamsButtonDisplay':           'text',
+'DefaultParamsButtonInfoText':             'LOAD DEFAULT PARAMETERS:\nLoad the Rope default parameters for this column.',
+'DefaultParamsButtonState':           False,
+'DefaultParamsButtonText':           'Load Defaults',
+'DelEmbedDisplay':          'text', 
+'DelEmbedIconHover':        './rope/media/rec.png',      
+'DelEmbedIconOff':          './rope/media/rec.png',
+'DelEmbedIconOn':           './rope/media/rec.png',
+'DelEmbedInfoText':             'DELETE EMBEDDING:\nDelete the currently selected embedding',
+'DelEmbedState':            False,          
+'DelEmbedText':             'Delete Emb',  
+'DelMarkerButtonDisplay':           'icon',
+'DelMarkerButtonIconHover':            './rope/media/remove_marker_hover.png',    
+'DelMarkerButtonIconOff':              './rope/media/remove_marker_off.png',
+'DelMarkerButtonIconOn':               './rope/media/remove_marker_off.png',
+'DelMarkerButtonInfoText':             'REMOVE MARKER:\nRemoves the parameter marker from the current frame.',
+'DelMarkerButtonState':           False,
+'FindFacesDisplay':         'text', 
+'FindFacesIcon':         './rope/media/tarface.png',
+'FindFacesIconHover':       './rope/media/rec.png',
+'FindFacesIconOff':         './rope/media/rec.png',
+'FindFacesIconOn':          './rope/media/rec.png',
+'FindFacesInfoText':             'FIND FACES:\nFinds all new faces in the current frame.',
+'FindFacesState':           False,  
+'FindFacesText':            'Find Faces',  
+'ImgDockState':             False,
+'ImgVidMode':               'Videos', 
+'ImgVidState':              False,
+'LoadParamsButtonDisplay':           'text',
+'LoadParamsButtonInfoText':             'LOAD SAVED PARAMETERS:\nLoads all parameters from this column if they have been previously saved. ',
+'LoadParamsButtonState':           False,
+'LoadParamsButtonText':           'Load Params',
+'LoadSFacesDisplay':         'both', 
+'LoadSFacesIcon':            './rope/media/save.png',
+'LoadSFacesIconHover':        './rope/media/save.png',     
+'LoadSFacesIconOff':          './rope/media/save.png',
+'LoadSFacesIconOn':           './rope/media/save.png',
+'LoadSFacesInfoText':             'SELECT SOURCE FACES FOLDER:\nSelects and loads Source Faces from Folder. Make sure the folder only contains <good> images.',
+'LoadSFacesState':          False,
+'LoadSFacesText':           'Select Faces Folder',  
+'LoadTVideosDisplay':         'both', 
+'LoadTVideosIconHover':        './rope/media/save.png', 
+'LoadTVideosIconOff':          './rope/media/save.png',
+'LoadTVideosIconOn':           './rope/media/save.png',
+'LoadTVideosInfoText':             'SELECT INPUT VIDEOS/IMAGES FOLDER:\nSelect and load media from folder.',
+'LoadTVideosState':         False,    
+'LoadTVideosText':           'Select Videos Folder',  
+'MaskViewDisplay':         'text',   
+'MaskViewInfoText':             'SHOW MASKS:\nDisplays the mask for a face side-by-side with the face. Useful for understanding the masking behaviors and results.',
+'MaskViewState':            False,    
+'MaskViewText':           'Show Mask', 
+'NextMarkerButtonDisplay':           'icon',
+'NextMarkerButtonIconHover':            './rope/media/next_marker_hover.png',    
+'NextMarkerButtonIconOff':              './rope/media/next_marker_off.png',
+'NextMarkerButtonIconOn':               './rope/media/next_marker_off.png',
+'NextMarkerButtonInfoText':             'NEXT MARKER:\nMove to the next marker.',
+'NextMarkerButtonState':           False,
+'OutputFolderDisplay':         'both', 
+'OutputFolderIconHover':        './rope/media/save.png', 
+'OutputFolderIconOff':          './rope/media/save.png',
+'OutputFolderIconOn':           './rope/media/save.png',
+'OutputFolderInfoText':             'SELECT SAVE FOLDER:\nSelect folder for saved videos and images.',
+'OutputFolderState':        False,   
+'OutputFolderText':           'Select Output Folder',  
+'PerfTestState':            False,
+'PlayDisplay':              'icon',   
+'PlayIconHover':            './rope/media/play_hover.png',
+'PlayIconOff':              './rope/media/play_off.png',
+'PlayIconOn':               './rope/media/play_on.png',
+'PlayInfoText':             'PLAY:\nPlays the video. Press again to stop playing',
+'PlayState':                False,
+'PrevMarkerButtonDisplay':           'icon',
+'PrevMarkerButtonIconHover':            './rope/media/previous_marker_hover.png',    
+'PrevMarkerButtonIconOff':              './rope/media/previous_marker_off.png',
+'PrevMarkerButtonIconOn':               './rope/media/previous_marker_off.png',
+'PrevMarkerButtonInfoText':             'PREVIOUS MARKER:\nMove to the previous marker.',
+'PrevMarkerButtonState':           False,
+'RecordDisplay':            'icon',     
+'RecordIconHover':          './rope/media/rec_hover.png',
+'RecordIconOff':            './rope/media/rec_off.png',
+'RecordIconOn':             './rope/media/rec_on.png',
+'RecordInfoText':             'RECORD:\nArms the PLAY button for recording. Press RECORD, then PLAY to record. Press PLAY again to stop recording.',  
+'RecordState':              False,       
+'SaveImageState':           False,
+'SaveParamsButtonDisplay':           'text',
+'SaveParamsButtonInfoText':             'SAVE PARAMETERS:\nSaves all parameters in this column.',
+'SaveParamsButtonState':            False,
+'SaveParamsButtonText':             'Save Params',
+'StartRopeDisplay':                 'both', 
+'StartRopeIconHover':               './rope/media/rope.png',    
+'StartRopeIconOff':                 './rope/media/rope.png',
+'StartRopeIconOn':                  './rope/media/rope.png',
+'StartRopeInfoText':                'STARTS ROPE:\nStarts up the Rope application.',
+'StartRopeState':                   False,    
+'StartRopeText':                    'Start Rope',  
+'SwapFacesDisplay':                 'text', 
+'SwapFacesInfoText':                'SWAP:\nSwap assigned Source Faces and Target Faces.',
+'SwapFacesState':                   False,          
+'SwapFacesText':                    'Swap Faces',  
+'TLBeginningDisplay':              'icon', 
+'TLBeginningIconHover':            './rope/media/tl_beg_hover.png',
+'TLBeginningIconOff':              './rope/media/tl_beg_off.png',
+'TLBeginningIconOn':               './rope/media/tl_beg_on.png',
+'TLBeginningInfoText':             'TIMELINE START:\nMove the timeline handle to the first frame.',
+'TLBeginningState':                 False,
+'TLLeftDisplay':                    'icon',   
+'TLLeftIconHover':                  './rope/media/tl_left_hover.png',
+'TLLeftIconOff':                    './rope/media/tl_left_off.png',
+'TLLeftIconOn':                     './rope/media/tl_left_on.png',
+'TLLeftInfoText':                   'TIMELEFT NUDGE LEFT:\nMove the timeline handle to the left 30 frames.',
+'TLLeftState':                      False,
+'TLRightDisplay':                   'icon',   
+'TLRightIconHover':                 './rope/media/tl_right_hover.png',    
+'TLRightIconOff':                   './rope/media/tl_right_off.png',
+'TLRightIconOn':                    './rope/media/tl_right_on.png',
+'TLRightInfoText':                  'TIMELEFT NUDGE RIGHT:\nMove the timeline handle to the RIGHT 30 frames.',
+'TLRightState':                     False,
+
+'SaveImageButtonDisplay':                   'text',   
+'SaveImageButtonInfoText':                  'SAVE IMAGE:\nSaves the current image to your Output Folder.',
+'SaveImageButtonState':                     False,
+'SaveImageButtonText':             'Save Image',
+
+'AutoSwapButtonDisplay':                   'text',   
+'AutoSwapButtonInfoText':                  'AUTOSWAP:\nAutomatcially applies your currently selected Input Face to new images.',
+'AutoSwapButtonState':                     False,
+'AutoSwapButtonText':             'Auto Swap',
+ 
+'ClearVramButtonDisplay':                   'text',   
+'ClearVramButtonInfoText':                  'CLEAR VRAM:\nClears models from your VRAM.',
+'ClearVramButtonState':                     False,
+'ClearVramButtonText':             'Clear VRAM',
+
+'GetNewEmbButtonDisplay':                   'text',
+'GetNewEmbButtonInfoText':                  'CLEAR VRAM:\nClears models from your VRAM.',
+'GetNewEmbButtonState':                     False,
+'GetNewEmbButtonText':             'Clear VRAM',
+
+
+'StopMarkerButtonnDisplay':                   'icon',
+'StopMarkerButtonIconHover':            './rope/media/previous_marker_hover.png',
+'StopMarkerButtonIconOff':              './rope/media/previous_marker_off.png',
+'StopMarkerButtonIconOn':               './rope/media/previous_marker_off.png',
+'StopMarkerButtonInfoText':                  'CLEAR VRAM:\nClears models from your VRAM.',
+'StopMarkerButtonState':                     False,
+'StopMarkerButtonText':             'Clear VRAM',
+ 
+#Switches       
+'ColorSwitchInfoText':              'RGB ADJUSTMENT:\nFine-tune the RGB color values of the swap.',
+'ColorSwitchState':                 False,
+'DiffSwitchInfoText':               'DIFFERENCER:\nAllow some of the original face to show in the swapped result when the difference between the two images is small. Can help bring back some texture to the swapped face',
+'DiffSwitchState':                  False,
+'FaceAdjSwitchInfoText':            'KPS and SCALE ADJUSTMENT:\nThis is an experimental feature to perform direct adjustments to the face landmarks found by the detector. There is also an option to adjust the scale of the swapped face.',
+'FaceAdjSwitchState':               False,
+#
+'LandmarksDetectionAdjSwitchInfoText': 'KPS ADJUSTMENT:\nThis is an experimental feature to perform direct adjustments to the face landmarks found by the detector. ',
+'LandmarksDetectionAdjSwitchState':    False,
+'LandmarksAlignModeFromPointsSwitchInfoText': 'KPS ADJUSTMENT ALIGN MODE FROM POINTS:\nThis is an experimental feature to perform direct adjustments to the face landmarks found from detector key points.',
+'LandmarksAlignModeFromPointsSwitchState':    False,
+'ShowLandmarksSwitchInfoText':      'Show Landmarks in realtime.',
+'ShowLandmarksSwitchState':         False,
+#
+'FaceParserSwitchInfoText':         'BACKGROUND MASK:\nAllow the unprocessed background from the orginal image to show in the final swap.',
+'FaceParserSwitchState':            False,
+'MouthParserSwitchInfoText':        'MOUTH MASK:\nAllow the mouth from the original face to show on the swapped face.',
+'MouthParserSwitchState':           False,
+'OccluderSwitchInfoText':           'OCCLUSION MASK:\nAllow objects occluding the face to show up in the swapped image.',
+'OccluderSwitchState':              False,
+'OrientSwitchInfoText':             'ORIENTATION:\nRotate the face detector to better detect faces at different angles',
+'OrientSwitchState':                False,
+'RestorerSwitchInfoText':           'FACE RESTORER:\nRestore the swapped image by upscaling.',
+'RestorerSwitchState':              False,
+'StrengthSwitchInfoText':           'SWAPPER STRENGTH:\nApply additional swapping iterations to increase the strength of the result, which may increase likeness',
+'StrengthSwitchState':              False,
+'CLIPSwitchInfoText':               'TEXT MASKING:\nUse descriptions to identify objects that will be present in the final swapped image.',
+'CLIPSwitchState':                  False,
+
+# Sliders
+'BlendSliderAmount':                5,
+'BlendSliderInc':                   1,  
+'BlendSliderInfoText':              'BLEND:\nCombined masks blending distance. Is not applied to the border masks.',
+'BlendSliderMax':                   100,
+'BlendSliderMin':                   0,
+'BorderBlurSliderAmount':           10,
+'BorderBlurSliderInc':              1,  
+'BorderBlurSliderInfoText':         'BORDER MASK BLEND:\nBorder mask blending distance.',
+'BorderBlurSliderMax':              64,
+'BorderBlurSliderMin':              0,
+'BorderBottomSliderAmount':         10, 
+'BorderBottomSliderInc':            1,  
+'BorderBottomSliderInfoText':       'BOTTOM BORDER DISTANCE:\nA rectangle with adjustable top, bottom, and sides that blends the swapped face rseult back into the original image.',
+'BorderBottomSliderMax':            64,
+'BorderBottomSliderMin':            0,
+'BorderSidesSliderAmount':          10, 
+'BorderSidesSliderInc':             1,  
+'BorderSidesSliderInfoText':        'SIDES BORDER DISTANCE:\nA rectangle with adjustable top, bottom, and sides that blends the swapped face result back into the original image.',
+'BorderSidesSliderMax':             64,
+'BorderSidesSliderMin':             0,
+'BorderTopSliderAmount':            10, 
+'BorderTopSliderInc':               1, 
+'BorderTopSliderInfoText':          'TOP BORDER DISTANCE:\nA rectangle with adjustable top, bottom, and sides that blends the swapped face result back into the original image.',
+'BorderTopSliderMax':               64,
+'BorderTopSliderMin':               0,
+'ColorBlueSliderAmount':            0,
+'ColorBlueSliderInc':               1,  
+'ColorBlueSliderInfoText':          'RGB BLUE ADJUSTMENT',
+'ColorBlueSliderMax':               100,
+'ColorBlueSliderMin':               -100,
+'ColorGreenSliderAmount':           0,
+'ColorGreenSliderInc':              1, 
+'ColorGreenSliderInfoText':         'RGB GREEN ADJUSTMENT',
+'ColorGreenSliderMax':              100,
+'ColorGreenSliderMin':              -100,
+'ColorRedSliderAmount':             0,
+'ColorRedSliderInc':                1,  
+'ColorRedSliderInfoText':           'RGB RED ADJUSTMENT',
+'ColorRedSliderMax':                100,
+'ColorRedSliderMin':                -100,
+'DetectScoreSliderAmount':          50,
+'DetectScoreSliderInc':             1,      
+'DetectScoreSliderInfoText':        'DETECTION SCORE LIMIT:\nDetermines the minimum score required for a face to be detected. Higher values require higher quality faces. E.g., if faces are flickering when at extreme angles, raising this will limit swapping attempts.',
+'DetectScoreSliderMax':             100,
+'DetectScoreSliderMin':             1,
+#
+'LandmarksDetectScoreSliderAmount':  50,
+'LandmarksDetectScoreSliderInc':     1,      
+'LandmarksDetectScoreSliderInfoText':'LANDMARKS DETECTION SCORE LIMIT:\nDetermines the minimum score required for a face to be detected. Higher values require higher quality faces. E.g., if faces are flickering when at extreme angles, raising this will limit swapping attempts.',
+'LandmarksDetectScoreSliderMax':     100,
+'LandmarksDetectScoreSliderMin':     1,
+#
+'DiffSliderAmount':                 4,   
+'DiffSliderInc':                    1,
+'DiffSliderInfoText':               'DIFFERENCING AMOUNT:\nHigher values relaxes the similarity constraint.',
+'DiffSliderMax':                    100,
+'DiffSliderMin':                    0,
+'FaceParserSliderAmount':           0,   
+'FaceParserSliderInc':              1,        
+'FaceParserSliderInfoText':         'BACKGROUND MASK AMOUNT:\nNegative/Positive values shrink and grow the mask.',
+'FaceParserSliderMax':              50,
+'FaceParserSliderMin':              -50,
+'FaceScaleSliderAmount':            0,
+'FaceScaleSliderInc':               1,    
+'FaceScaleSliderInfoText':          'FACE SCALE AMOUNT',
+'FaceScaleSliderMax':               20,
+'FaceScaleSliderMin':               -20,
+'KPSScaleSliderAmount':             0, 
+'KPSScaleSliderInc':                1,  
+'KPSScaleSliderInfoText':           'KPS SCALE AMOUNT:\nGrows and shrinks the detection point distances.',
+'KPSScaleSliderMax':                100,
+'KPSScaleSliderMin':                -100,
+'KPSXSliderAmount':                 0, 
+'KPSXSliderInc':                    1,      
+'KPSXSliderInfoText':               'KPS X-DIRECTION AMOUNT:\nShifts the detection points left and right',
+'KPSXSliderMax':                    100,
+'KPSXSliderMin':                    -100,
+'KPSYSliderAmount':                 0, 
+'KPSYSliderInc':                    1,  
+'KPSYSliderInfoText':               'KPS Y-DIRECTION AMOUNT:\nShifts the detection points lup and down',
+'KPSYSliderMax':                    100,
+'KPSYSliderMin':                    -100,
+'MouthParserSliderAmount':          0, 
+'MouthParserSliderInc':             1,      
+'MouthParserSliderInfoText':        'MOUTH MASK AMOUNT:\nAdjust the size of the mask. Negative values only mask the inside of the mouth, including the tongue. Positive values also include lips',
+'MouthParserSliderMax':             50,
+'MouthParserSliderMin':             -50,
+'OccluderSliderAmount':             0,
+'OccluderSliderInc':                1,
+'OccluderSliderInfoText':           'OCCLUDER AMOUNT:\nGrows or shrinks the occluded region',
+'OccluderSliderMax':                100,
+'OccluderSliderMin':                -100,
+'OrientSliderAmount':               0,
+'OrientSliderInc':                  90,
+'OrientSliderInfoText':             'ORIENTATION ANGLE:\nSet this to the angle of the input face angle to help with laying down/upside down/etc. Angles are read clockwise.',
+'OrientSliderMax':                  270,
+'OrientSliderMin':                  0,
+'RestorerSliderAmount':             100,
+'RestorerSliderInc':                5,
+'RestorerSliderInfoText':           'RESTORER AMOUNT:\nBlends the Restored results back into the original swap.',
+'RestorerSliderMax':                100,
+'RestorerSliderMin':                0,
+'StrengthSliderAmount':             100,
+'StrengthSliderInc':                25,    
+'StrengthSliderInfoText':           'STRENGTH AMOUNT:\nIncrease up to 5x additional swaps (500%). 200% is generally a good result. Set to 0 to turn off swapping but allow the rest of the pipeline to apply to the original image.',
+'StrengthSliderMax':                500,
+'StrengthSliderMin':                0,
+'ThreadsSliderAmount':              5,
+'ThreadsSliderInc':                 1,    
+'ThreadsSliderInfoText':            'EXECUTION THREADS:\nSet number of execution threads while playing and recording. Depends strongly on GPU VRAM. 5 threads for 24GB.',
+'ThreadsSliderMax':                 20,
+'ThreadsSliderMin':                 1,
+'ThresholdSliderAmount':            55,
+'ThresholdSliderInc':               1,
+'ThresholdSliderInfoText':          'THRESHHOLD AMOUNT:\nRaise to reduce faces hopping around when swapping multiple people. A higher value is stricter.',
+'ThresholdSliderMax':               100,
+'ThresholdSliderMin':               0,
+'VideoQualSliderAmount':            18,
+'VideoQualSliderInc':               1,      
+'VideoQualSliderInfoText':          'VIDEO QUALITY:\nThe encoding quality of the recorded video. 0 is best, 50 is worst, 18 is mostly lossless. File size increases with a lower quality number.',
+'VideoQualSliderMax':               50,
+'VideoQualSliderMin':               0,
+
+'CLIPSliderAmount':                 50,
+'CLIPSliderInc':                    1,
+'CLIPSliderInfoText':               'TEXT MASKING STENGTH:\nIncrease to strengthen the effect.',
+'CLIPSliderMax':                    100,
+'CLIPSliderMin':                    0,
+
+'ColorGammaSliderAmount':                 1,
+'ColorGammaSliderInc':                    0.02,
+'ColorGammaSliderInfoText':               'GAMMA VALUE:\nChanges Gamma.',
+'ColorGammaSliderMax':                    2,
+'ColorGammaSliderMin':                    0,
+
+
+# Text Selection
+'DetectTypeTextSelInfoText':        'FACE DETECTION MODEL:\nSelect the face detection model. Mostly only subtle differences, but can significant differences when the face is at extreme angles or covered.',
+'DetectTypeTextSelMode':            'Retinaface',
+'DetectTypeTextSelModes':           ['Retinaface', 'Yolov8', 'SCRDF', 'Yunet'],
+#
+'LandmarksDetectTypeTextSelInfoText': 'LANDMARKS FACE DETECTION MODEL:\nSelect the landmarks face detection model. Mostly only subtle differences, but can significant differences when the face is at extreme angles or covered.',
+'LandmarksDetectTypeTextSelMode':     '98',
+'LandmarksDetectTypeTextSelModes':    ['5', '68', '3d68', '98', '106', '478'],
+#
+'PreviewModeTextSelInfoText':       '',
+'PreviewModeTextSelMode':           'Video',
+'PreviewModeTextSelModes':          ['Video', 'Image','Theater'],
+'RecordTypeTextSelInfoText':        'VIDEO RECORDING LIBRARY:\nSelect the recording library used for video recording. FFMPEG uses the Video Quality slider to adjust the size and quality of the final video. OPENCV has no options but is faster and produces good results.',
+'RecordTypeTextSelMode':            'FFMPEG',
+'RecordTypeTextSelModes':           ['FFMPEG', 'OPENCV'],
+'RestorerDetTypeTextSelInfoText':   'ALIGNMENT:\nSelect how the face is aligned for the Restorer. Original preserves facial features and expressions, but can show some artifacts. Reference softens features. Blend is closer to Reference but is much faster.',
+'RestorerDetTypeTextSelMode':       'Blend',
+'RestorerDetTypeTextSelModes':      ['Original', 'Blend', 'Reference'],  
+'RestorerTypeTextSelInfoText':      'RESTORER TYPE:\nSelect the Restorer type.\nSpeed: GPEN256>GFPGAN>CF>GPEN512',
+'RestorerTypeTextSelMode':          'GFPGAN',
+'RestorerTypeTextSelModes':         ['GFPGAN', 'CF', 'GPEN256', 'GPEN512', 'GPEN1024'],
+'MergeTextSelInfoText':      'INPUT FACES MERGE MATH:\nWhen shift-clicking face for merging, determines how the embedding vectors are combined.',
+'MergeTextSelMode':          'Mean',
+'MergeTextSelModes':         ['Mean', 'Median'],
+'SwapperTypeTextSelInfoText':      'SWAPPER OUTPUT RESOLUTION:\nDetermines the resolution of the swapper output.',
+'SwapperTypeTextSelMode':          '128',
+'SwapperTypeTextSelModes':         ['128', '256', '512'],
+
+
+
+# Text Entry
+'CLIPTextEntry':    '',
+'CLIPTextEntryInfoText':            'TEXT MASKING ENTRY:\nTo use, type a word(s) in the box separated by commas and press <enter>.',
+}
+
+PARAM_VARS =    {
+
+    'CLIPState':                False,
+    'CLIPMode':                 0, 
+    'CLIPModes':                ['CLIP'], 
+    'CLIPAmount':               [50],
+    'CLIPMin':                  0,
+    'CLIPMax':                  100,
+    'CLIPInc':                  1,                                                  
+    'CLIPUnit':                 '%', 
+    'CLIPIcon':                 './rope/media/CLIP.png',
+    'CLIPMessage':              'CLIP - Text based occluder. Occluded objects are visible in the final image (occluded from the mask). [LB: on/off, MW: strength]',                              
+    'CLIPFunction':         False,
+
+    "CLIPText":                 '',
+}   
+ 
+PARAMS =   { 
+   
+    'ClearmemFunction':         'self.clear_mem()',
+    'PerfTestFunction':         'self.toggle_perf_test()',
+    'ImgVidFunction':         'self.toggle_vid_img()',
+    'AutoSwapFunction':         'self.toggle_auto_swap()',
+    'SaveImageFunction':         'self.save_image()',
+
+    'ClearmemIcon':            './rope/media/clear_mem.png',
+    'SaveImageIcon':            './rope/media/save_disk.png', 
+    'PerfTestIcon':            './rope/media/test.png',
+    'RefDelIcon':          './rope/media/construction.png',
+    'TransformIcon':          './rope/media/scale.png',    
+    'ThresholdIcon':            './rope/media/thresh.png',
+    'LoadSFacesIcon':            './rope/media/save.png',
+    'BorderIcon':                 './rope/media/maskup.png',
+    'OccluderIcon':             './rope/media/occluder.png',
+    'ColorIcon':            './rope/media/rgb.png',
+    'StrengthIcon':             './rope/media/strength.png',
+    'OrientationIcon':          './rope/media/orient.png',
+    'DiffIcon':                 './rope/media/diff.png',
+    'MouthParserIcon':           './rope/media/parse.png',
+    'AudioIcon':            './rope/media/rgb.png',    
+    'VideoQualityIcon':            './rope/media/tarface.png',    
+    'MaskViewIcon':             './rope/media/maskblur.png',
+    'BlurIcon':                 './rope/media/blur.png',    
+    'ToggleStopIcon':            './rope/media/STOP.png',    
+     'DelEmbedIcon':            './rope/media/delemb.png',   
+    'ImgVidIcon':            './rope/media/imgvid.png',    
+    
+    
+    
+    'ImgVidMessage':         'IMAGE/VIDEO - Toggle between Image and Video folder view.',      
+    'ToggleStopMessage':         'STOP MARKER - Sets a frame that will stop the video playing/recording.',      
+    'AutoSwapMessage':         'AUTO SWAP - Automatically swaps the first person in an image to the selcted source faces [LB: Turn on/off]',      
+    'SaveImageMessage':         'SAVE IMAGE - Save image to output folder',      
+    'ClearmemMessage':         'CLEAR VRAM - Clears all models from VRAM [LB: Clear]',      
+    'PerfTestMessage':         'PERFORMANCE DATA - Displays timing data in the console for critical Rope functions. [LB: on/off]',
+    'RefDelMessage':       'REFERENCE DELTA - Modify the reference points. Turn on mask preview to see adjustments. [LB: on/off, RB: translate x/y, and scale, MW: amount]' ,
+    'ThresholdMessage':         'THRESHOLD - Threshold for determining if Target Faces match faces in a frame. Lower is stricter. [LB: use amount/match all, MW: value]',
+    'TransformMessage':       'SCALE - Adjust the scale of the face. Use with Background parser to blend into the image. [LB: on/off, MW: amount]',     
+    'PlayMessage':         'PLAY - Plays the video. Press again to stop playing',  
+ 
+     }   
+     

rope/FaceUtil.py

@@ -0,0 +1,405 @@
+import cv2
+import math
+import numpy as np
+from skimage import transform as trans
+import torch
+import torchvision
+torchvision.disable_beta_transforms_warning()
+from torchvision.transforms import v2
+from numpy.linalg import norm as l2norm
+
+arcface_src = np.array(
+    [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366],
+     [41.5493, 92.3655], [70.7299, 92.2041]],
+    dtype=np.float32)
+
+arcface_src = np.expand_dims(arcface_src, axis=0)
+
+def pad_image_by_size(img, image_size):
+    w, h = math.ceil(img.size(dim=2)), math.ceil(img.size(dim=1))
+    if w < image_size or h < image_size:
+        # add right, bottom pading to the image if its size is less than image_size value
+        add = image_size - min(w, h)
+        img = torch.nn.functional.pad(img, (0, add, 0, add), 'constant', 0)
+
+    return img
+
+def transform(img, center, output_size, scale, rotation):
+    # pad image by image size
+    img = pad_image_by_size(img, output_size)
+
+    scale_ratio = scale
+    rot = float(rotation) * np.pi / 180.0
+    t1 = trans.SimilarityTransform(scale=scale_ratio)
+    cx = center[0] * scale_ratio
+    cy = center[1] * scale_ratio
+    t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
+    t3 = trans.SimilarityTransform(rotation=rot)
+    t4 = trans.SimilarityTransform(translation=(output_size / 2,
+                                                output_size / 2))
+    t = t1 + t2 + t3 + t4
+    M = t.params[0:2]
+
+    cropped = v2.functional.affine(img, t.rotation, (t.translation[0], t.translation[1]) , t.scale, 0, interpolation=v2.InterpolationMode.BILINEAR, center = (0,0) )
+    cropped = v2.functional.crop(cropped, 0,0, output_size, output_size)
+
+    return cropped, M
+
+def trans_points2d(pts, M):
+    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
+    for i in range(pts.shape[0]):
+        pt = pts[i]
+        new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
+        new_pt = np.dot(M, new_pt)
+        #print('new_pt', new_pt.shape, new_pt)
+        new_pts[i] = new_pt[0:2]
+
+    return new_pts
+
+def trans_points3d(pts, M):
+    scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1])
+    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
+    for i in range(pts.shape[0]):
+        pt = pts[i]
+        new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
+        new_pt = np.dot(M, new_pt)
+        #print('new_pt', new_pt.shape, new_pt)
+        new_pts[i][0:2] = new_pt[0:2]
+        new_pts[i][2] = pts[i][2] * scale
+
+    return new_pts
+
+def trans_points(pts, M):
+    if pts.shape[1] == 2:
+        return trans_points2d(pts, M)
+    else:
+        return trans_points3d(pts, M)
+
+def estimate_affine_matrix_3d23d(X, Y):
+    ''' Using least-squares solution
+    Args:
+        X: [n, 3]. 3d points(fixed)
+        Y: [n, 3]. corresponding 3d points(moving). Y = PX
+    Returns:
+        P_Affine: (3, 4). Affine camera matrix (the third row is [0, 0, 0, 1]).
+    '''
+    X_homo = np.hstack((X, np.ones([X.shape[0],1]))) #n x 4
+    P = np.linalg.lstsq(X_homo, Y,rcond=None)[0].T # Affine matrix. 3 x 4
+    return P
+
+def P2sRt(P):
+    ''' decompositing camera matrix P
+    Args:
+        P: (3, 4). Affine Camera Matrix.
+    Returns:
+        s: scale factor.
+        R: (3, 3). rotation matrix.
+        t: (3,). translation.
+    '''
+    t = P[:, 3]
+    R1 = P[0:1, :3]
+    R2 = P[1:2, :3]
+    s = (np.linalg.norm(R1) + np.linalg.norm(R2))/2.0
+    r1 = R1/np.linalg.norm(R1)
+    r2 = R2/np.linalg.norm(R2)
+    r3 = np.cross(r1, r2)
+
+    R = np.concatenate((r1, r2, r3), 0)
+    return s, R, t
+
+def matrix2angle(R):
+    ''' get three Euler angles from Rotation Matrix
+    Args:
+        R: (3,3). rotation matrix
+    Returns:
+        x: pitch
+        y: yaw
+        z: roll
+    '''
+    sy = math.sqrt(R[0,0] * R[0,0] +  R[1,0] * R[1,0])
+
+    singular = sy < 1e-6
+
+    if  not singular :
+        x = math.atan2(R[2,1] , R[2,2])
+        y = math.atan2(-R[2,0], sy)
+        z = math.atan2(R[1,0], R[0,0])
+    else :
+        x = math.atan2(-R[1,2], R[1,1])
+        y = math.atan2(-R[2,0], sy)
+        z = 0
+
+    # rx, ry, rz = np.rad2deg(x), np.rad2deg(y), np.rad2deg(z)
+    rx, ry, rz = x*180/np.pi, y*180/np.pi, z*180/np.pi
+    return rx, ry, rz
+
+def warp_face_by_bounding_box(img, bboxes, image_size=112):
+    # pad image by image size
+    img = pad_image_by_size(img, image_size)
+
+    # Set source points from bounding boxes
+    source_points = np.array([ [ bboxes[0], bboxes[1] ], [ bboxes[2], bboxes[1] ], [ bboxes[0], bboxes[3] ], [ bboxes[2], bboxes[3] ] ]).astype(np.float32)
+
+    # Set target points from image size
+    target_points = np.array([ [ 0, 0 ], [ image_size, 0 ], [ 0, image_size ], [ image_size, image_size ] ]).astype(np.float32)
+
+    # Find transform
+    tform = trans.SimilarityTransform()
+    tform.estimate(source_points, target_points)
+    
+    # Transform
+    img = v2.functional.affine(img, tform.rotation, (tform.translation[0], tform.translation[1]) , tform.scale, 0, interpolation=v2.InterpolationMode.BILINEAR, center = (0,0) ) 
+    img = v2.functional.crop(img, 0,0, image_size, image_size)
+    M = tform.params[0:2]
+
+    return img, M
+
+def warp_face_by_face_landmark_5(img, kpss, image_size=112, normalized = False, interpolation=v2.InterpolationMode.BILINEAR, custom_arcface_src = None):
+    # pad image by image size
+    img = pad_image_by_size(img, image_size)
+
+    M, pose_index = estimate_norm(kpss, image_size, normalized, custom_arcface_src)
+    #warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
+    t = trans.SimilarityTransform()
+    t.params[0:2] = M
+    img = v2.functional.affine(img, t.rotation*57.2958, (t.translation[0], t.translation[1]) , t.scale, 0, interpolation=interpolation, center = (0, 0) )
+    img = v2.functional.crop(img, 0,0, image_size, image_size)
+
+    return img, M
+
+# lmk is prediction; src is template
+def estimate_norm(lmk, image_size=112, normalized = False, custom_arcface_src = None):
+    assert lmk.shape == (5, 2)
+    tform = trans.SimilarityTransform()
+    lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1)
+    min_M = []
+    min_index = []
+    min_error = float('inf')
+
+    if custom_arcface_src is None:
+        if normalized == False:
+            if image_size == 112:
+                src = arcface_src
+            else:
+                src = float(image_size) / 112.0 * arcface_src
+        else:
+            factor = float(image_size) / 128.0
+            src = arcface_src * factor
+            src[:, 0] += (factor * 8.0)
+    else:
+        src = custom_arcface_src
+
+    for i in np.arange(src.shape[0]):
+        tform.estimate(lmk, src[i])
+        M = tform.params[0:2, :]
+        results = np.dot(M, lmk_tran.T)
+        results = results.T
+        error = np.sum(np.sqrt(np.sum((results - src[i])**2, axis=1)))
+        #         print(error)
+        if error < min_error:
+            min_error = error
+            min_M = M
+            min_index = i
+    return min_M, min_index
+
+def invertAffineTransform(M):
+    t = trans.SimilarityTransform()
+    t.params[0:2] = M
+    IM = t.inverse.params[0:2, :]
+
+    return IM
+
+def warp_face_by_bounding_box_for_landmark_68(img, bbox, input_size):
+    """
+    :param img: raw image
+    :param bbox: the bbox for the face
+    :param input_size: tuple input image size
+    :return:
+    """
+    # pad image by image size
+    img = pad_image_by_size(img, input_size[0])
+
+    scale = 195 / np.subtract(bbox[2:], bbox[:2]).max()
+    translation = (256 - np.add(bbox[2:], bbox[:2]) * scale) * 0.5
+    rotation = 0
+
+    t1 = trans.SimilarityTransform(scale=scale)
+    t2 = trans.SimilarityTransform(rotation=rotation)
+    t3 = trans.SimilarityTransform(translation=translation)
+
+    t = t1 + t2 + t3
+    affine_matrix = np.array([ [ scale, 0, translation[0] ], [ 0, scale, translation[1] ] ])
+
+    crop_image = v2.functional.affine(img, t.rotation, (t.translation[0], t.translation[1]) , t.scale, 0, interpolation=v2.InterpolationMode.BILINEAR, center = (0,0) ) 
+    crop_image = v2.functional.crop(crop_image, 0,0, input_size[1], input_size[0])
+
+    if torch.mean(crop_image.to(dtype=torch.float32)[0, :, :]) < 30:
+        crop_image = cv2.cvtColor(crop_image.permute(1, 2, 0).to('cpu').numpy(), cv2.COLOR_RGB2Lab)
+        crop_image[:, :, 0] = cv2.createCLAHE(clipLimit = 2).apply(crop_image[:, :, 0])
+        crop_image = torch.from_numpy(cv2.cvtColor(crop_image, cv2.COLOR_Lab2RGB)).to('cuda').permute(2, 0, 1)
+
+    return crop_image, affine_matrix
+
+def warp_face_by_bounding_box_for_landmark_98(img, bbox_org, input_size):
+    """
+    :param img: raw image
+    :param bbox: the bbox for the face
+    :param input_size: tuple input image size
+    :return:
+    """
+    # pad image by image size
+    img = pad_image_by_size(img, input_size[0])
+    
+    ##preprocess
+    bbox = bbox_org.copy()
+    min_face = 20
+    base_extend_range = [0.2, 0.3]
+    bbox_width = bbox[2] - bbox[0]
+    bbox_height = bbox[3] - bbox[1]
+    if bbox_width <= min_face or bbox_height <= min_face:
+        return None, None
+    add = int(max(bbox_width, bbox_height))
+
+    bimg = torch.nn.functional.pad(img, (add, add, add, add), 'constant', 0)
+
+    bbox += add
+
+    face_width = (1 + 2 * base_extend_range[0]) * bbox_width
+    center = [(bbox[0] + bbox[2]) // 2, (bbox[1] + bbox[3]) // 2]
+
+    ### make the box as square
+    bbox[0] = center[0] - face_width // 2
+    bbox[1] = center[1] - face_width // 2
+    bbox[2] = center[0] + face_width // 2
+    bbox[3] = center[1] + face_width // 2
+
+    # crop
+    bbox = bbox.astype(np.int32)
+    crop_image = bimg[:, bbox[1]:bbox[3], bbox[0]:bbox[2]]
+
+    h, w = (crop_image.size(dim=1), crop_image.size(dim=2))
+    
+    t_resize = v2.Resize((input_size[1], input_size[0]), antialias=False)
+    crop_image = t_resize(crop_image)
+
+    return crop_image, [h, w, bbox[1], bbox[0], add]
+
+def create_bounding_box_from_face_landmark_106_98_68(face_landmark_106_98_68):
+    min_x, min_y = np.min(face_landmark_106_98_68, axis = 0)
+    max_x, max_y = np.max(face_landmark_106_98_68, axis = 0)
+    bounding_box = np.array([ min_x, min_y, max_x, max_y ]).astype(np.int16)
+    return bounding_box
+
+def convert_face_landmark_68_to_5(face_landmark_68, face_landmark_68_score):
+    face_landmark_5 = np.array(
+    [
+        np.mean(face_landmark_68[36:42], axis = 0),
+        np.mean(face_landmark_68[42:48], axis = 0),
+        face_landmark_68[30],
+        face_landmark_68[48],
+        face_landmark_68[54]
+    ])
+
+    if np.any(face_landmark_68_score):
+        face_landmark_5_score = np.array(
+        [
+            np.mean(face_landmark_68_score[36:42], axis = 0),
+            np.mean(face_landmark_68_score[42:48], axis = 0),
+            face_landmark_68_score[30],
+            face_landmark_68_score[48],
+            face_landmark_68_score[54]
+        ])
+    else:
+        face_landmark_5_score = np.array([])
+
+    return face_landmark_5, face_landmark_5_score
+
+def convert_face_landmark_98_to_5(face_landmark_98, face_landmark_98_score):
+    face_landmark_5 = np.array(
+    [
+        face_landmark_98[96], # eye left
+        face_landmark_98[97], # eye-right
+        face_landmark_98[54], # nose,
+        face_landmark_98[76], # lip left
+        face_landmark_98[82]  # lip right
+    ])
+    
+    face_landmark_5_score = np.array(
+    [
+        face_landmark_98_score[96], # eye left
+        face_landmark_98_score[97], # eye-right
+        face_landmark_98_score[54], # nose,
+        face_landmark_98_score[76], # lip left
+        face_landmark_98_score[82]  # lip right        
+    ])
+
+    return face_landmark_5, face_landmark_5_score
+
+def convert_face_landmark_106_to_5(face_landmark_106):
+    face_landmark_5 = np.array(
+    [
+        face_landmark_106[38], # eye left
+        face_landmark_106[88], # eye-right
+        face_landmark_106[86], # nose,
+        face_landmark_106[52], # lip left
+        face_landmark_106[61]  # lip right
+    ])
+
+    return face_landmark_5
+
+def convert_face_landmark_478_to_5(face_landmark_478):
+    face_landmark_5 = np.array(
+    [
+        face_landmark_478[468], # eye left
+        #np.array([(face_landmark_478[159][0] + face_landmark_478[145][0]) / 2, (face_landmark_478[159][1] + face_landmark_478[145][1]) / 2]), # eye left (145-159)
+        face_landmark_478[473], # eye-right
+        #np.array([(face_landmark_478[386][0] + face_landmark_478[374][0]) / 2, (face_landmark_478[386][1] + face_landmark_478[374][1]) / 2]), # eye-right (374-386)
+        face_landmark_478[4], # nose, 4, 1
+        face_landmark_478[61], # lip left ? 61, 57
+        face_landmark_478[291]  # lip right ? 291, 287
+    ])
+
+    return face_landmark_5
+
+def test_bbox_landmarks(img, bbox, kpss):
+        image = img.permute(1,2,0).to('cpu').numpy().copy()
+        if len(bbox) > 0:
+            box = bbox.astype(int)
+            color = (255, 0, 0)
+            cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), color, 2)
+        
+        if len(kpss) > 0:
+            for i in range(kpss.shape[0]):
+                kps = kpss[i].astype(int)
+                color = (0, 0, 255)
+                cv2.circle(image, (kps[0], kps[1]), 1, color,
+                           2)
+
+        cv2.imshow('image', image) 
+        cv2.waitKey(0)         
+        cv2.destroyAllWindows()
+
+def test_multi_bbox_landmarks(img, bboxes, kpss):
+    if len(bboxes) > 0 and len(kpss) > 0:
+        for i in range(np.array(kpss).shape[0]):
+            test_bbox_landmarks(img, bboxes[i], kpss[i])
+
+def detect_img_color(img):
+    frame = img.permute(1,2,0)
+
+    b = frame[:, :, :1] 
+    g = frame[:, :, 1:2] 
+    r = frame[:, :, 2:] 
+  
+    # computing the mean 
+    b_mean = torch.mean(b.to(float)) 
+    g_mean = torch.mean(g.to(float)) 
+    r_mean = torch.mean(r.to(float)) 
+
+    # displaying the most prominent color 
+    if (b_mean > g_mean and b_mean > r_mean): 
+        return 'BGR'
+    elif (g_mean > r_mean and g_mean > b_mean): 
+        return 'GBR'
+
+    return 'RGB'

rope/GUIElements.py

@@ -0,0 +1,1248 @@
+import tkinter as tk
+from tkinter import font
+from PIL import Image, ImageTk
+
+from  rope.Dicts import DEFAULT_DATA
+import rope.Styles as style
+
+#import inspect print(inspect.currentframe().f_back.f_code.co_name, 'resize_image')
+
+class Separator_x():
+    def __init__(self, parent, x, y): 
+        self.parent = parent
+        self.x = x
+        self.y = y
+        self.parent.update()
+        self.blank = tk.PhotoImage()
+        self.sep = tk.Label(self.parent, bg='#090909', image=self.blank, compound='c', border=0, width=self.parent.winfo_width(), height=1)
+        self.sep.place(x=self.x, y=self.y)
+        # self.parent.bind('<Configure>', self.update_sep_after_window_resize)
+    
+    # def update_sep_after_window_resize(self, event):
+        # self.parent.update()
+        # self.sep.configure(width=self.parent.winfo_width())
+        
+    def hide(self):
+        self.sep.place_forget()
+
+    def unhide(self):
+        self.parent.update()
+        self.sep.place(x=self.x, y=self.y)  
+        self.sep.configure(width=self.parent.winfo_width())
+   
+
+class Separator_y():
+    def __init__(self, parent, x, y): 
+        self.parent = parent
+        self.x = x
+        self.y = y        
+        self.parent.update()
+        self.blank = tk.PhotoImage()
+        self.sep = tk.Label(self.parent, bg='#090909', image=self.blank, compound='c', border=0, width=1, height=self.parent.winfo_height())
+        self.sep.place(x=self.x, y=self.y) 
+        # self.parent.bind('<Configure>', self.update_sep_after_window_resize)
+    
+    # def update_sep_after_window_resize(self, event):
+        # self.parent.update()
+        # self.sep.configure(height=self.parent.winfo_height())
+        
+    def hide(self):
+        self.sep.place_forget()
+
+    def unhide(self):
+        self.parent.update()
+        self.sep.place(x=self.x, y=self.y)  
+        self.sep.configure(height=self.parent.winfo_height())          
+
+class Text():
+    def __init__(self, parent, text, style_level, x, y, width, height):
+        self.blank = tk.PhotoImage()
+        
+        if style_level == 1:
+            self.style = style.text_1       
+        elif style_level == 2:
+            self.style = style.text_2
+        elif style_level == 3:
+            self.style = style.text_3
+            
+        self.label = tk.Label(parent, self.style, image=self.blank, compound='c', text=text, anchor='w', width=width, height=height)
+        self.label.place(x=x, y=y)
+    
+    def configure(self, text):
+        self.label.configure(text=text)
+
+class Scrollbar_y():
+    def __init__(self, parent, child): 
+
+        self.child = child
+
+        self.trough_short_dim = 15
+        self.trough_long_dim = []
+        self.handle_short_dim = self.trough_short_dim*0.5
+
+        self.top_of_handle = []
+        self.middle_of_handle = []
+        self.bottom_of_handle = []
+
+        self.old_coord = 0
+                
+        # Child data
+        self.child.bind('<Configure>', self.resize_scrollbar)
+
+        # Set the canvas
+        self.scrollbar_canvas = parent
+        self.scrollbar_canvas.configure(width=self.trough_short_dim)
+        self.scrollbar_canvas.bind("<MouseWheel>", self.scroll)
+        self.scrollbar_canvas.bind("<ButtonPress-1>", self.scroll)
+        self.scrollbar_canvas.bind("<B1-Motion>", self.scroll)     
+        
+        # Draw handle        
+        self.resize_scrollbar(None)
+
+    def resize_scrollbar(self, event): # on window updates
+        self.child.update()
+        self.child.configure(scrollregion=self.child.bbox("all"))
+        
+        # Reconfigure data
+        self.trough_long_dim = self.child.winfo_height()
+        self.scrollbar_canvas.delete('all')   
+        self.scrollbar_canvas.configure(height=self.trough_long_dim)          
+        
+        # Redraw the scrollbar
+        x1 = (self.trough_short_dim-self.handle_short_dim)/2
+        x2 = self.trough_short_dim-x1
+        y1 = self.child.yview()[0]*self.trough_long_dim
+        y2 = self.child.yview()[1]*self.trough_long_dim
+
+        self.middle_of_handle = self.scrollbar_canvas.create_rectangle(x1, y1, x2, y2, fill='grey25', outline='')
+
+    def scroll(self, event):
+        delta = 0
+    
+        # Get handle dimensions        
+        handle_y1 = self.scrollbar_canvas.coords(self.middle_of_handle)[1]
+        handle_y2 = self.scrollbar_canvas.coords(self.middle_of_handle)[3]
+        handle_center = (handle_y2-handle_y1)/2 + handle_y1
+        handle_length = handle_y2-handle_y1
+
+        if event.type == '38': # mousewheel
+            delta = -int(event.delta/20.0)
+
+        elif event.type == '4': # l-button press        
+            # If the mouse coord is within the handle dont jump the handle
+            if event.y > handle_y1 and event.y<handle_y2:
+                self.old_coord = event.y
+            else:
+                self.old_coord = handle_center
+
+            delta = event.y-self.old_coord
+
+        elif event.type == '6': # l-button drag
+            delta = event.y-self.old_coord
+
+        # Do some bounding
+        if handle_y1+delta<0:
+            delta = -handle_y1
+        elif handle_y2+delta>self.trough_long_dim:
+            delta = self.trough_long_dim-handle_y2
+
+        # update the scrollbar
+        self.scrollbar_canvas.move(self.middle_of_handle, 0, delta)
+
+        # Get the new handle postition to calculate the change for the child
+        handle_y1 = self.scrollbar_canvas.coords(self.middle_of_handle)[1]
+
+        # Move the child
+        self.child.yview_moveto(handle_y1/self.trough_long_dim)
+        
+        self.old_coord = event.y
+        
+    def set(self, value):
+        handle_y1 = self.scrollbar_canvas.coords(self.middle_of_handle)[1]
+        handle_y2 = self.scrollbar_canvas.coords(self.middle_of_handle)[3]
+        handle_center = (handle_y2-handle_y1)/2 + handle_y1
+
+        coord_del = self.scrollbar_canvas.winfo_height()*value-handle_center
+        self.old_coord = self.scrollbar_canvas.winfo_height()*value
+
+        self.scrollbar_canvas.move(self.middle_of_handle, 0, coord_del)
+
+    def hide(self):
+        pass
+
+    def unhide(self):
+        pass   
+
+class Timeline():
+    def __init__(self, parent, widget, temp_toggle_swapper, add_action):  
+        self.parent = parent
+        self.add_action = add_action
+        self.temp_toggle_swapper = temp_toggle_swapper
+
+        self.frame_length = 0 
+        self.height = 20
+        self.counter_width = 40        
+
+        self.entry_string = tk.StringVar()
+        self.entry_string.set(0)
+
+        self.last_position = 0
+        
+        # Widget variables
+        self.max_ = 100#video_length 
+
+        self.handle = []
+        self.slider_left = []
+        self.slider_right = []
+           
+        # Event trigget for window resize
+        self.parent.bind('<Configure>', self.window_resize)
+
+        # Add the Slider Canvas to the frame
+        self.slider = tk.Canvas(self.parent, style.timeline_canvas, height=self.height)
+        self.slider.place(x=0, y=0)
+        self.slider.bind('<B1-Motion>', lambda e: self.update_timeline_handle(e, True))
+        self.slider.bind('<ButtonPress-1>', lambda e: self.update_timeline_handle(e, True))
+        self.slider.bind('<ButtonRelease-1>', lambda e: self.update_timeline_handle(e, True))
+        self.slider.bind('<MouseWheel>', lambda e: self.update_timeline_handle(e, True))
+
+        # Add the Entry to the frame
+        self.entry_width = 40
+        self.entry = tk.Entry(self.parent, style.entry_3, textvariable=self.entry_string)
+        self.entry.bind('<Return>', lambda event: self.entry_input(event))      
+
+    def draw_timeline(self):
+        self.slider.delete('all')
+        
+        # Configure widths and placements
+        self.slider.configure(width=self.frame_length)
+        self.entry.place(x=self.parent.winfo_width()-self.counter_width, y=0)  
+
+        # Draw the slider
+        slider_pad = 20
+        entry_pad = 20
+        self.slider_left = slider_pad
+        self.slider_right = self.frame_length-entry_pad-self.entry_width 
+        slider_center = (self.height)/2
+
+        line_loc = self.pos2coord(self.last_position)
+
+        line_height = 8
+        line_width = 1.5
+        line_x1 = line_loc-line_width
+        line_y1 = slider_center -line_height
+        line_x2 = line_loc+line_width
+        line_y2 = slider_center +line_height        
+
+        
+        trough_x1 = self.slider_left
+        trough_y1 = slider_center-1
+        trough_x2 = self.slider_right
+        trough_y2 = slider_center+1  
+
+        self.slider.create_rectangle(trough_x1, trough_y1, trough_x2, trough_y2, fill='#43474D', outline='')        
+        self.handle = self.slider.create_rectangle(line_x1, line_y1, line_x2, line_y2, fill='#FFFFFF', outline='')    
+
+    def coord2pos(self, coord):
+        return float(coord-self.slider_left)*self.max_/(self.slider_right-self.slider_left)
+        
+    def pos2coord(self, pos):
+        return float(float(pos)*(self.slider_right-self.slider_left)/self.max_ + self.slider_left)
+  
+
+    def update_timeline_handle(self, event, also_update_entry=False):
+        requested = True
+
+        if isinstance(event, float):
+            position = event
+            requested = False
+        else:
+            if event.type == '38': # mousewheel
+                position = self.last_position+int(event.delta/120.0)
+                
+            elif event.type == '4': # l-button press
+                x_coord = float(event.x)
+                position = self.coord2pos(x_coord)
+                
+                # Turn off swapping
+                self.temp_toggle_swapper('off')
+                self.add_action("play_video", "stop")
+                
+            elif event.type == '5': # l-button release 
+                x_coord = float(event.x)
+                position = self.coord2pos(x_coord)       
+                
+                # Turn on swapping, if it was already on and request new frame
+                self.temp_toggle_swapper('on')
+
+            elif event.type == '6': # l-button drag
+                x_coord = float(event.x)
+                position = self.coord2pos(x_coord)
+
+        # constrain mousewheel movement
+        if position < 0: position = 0
+        elif position > self.max_: position = self.max_
+
+        # Find closest position increment
+        position = round(position)    
+
+        # moving sends many events, so only update when the next frame is reached
+        if position != self.last_position:
+            # Move handle to coordinate based on position
+            self.slider.move(self.handle, self.pos2coord(position) - self.pos2coord(self.last_position), 0)
+            
+            if requested:
+                self.add_action("get_requested_video_frame", position)          
+
+            # Save for next time           
+            self.last_position = position
+            
+            if also_update_entry:
+                self.entry_string.set(str(position))
+
+    def entry_input(self, event): 
+    # event.char
+        self.entry.update()
+        try:
+            input_num = float(self.entry_string.get())
+            self.update_timeline_handle(input_num, False)
+        except:
+            return
+    
+    def set(self, value):
+        self.update_timeline_handle(float(value), also_update_entry=True)
+
+    def get(self):
+        return int(self.last_position)
+        
+        
+    def set_length(self, value):
+        self.max_ = value
+        self.update_timeline_handle(float(self.last_position), also_update_entry=True)
+
+    def get_length(self):
+        return int(self.max_)
+        
+    # Event when the window is resized
+    def window_resize(self, event):
+        self.parent.update()
+        self.frame_length = self.parent.winfo_width()
+        self.draw_timeline()
+          
+
+
+        
+class Button():
+    def __init__(self, parent, name, style_level, function, args, data_type, x, y, width=125, height=20):          
+        self.default_data = DEFAULT_DATA
+        self.name = name
+        self.function = function
+        self.args = args
+        self.info = []
+        self.state = []
+        self.hold_state = []
+        self.error = []
+        self.data_type = data_type
+        
+        if style_level == 1:
+            self.button_style = style.button_1        
+        elif style_level == 2:
+            self.button_style = style.button_2 
+        elif style_level == 3:
+            self.button_style = style.button_3
+
+
+        # Add Icon
+        if self.default_data[self.name+'Display'] == 'both':   
+            img = Image.open(self.default_data[self.name+'IconOn'])
+            resized_image= img.resize((20,20), Image.ANTIALIAS)
+            self.icon_on = ImageTk.PhotoImage(resized_image)
+            img = Image.open(self.default_data[self.name+'IconOff'])
+            resized_image= img.resize((20,20), Image.ANTIALIAS)
+            self.icon_off = ImageTk.PhotoImage(resized_image)
+            img = Image.open(self.default_data[self.name+'IconHover'])
+            resized_image= img.resize((20,20), Image.ANTIALIAS)
+            self.icon_hover = ImageTk.PhotoImage(resized_image)
+            
+            text = ' '+self.default_data[self.name+'Text']
+            
+        elif self.default_data[self.name+'Display'] == 'icon':   
+            img = Image.open(self.default_data[self.name+'IconOn'])
+            resized_image= img.resize((20,20), Image.ANTIALIAS)
+            self.icon_on = ImageTk.PhotoImage(resized_image)
+            img = Image.open(self.default_data[self.name+'IconOff'])
+            resized_image= img.resize((20,20), Image.ANTIALIAS)
+            self.icon_off = ImageTk.PhotoImage(resized_image)
+            img = Image.open(self.default_data[self.name+'IconHover'])
+            resized_image= img.resize((20,20), Image.ANTIALIAS)
+            self.icon_hover = ImageTk.PhotoImage(resized_image)            
+            
+            text = ''
+            
+        elif self.default_data[self.name+'Display'] == 'text':
+            self.icon_on = tk.PhotoImage()
+            self.icon_off = tk.PhotoImage()
+            self.icon_hover = tk.PhotoImage()
+            
+            text = ' '+self.default_data[self.name+'Text']
+
+        # Create Button and place
+        self.button = tk.Button(parent, self.button_style, compound='left', text=text, anchor='w')
+        self.button.configure(width=width, height=height)
+        self.button.place(x=x, y=y)   
+
+        self.button.bind("<Enter>", lambda event: self.on_enter())
+        self.button.bind("<Leave>", lambda event: self.on_leave())     
+        
+        if self.function != None:
+            if self.args != None:
+                self.button.configure(command=lambda: self.function(self.args))
+            else:
+                self.button.configure(command=lambda: self.function())
+                
+        # Set inital state
+        self.button.configure(image=self.icon_on) 
+        
+        if self.default_data[self.name+'State']:
+            self.enable_button()
+
+        else:
+            self.disable_button()
+    
+    def add_info_frame(self, info):
+        self.info = info
+        
+    
+    def on_enter(self):
+        if self.info:
+            self.info.configure(text=self.default_data[self.name+'InfoText'])
+    
+        if not self.state and not self.error:
+            self.button.configure(image=self.icon_hover)    
+            self.button.configure(fg='#B1B1B2') 
+
+    def on_leave(self):
+        if not self.state and not self.error:
+
+            self.button.configure(image=self.icon_off)  
+            self.button.configure(fg='#828282') 
+
+    def enable_button(self):
+
+        self.button.configure(image=self.icon_on)
+        self.button.configure(fg='#FFFFFF') 
+        self.state = True
+        self.error = False
+        
+    def disable_button(self):
+
+        self.button.configure(image=self.icon_off)
+        self.button.configure(fg='#828282') 
+        self.state = False
+        self.error = False
+
+    def toggle_button(self):
+        self.state = not self.state
+        
+        if self.state:
+            self.button.configure(image=self.icon_on)
+            self.button.configure(fg='#FFFFFF') 
+        else:
+            self.button.configure(image=self.icon_off)
+            self.button.configure(fg='#828282') 
+       
+    def temp_disable_button(self):
+        self.hold_state = self.state
+        self.state = False
+        
+    def temp_enable_button(self):
+        self.state = self.hold_state
+
+    def error_button(self):
+
+        self.button.configure(image=self.icon_off)
+        self.button.configure(fg='light goldenrod')
+        self.state = False 
+        self.error = True
+        
+    def get(self):
+        return self.state
+        
+    def set(self, value, request_frame=True):
+        if value:
+            self.enable_button()
+
+        elif not value:
+            self.disable_button()
+        if request_frame:
+            if self.function != None:
+                if self.args != None:
+                    self.function(self.args)
+                else:
+                    self.function()            
+       
+    def hide(self):
+        pass
+        
+    def unhide(self):
+        pass
+
+    def get_data_type(self):
+        return self.data_type
+        
+    def load_default(self):
+        self.set(self.default_data[self.name+'State'])  
+
+class TextSelection():
+    def __init__(self, parent, name, display_text, style_level, function, argument, data_type, width, height, x, y, text_percent):
+        self.blank = tk.PhotoImage()
+
+        self.default_data = DEFAULT_DATA
+        # Capture inputs as instance variables
+        self.parent = parent
+        self.name = name
+        self.function = function
+        self.argument = argument
+        self.data_type = data_type
+        self.width = width
+        self.height = height
+        self.style = []
+        self.info = []
+
+        if style_level == 3:
+            self.frame_style = style.canvas_frame_label_3 
+            self.text_style = style.text_3
+            self.sel_off_style = style.text_selection_off_3
+            self.sel_on_style = style.text_selection_on_3
+        
+        if style_level == 2:
+            self.frame_style = style.canvas_frame_label_2
+            self.text_style = style.text_2
+            self.sel_off_style = style.text_selection_off_2
+            self.sel_on_style = style.text_selection_on_2
+        
+        self.display_text = display_text+' '
+        
+        self.textselect_label = {}
+        
+        # Initial data
+        self.selection = self.default_data[self.name+'Mode']
+        
+        # Frame to hold everything
+        self.ts_frame = tk.Frame(self.parent, self.frame_style, width=self.width, height=self.height)
+        self.ts_frame.place(x=x, y=y)
+        self.ts_frame.bind("<Enter>", lambda event: self.on_enter())
+        
+        self.text_width = int(width*(1.0-text_percent)) 
+        
+        # Create the text on the left
+        self.text_label = tk.Label(self.ts_frame, self.text_style, image=self.blank, compound='c', text=self.display_text, anchor='e', width=self.text_width, height=height)
+        self.text_label.place(x=0, y=0)
+        
+        # Loop through the parameter modes, create a label 
+        # Gotta find the size of the buttons according to the font
+        self.font = tk.font.Font(family="Segoe UI", size=10, weight="normal")
+        x_spacing = self.text_width + 10
+
+        
+        for mode in self.default_data[self.name+'Modes']:
+            # Get size of text in pixels
+            m_len = self.font.measure(mode)
+            
+            # Create a label with the text
+            self.textselect_label[mode] = tk.Label(self.ts_frame, self.sel_off_style, text=mode, image=self.blank, compound='c', anchor='c', width=m_len, height=height)
+            self.textselect_label[mode].place(x=x_spacing, y=0)
+            self.textselect_label[mode].bind("<ButtonRelease-1>", lambda event, mode=mode: self.select_ui_text_selection(mode))
+            
+            # Initial value
+            if mode==self.selection:
+                self.textselect_label[mode].configure(self.sel_on_style)
+            
+            x_spacing = x_spacing + m_len+10
+
+    def select_ui_text_selection(self, selection, request_frame=True):
+        # Loop over all of the Modes
+        for mode in self.default_data[self.name+'Modes']:
+
+            # If the Mode has been selected
+            if mode==selection:      
+                # Set state to true
+                self.textselect_label[mode].configure(self.sel_on_style)
+                self.selection = mode
+                if request_frame:
+                    self.function(self.argument, self.name)
+
+            else:
+                self.textselect_label[mode].configure(self.sel_off_style)  
+
+    def add_info_frame(self, info):
+        self.info = info 
+        
+    def on_enter(self):
+        if self.info:
+            self.info.configure(text=self.default_data[self.name+'InfoText'])
+ 
+    def get(self):
+        return self.selection
+    
+    def set(self, value, request_frame=True):
+        self.select_ui_text_selection(value, request_frame)
+        
+    def hide(self):
+        pass
+        
+    def unhide(self):
+        pass 
+        
+    def get_data_type(self):
+        return self.data_type  
+
+    def load_default(self):
+        self.set(self.default_data[self.name+'Mode'])           
+ 
+
+class Switch2():
+    def __init__(self, parent, name, display_text, style_level, function, argument, width, height, x, y, toggle_x=0, toggle_width=40):
+        self.blank = tk.PhotoImage()
+        self.default_data = DEFAULT_DATA
+        # Capture inputs as instance variables
+        self.parent = parent
+        self.name = name
+        self.function = function
+        self.argument = argument
+        self.data_type = argument
+        self.width = width
+        self.height = height
+        self.x = x
+        self.y = y
+        self.style = []
+        self.info = []
+        
+        # Initial Value
+        self.state = self.default_data[name+'State']
+        
+        if style_level == 3:
+            self.frame_style = style.canvas_frame_label_3 
+            self.text_style = style.text_3
+            self.entry_style = style.entry_3 
+      
+        self.display_text = display_text
+        # Load Icons
+        self.img = Image.open(style.icon['IconOff'])
+        self.img = self.img.resize((40,40), Image.ANTIALIAS)
+        self.icon_off = ImageTk.PhotoImage(self.img)    
+        
+        self.img = Image.open(style.icon['IconOn'])
+        self.img = self.img.resize((40,40), Image.ANTIALIAS)
+        self.icon_on = ImageTk.PhotoImage(self.img) 
+
+        # Frame to hold everything
+        self.switch_frame = tk.Frame(self.parent, self.frame_style, width=self.width, height=self.height)
+        self.switch_frame.place(x=self.x, y=self.y)
+        self.switch_frame.bind("<Enter>", lambda event: self.on_enter())
+ 
+
+        #toggle_width = 40
+        text_width = self.width-toggle_width
+        
+        # Toggle Switch
+        self.switch = tk.Label(self.switch_frame, style.parameter_switch_3, image=self.icon_off, width=toggle_width, height=self.height)
+        if self.state:
+            self.switch.configure(image=self.icon_on)
+        #self.switch.place(x=0, y=2)
+        self.switch.place(x=toggle_x, y=2)
+        self.switch.bind("<ButtonRelease-1>", lambda event: self.toggle_switch(event))
+        
+        # Text
+        self.switch_text = tk.Label(self.switch_frame, style.parameter_switch_3, image=self.blank, compound='right', text=self.display_text, anchor='w', width=text_width, height=height-2)
+        #self.switch_text.place(x=50, y=0)
+        self.switch_text.place(x=toggle_x + toggle_width + 10, y=0)
+
+    def toggle_switch(self, event, set_value=None, request_frame=True):
+        # flip state
+        if set_value==None:
+            self.state = not self.state
+        else:
+            self.state = set_value
+            
+        if self.state:
+            self.switch.configure(image=self.icon_on)
+            
+        else:
+            self.switch.configure(image=self.icon_off)
+
+        if request_frame:
+            self.function(self.argument, self.name, use_markers=False)
+    
+    def add_info_frame(self, info):
+        self.info = info
+        
+    def on_enter(self):
+        if self.info:
+            self.info.configure(text=self.default_data[self.name+'InfoText'])
+
+    def hide(self):
+        self.switch_frame.place_forget()
+        self.switch.place_forget()
+        self.switch_text.place_forget()
+        
+    def unhide(self):
+        self.switch_frame.place(x=self.x, y=self.y)    
+        self.switch.place(x=0, y=2)
+        self.switch_text.place(x=50, y=0)
+        
+    def set(self, value, request_frame=True):
+        self.toggle_switch(None, value, request_frame)
+
+    def get(self):
+        return self.state   
+
+    def get_data_type(self):
+        return self.data_type 
+
+    def load_default(self):
+        self.set(self.default_data[self.name+'State'])        
+                
+class Slider2():
+    def __init__(self, parent, name, display_text, style_level, function, argument, width, height, x, y, slider_percent):
+
+        # self.constants = CONSTANTS
+        self.default_data = DEFAULT_DATA
+        self.blank = tk.PhotoImage() 
+        
+        # Capture inputs as instance variables
+        self.parent = parent
+        self.name = name
+        self.function = function
+        self.data_type = argument
+        self.x = x
+        self.y = y
+        self.slider_percent = slider_percent
+        self.width = width
+        self.height = height
+        self.info = []
+        
+        # Initial Value
+        self.amount = self.default_data[name+'Amount']
+        
+        if style_level == 1:
+            self.frame_style = style.canvas_frame_label_1 
+            self.text_style = style.text_1
+            self.entry_style = style.entry_3          
+
+        elif style_level == 3:
+            self.frame_style = style.canvas_frame_label_3 
+            self.text_style = style.text_3
+            self.entry_style = style.entry_3    
+        
+        # UI-controlled variables        
+        self.entry_string = tk.StringVar()
+        self.entry_string.set(self.amount)        
+        
+        # Widget variables
+        self.min_ = self.default_data[name+'Min']
+        self.max_ = self.default_data[name+'Max'] 
+        self.inc_ = self.default_data[name+'Inc']   
+        self.display_text = display_text+' '         
+
+        # Set up spacing
+        # |----------------------|slider_pad|-slider-|entry_pad|-|
+        # |---1-slider_percent---|---slider_percent---|
+        # |--------------------width------------------|        
+        
+        # Create a frame to hold it all
+        self.frame_x = x
+        self.frame_y = y
+        self.frame_width = width
+        self.frame_height = height
+    
+        self.frame = tk.Frame(self.parent, self.frame_style, width=self.frame_width, height=self.frame_height)
+        self.frame.place(x=self.frame_x, y=self.frame_y)
+        self.frame.bind("<Enter>", lambda event: self.on_enter())
+
+
+        # Add the slider Label text to the frame
+        self.txt_label_x = 0
+        self.txt_label_y = 0
+        self.txt_label_width = int(width*(1.0-slider_percent))        
+
+        self.label = tk.Label(self.frame, self.text_style, image=self.blank, compound='c', text=self.display_text, anchor='e', width=self.txt_label_width, height=self.height)
+        self.label.place(x=self.txt_label_x, y=self.txt_label_y)
+        
+        # Add the Slider Canvas to the frame
+        self.slider_canvas_x = self.txt_label_width
+        self.slider_canvas_y = 0
+        self.slider_canvas_width = width-self.txt_label_width
+        
+        self.slider = tk.Canvas(self.frame, self.frame_style, width=self.slider_canvas_width, height=self.height)
+        self.slider.place(x=self.slider_canvas_x, y=self.slider_canvas_y)
+        self.slider.bind('<B1-Motion>', lambda e: self.update_handle(e, True))
+        self.slider.bind('<MouseWheel>', lambda e: self.update_handle(e, True))         
+
+        # Add the Entry to the frame
+        self.entry_width = 60
+        self.entry_x = self.frame_width-self.entry_width 
+        self.entry_y = 0
+
+        self.entry = tk.Entry(self.frame, self.entry_style, textvariable=self.entry_string)
+        self.entry.place(x=self.entry_x, y=self.entry_y)
+        self.entry.bind('<Return>', lambda event: self.entry_input(event))      
+
+        # Draw the slider
+        self.slider_pad = 20
+        self.entry_pad = 20
+        self.slider_left = self.slider_pad
+        self.slider_right = self.slider_canvas_width-self.entry_pad-self.entry_width 
+        self.slider_center = (self.height+1)/2
+
+        self.oval_loc = self.pos2coord(self.amount)
+        self.oval_radius = 5
+        self.oval_x1 = self.oval_loc-self.oval_radius
+        self.oval_y1 = self.slider_center-self.oval_radius
+        self.oval_x2 = self.oval_loc+self.oval_radius
+        self.oval_y2 = self.slider_center+self.oval_radius
+        
+        self.trough_x1 = self.slider_left
+        self.trough_y1 = self.slider_center-2
+        self.trough_x2 = self.slider_right
+        self.trough_y2 = self.slider_center+2  
+
+        self.slider.create_rectangle(self.trough_x1, self.trough_y1, self.trough_x2, self.trough_y2, fill='#1F1F1F', outline='')        
+        self.handle = self.slider.create_oval(self.oval_x1, self.oval_y1, self.oval_x2, self.oval_y2, fill='#919191', outline='')
+        
+    def coord2pos(self, coord):
+        return float((coord-self.slider_left)*(self.max_-self.min_)/(self.slider_right-self.slider_left) + self.min_)
+        
+    def pos2coord(self, pos):
+        return float((float(pos)-self.min_)*(self.slider_right-self.slider_left)/(self.max_-self.min_) + self.slider_left)
+
+    def update_handle(self, event, also_update_entry=False, request_frame=True):
+        if isinstance(event, float):
+            position = event
+
+        elif event.type == '38':
+            position = self.amount+self.inc_*int(event.delta/120.0)
+
+        elif event.type == '6':
+            x_coord = float(event.x)
+            position = self.coord2pos(x_coord)
+            
+        # constrain mousewheel movement
+        if position < self.min_: position = self.min_
+        elif position > self.max_: position = self.max_
+        
+        # Find closest position increment
+        position_inc = round((position-self.min_) / self.inc_)      
+        position = (position_inc * self.inc_)+self.min_
+
+        # moving sends many events, so only update when the next frame is reached
+        if position != self.amount:
+            # Move handle to coordinate based on position
+            self.slider.move(self.handle, self.pos2coord(position) - self.pos2coord(self.amount), 0)
+
+            # Save for next time           
+            self.amount = position
+            
+            if also_update_entry:
+                self.entry_string.set(str(position))
+            
+            if request_frame:
+                self.function(self.data_type, self.name, use_markers=False)
+                
+            # return True
+        # return False
+    
+    def add_info_frame(self, info):
+        self.info = info
+    
+    def on_enter(self):
+        if self.info:
+            self.info.configure(text=self.default_data[self.name+'InfoText'])
+        
+    def entry_input(self, event): 
+    # event.char
+        self.entry.update()
+        try:
+            input_num = float(self.entry_string.get())
+            self.update_handle(input_num, False)
+        except:
+            return
+    
+    def set(self, value, request_frame=True):
+        self.update_handle(float(value), True)
+
+    def get(self):
+        return self.amount
+        
+    def hide(self):
+        self.frame.place_forget()
+        self.label.place_forget()
+        self.slider.place_forget()
+        self.entry.place_forget()
+        
+    def unhide(self):
+        self.frame.place(x=self.frame_x, y=self.frame_y)    
+        self.label.place(x=self.txt_label_x, y=self.txt_label_y)
+        self.slider.place(x=self.slider_canvas_x, y=self.slider_canvas_y)    
+        self.entry.place(x=self.entry_x, y=self.entry_y)  
+            
+    # def save_to_file(self, filename, data):
+        # with open(filename, 'w') as outfile:
+            # json.dump(data, outfile)
+            
+    def get_data_type(self):
+        return self.data_type 
+
+    def load_default(self):
+        self.set(self.default_data[self.name+'Amount'])
+
+
+class Slider3():
+    def __init__(self, parent, name, display_text, style_level, function, argument, width, height, x, y, slider_percent):
+
+        # self.constants = CONSTANTS
+        # self.default_data = DEFAULT_DATA
+        self.blank = tk.PhotoImage()
+
+        # Capture inputs as instance variables
+        self.parent = parent
+        self.name = name
+        self.function = function
+        self.data_type = argument
+        self.x = x
+        self.y = y
+        self.slider_percent = slider_percent
+        self.width = width
+        self.height = height
+        self.info = []
+
+        # Initial Value
+        self.amount = 0
+
+        if style_level == 1:
+            self.frame_style = style.canvas_frame_label_1
+            self.text_style = style.text_1
+            self.entry_style = style.entry_3
+
+        elif style_level == 3:
+            self.frame_style = style.canvas_frame_label_3
+            self.text_style = style.text_3
+            self.entry_style = style.entry_3
+
+            # UI-controlled variables
+        self.entry_string = tk.StringVar()
+        self.entry_string.set(self.amount)
+
+        # Widget variables
+        self.min_ = -2
+        self.max_ = 2
+        self.inc_ = 0.001
+        self.display_text = display_text + ' '
+
+        # Set up spacing
+        # |----------------------|slider_pad|-slider-|entry_pad|-|
+        # |---1-slider_percent---|---slider_percent---|
+        # |--------------------width------------------|
+
+        # Create a frame to hold it all
+        self.frame_x = x
+        self.frame_y = y
+        self.frame_width = width
+        self.frame_height = height
+
+        self.frame = tk.Frame(self.parent, self.frame_style, width=self.frame_width, height=self.frame_height)
+        self.frame.place(x=self.frame_x, y=self.frame_y)
+        # self.frame.bind("<Enter>", lambda event: self.on_enter())
+
+
+        # Add the slider Label text to the frame
+        self.txt_label_x = 0
+        self.txt_label_y = 0
+        self.txt_label_width = int(width * (1.0 - slider_percent))
+
+        self.label = tk.Label(self.frame, self.text_style, image=self.blank, compound='c', text=self.display_text, anchor='e', width=self.txt_label_width, height=self.height)
+        self.label.place(x=self.txt_label_x, y=self.txt_label_y)
+
+        # Add the Slider Canvas to the frame
+        self.slider_canvas_x = self.txt_label_width
+        self.slider_canvas_y = 0
+        self.slider_canvas_width = width - self.txt_label_width
+
+        self.slider = tk.Canvas(self.frame, self.frame_style, width=self.slider_canvas_width, height=self.height)
+        self.slider.place(x=self.slider_canvas_x, y=self.slider_canvas_y)
+        self.slider.bind('<B1-Motion>', lambda e: self.update_handle(e, True))
+        self.slider.bind('<MouseWheel>', lambda e: self.update_handle(e, True))
+
+        # Add the Entry to the frame
+        self.entry_width = 60
+        self.entry_x = self.frame_width - self.entry_width
+        self.entry_y = 0
+
+        self.entry = tk.Entry(self.frame, self.entry_style, textvariable=self.entry_string)
+        self.entry.place(x=self.entry_x, y=self.entry_y)
+        self.entry.bind('<Return>', lambda event: self.entry_input(event))
+
+        # Draw the slider
+        self.slider_pad = 20
+        self.entry_pad = 20
+        self.slider_left = self.slider_pad
+        self.slider_right = self.slider_canvas_width - self.entry_pad - self.entry_width
+        self.slider_center = (self.height + 1) / 2
+
+        self.oval_loc = self.pos2coord(self.amount)
+        self.oval_radius = 5
+        self.oval_x1 = self.oval_loc - self.oval_radius
+        self.oval_y1 = self.slider_center - self.oval_radius
+        self.oval_x2 = self.oval_loc + self.oval_radius
+        self.oval_y2 = self.slider_center + self.oval_radius
+
+        self.trough_x1 = self.slider_left
+        self.trough_y1 = self.slider_center - 2
+        self.trough_x2 = self.slider_right
+        self.trough_y2 = self.slider_center + 2
+
+        self.slider.create_rectangle(self.trough_x1, self.trough_y1, self.trough_x2, self.trough_y2, fill='#1F1F1F', outline='')
+        self.handle = self.slider.create_oval(self.oval_x1, self.oval_y1, self.oval_x2, self.oval_y2, fill='#919191', outline='')
+
+    def coord2pos(self, coord):
+        return float((coord - self.slider_left) * (self.max_ - self.min_) / (self.slider_right - self.slider_left) + self.min_)
+
+    def pos2coord(self, pos):
+        return float((float(pos) - self.min_) * (self.slider_right - self.slider_left) / (self.max_ - self.min_) + self.slider_left)
+
+    def update_handle(self, event, also_update_entry=False, request_frame=True):
+        if isinstance(event, float):
+            position = event
+
+        elif event.type == '38':
+            position = self.amount + self.inc_ * int(event.delta / 120.0)
+
+        elif event.type == '6':
+            x_coord = float(event.x)
+            position = self.coord2pos(x_coord)
+
+        # constrain mousewheel movement
+        if position < self.min_:
+            position = self.min_
+        elif position > self.max_:
+            position = self.max_
+
+        # Find closest position increment
+        position_inc = round((position - self.min_) / self.inc_)
+        position = (position_inc * self.inc_) + self.min_
+
+        # moving sends many events, so only update when the next frame is reached
+        if position != self.amount:
+            # Move handle to coordinate based on position
+            self.slider.move(self.handle, self.pos2coord(position) - self.pos2coord(self.amount), 0)
+
+            # Save for next time
+            self.amount = position
+
+            if also_update_entry:
+                self.entry_string.set(str(position))
+
+            if request_frame:
+                self.function(self.data_type)
+
+            # return True
+        # return False
+
+    def add_info_frame(self, info):
+        self.info = info
+
+    # def on_enter(self):
+    #     if self.info:
+    #         self.info.configure(text=self.default_data[self.name + 'InfoText'])
+
+    def entry_input(self, event):
+        # event.char
+        self.entry.update()
+        try:
+            input_num = float(self.entry_string.get())
+            self.update_handle(input_num, False)
+        except:
+            return
+
+    def set(self, value, request_frame=True):
+        self.update_handle(float(value), True, request_frame)
+
+    def get(self):
+        return self.amount
+
+    def hide(self):
+        self.frame.place_forget()
+        self.label.place_forget()
+        self.slider.place_forget()
+        self.entry.place_forget()
+
+    def unhide(self):
+        self.frame.place(x=self.frame_x, y=self.frame_y)
+        self.label.place(x=self.txt_label_x, y=self.txt_label_y)
+        self.slider.place(x=self.slider_canvas_x, y=self.slider_canvas_y)
+        self.entry.place(x=self.entry_x, y=self.entry_y)
+
+        # def save_to_file(self, filename, data):
+        # with open(filename, 'w') as outfile:
+        # json.dump(data, outfile)
+
+    def get_data_type(self):
+        return self.data_type
+
+    def load_default(self):
+        self.set(0)
+
+class Text_Entry():
+    def __init__(self, parent, name, display_text, style_level, function, data_type, width, height, x, y, text_percent):
+        self.blank = tk.PhotoImage()
+
+        self.default_data = DEFAULT_DATA
+        # Capture inputs as instance variables
+        self.parent = parent
+        self.name = name
+        self.function = function
+        self.data_type = data_type
+        self.width = width
+        self.height = height
+        self.style = []
+        self.info = []
+
+        if style_level == 3:
+            self.frame_style = style.canvas_frame_label_3 
+            self.text_style = style.text_3
+            self.sel_off_style = style.text_selection_off_3
+            self.sel_on_style = style.text_selection_on_3
+        
+        if style_level == 2:
+            self.frame_style = style.canvas_frame_label_2
+            self.text_style = style.text_2
+            self.sel_off_style = style.text_selection_off_2
+            self.sel_on_style = style.text_selection_on_2
+        
+        self.display_text = display_text+' '
+        
+     
+        # Initial data
+        self.entry_text = tk.StringVar()
+        self.entry_text.set(self.default_data[self.name])
+        
+        # Frame to hold everything
+        self.ts_frame = tk.Frame(self.parent, self.frame_style, width=self.width, height=self.height)
+        self.ts_frame.place(x=x, y=y)
+        self.ts_frame.bind("<Enter>", lambda event: self.on_enter())
+        
+        self.text_width = int(width*(1.0-text_percent))
+        
+        # Create the text on the left
+        self.text_label = tk.Label(self.ts_frame, self.text_style, image=self.blank, compound='c', text=self.display_text, anchor='e', width=self.text_width, height=height)
+        self.text_label.place(x=0, y=0)
+        
+        
+        
+        self.entry = tk.Entry(self.ts_frame, style.entry_2, textvariable=self.entry_text)
+        self.entry.place(x=self.text_width+20, y=0, width = self.width-self.text_width-50, height=15) 
+        self.entry.bind("<Return>", lambda event: self.send_text(self.entry_text.get())) 
+        
+    def send_text(self, text):
+        self.function(self.data_type, self.name, use_markers=False)
+
+    def add_info_frame(self, info):
+        self.info = info 
+        
+    def on_enter(self):
+        if self.info:
+            self.info.configure(text=self.default_data[self.name+'InfoText'])
+ 
+    def get(self):
+        return self.entry_text.get()
+    
+    def set(self, value, request_frame=True):
+        pass
+        # self.select_ui_text_selection(value, request_frame)
+        
+    def hide(self):
+        pass
+        
+    def unhide(self):
+        pass 
+        
+    def get_data_type(self):
+        return self.data_type  
+
+    def load_default(self):
+        pass
+        # self.set(self.default_data[self.name+'Mode']) 
+        
+class VRAM_Indicator():
+    def __init__(self, parent, style_level, width, height, x, y):
+        self.parent = parent
+        self.width = width
+        self.height = height
+        self.x = x
+        self.y = y
+        self.blank = tk.PhotoImage()
+        
+        self.used = 0
+        self.total = 1
+        
+        if style_level == 3:
+            self.frame_style = style.canvas_frame_label_3 
+            self.text_style = style.text_3
+            self.sel_off_style = style.text_selection_off_3
+            self.sel_on_style = style.text_selection_on_3
+        
+        if style_level == 2:
+            self.frame_style = style.canvas_frame_label_2
+            self.text_style = style.text_2
+            self.sel_off_style = style.text_selection_off_2
+            self.sel_on_style = style.text_selection_on_2
+        
+        if style_level == 1:
+            self.frame_style = style.canvas_frame_label_1
+
+        self.frame = tk.Frame(self.parent, self.frame_style, width=self.width, height=self.height)
+        self.frame.place(x=self.x, y=self.y)
+        
+        self.label_name = tk.Label(self.frame, self.frame_style, image=self.blank, compound='c', fg='#b1b1b2', font=("Segoe UI", 9), width=50, text='VRAM', height=self.height)
+        self.label_name.place(x=0, y=0)
+
+        
+        # self.label_value = tk.Label(self.frame, self.frame_style, bg='yellow', image=self.blank, compound='c', fg='#D0D0D0', font=("Segoe UI", 9), justify='right', width=100, text='VRAM', height=self.height)
+        # self.label_value.place(x=200, y=0)
+        
+        
+        self.canvas = tk.Canvas(self.frame, self.frame_style, highlightthickness =2, highlightbackground='#b1b1b2', width=self.width-60, height=self.height-4)
+        self.canvas.place(x=50, y=0)
+        
+    def update_display(self):
+        self.canvas.delete('all')
+        width = self.canvas.winfo_width()
+
+        try:
+            ratio = self.used/self.total
+        except ZeroDivisionError:
+            ratio = 1
+        
+        if ratio>0.9:
+            color = '#d10303'
+        else:
+            color = '#b1b1b2'
+        width = ratio*width
+        
+        self.canvas.create_rectangle(0, 0, width, self.height, fill=color)
+        
+        # text = str(self.used)+' / '+str(self.total)+' MB'        
+        # self.label_value.configure(text=text)
+    
+    def set(self, used, total):
+        self.used = used
+        self.total = total
+        
+        self.update_display()
+        
+    def hide(self):
+        pass
+ 
+    def unhide(self):
+        pass  
+        

rope/Models.py

@@ -0,0 +1,1961 @@
+import cv2
+import numpy as np
+from skimage import transform as trans
+import torch
+import torchvision
+torchvision.disable_beta_transforms_warning()
+from torchvision.transforms import v2
+from numpy.linalg import norm as l2norm
+import onnxruntime
+import onnx
+from itertools import product as product
+import subprocess as sp
+onnxruntime.set_default_logger_severity(4)
+onnxruntime.log_verbosity_level = -1
+import rope.FaceUtil as faceutil
+import pickle
+
+class Models():
+    def __init__(self):
+        self.arcface_dst = np.array( [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], [41.5493, 92.3655], [70.7299, 92.2041]], dtype=np.float32)
+        self.providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
+
+        self.retinaface_model = []
+        self.yoloface_model = []
+        self.scrdf_model = []
+        self.yunet_model = []
+        self.face_landmark_68_model = []
+        self.face_landmark_3d68_model = []
+        self.mean_lmk = []
+        self.face_landmark_98_model = []
+        self.face_landmark_106_model = []
+        self.face_landmark_478_model = []
+        self.face_blendshapes_model = []
+        self.resnet50_model, self.anchors  = [], []
+
+        self.insight106_model = []
+
+        self.recognition_model = []
+        self.swapper_model = []
+        self.swapper_model_kps = []
+        self.swapper_model_swap = []
+
+        self.emap = []
+        self.GFPGAN_model = []
+        self.GPEN_256_model = []
+        self.GPEN_512_model = []
+        self.GPEN_1024_model = []
+        self.codeformer_model = []
+
+        self.occluder_model = []
+        self.faceparser_model = []
+
+        self.syncvec = torch.empty((1,1), dtype=torch.float32, device='cuda:0')
+
+        self.normalize = v2.Normalize(mean = [ 0., 0., 0. ],
+                                      std = [ 1/1.0, 1/1.0, 1/1.0 ])
+
+        self.LandmarksSubsetIdxs = [
+            0, 1, 4, 5, 6, 7, 8, 10, 13, 14, 17, 21, 33, 37, 39,
+            40, 46, 52, 53, 54, 55, 58, 61, 63, 65, 66, 67, 70, 78, 80,
+            81, 82, 84, 87, 88, 91, 93, 95, 103, 105, 107, 109, 127, 132, 133,
+            136, 144, 145, 146, 148, 149, 150, 152, 153, 154, 155, 157, 158, 159, 160,
+            161, 162, 163, 168, 172, 173, 176, 178, 181, 185, 191, 195, 197, 234, 246,
+            249, 251, 263, 267, 269, 270, 276, 282, 283, 284, 285, 288, 291, 293, 295,
+            296, 297, 300, 308, 310, 311, 312, 314, 317, 318, 321, 323, 324, 332, 334,
+            336, 338, 356, 361, 362, 365, 373, 374, 375, 377, 378, 379, 380, 381, 382,
+            384, 385, 386, 387, 388, 389, 390, 397, 398, 400, 402, 405, 409, 415, 454,
+            466, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477
+        ]
+
+    def get_gpu_memory(self):
+        command = "nvidia-smi --query-gpu=memory.total --format=csv"
+        memory_total_info = sp.check_output(command.split()).decode('ascii').split('\n')[:-1][1:]
+        memory_total = [int(x.split()[0]) for i, x in enumerate(memory_total_info)]
+
+        command = "nvidia-smi --query-gpu=memory.free --format=csv"
+        memory_free_info = sp.check_output(command.split()).decode('ascii').split('\n')[:-1][1:]
+        memory_free = [int(x.split()[0]) for i, x in enumerate(memory_free_info)]
+
+        memory_used = memory_total[0] - memory_free[0]
+
+        return memory_used, memory_total[0]
+
+    def run_detect(self, img, detect_mode='Retinaface', max_num=1, score=0.5, use_landmark_detection=False, landmark_detect_mode='98', landmark_score=0.5, from_points=False):
+        bboxes = []
+        kpss = []
+
+        if detect_mode=='Retinaface':
+            if not self.retinaface_model:
+                self.retinaface_model = onnxruntime.InferenceSession('./models/det_10g.onnx', providers=self.providers)
+
+            bboxes, kpss = self.detect_retinaface(img, max_num=max_num, score=score, use_landmark_detection=use_landmark_detection, landmark_detect_mode=landmark_detect_mode, landmark_score=landmark_score, from_points=from_points)
+
+        elif detect_mode=='SCRDF':
+            if not self.scrdf_model:
+                self.scrdf_model = onnxruntime.InferenceSession('./models/scrfd_2.5g_bnkps.onnx', providers=self.providers)
+
+            bboxes, kpss = self.detect_scrdf(img, max_num=max_num, score=score, use_landmark_detection=use_landmark_detection, landmark_detect_mode=landmark_detect_mode, landmark_score=landmark_score, from_points=from_points)
+
+        elif detect_mode=='Yolov8':
+            if not self.yoloface_model:
+                self.yoloface_model = onnxruntime.InferenceSession('./models/yoloface_8n.onnx', providers=self.providers)
+                #self.insight106_model = onnxruntime.InferenceSession('./models/2d106det.onnx', providers=self.providers)
+
+            bboxes, kpss = self.detect_yoloface(img, max_num=max_num, score=score, use_landmark_detection=use_landmark_detection, landmark_detect_mode=landmark_detect_mode, landmark_score=landmark_score, from_points=from_points)
+
+        elif detect_mode=='Yunet':
+            if not self.yunet_model:
+                self.yunet_model = onnxruntime.InferenceSession('./models/yunet_n_640_640.onnx', providers=self.providers)
+
+            bboxes, kpss = self.detect_yunet(img, max_num=max_num, score=score, use_landmark_detection=use_landmark_detection, landmark_detect_mode=landmark_detect_mode, landmark_score=landmark_score, from_points=from_points)
+
+        return bboxes, kpss
+
+    def run_detect_landmark(self, img, bbox, det_kpss, detect_mode='98', score=0.5, from_points=False):
+        kpss = []
+        scores = []
+
+        if detect_mode=='5':
+            if not self.resnet50_model:
+                self.resnet50_model = onnxruntime.InferenceSession("./models/res50.onnx", providers=self.providers)
+
+                feature_maps = [[64, 64], [32, 32], [16, 16]]
+                min_sizes = [[16, 32], [64, 128], [256, 512]]
+                steps = [8, 16, 32]
+                image_size = 512
+
+                for k, f in enumerate(feature_maps):
+                    min_size_array = min_sizes[k]
+                    for i, j in product(range(f[0]), range(f[1])):
+                        for min_size in min_size_array:
+                            s_kx = min_size / image_size
+                            s_ky = min_size / image_size
+                            dense_cx = [x * steps[k] / image_size for x in [j + 0.5]]
+                            dense_cy = [y * steps[k] / image_size for y in [i + 0.5]]
+                            for cy, cx in product(dense_cy, dense_cx):
+                                self.anchors += [cx, cy, s_kx, s_ky]
+
+            kpss, scores = self.detect_face_landmark_5(img, bbox=bbox, det_kpss=det_kpss, from_points=from_points)
+
+        elif detect_mode=='68':
+            if not self.face_landmark_68_model:
+                self.face_landmark_68_model = onnxruntime.InferenceSession('./models/2dfan4.onnx', providers=self.providers)
+
+            kpss, scores = self.detect_face_landmark_68(img, bbox=bbox, det_kpss=det_kpss, convert68_5=True, from_points=from_points)
+
+        elif detect_mode=='3d68':
+            if not self.face_landmark_3d68_model:
+                self.face_landmark_3d68_model = onnxruntime.InferenceSession('./models/1k3d68.onnx', providers=self.providers)
+                with open('./models/meanshape_68.pkl', 'rb') as f:
+                    self.mean_lmk = pickle.load(f)
+
+            kpss, scores = self.detect_face_landmark_3d68(img, bbox=bbox, det_kpss=det_kpss, convert68_5=True, from_points=from_points)
+
+            return kpss, scores
+
+        elif detect_mode=='98':
+            if not self.face_landmark_98_model:
+                self.face_landmark_98_model = onnxruntime.InferenceSession('./models/peppapig_teacher_Nx3x256x256.onnx', providers=self.providers)
+
+            kpss, scores = self.detect_face_landmark_98(img, bbox=bbox, det_kpss=det_kpss, convert98_5=True, from_points=from_points)
+
+        elif detect_mode=='106':
+            if not self.face_landmark_106_model:
+                self.face_landmark_106_model = onnxruntime.InferenceSession('./models/2d106det.onnx', providers=self.providers)
+
+            kpss, scores = self.detect_face_landmark_106(img, bbox=bbox, det_kpss=det_kpss, convert106_5=True, from_points=from_points)
+
+            return kpss, scores
+
+        elif detect_mode=='478':
+            if not self.face_landmark_478_model:
+                self.face_landmark_478_model = onnxruntime.InferenceSession('./models/face_landmarks_detector_Nx3x256x256.onnx', providers=self.providers)
+
+            if not self.face_blendshapes_model:
+                self.face_blendshapes_model = onnxruntime.InferenceSession('./models/face_blendshapes_Nx146x2.onnx', providers=self.providers)
+
+            kpss, scores = self.detect_face_landmark_478(img, bbox=bbox, det_kpss=det_kpss, convert478_5=True, from_points=from_points)
+
+            return kpss, scores
+
+        if len(kpss) > 0:
+            if len(scores) > 0:
+                if np.mean(scores) >= score:
+                    return kpss, scores
+            else:
+                return kpss, scores
+
+        return [], []
+
+    def delete_models(self):
+        self.retinaface_model = []
+        self.yoloface_model = []
+        self.scrdf_model = []
+        self.yunet_model = []
+        self.face_landmark_68_model = []
+        self.face_landmark_3d68_model = []
+        self.mean_lmk = []
+        self.face_landmark_98_model = []
+        self.face_landmark_106_model = []
+        self.face_landmark_478_model = []
+        self.face_blendshapes_model = []
+        self.resnet50_model = []
+        self.insight106_model = []
+        self.recognition_model = []
+        self.swapper_model = []
+        self.GFPGAN_model = []
+        self.GPEN_256_model = []
+        self.GPEN_512_model = []
+        self.GPEN_1024_model = []
+        self.codeformer_model = []
+        self.occluder_model = []
+        self.faceparser_model = []
+
+    def run_recognize(self, img, kps):
+        if not self.recognition_model:
+            self.recognition_model = onnxruntime.InferenceSession('./models/w600k_r50.onnx', providers=self.providers)
+
+        embedding, cropped_image = self.recognize(img, kps)
+        return embedding, cropped_image
+
+    def calc_swapper_latent(self, source_embedding):
+        if not self.swapper_model:
+            graph = onnx.load("./models/inswapper_128.fp16.onnx").graph
+            self.emap = onnx.numpy_helper.to_array(graph.initializer[-1])
+
+        n_e = source_embedding / l2norm(source_embedding)
+        latent = n_e.reshape((1,-1))
+        latent = np.dot(latent, self.emap)
+        latent /= np.linalg.norm(latent)
+        return latent
+
+    def run_swapper(self, image, embedding, output):
+        if not self.swapper_model:
+            cuda_options = {"arena_extend_strategy": "kSameAsRequested", 'cudnn_conv_algo_search': 'DEFAULT'}
+            sess_options = onnxruntime.SessionOptions()
+            sess_options.enable_cpu_mem_arena = False
+
+            # self.swapper_model = onnxruntime.InferenceSession( "./models/inswapper_128_last_cubic.onnx", sess_options, providers=[('CUDAExecutionProvider', cuda_options), 'CPUExecutionProvider'])
+
+            self.swapper_model = onnxruntime.InferenceSession( "./models/inswapper_128.fp16.onnx", providers=self.providers)
+
+        io_binding = self.swapper_model.io_binding()
+        io_binding.bind_input(name='target', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,128,128), buffer_ptr=image.data_ptr())
+        io_binding.bind_input(name='source', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,512), buffer_ptr=embedding.data_ptr())
+        io_binding.bind_output(name='output', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,128,128), buffer_ptr=output.data_ptr())
+
+        self.syncvec.cpu()
+        self.swapper_model.run_with_iobinding(io_binding)
+
+    def run_swap_stg1(self, embedding):
+
+        # Load model
+        if not self.swapper_model_kps:
+            self.swapper_model_kps = onnxruntime.InferenceSession( "./models/inswapper_kps.onnx", providers=self.providers)
+
+        # Wacky data structure
+        io_binding = self.swapper_model_kps.io_binding()
+        kps_1 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous()
+        kps_2 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous()
+        kps_3 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous()
+        kps_4 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous()
+        kps_5 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous()
+        kps_6 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous()
+        kps_7 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous()
+        kps_8 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous()
+        kps_9 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous()
+        kps_10 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous()
+        kps_11 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous()
+        kps_12 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous()
+
+        # Bind the data structures
+        io_binding.bind_input(name='source', device_type='cuda', device_id=0, element_type=np.float32, shape=(1, 512), buffer_ptr=embedding.data_ptr())
+        io_binding.bind_output(name='1', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_1.data_ptr())
+        io_binding.bind_output(name='2', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_2.data_ptr())
+        io_binding.bind_output(name='3', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_3.data_ptr())
+        io_binding.bind_output(name='4', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_4.data_ptr())
+        io_binding.bind_output(name='5', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_5.data_ptr())
+        io_binding.bind_output(name='6', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_6.data_ptr())
+        io_binding.bind_output(name='7', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_7.data_ptr())
+        io_binding.bind_output(name='8', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_8.data_ptr())
+        io_binding.bind_output(name='9', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_9.data_ptr())
+        io_binding.bind_output(name='10', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_10.data_ptr())
+        io_binding.bind_output(name='11', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_11.data_ptr())
+        io_binding.bind_output(name='12', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_12.data_ptr())
+
+        self.syncvec.cpu()
+        self.swapper_model_kps.run_with_iobinding(io_binding)
+
+        # List of pointers
+        holder = []
+        holder.append(kps_1)
+        holder.append(kps_2)
+        holder.append(kps_3)
+        holder.append(kps_4)
+        holder.append(kps_5)
+        holder.append(kps_6)
+        holder.append(kps_7)
+        holder.append(kps_8)
+        holder.append(kps_9)
+        holder.append(kps_10)
+        holder.append(kps_11)
+        holder.append(kps_12)
+
+        return holder
+
+
+    def run_swap_stg2(self, image, holder, output):
+        if not self.swapper_model_swap:
+            self.swapper_model_swap = onnxruntime.InferenceSession( "./models/inswapper_swap.onnx", providers=self.providers)
+
+        io_binding = self.swapper_model_swap.io_binding()
+        io_binding.bind_input(name='target', device_type='cuda', device_id=0, element_type=np.float32, shape=(1, 3, 128, 128), buffer_ptr=image.data_ptr())
+        io_binding.bind_input(name='onnx::Unsqueeze_170', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[0].data_ptr())
+        io_binding.bind_input(name='onnx::Unsqueeze_224', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[1].data_ptr())
+        io_binding.bind_input(name='onnx::Unsqueeze_278', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[2].data_ptr())
+        io_binding.bind_input(name='onnx::Unsqueeze_332', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[3].data_ptr())
+        io_binding.bind_input(name='onnx::Unsqueeze_386', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[4].data_ptr())
+        io_binding.bind_input(name='onnx::Unsqueeze_440', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[5].data_ptr())
+        io_binding.bind_input(name='onnx::Unsqueeze_494', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[6].data_ptr())
+        io_binding.bind_input(name='onnx::Unsqueeze_548', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[7].data_ptr())
+        io_binding.bind_input(name='onnx::Unsqueeze_602', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[8].data_ptr())
+        io_binding.bind_input(name='onnx::Unsqueeze_656', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[9].data_ptr())
+        io_binding.bind_input(name='onnx::Unsqueeze_710', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[10].data_ptr())
+        io_binding.bind_input(name='onnx::Unsqueeze_764', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[11].data_ptr())
+        io_binding.bind_output(name='output', device_type='cuda', device_id=0, element_type=np.float32, shape=(1, 3, 128, 128), buffer_ptr=output.data_ptr())
+
+        self.syncvec.cpu()
+        self.swapper_model_swap.run_with_iobinding(io_binding)
+    def run_GFPGAN(self, image, output):
+        if not self.GFPGAN_model:
+            # cuda_options = {"arena_extend_strategy": "kSameAsRequested", 'cudnn_conv_algo_search': 'DEFAULT'}
+            # sess_options = onnxruntime.SessionOptions()
+            # sess_options.enable_cpu_mem_arena = False
+
+            # self.GFPGAN_model = onnxruntime.InferenceSession( "./models/GFPGANv1.4.onnx", sess_options, providers=[("CUDAExecutionProvider", cuda_options), 'CPUExecutionProvider'])
+
+            self.GFPGAN_model = onnxruntime.InferenceSession( "./models/GFPGANv1.4.onnx", providers=self.providers)
+
+        io_binding = self.GFPGAN_model.io_binding()
+        io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=image.data_ptr())
+        io_binding.bind_output(name='output', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=output.data_ptr())
+
+        self.syncvec.cpu()
+        self.GFPGAN_model.run_with_iobinding(io_binding)
+
+    def run_GPEN_1024(self, image, output):
+        if not self.GPEN_1024_model:
+            self.GPEN_1024_model = onnxruntime.InferenceSession( "./models/GPEN-BFR-1024.onnx", providers=self.providers)
+ 
+        io_binding = self.GPEN_1024_model.io_binding()
+        io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,1024,1024), buffer_ptr=image.data_ptr())
+        io_binding.bind_output(name='output', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,1024,1024), buffer_ptr=output.data_ptr())
+
+        self.syncvec.cpu()
+        self.GPEN_1024_model.run_with_iobinding(io_binding)
+
+    def run_GPEN_512(self, image, output):
+        if not self.GPEN_512_model:
+            self.GPEN_512_model = onnxruntime.InferenceSession( "./models/GPEN-BFR-512.onnx", providers=self.providers)
+
+        io_binding = self.GPEN_512_model.io_binding()
+        io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=image.data_ptr())
+        io_binding.bind_output(name='output', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=output.data_ptr())
+
+        self.syncvec.cpu()
+        self.GPEN_512_model.run_with_iobinding(io_binding)
+
+    def run_GPEN_256(self, image, output):
+        if not self.GPEN_256_model:
+            self.GPEN_256_model = onnxruntime.InferenceSession( "./models/GPEN-BFR-256.onnx", providers=self.providers)
+
+        io_binding = self.GPEN_256_model.io_binding()
+        io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,256,256), buffer_ptr=image.data_ptr())
+        io_binding.bind_output(name='output', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,256,256), buffer_ptr=output.data_ptr())
+
+        self.syncvec.cpu()
+        self.GPEN_256_model.run_with_iobinding(io_binding)
+
+    def run_codeformer(self, image, output):
+        if not self.codeformer_model:
+            self.codeformer_model = onnxruntime.InferenceSession( "./models/codeformer_fp16.onnx", providers=self.providers)
+
+        io_binding = self.codeformer_model.io_binding()
+        io_binding.bind_input(name='x', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=image.data_ptr())
+        w = np.array([0.9], dtype=np.double)
+        io_binding.bind_cpu_input('w', w)
+        io_binding.bind_output(name='y', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=output.data_ptr())
+
+        self.syncvec.cpu()
+        self.codeformer_model.run_with_iobinding(io_binding)
+
+    def run_occluder(self, image, output):
+        if not self.occluder_model:
+            self.occluder_model = onnxruntime.InferenceSession("./models/occluder.onnx", providers=self.providers)
+
+        io_binding = self.occluder_model.io_binding()
+        io_binding.bind_input(name='img', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,256,256), buffer_ptr=image.data_ptr())
+        io_binding.bind_output(name='output', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,1,256,256), buffer_ptr=output.data_ptr())
+
+        # torch.cuda.synchronize('cuda')
+        self.syncvec.cpu()
+        self.occluder_model.run_with_iobinding(io_binding)
+
+    def run_faceparser(self, image, output):
+        if not self.faceparser_model:
+            self.faceparser_model = onnxruntime.InferenceSession("./models/faceparser_fp16.onnx", providers=self.providers)
+
+        io_binding = self.faceparser_model.io_binding()
+        io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=image.data_ptr())
+        io_binding.bind_output(name='out', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,19,512,512), buffer_ptr=output.data_ptr())
+
+        # torch.cuda.synchronize('cuda')
+        self.syncvec.cpu()
+        self.faceparser_model.run_with_iobinding(io_binding)
+
+    def detect_retinaface(self, img, max_num, score, use_landmark_detection, landmark_detect_mode, landmark_score, from_points):
+        if use_landmark_detection:
+            img_landmark = img.clone()
+
+        # Resize image to fit within the input_size
+        input_size = (640, 640)
+        im_ratio = torch.div(img.size()[1], img.size()[2])
+
+        # model_ratio = float(input_size[1]) / input_size[0]
+        model_ratio = 1.0
+        if im_ratio>model_ratio:
+            new_height = input_size[1]
+            new_width = int(new_height / im_ratio)
+        else:
+            new_width = input_size[0]
+            new_height = int(new_width * im_ratio)
+        det_scale = torch.div(new_height,  img.size()[1])
+
+        resize = v2.Resize((new_height, new_width), antialias=True)
+        img = resize(img)
+        img = img.permute(1,2,0)
+
+        det_img = torch.zeros((input_size[1], input_size[0], 3), dtype=torch.float32, device='cuda:0')
+        det_img[:new_height,:new_width,  :] = img
+
+        # Switch to BGR and normalize
+        det_img = det_img[:, :, [2,1,0]]
+        det_img = torch.sub(det_img, 127.5)
+        det_img = torch.div(det_img, 128.0)
+        det_img = det_img.permute(2, 0, 1) #3,128,128
+
+        # Prepare data and find model parameters
+        det_img = torch.unsqueeze(det_img, 0).contiguous()
+
+        io_binding = self.retinaface_model.io_binding()
+        io_binding.bind_input(name='input.1', device_type='cuda', device_id=0, element_type=np.float32,  shape=det_img.size(), buffer_ptr=det_img.data_ptr())
+
+        io_binding.bind_output('448', 'cuda')
+        io_binding.bind_output('471', 'cuda')
+        io_binding.bind_output('494', 'cuda')
+        io_binding.bind_output('451', 'cuda')
+        io_binding.bind_output('474', 'cuda')
+        io_binding.bind_output('497', 'cuda')
+        io_binding.bind_output('454', 'cuda')
+        io_binding.bind_output('477', 'cuda')
+        io_binding.bind_output('500', 'cuda')
+
+        # Sync and run model
+        self.syncvec.cpu()
+        self.retinaface_model.run_with_iobinding(io_binding)
+
+        net_outs = io_binding.copy_outputs_to_cpu()
+
+        input_height = det_img.shape[2]
+        input_width = det_img.shape[3]
+
+        fmc = 3
+        center_cache = {}
+        scores_list = []
+        bboxes_list = []
+        kpss_list = []
+        for idx, stride in enumerate([8, 16, 32]):
+            scores = net_outs[idx]
+            bbox_preds = net_outs[idx+fmc]
+            bbox_preds = bbox_preds * stride
+
+            kps_preds = net_outs[idx+fmc*2] * stride
+            height = input_height // stride
+            width = input_width // stride
+            K = height * width
+            key = (height, width, stride)
+            if key in center_cache:
+                anchor_centers = center_cache[key]
+            else:
+                anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32)
+                anchor_centers = (anchor_centers * stride).reshape( (-1, 2) )
+                anchor_centers = np.stack([anchor_centers]*2, axis=1).reshape( (-1,2) )
+                if len(center_cache)<100:
+                    center_cache[key] = anchor_centers
+
+            pos_inds = np.where(scores>=score)[0]
+
+            x1 = anchor_centers[:, 0] - bbox_preds[:, 0]
+            y1 = anchor_centers[:, 1] - bbox_preds[:, 1]
+            x2 = anchor_centers[:, 0] + bbox_preds[:, 2]
+            y2 = anchor_centers[:, 1] + bbox_preds[:, 3]
+
+            bboxes = np.stack([x1, y1, x2, y2], axis=-1)
+
+            pos_scores = scores[pos_inds]
+            pos_bboxes = bboxes[pos_inds]
+            scores_list.append(pos_scores)
+            bboxes_list.append(pos_bboxes)
+
+            preds = []
+            for i in range(0, kps_preds.shape[1], 2):
+                px = anchor_centers[:, i%2] + kps_preds[:, i]
+                py = anchor_centers[:, i%2+1] + kps_preds[:, i+1]
+
+                preds.append(px)
+                preds.append(py)
+            kpss = np.stack(preds, axis=-1)
+            #kpss = kps_preds
+            kpss = kpss.reshape( (kpss.shape[0], -1, 2) )
+            pos_kpss = kpss[pos_inds]
+            kpss_list.append(pos_kpss)
+
+        scores = np.vstack(scores_list)
+        scores_ravel = scores.ravel()
+        order = scores_ravel.argsort()[::-1]
+
+        det_scale = det_scale.numpy()###
+
+        bboxes = np.vstack(bboxes_list) / det_scale
+
+        kpss = np.vstack(kpss_list) / det_scale
+        pre_det = np.hstack((bboxes, scores)).astype(np.float32, copy=False)
+        pre_det = pre_det[order, :]
+
+        dets = pre_det
+        thresh = 0.4
+        x1 = dets[:, 0]
+        y1 = dets[:, 1]
+        x2 = dets[:, 2]
+        y2 = dets[:, 3]
+        scoresb = dets[:, 4]
+
+        areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+        orderb = scoresb.argsort()[::-1]
+
+        keep = []
+        while orderb.size > 0:
+            i = orderb[0]
+            keep.append(i)
+            xx1 = np.maximum(x1[i], x1[orderb[1:]])
+            yy1 = np.maximum(y1[i], y1[orderb[1:]])
+            xx2 = np.minimum(x2[i], x2[orderb[1:]])
+            yy2 = np.minimum(y2[i], y2[orderb[1:]])
+
+            w = np.maximum(0.0, xx2 - xx1 + 1)
+            h = np.maximum(0.0, yy2 - yy1 + 1)
+
+            inter = w * h
+            ovr = inter / (areas[i] + areas[orderb[1:]] - inter)
+
+            inds = np.where(ovr <= thresh)[0]
+            orderb = orderb[inds + 1]
+
+        det = pre_det[keep, :]
+
+        kpss = kpss[order,:,:]
+        kpss = kpss[keep,:,:]
+
+        if max_num > 0 and det.shape[0] > max_num:
+            area = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1])
+            det_img_center = det_img.shape[0] // 2, det_img.shape[1] // 2
+            offsets = np.vstack([
+                (det[:, 0] + det[:, 2]) / 2 - det_img_center[1],
+                (det[:, 1] + det[:, 3]) / 2 - det_img_center[0]
+            ])
+            offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
+
+            values = area - offset_dist_squared * 2.0  # some extra weight on the centering
+            bindex = np.argsort(values)[::-1]  # some extra weight on the centering
+            bindex = bindex[0:max_num]
+
+            det = det[bindex, :]
+            if kpss is not None:
+                kpss = kpss[bindex, :]
+
+        score_values = det[:, 4]
+        # delete score column
+        det = np.delete(det, 4, 1)
+
+        if use_landmark_detection and len(kpss) > 0:
+            for i in range(kpss.shape[0]):
+                landmark_kpss, landmark_scores = self.run_detect_landmark(img_landmark, det[i], kpss[i], landmark_detect_mode, landmark_score, from_points)
+                if len(landmark_kpss) > 0:
+                    if len(landmark_scores) > 0:
+                        #print(np.mean(landmark_scores))
+                        #print(np.mean(score_values[i]))
+                        if np.mean(landmark_scores) > np.mean(score_values[i]):
+                            kpss[i] = landmark_kpss
+                    else:
+                        kpss[i] = landmark_kpss
+
+        return det, kpss
+
+    def detect_retinaface2(self, img, max_num, score):
+
+        # Resize image to fit within the input_size
+        input_size = (640, 640)
+        im_ratio = torch.div(img.size()[1], img.size()[2])
+
+        # model_ratio = float(input_size[1]) / input_size[0]
+        model_ratio = 1.0
+        if im_ratio > model_ratio:
+            new_height = input_size[1]
+            new_width = int(new_height / im_ratio)
+        else:
+            new_width = input_size[0]
+            new_height = int(new_width * im_ratio)
+        det_scale = torch.div(new_height, img.size()[1])
+
+        resize = v2.Resize((new_height, new_width), antialias=True)
+        img = resize(img)
+        img = img.permute(1, 2, 0)
+
+        det_img = torch.zeros((input_size[1], input_size[0], 3), dtype=torch.float32, device='cuda:0')
+        det_img[:new_height, :new_width, :] = img
+
+        # Switch to BGR and normalize
+        det_img = det_img[:, :, [2, 1, 0]]
+        det_img = torch.sub(det_img, 127.5)
+        det_img = torch.div(det_img, 128.0)
+        det_img = det_img.permute(2, 0, 1)  # 3,128,128
+
+        # Prepare data and find model parameters
+        det_img = torch.unsqueeze(det_img, 0).contiguous()
+
+        io_binding = self.retinaface_model.io_binding()
+        io_binding.bind_input(name='input.1', device_type='cuda', device_id=0, element_type=np.float32, shape=det_img.size(), buffer_ptr=det_img.data_ptr())
+
+        io_binding.bind_output('448', 'cuda')
+        io_binding.bind_output('471', 'cuda')
+        io_binding.bind_output('494', 'cuda')
+        io_binding.bind_output('451', 'cuda')
+        io_binding.bind_output('474', 'cuda')
+        io_binding.bind_output('497', 'cuda')
+        io_binding.bind_output('454', 'cuda')
+        io_binding.bind_output('477', 'cuda')
+        io_binding.bind_output('500', 'cuda')
+
+        # Sync and run model
+        self.syncvec.cpu()
+        self.retinaface_model.run_with_iobinding(io_binding)
+
+        net_outs = io_binding.copy_outputs_to_cpu()
+
+        input_height = det_img.shape[2]
+        input_width = det_img.shape[3]
+
+        fmc = 3
+        center_cache = {}
+        scores_list = []
+        bboxes_list = []
+        kpss_list = []
+        for idx, stride in enumerate([8, 16, 32]):
+            scores = net_outs[idx]
+            bbox_preds = net_outs[idx + fmc]
+            bbox_preds = bbox_preds * stride
+
+            kps_preds = net_outs[idx + fmc * 2] * stride
+            height = input_height // stride
+            width = input_width // stride
+            K = height * width
+            key = (height, width, stride)
+            if key in center_cache:
+                anchor_centers = center_cache[key]
+            else:
+                anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32)
+                anchor_centers = (anchor_centers * stride).reshape((-1, 2))
+                anchor_centers = np.stack([anchor_centers] * 2, axis=1).reshape((-1, 2))
+                if len(center_cache) < 100:
+                    center_cache[key] = anchor_centers
+
+            pos_inds = np.where(scores >= score)[0]
+
+            x1 = anchor_centers[:, 0] - bbox_preds[:, 0]
+            y1 = anchor_centers[:, 1] - bbox_preds[:, 1]
+            x2 = anchor_centers[:, 0] + bbox_preds[:, 2]
+            y2 = anchor_centers[:, 1] + bbox_preds[:, 3]
+
+            bboxes = np.stack([x1, y1, x2, y2], axis=-1)
+
+            pos_scores = scores[pos_inds]
+            pos_bboxes = bboxes[pos_inds]
+            scores_list.append(pos_scores)
+            bboxes_list.append(pos_bboxes)
+
+            preds = []
+            for i in range(0, kps_preds.shape[1], 2):
+                px = anchor_centers[:, i % 2] + kps_preds[:, i]
+                py = anchor_centers[:, i % 2 + 1] + kps_preds[:, i + 1]
+
+                preds.append(px)
+                preds.append(py)
+            kpss = np.stack(preds, axis=-1)
+            # kpss = kps_preds
+            kpss = kpss.reshape((kpss.shape[0], -1, 2))
+            pos_kpss = kpss[pos_inds]
+            kpss_list.append(pos_kpss)
+        # result_boxes = cv2.dnn.NMSBoxes(bboxes_list, scores_list, 0.25, 0.45, 0.5)
+        # print(result_boxes)
+        scores = np.vstack(scores_list)
+        scores_ravel = scores.ravel()
+        order = scores_ravel.argsort()[::-1]
+
+        det_scale = det_scale.numpy()  ###
+
+        bboxes = np.vstack(bboxes_list) / det_scale
+
+        kpss = np.vstack(kpss_list) / det_scale
+        pre_det = np.hstack((bboxes, scores)).astype(np.float32, copy=False)
+        pre_det = pre_det[order, :]
+
+
+
+        dets = pre_det
+        thresh = 0.4
+        x1 = dets[:, 0]
+        y1 = dets[:, 1]
+        x2 = dets[:, 2]
+        y2 = dets[:, 3]
+        scoresb = dets[:, 4]
+
+        areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+        orderb = scoresb.argsort()[::-1]
+
+        keep = []
+        person_id = 0
+        people = {}
+
+        while orderb.size > 0:
+            # Add first box in list
+            i = orderb[0]
+            keep.append(i)
+
+            people[person_id] = orderb[0]
+
+
+            # Find overlap of remaining boxes
+            xx1 = np.maximum(x1[i], x1[orderb[1:]])
+            yy1 = np.maximum(y1[i], y1[orderb[1:]])
+            xx2 = np.minimum(x2[i], x2[orderb[1:]])
+            yy2 = np.minimum(y2[i], y2[orderb[1:]])
+
+            w = np.maximum(0.0, xx2 - xx1 + 1)
+            h = np.maximum(0.0, yy2 - yy1 + 1)
+
+            inter = w * h
+
+
+            ovr = inter / (areas[i] + areas[orderb[1:]] - inter)
+
+
+            inds0 = np.where(ovr > thresh)[0]
+            people[person_id] = np.hstack((people[person_id], orderb[inds0+1])).astype(np.int, copy=False)
+
+
+            # identify where there is no overlap (<thresh)
+            inds = np.where(ovr <= thresh)[0]
+            # print(len(inds))
+
+
+            orderb = orderb[inds+1]
+            person_id += 1
+
+
+
+        det = pre_det[keep, :]
+
+
+        kpss = kpss[order, :, :]
+        # print('order', kpss)
+        # kpss = kpss[keep, :, :]
+        # print('keep',kpss)
+
+        kpss_ave = []
+        for person in people:
+            # print(kpss[people[person], :, :])
+            # print('mean', np.mean(kpss[people[person], :, :], axis=0))
+            # print(kpss[people[person], :, :].shape)
+            kpss_ave.append(np.mean(kpss[people[person], :, :], axis=0).tolist())
+
+
+        if max_num > 0 and det.shape[0] > max_num:
+            area = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1])
+            det_img_center = det_img.shape[0] // 2, det_img.shape[1] // 2
+            offsets = np.vstack([
+                (det[:, 0] + det[:, 2]) / 2 - det_img_center[1],
+                (det[:, 1] + det[:, 3]) / 2 - det_img_center[0]
+            ])
+            offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
+
+            values = area - offset_dist_squared * 2.0  # some extra weight on the centering
+            bindex = np.argsort(values)[::-1]  # some extra weight on the centering
+            bindex = bindex[0:max_num]
+
+            det = det[bindex, :]
+            if kpss is not None:
+                kpss = kpss[bindex, :]
+
+        # return kpss_ave
+
+        # delete score column
+        det = np.delete(det, 4, 1)
+
+        return kpss_ave
+
+    def detect_scrdf(self, img, max_num, score, use_landmark_detection, landmark_detect_mode, landmark_score, from_points):
+        if use_landmark_detection:
+            img_landmark = img.clone()
+
+        # Resize image to fit within the input_size
+        input_size = (640, 640)
+        im_ratio = torch.div(img.size()[1], img.size()[2])
+
+        model_ratio = float(input_size[1]) / input_size[0]
+        if im_ratio>model_ratio:
+            new_height = input_size[1]
+            new_width = int(new_height / im_ratio)
+        else:
+            new_width = input_size[0]
+            new_height = int(new_width * im_ratio)
+        det_scale = torch.div(new_height,  img.size()[1])
+
+        resize = v2.Resize((new_height, new_width), antialias=True)
+        img = resize(img)
+        img = img.permute(1,2,0)
+
+        det_img = torch.zeros((input_size[1], input_size[0], 3), dtype=torch.float32, device='cuda:0')
+        det_img[:new_height,:new_width,  :] = img
+
+        # Switch to BGR and normalize
+        det_img = det_img[:, :, [2,1,0]]
+        det_img = torch.sub(det_img, 127.5)
+        det_img = torch.div(det_img, 128.0)
+        det_img = det_img.permute(2, 0, 1) #3,128,128
+
+        # Prepare data and find model parameters
+        det_img = torch.unsqueeze(det_img, 0).contiguous()
+        input_name = self.scrdf_model.get_inputs()[0].name
+
+        outputs = self.scrdf_model.get_outputs()
+        output_names = []
+        for o in outputs:
+            output_names.append(o.name)
+
+        io_binding = self.scrdf_model.io_binding()
+        io_binding.bind_input(name=input_name, device_type='cuda', device_id=0, element_type=np.float32,  shape=det_img.size(), buffer_ptr=det_img.data_ptr())
+
+        for i in range(len(output_names)):
+            io_binding.bind_output(output_names[i], 'cuda')
+
+        # Sync and run model
+        syncvec = self.syncvec.cpu()
+        self.scrdf_model.run_with_iobinding(io_binding)
+
+        net_outs = io_binding.copy_outputs_to_cpu()
+
+        input_height = det_img.shape[2]
+        input_width = det_img.shape[3]
+
+        fmc = 3
+        center_cache = {}
+        scores_list = []
+        bboxes_list = []
+        kpss_list = []
+        for idx, stride in enumerate([8, 16, 32]):
+            scores = net_outs[idx]
+            bbox_preds = net_outs[idx+fmc]
+            bbox_preds = bbox_preds * stride
+
+            kps_preds = net_outs[idx+fmc*2] * stride
+            height = input_height // stride
+            width = input_width // stride
+            K = height * width
+            key = (height, width, stride)
+            if key in center_cache:
+                anchor_centers = center_cache[key]
+            else:
+                anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32)
+                anchor_centers = (anchor_centers * stride).reshape( (-1, 2) )
+                anchor_centers = np.stack([anchor_centers]*2, axis=1).reshape( (-1,2) )
+                if len(center_cache)<100:
+                    center_cache[key] = anchor_centers
+
+            pos_inds = np.where(scores>=score)[0]
+
+            x1 = anchor_centers[:, 0] - bbox_preds[:, 0]
+            y1 = anchor_centers[:, 1] - bbox_preds[:, 1]
+            x2 = anchor_centers[:, 0] + bbox_preds[:, 2]
+            y2 = anchor_centers[:, 1] + bbox_preds[:, 3]
+
+            bboxes = np.stack([x1, y1, x2, y2], axis=-1)
+
+            pos_scores = scores[pos_inds]
+            pos_bboxes = bboxes[pos_inds]
+            scores_list.append(pos_scores)
+            bboxes_list.append(pos_bboxes)
+
+            preds = []
+            for i in range(0, kps_preds.shape[1], 2):
+                px = anchor_centers[:, i%2] + kps_preds[:, i]
+                py = anchor_centers[:, i%2+1] + kps_preds[:, i+1]
+
+                preds.append(px)
+                preds.append(py)
+            kpss = np.stack(preds, axis=-1)
+            #kpss = kps_preds
+            kpss = kpss.reshape( (kpss.shape[0], -1, 2) )
+            pos_kpss = kpss[pos_inds]
+            kpss_list.append(pos_kpss)
+
+        scores = np.vstack(scores_list)
+        scores_ravel = scores.ravel()
+        order = scores_ravel.argsort()[::-1]
+
+        det_scale = det_scale.numpy()###
+
+        bboxes = np.vstack(bboxes_list) / det_scale
+
+        kpss = np.vstack(kpss_list) / det_scale
+        pre_det = np.hstack((bboxes, scores)).astype(np.float32, copy=False)
+        pre_det = pre_det[order, :]
+
+        dets = pre_det
+        thresh = 0.4
+        x1 = dets[:, 0]
+        y1 = dets[:, 1]
+        x2 = dets[:, 2]
+        y2 = dets[:, 3]
+        scoresb = dets[:, 4]
+
+        areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+        orderb = scoresb.argsort()[::-1]
+
+        keep = []
+        while orderb.size > 0:
+            i = orderb[0]
+            keep.append(i)
+            xx1 = np.maximum(x1[i], x1[orderb[1:]])
+            yy1 = np.maximum(y1[i], y1[orderb[1:]])
+            xx2 = np.minimum(x2[i], x2[orderb[1:]])
+            yy2 = np.minimum(y2[i], y2[orderb[1:]])
+
+            w = np.maximum(0.0, xx2 - xx1 + 1)
+            h = np.maximum(0.0, yy2 - yy1 + 1)
+
+            inter = w * h
+            ovr = inter / (areas[i] + areas[orderb[1:]] - inter)
+
+            inds = np.where(ovr <= thresh)[0]
+            orderb = orderb[inds + 1]
+
+        det = pre_det[keep, :]
+
+        kpss = kpss[order,:,:]
+        kpss = kpss[keep,:,:]
+
+        if max_num > 0 and det.shape[0] > max_num:
+            area = (det[:, 2] - det[:, 0]) * (det[:, 3] -
+                                                    det[:, 1])
+            det_img_center = det_img.shape[0] // 2, det_img.shape[1] // 2
+            offsets = np.vstack([
+                (det[:, 0] + det[:, 2]) / 2 - det_img_center[1],
+                (det[:, 1] + det[:, 3]) / 2 - det_img_center[0]
+            ])
+            offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
+
+            values = area - offset_dist_squared * 2.0  # some extra weight on the centering
+            bindex = np.argsort(values)[::-1]  # some extra weight on the centering
+            bindex = bindex[0:max_num]
+
+            det = det[bindex, :]
+            if kpss is not None:
+                kpss = kpss[bindex, :]
+
+        score_values = det[:, 4]
+        # delete score column
+        det = np.delete(det, 4, 1)
+
+        if use_landmark_detection and len(kpss) > 0:
+            for i in range(kpss.shape[0]):
+                landmark_kpss, landmark_scores = self.run_detect_landmark(img_landmark, det[i], kpss[i], landmark_detect_mode, landmark_score, from_points)
+                if len(landmark_kpss) > 0:
+                    if len(landmark_scores) > 0:
+                        #print(np.mean(landmark_scores))
+                        #print(np.mean(score_values[i]))
+                        if np.mean(landmark_scores) > np.mean(score_values[i]):
+                            kpss[i] = landmark_kpss
+                    else:
+                        kpss[i] = landmark_kpss
+
+        return det, kpss
+
+    def detect_yoloface(self, img, max_num, score, use_landmark_detection, landmark_detect_mode, landmark_score, from_points):
+        if use_landmark_detection:
+            img_landmark = img.clone()
+
+        height = img.size(dim=1)
+        width = img.size(dim=2)
+        length = max((height, width))
+
+        image = torch.zeros((length, length, 3), dtype=torch.uint8, device='cuda')
+        img = img.permute(1,2,0)
+
+        image[0:height, 0:width] = img
+        scale = length/640.0
+        image = torch.div(image, 255.0)
+
+        t640 = v2.Resize((640, 640), antialias=False)
+        image = image.permute(2, 0, 1)
+        image = t640(image)
+
+        image = torch.unsqueeze(image, 0).contiguous()
+
+        io_binding = self.yoloface_model.io_binding()
+        io_binding.bind_input(name='images', device_type='cuda', device_id=0, element_type=np.float32,  shape=image.size(), buffer_ptr=image.data_ptr())
+        io_binding.bind_output('output0', 'cuda')
+
+        # Sync and run model
+        self.syncvec.cpu()
+        self.yoloface_model.run_with_iobinding(io_binding)
+
+        net_outs = io_binding.copy_outputs_to_cpu()
+
+        outputs = np.squeeze(net_outs).T
+
+        bbox_raw, score_raw, kps_raw = np.split(outputs, [4, 5], axis=1)
+
+        bbox_list = []
+        score_list = []
+        kps_list = []
+        keep_indices = np.where(score_raw > score)[0]
+
+        if keep_indices.any():
+            bbox_raw, kps_raw, score_raw = bbox_raw[keep_indices], kps_raw[keep_indices], score_raw[keep_indices]
+
+            bbox_raw = bbox_raw * scale
+
+            for bbox in bbox_raw:
+                bbox_list.append(np.array([(bbox[0]-bbox[2]/2), (bbox[1]-bbox[3]/2), (bbox[0]+bbox[2]/2), (bbox[1]+bbox[3]/2)]))
+
+            kps_raw = kps_raw * scale
+
+            for kps in kps_raw:
+                indexes = np.arange(0, len(kps), 3)
+                temp_kps = []
+                for index in indexes:
+                    temp_kps.append([kps[index], kps[index + 1]])
+                kps_list.append(np.array(temp_kps))
+            score_list = score_raw.ravel().tolist()
+
+        result_boxes = cv2.dnn.NMSBoxes(bbox_list, score_list, 0.25, 0.45, 0.5)
+
+        bboxes_list = []
+        kpss_list = []
+        for r in result_boxes:
+            if r==max_num:
+                break
+            if use_landmark_detection and len(kps_list[r]) > 0:
+                landmark_kpss, landmark_scores = self.run_detect_landmark(img_landmark, bbox_list[r], kps_list[r], landmark_detect_mode, landmark_score, from_points)
+                if len(landmark_kpss) > 0:
+                    if len(landmark_scores) > 0:
+                        #print(np.mean(landmark_scores))
+                        #print(np.mean(score_list[r]))
+                        if np.mean(landmark_scores) > np.mean(score_list[r]):
+                            kps_list[r] = landmark_kpss
+                    else:
+                        kps_list[r] = landmark_kpss
+
+            bboxes_list.append(bbox_list[r])
+            kpss_list.append(kps_list[r])
+
+        return np.array(bboxes_list), np.array(kpss_list)
+
+    def detect_yoloface2(self, image_in, max_num, score):
+        img = image_in.detach().clone()
+
+        height = img.size(dim=1)
+        width = img.size(dim=2)
+        length = max((height, width))
+
+        image = torch.zeros((length, length, 3), dtype=torch.uint8,
+                            device='cuda')
+        img = img.permute(1, 2, 0)
+
+        image[0:height, 0:width] = img
+        scale = length / 640.0
+        image = torch.div(image, 255.0)
+
+        t640 = v2.Resize((640, 640), antialias=False)
+        image = image.permute(2, 0, 1)
+        image = t640(image)
+
+        image = torch.unsqueeze(image, 0).contiguous()
+
+        io_binding = self.yoloface_model.io_binding()
+        io_binding.bind_input(name='images', device_type='cuda', device_id=0,
+                              element_type=np.float32, shape=image.size(),
+                              buffer_ptr=image.data_ptr())
+        io_binding.bind_output('output0', 'cuda')
+
+        # Sync and run model
+        self.syncvec.cpu()
+        self.yoloface_model.run_with_iobinding(io_binding)
+
+        net_outs = io_binding.copy_outputs_to_cpu()
+
+        outputs = np.squeeze(net_outs).T
+
+        bbox_raw, score_raw, kps_raw = np.split(outputs, [4, 5], axis=1)
+
+        bbox_list = []
+        score_list = []
+        kps_list = []
+        keep_indices = np.where(score_raw > score)[0]
+
+        if keep_indices.any():
+            bbox_raw, kps_raw, score_raw = bbox_raw[keep_indices], kps_raw[
+                keep_indices], score_raw[keep_indices]
+            for bbox in bbox_raw:
+                bbox_list.append(np.array(
+                    [(bbox[0] - bbox[2] / 2), (bbox[1] - bbox[3] / 2),
+                     (bbox[0] + bbox[2] / 2), (bbox[1] + bbox[3] / 2)]))
+            kps_raw = kps_raw * scale
+
+            for kps in kps_raw:
+                indexes = np.arange(0, len(kps), 3)
+                temp_kps = []
+                for index in indexes:
+                    temp_kps.append([kps[index], kps[index + 1]])
+                kps_list.append(np.array(temp_kps))
+            score_list = score_raw.ravel().tolist()
+
+        result_boxes = cv2.dnn.NMSBoxes(bbox_list, score_list, 0.25, 0.45, 0.5)
+
+        result = []
+        for r in result_boxes:
+            if r == max_num:
+                break
+            bbox_list = bbox_list[r]
+            result.append(kps_list[r])
+        bbox_list = bbox_list*scale
+        # print(bbox_list)
+        # print(bbox_list*scale)
+
+        # img = image_in.detach().clone()
+        # test = image_in.permute(1, 2, 0)
+        # test = test.cpu().numpy()
+        # cv2.imwrite('1.jpg', test)
+
+        # b_scale = 50
+        # bbox_list[0] = bbox_list[0] - b_scale
+        # bbox_list[1] = bbox_list[1] - b_scale
+        # bbox_list[2] = bbox_list[2] + b_scale
+        # bbox_list[3] = bbox_list[3] + b_scale
+
+        img = image_in.detach().clone()
+
+        img = img[:, int(bbox_list[1]):int(bbox_list[3]), int(bbox_list[0]):int(bbox_list[2])]
+        # print(img.size())
+
+
+        height = img.size(dim=1)
+        width = img.size(dim=2)
+        length = max((height, width))
+
+        image = torch.zeros((length, length, 3), dtype=torch.uint8, device='cuda')
+        img = img.permute(1,2,0)
+
+        image[0:height, 0:width] = img
+        scale = length/192
+        image = torch.div(image, 255.0)
+
+
+        t192 = v2.Resize((192, 192), antialias=False)
+        image = image.permute(2, 0, 1)
+        image = t192(image)
+
+        test = image_in.detach().clone().permute(1, 2, 0)
+        test = test.cpu().numpy()
+
+        input_mean = 0.0
+        input_std = 1.0
+
+        self.lmk_dim = 2
+        self.lmk_num = 106
+
+        bbox = bbox_list
+        w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1])
+        center = (bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2
+        rotate = 0
+        _scale = 192 / (max(w, h) * 1.5)
+        # print('param:', img.shape, bbox, center, self.input_size, _scale, rotate)
+        aimg, M = self.transform(test, center, 192, _scale, rotate)
+        input_size = tuple(aimg.shape[0:2][::-1])
+        # assert input_size==self.input_size
+        blob = cv2.dnn.blobFromImage(aimg, 1.0 / input_std, input_size, ( input_mean, input_mean, input_mean), swapRB=True)
+        pred = self.insight106_model.run(['fc1'], {'data': blob})[0][0]
+        if pred.shape[0] >= 3000:
+            pred = pred.reshape((-1, 3))
+        else:
+            pred = pred.reshape((-1, 2))
+        if self.lmk_num < pred.shape[0]:
+            pred = pred[self.lmk_num * -1:, :]
+        pred[:, 0:2] += 1
+        pred[:, 0:2] *= 96
+        if pred.shape[1] == 3:
+            pred[:, 2] *= (106)
+
+        IM = cv2.invertAffineTransform(M)
+        pred = self.trans_points2d(pred, IM)
+        # face[self.taskname] = pred
+        # if self.require_pose:
+        #     P = transform.estimate_affine_matrix_3d23d(self.mean_lmk, pred)
+        #     s, R, t = transform.P2sRt(P)
+        #     rx, ry, rz = transform.matrix2angle(R)
+        #     pose = np.array([rx, ry, rz], dtype=np.float32)
+        #     face['pose'] = pose  # pitch, yaw, roll
+        # print(pred.shape)
+        # print(pred)
+
+        for point in pred:
+            test[int(point[1])] [int(point[0])] [0] = 255
+            test[int(point[1])] [int(point[0])] [1] = 255
+            test[int(point[1])] [int(point[0])] [2] = 255
+        cv2.imwrite('2.jpg', test)
+
+        predd = []
+        predd.append(pred[38])
+        predd.append(pred[88])
+        # predd.append(pred[86])
+        # predd.append(pred[52])
+        # predd.append(pred[61])
+
+        predd.append(kps_list[0][2])
+        predd.append(kps_list[0][3])
+        predd.append(kps_list[0][4])
+
+        # for point in predd:
+        #     test[int(point[1])] [int(point[0])] [0] = 255
+        #     test[int(point[1])] [int(point[0])] [1] = 255
+        #     test[int(point[1])] [int(point[0])] [2] = 255
+        # cv2.imwrite('2.jpg', test)
+        preddd=[]
+        preddd.append(predd)
+        return np.array(preddd)
+    def transform(self, data, center, output_size, scale, rotation):
+        scale_ratio = scale
+        rot = float(rotation) * np.pi / 180.0
+        # translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
+        t1 = trans.SimilarityTransform(scale=scale_ratio)
+        cx = center[0] * scale_ratio
+        cy = center[1] * scale_ratio
+        t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
+        t3 = trans.SimilarityTransform(rotation=rot)
+        t4 = trans.SimilarityTransform(translation=(output_size / 2,
+                                                    output_size / 2))
+        t = t1 + t2 + t3 + t4
+        M = t.params[0:2]
+        cropped = cv2.warpAffine(data,
+                                 M, (output_size, output_size),
+                                 borderValue=0.0)
+        return cropped, M
+
+    def trans_points2d(self, pts, M):
+        new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
+        for i in range(pts.shape[0]):
+            pt = pts[i]
+            new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
+            new_pt = np.dot(M, new_pt)
+            # print('new_pt', new_pt.shape, new_pt)
+            new_pts[i] = new_pt[0:2]
+
+        return new_pts
+
+        # image = torch.unsqueeze(image, 0).contiguous()
+        #
+        # io_binding = self.insight106_model.io_binding()
+        # io_binding.bind_input(name='data', device_type='cuda', device_id=0, element_type=np.float32,  shape=image.size(), buffer_ptr=image.data_ptr())
+        # io_binding.bind_output('fc1', 'cuda')
+        #
+        # # Sync and run model
+        # self.syncvec.cpu()
+        # self.insight106_model.run_with_iobinding(io_binding)
+        #
+        # net_outs = io_binding.copy_outputs_to_cpu()
+        # print(net_outs)
+        # net_outs[0][0] = net_outs[0][0]+1.
+        # net_outs[0][0] = net_outs[0][0]/2.
+        # net_outs[0][0] = net_outs[0][0]*96
+        #
+        # # net_outs[0] = net_outs[0]*scale
+        # # print(net_outs)
+        # test=test*255.0
+        # for i in range(0, len(net_outs[0][0]), 2):
+        #     test[int(net_outs[0][0][i+1])] [int(net_outs[0][0][i])] [0] = 255
+        #     test[int(net_outs[0][0][i+1])] [int(net_outs[0][0][i])] [1] = 255
+        #     test[int(net_outs[0][0][i+1])] [int(net_outs[0][0][i])] [2] = 255
+        # cv2.imwrite('2.jpg', test)
+        #
+        # return np.array(result)
+    def detect_yunet(self, img, max_num, score, use_landmark_detection, landmark_detect_mode, landmark_score, from_points):
+        if use_landmark_detection:
+            img_landmark = img.clone()
+
+        height = img.size(dim=1)
+        width = img.size(dim=2)
+        input_size = (640, 640)
+        im_ratio = float(height) / width
+        model_ratio = float(input_size[1]) / input_size[0]
+        if im_ratio > model_ratio:
+            new_height = input_size[1]
+            new_width = int(new_height / im_ratio)
+        else:
+            new_width = input_size[0]
+            new_height = int(new_width * im_ratio)
+        det_scale = float(new_height) / height
+
+        t640 = v2.Resize((new_height, new_width), antialias=False)
+        img = t640(img)
+
+        # Switch to BGR
+        img = img.permute(1,2,0)
+        img = img[:, :, [2,1,0]]
+
+        image = torch.zeros((input_size[1], input_size[0], 3), dtype=torch.uint8, device='cuda')
+        image[:new_height, :new_width, :] = img
+
+        image = image.permute(2, 0, 1)
+        image = torch.unsqueeze(image, 0).contiguous()
+        image = image.to(dtype=torch.float32)
+
+        input_name = self.yunet_model.get_inputs()[0].name
+        outputs = self.yunet_model.get_outputs()
+        output_names = []
+        for o in outputs:
+            output_names.append(o.name)
+
+        io_binding = self.yunet_model.io_binding() 
+        io_binding.bind_input(name=input_name, device_type='cuda', device_id=0, element_type=np.float32,  shape=image.size(), buffer_ptr=image.data_ptr())
+
+        for i in range(len(output_names)):
+            io_binding.bind_output(output_names[i], 'cuda')
+
+        # Sync and run model
+        syncvec = self.syncvec.cpu()
+        self.yunet_model.run_with_iobinding(io_binding)
+        net_outs = io_binding.copy_outputs_to_cpu()
+
+        strides = [8, 16, 32]
+        scores, bboxes, kpss = [], [], []
+        for idx, stride in enumerate(strides):
+            cls_pred = net_outs[idx].reshape(-1, 1)
+            obj_pred = net_outs[idx + len(strides)].reshape(-1, 1)
+            reg_pred = net_outs[idx + len(strides) * 2].reshape(-1, 4)
+            kps_pred = net_outs[idx + len(strides) * 3].reshape(
+                -1, 5 * 2)
+
+            anchor_centers = np.stack(
+                np.mgrid[:(input_size[1] // stride), :(input_size[0] //
+                                                       stride)][::-1],
+                axis=-1)
+            anchor_centers = (anchor_centers * stride).astype(
+                np.float32).reshape(-1, 2)
+
+            bbox_cxy = reg_pred[:, :2] * stride + anchor_centers[:]
+            bbox_wh = np.exp(reg_pred[:, 2:]) * stride
+            tl_x = (bbox_cxy[:, 0] - bbox_wh[:, 0] / 2.)
+            tl_y = (bbox_cxy[:, 1] - bbox_wh[:, 1] / 2.)
+            br_x = (bbox_cxy[:, 0] + bbox_wh[:, 0] / 2.)
+            br_y = (bbox_cxy[:, 1] + bbox_wh[:, 1] / 2.)
+
+            bboxes.append(np.stack([tl_x, tl_y, br_x, br_y], -1))
+            # for nk in range(5):
+            per_kps = np.concatenate(
+                [((kps_pred[:, [2 * i, 2 * i + 1]] * stride) + anchor_centers)
+                 for i in range(5)],
+                axis=-1)
+
+            kpss.append(per_kps)
+            scores.append(cls_pred * obj_pred)
+
+        scores = np.concatenate(scores, axis=0).reshape(-1)
+        bboxes = np.concatenate(bboxes, axis=0)
+        kpss = np.concatenate(kpss, axis=0)
+        score_mask = (scores > score)
+        scores = scores[score_mask]
+        bboxes = bboxes[score_mask]
+        kpss = kpss[score_mask]
+
+        bboxes /= det_scale
+        kpss /= det_scale
+        pre_det = np.hstack((bboxes, scores[:, None]))
+
+        dets = pre_det
+        thresh = 0.4
+        x1 = dets[:, 0]
+        y1 = dets[:, 1]
+        x2 = dets[:, 2]
+        y2 = dets[:, 3]
+        scoresb = dets[:, -1]
+
+        areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+        order = scoresb.argsort()[::-1]
+
+        keep = []
+        while order.size > 0:
+            i = order[0]
+            keep.append(i)
+            xx1 = np.maximum(x1[i], x1[order[1:]])
+            yy1 = np.maximum(y1[i], y1[order[1:]])
+            xx2 = np.minimum(x2[i], x2[order[1:]])
+            yy2 = np.minimum(y2[i], y2[order[1:]])
+
+            w = np.maximum(0.0, xx2 - xx1 + 1)
+            h = np.maximum(0.0, yy2 - yy1 + 1)
+            inter = w * h
+            ovr = inter / (areas[i] + areas[order[1:]] - inter)
+
+            inds = np.where(ovr <= thresh)[0]
+            order = order[inds + 1]
+
+        kpss = kpss[keep, :]
+        bboxes = pre_det[keep, :]
+        score_values = bboxes[:, 4]
+
+        bbox_list = []
+        kps_list = []
+        for i in range(bboxes.shape[0]):
+            if i==max_num:
+                    break
+            box = np.array((bboxes[i][0], bboxes[i][1], bboxes[i][2], bboxes[i][3]))
+            bbox_list.append(box)
+
+            if kpss is not None:
+                kps = kpss[i].reshape(-1, 2)
+                if use_landmark_detection and len(kps) > 0:
+                    landmark_kpss, landmark_scores = self.run_detect_landmark(img_landmark, box, kps, landmark_detect_mode, landmark_score, from_points)
+                    if len(landmark_kpss) > 0:
+                        if len(landmark_scores) > 0:
+                            #print(np.mean(landmark_scores))
+                            #print(np.mean(score_values[i]))
+                            if np.mean(landmark_scores) > np.mean(score_values[i]):
+                                kps = landmark_kpss
+                        else:
+                            kps = landmark_kpss
+
+                kps_list.append(kps)
+
+        return np.array(bbox_list), np.array(kps_list)
+
+    def detect_face_landmark_5(self, img, bbox, det_kpss, from_points=False):
+        if from_points == False:
+            w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1])
+            center = (bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2
+            rotate = 0
+            _scale = 512.0  / (max(w, h)*1.5)
+            image, M = faceutil.transform(img, center, 512, _scale, rotate)
+        else:
+            image, M = faceutil.warp_face_by_face_landmark_5(img, det_kpss, 512, normalized=True)
+
+        image = image.permute(1,2,0)
+
+        mean = torch.tensor([104, 117, 123], dtype=torch.float32, device='cuda')
+        image = torch.sub(image, mean)
+
+        image = image.permute(2,0,1)
+        image = torch.reshape(image, (1, 3, 512, 512))
+
+        height, width = (512, 512)
+        tmp = [width, height, width, height, width, height, width, height, width, height]
+        scale1 = torch.tensor(tmp, dtype=torch.float32, device='cuda')
+
+        conf = torch.empty((1,10752,2), dtype=torch.float32, device='cuda').contiguous()
+        landmarks = torch.empty((1,10752,10), dtype=torch.float32, device='cuda').contiguous()
+
+        io_binding = self.resnet50_model.io_binding()
+        io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=image.data_ptr())
+        io_binding.bind_output(name='conf', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,10752,2), buffer_ptr=conf.data_ptr())
+        io_binding.bind_output(name='landmarks', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,10752,10), buffer_ptr=landmarks.data_ptr())
+
+        torch.cuda.synchronize('cuda')
+        self.resnet50_model.run_with_iobinding(io_binding)
+
+        scores = torch.squeeze(conf)[:, 1]
+        priors = torch.tensor(self.anchors).view(-1, 4)
+        priors = priors.to('cuda')
+
+        pre = torch.squeeze(landmarks, 0)
+
+        tmp = (priors[:, :2] + pre[:, :2] * 0.1 * priors[:, 2:], priors[:, :2] + pre[:, 2:4] * 0.1 * priors[:, 2:], priors[:, :2] + pre[:, 4:6] * 0.1 * priors[:, 2:], priors[:, :2] + pre[:, 6:8] * 0.1 * priors[:, 2:], priors[:, :2] + pre[:, 8:10] * 0.1 * priors[:, 2:])
+        landmarks = torch.cat(tmp, dim=1)
+        landmarks = torch.mul(landmarks, scale1)
+
+        landmarks = landmarks.cpu().numpy()
+
+        # ignore low scores
+        score=.1
+        inds = torch.where(scores>score)[0]
+        inds = inds.cpu().numpy()  
+        scores = scores.cpu().numpy()  
+
+        landmarks, scores = landmarks[inds], scores[inds]    
+
+        # sort
+        order = scores.argsort()[::-1]
+
+        if len(order) > 0:
+            landmarks = landmarks[order][0]
+            scores = scores[order][0]
+
+            landmarks = np.array([[landmarks[i], landmarks[i + 1]] for i in range(0,10,2)])
+
+            IM = faceutil.invertAffineTransform(M)
+            landmarks = faceutil.trans_points2d(landmarks, IM)
+            scores = np.array([scores])
+            
+            #faceutil.test_bbox_landmarks(img, bbox, landmarks)
+            #print(scores)
+                    
+            return landmarks, scores
+
+        return [], []
+
+    def detect_face_landmark_68(self, img, bbox, det_kpss, convert68_5=True, from_points=False):
+        if from_points == False:
+            crop_image, affine_matrix = faceutil.warp_face_by_bounding_box_for_landmark_68(img, bbox, (256, 256))
+        else:
+            crop_image, affine_matrix = faceutil.warp_face_by_face_landmark_5(img, det_kpss, 256, normalized=True)
+        '''
+        cv2.imshow('image', crop_image.permute(1, 2, 0).to('cpu').numpy())
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        '''
+        crop_image = crop_image.to(dtype=torch.float32)
+        crop_image = torch.div(crop_image, 255.0)
+        crop_image = torch.unsqueeze(crop_image, 0).contiguous()
+
+        io_binding = self.face_landmark_68_model.io_binding()
+        io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32,  shape=crop_image.size(), buffer_ptr=crop_image.data_ptr())
+
+        io_binding.bind_output('landmarks_xyscore', 'cuda')
+        io_binding.bind_output('heatmaps', 'cuda')
+
+        # Sync and run model
+        syncvec = self.syncvec.cpu()
+        self.face_landmark_68_model.run_with_iobinding(io_binding)
+        net_outs = io_binding.copy_outputs_to_cpu()
+        face_landmark_68 = net_outs[0]
+        face_heatmap = net_outs[1]
+
+        face_landmark_68 = face_landmark_68[:, :, :2][0] / 64.0
+        face_landmark_68 = face_landmark_68.reshape(1, -1, 2) * 256.0
+        face_landmark_68 = cv2.transform(face_landmark_68, cv2.invertAffineTransform(affine_matrix))
+
+        face_landmark_68 = face_landmark_68.reshape(-1, 2)
+        face_landmark_68_score = np.amax(face_heatmap, axis = (2, 3))
+        face_landmark_68_score = face_landmark_68_score.reshape(-1, 1)
+
+        if convert68_5:
+            face_landmark_68, face_landmark_68_score = faceutil.convert_face_landmark_68_to_5(face_landmark_68, face_landmark_68_score)
+
+        #faceutil.test_bbox_landmarks(img, bbox, face_landmark_68)
+
+        return face_landmark_68, face_landmark_68_score
+
+    def detect_face_landmark_3d68(self, img, bbox, det_kpss, convert68_5=True, from_points=False):
+        if from_points == False:
+            w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1])
+            center = (bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2
+            rotate = 0
+            _scale = 192  / (max(w, h)*1.5)
+            #print('param:', img.size(), bbox, center, (192, 192), _scale, rotate)
+            aimg, M = faceutil.transform(img, center, 192, _scale, rotate)
+        else:
+            aimg, M = faceutil.warp_face_by_face_landmark_5(img, det_kpss, image_size=192, normalized=True)
+        '''
+        cv2.imshow('image', aimg.permute(1.2.0).to('cpu').numpy())
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        '''
+        aimg = torch.unsqueeze(aimg, 0).contiguous()
+        aimg = aimg.to(dtype=torch.float32)
+        aimg = self.normalize(aimg)
+        io_binding = self.face_landmark_3d68_model.io_binding()
+        io_binding.bind_input(name='data', device_type='cuda', device_id=0, element_type=np.float32,  shape=aimg.size(), buffer_ptr=aimg.data_ptr())
+
+        io_binding.bind_output('fc1', 'cuda')
+
+        # Sync and run model
+        syncvec = self.syncvec.cpu()
+        self.face_landmark_3d68_model.run_with_iobinding(io_binding)
+        pred = io_binding.copy_outputs_to_cpu()[0][0]
+
+        if pred.shape[0] >= 3000:
+            pred = pred.reshape((-1, 3))
+        else:
+            pred = pred.reshape((-1, 2))
+        if 68 < pred.shape[0]:
+            pred = pred[68*-1:,:]
+        pred[:, 0:2] += 1
+        pred[:, 0:2] *= (192 // 2)
+        if pred.shape[1] == 3:
+            pred[:, 2] *= (192 // 2)
+
+        #IM = cv2.invertAffineTransform(M)
+        IM = faceutil.invertAffineTransform(M)
+        pred = faceutil.trans_points3d(pred, IM)
+
+        # at moment we don't use 3d points
+        '''
+        P = faceutil.estimate_affine_matrix_3d23d(self.mean_lmk, pred)
+        s, R, t = faceutil.P2sRt(P)
+        rx, ry, rz = faceutil.matrix2angle(R)
+        pose = np.array( [rx, ry, rz], dtype=np.float32 ) #pitch, yaw, roll
+        '''
+
+        # convert from 3d68 to 2d68 keypoints
+        landmark2d68 = np.array(pred[:, [0, 1]])
+
+        if convert68_5:
+            # convert from 68 to 5 keypoints
+            landmark2d68, _ = faceutil.convert_face_landmark_68_to_5(landmark2d68, [])
+
+        #faceutil.test_bbox_landmarks(img, bbox, landmark2d68)
+
+        return landmark2d68, []
+
+    def detect_face_landmark_98(self, img, bbox, det_kpss, convert98_5=True, from_points=False):
+        if from_points == False:
+            crop_image, detail = faceutil.warp_face_by_bounding_box_for_landmark_98(img, bbox, (256, 256))
+        else:
+            crop_image, M = faceutil.warp_face_by_face_landmark_5(img, det_kpss, image_size=256, normalized=True)
+            #crop_image2 = crop_image.clone()
+            h, w = (crop_image.size(dim=1), crop_image.size(dim=2))
+        '''
+        cv2.imshow('image', crop_image.permute(1, 2, 0).to('cpu').numpy())
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        '''
+        landmark = []
+        landmark_score = []
+        if crop_image is not None:
+            crop_image = crop_image.to(dtype=torch.float32)
+            crop_image = torch.div(crop_image, 255.0)
+            crop_image = torch.unsqueeze(crop_image, 0).contiguous()
+
+            io_binding = self.face_landmark_98_model.io_binding()
+            io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32,  shape=crop_image.size(), buffer_ptr=crop_image.data_ptr())
+
+            io_binding.bind_output('landmarks_xyscore', 'cuda')
+
+            # Sync and run model
+            syncvec = self.syncvec.cpu()
+            self.face_landmark_98_model.run_with_iobinding(io_binding)
+            landmarks_xyscore = io_binding.copy_outputs_to_cpu()[0]
+
+            if len(landmarks_xyscore) > 0:
+                for one_face_landmarks in landmarks_xyscore:
+                    landmark_score = one_face_landmarks[:, [2]].reshape(-1)
+                    landmark = one_face_landmarks[:, [0, 1]].reshape(-1,2)
+
+                    ##recorver, and grouped as [98,2]
+                    if from_points == False:
+                        landmark[:, 0] = landmark[:, 0] * detail[1] + detail[3] - detail[4]
+                        landmark[:, 1] = landmark[:, 1] * detail[0] + detail[2] - detail[4]
+                    else:
+                        landmark[:, 0] = landmark[:, 0] * w
+                        landmark[:, 1] = landmark[:, 1] * h
+                        #lmk = landmark.copy()
+                        #lmk_score = landmark_score.copy()
+
+                        #IM = cv2.invertAffineTransform(M)
+                        IM = faceutil.invertAffineTransform(M)
+                        landmark = faceutil.trans_points2d(landmark, IM)
+
+                    if convert98_5:
+                        landmark, landmark_score = faceutil.convert_face_landmark_98_to_5(landmark, landmark_score)
+                        #lmk, lmk_score = faceutil.convert_face_landmark_98_to_5(lmk, lmk_score)
+
+                    #faceutil.test_bbox_landmarks(crop_image2, [], lmk)
+                    #faceutil.test_bbox_landmarks(img, bbox, landmark)
+                    #faceutil.test_bbox_landmarks(img, bbox, det_kpss)
+
+        return landmark, landmark_score
+
+    def detect_face_landmark_106(self, img, bbox, det_kpss, convert106_5=True, from_points=False):
+        if from_points == False:
+            w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1])
+            center = (bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2
+            rotate = 0
+            _scale = 192  / (max(w, h)*1.5)
+            #print('param:', img.size(), bbox, center, (192, 192), _scale, rotate)
+            aimg, M = faceutil.transform(img, center, 192, _scale, rotate)
+        else:
+            aimg, M = faceutil.warp_face_by_face_landmark_5(img, det_kpss, image_size=192, normalized=True)
+        '''
+        cv2.imshow('image', aimg.permute(1.2.0).to('cpu').numpy())
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        '''
+        aimg = torch.unsqueeze(aimg, 0).contiguous()
+        aimg = aimg.to(dtype=torch.float32)
+        aimg = self.normalize(aimg)
+        io_binding = self.face_landmark_106_model.io_binding()
+        io_binding.bind_input(name='data', device_type='cuda', device_id=0, element_type=np.float32,  shape=aimg.size(), buffer_ptr=aimg.data_ptr())
+
+        io_binding.bind_output('fc1', 'cuda')
+
+        # Sync and run model
+        syncvec = self.syncvec.cpu()
+        self.face_landmark_106_model.run_with_iobinding(io_binding)
+        pred = io_binding.copy_outputs_to_cpu()[0][0]
+
+        if pred.shape[0] >= 3000:
+            pred = pred.reshape((-1, 3))
+        else:
+            pred = pred.reshape((-1, 2))
+
+        if 106 < pred.shape[0]:
+            pred = pred[106*-1:,:]
+
+        pred[:, 0:2] += 1
+        pred[:, 0:2] *= (192 // 2)
+        if pred.shape[1] == 3:
+            pred[:, 2] *= (192 // 2)
+
+        #IM = cv2.invertAffineTransform(M)
+        IM = faceutil.invertAffineTransform(M)
+        pred = faceutil.trans_points(pred, IM)
+
+        if pred is not None:
+            if convert106_5:
+                # convert from 106 to 5 keypoints
+                pred = faceutil.convert_face_landmark_106_to_5(pred)
+
+        #faceutil.test_bbox_landmarks(img, bbox, pred)
+
+        return pred, []
+
+    def detect_face_landmark_478(self, img, bbox, det_kpss, convert478_5=True, from_points=False):
+        if from_points == False:
+            w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1])
+            center = (bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2
+            rotate = 0
+            _scale = 256.0  / (max(w, h)*1.5)
+            #print('param:', img.size(), bbox, center, (192, 192), _scale, rotate)
+            aimg, M = faceutil.transform(img, center, 256, _scale, rotate)
+        else:
+            aimg, M = faceutil.warp_face_by_face_landmark_5(img, det_kpss, 256, normalized=False)
+            #aimg2 = aimg.clone()
+        '''
+        cv2.imshow('image', aimg.permute(1,2,0).to('cpu').numpy())
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        '''
+        aimg = torch.unsqueeze(aimg, 0).contiguous()
+        aimg = aimg.to(dtype=torch.float32)
+        aimg = torch.div(aimg, 255.0)
+        io_binding = self.face_landmark_478_model.io_binding()
+        io_binding.bind_input(name='input_12', device_type='cuda', device_id=0, element_type=np.float32,  shape=aimg.size(), buffer_ptr=aimg.data_ptr())
+
+        io_binding.bind_output('Identity', 'cuda')
+        io_binding.bind_output('Identity_1', 'cuda')
+        io_binding.bind_output('Identity_2', 'cuda')
+
+        # Sync and run model
+        syncvec = self.syncvec.cpu()
+        self.face_landmark_478_model.run_with_iobinding(io_binding)
+        landmarks, faceflag, blendshapes = io_binding.copy_outputs_to_cpu()
+        landmarks = landmarks.reshape( (1,478,3))
+
+        landmark = []
+        landmark_score = []
+        if len(landmarks) > 0:
+            for one_face_landmarks in landmarks:
+                #lmk = one_face_landmarks.copy()
+                landmark = one_face_landmarks
+                #IM = cv2.invertAffineTransform(M)
+                IM = faceutil.invertAffineTransform(M)
+                landmark = faceutil.trans_points3d(landmark, IM)
+                '''
+                P = faceutil.estimate_affine_matrix_3d23d(self.mean_lmk, landmark)
+                s, R, t = faceutil.P2sRt(P)
+                rx, ry, rz = faceutil.matrix2angle(R)
+                pose = np.array( [rx, ry, rz], dtype=np.float32 ) #pitch, yaw, roll
+                '''
+                landmark = landmark[:, [0, 1]].reshape(-1,2)
+                #lmk = lmk[:, [0, 1]].reshape(-1,2)
+
+                #get scores
+                landmark_for_score = landmark[self.LandmarksSubsetIdxs]
+                landmark_for_score = landmark_for_score[:, :2]
+                landmark_for_score = np.expand_dims(landmark_for_score, axis=0)
+                landmark_for_score = landmark_for_score.astype(np.float32)
+                landmark_for_score = torch.from_numpy(landmark_for_score).to('cuda')
+    
+                io_binding_bs = self.face_blendshapes_model.io_binding()
+                io_binding_bs.bind_input(name='input_points', device_type='cuda', device_id=0, element_type=np.float32,  shape=tuple(landmark_for_score.shape), buffer_ptr=landmark_for_score.data_ptr())
+                io_binding_bs.bind_output('output', 'cuda')
+
+                # Sync and run model
+                syncvec = self.syncvec.cpu()
+                self.face_blendshapes_model.run_with_iobinding(io_binding_bs)
+                landmark_score = io_binding_bs.copy_outputs_to_cpu()[0]
+                
+                if convert478_5:
+                    # convert from 478 to 5 keypoints
+                    landmark = faceutil.convert_face_landmark_478_to_5(landmark)
+                    #lmk = faceutil.convert_face_landmark_478_to_5(lmk)
+
+                #faceutil.test_bbox_landmarks(aimg2, [], lmk)
+                #faceutil.test_bbox_landmarks(img, bbox, landmark)
+                #faceutil.test_bbox_landmarks(img, bbox, det_kpss)
+
+        #return landmark, landmark_score
+        return landmark, []
+
+    def recognize(self, img, face_kps):
+        '''
+        # Find transform
+        dst = self.arcface_dst.copy()
+        dst[:, 0] += 8.0
+
+        tform = trans.SimilarityTransform()
+        tform.estimate(face_kps, dst)
+
+        # Transform
+        img = v2.functional.affine(img, tform.rotation*57.2958, (tform.translation[0], tform.translation[1]) , tform.scale, 0, center = (0,0) ) 
+        img = v2.functional.crop(img, 0,0, 128, 128)
+        img = v2.Resize((112, 112), interpolation=v2.InterpolationMode.BILINEAR, antialias=False)(img)
+        '''
+        # Find transform 
+        tform = trans.SimilarityTransform()
+        tform.estimate(face_kps, self.arcface_dst)
+
+        # Transform
+        img = v2.functional.affine(img, tform.rotation*57.2958, (tform.translation[0], tform.translation[1]) , tform.scale, 0, center = (0,0) ) 
+        img = v2.functional.crop(img, 0,0, 112, 112)
+
+        cropped_image = img
+        # Switch to BGR and normalize
+        img = img.permute(1,2,0) #112,112,3
+        img = img[:, :, [2,1,0]]
+        img = torch.sub(img, 127.5)
+        img = torch.div(img, 127.5)
+        img = img.permute(2, 0, 1) #3,112,112
+
+        # Prepare data and find model parameters
+        img = torch.unsqueeze(img, 0).contiguous()
+        input_name = self.recognition_model.get_inputs()[0].name
+
+        outputs = self.recognition_model.get_outputs()
+        output_names = []
+        for o in outputs:
+            output_names.append(o.name)
+
+        io_binding = self.recognition_model.io_binding()
+        io_binding.bind_input(name=input_name, device_type='cuda', device_id=0, element_type=np.float32,  shape=img.size(), buffer_ptr=img.data_ptr())
+
+        for i in range(len(output_names)):
+            io_binding.bind_output(output_names[i], 'cuda')
+
+        # Sync and run model
+        self.syncvec.cpu()
+        self.recognition_model.run_with_iobinding(io_binding)
+
+        # Return embedding
+        return np.array(io_binding.copy_outputs_to_cpu()).flatten(), cropped_image
+
+    def resnet50(self, image, score=.5):
+        if not self.resnet50_model:
+            self.resnet50_model = onnxruntime.InferenceSession("./models/res50.onnx", providers=self.providers)
+
+            feature_maps = [[64, 64], [32, 32], [16, 16]]
+            min_sizes = [[16, 32], [64, 128], [256, 512]]
+            steps = [8, 16, 32]
+            image_size = 512
+
+            for k, f in enumerate(feature_maps):
+                min_size_array = min_sizes[k]
+                for i, j in product(range(f[0]), range(f[1])):
+                    for min_size in min_size_array:
+                        s_kx = min_size / image_size
+                        s_ky = min_size / image_size
+                        dense_cx = [x * steps[k] / image_size for x in [j + 0.5]]
+                        dense_cy = [y * steps[k] / image_size for y in [i + 0.5]]
+                        for cy, cx in product(dense_cy, dense_cx):
+                            self.anchors += [cx, cy, s_kx, s_ky]
+
+        # image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
+        image = image.permute(1,2,0)
+
+        # image = image - [104, 117, 123]
+        mean = torch.tensor([104, 117, 123], dtype=torch.float32, device='cuda')
+        image = torch.sub(image, mean)
+
+        # image = image.transpose(2, 0, 1)
+        # image = np.float32(image[np.newaxis,:,:,:])
+        image = image.permute(2,0,1)
+        image = torch.reshape(image, (1, 3, 512, 512))
+
+        height, width = (512, 512)
+        tmp = [width, height, width, height, width, height, width, height, width, height]
+        scale1 = torch.tensor(tmp, dtype=torch.float32, device='cuda')
+
+        # ort_inputs = {"input": image}
+        conf = torch.empty((1,10752,2), dtype=torch.float32, device='cuda').contiguous()
+        landmarks = torch.empty((1,10752,10), dtype=torch.float32, device='cuda').contiguous()
+
+        io_binding = self.resnet50_model.io_binding()
+        io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=image.data_ptr())
+        io_binding.bind_output(name='conf', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,10752,2), buffer_ptr=conf.data_ptr())
+        io_binding.bind_output(name='landmarks', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,10752,10), buffer_ptr=landmarks.data_ptr())
+
+        # _, conf, landmarks = self.resnet_model.run(None, ort_inputs)
+        torch.cuda.synchronize('cuda')
+        self.resnet50_model.run_with_iobinding(io_binding)
+
+        # conf = torch.from_numpy(conf)
+        # scores = conf.squeeze(0).numpy()[:, 1]
+        scores = torch.squeeze(conf)[:, 1]
+
+        # landmarks = torch.from_numpy(landmarks)
+        # landmarks = landmarks.to('cuda')
+
+        priors = torch.tensor(self.anchors).view(-1, 4)
+        priors = priors.to('cuda')
+
+        # pre = landmarks.squeeze(0)
+        pre = torch.squeeze(landmarks, 0)
+
+        tmp = (priors[:, :2] + pre[:, :2] * 0.1 * priors[:, 2:], priors[:, :2] + pre[:, 2:4] * 0.1 * priors[:, 2:], priors[:, :2] + pre[:, 4:6] * 0.1 * priors[:, 2:], priors[:, :2] + pre[:, 6:8] * 0.1 * priors[:, 2:], priors[:, :2] + pre[:, 8:10] * 0.1 * priors[:, 2:])
+        landmarks = torch.cat(tmp, dim=1)
+        # landmarks = landmarks * scale1
+        landmarks = torch.mul(landmarks, scale1)
+
+        landmarks = landmarks.cpu().numpy()
+
+        # ignore low scores
+        inds = torch.where(scores>score)[0]
+        inds = inds.cpu().numpy()  
+        scores = scores.cpu().numpy()  
+
+        landmarks, scores = landmarks[inds], scores[inds]    
+
+        # sort
+        order = scores.argsort()[::-1]
+        landmarks = landmarks[order][0]
+
+        return np.array([[landmarks[i], landmarks[i + 1]] for i in range(0,10,2)])

rope/Styles.py

@@ -0,0 +1,293 @@
+bg = 'black'
+main = '#1A1A1A' #Not as Dark Grey '#1A1A1A'
+main2 = '#151515' #Dark Grey '#151515'
+main3 = '#28282E' #Light Grey '#28282E'
+
+
+
+canvas_frame_label_1 = {  
+    'bg':                       main2,
+    'bd':                       '0',
+    'relief':                   'flat',
+    'highlightthickness':       '0'
+    } 
+
+canvas_frame_label_2 =      {  
+    'bg':                       main2,
+    'bd':                       '0',    
+    'relief':                   'flat',
+    'highlightthickness':       '0'
+    }  
+    
+canvas_frame_label_3 = {  
+    'bg':                       main,
+    'bd':                       '0',
+    'relief':                   'flat',
+    'highlightthickness':       '0'
+    }
+    
+info_label = {  
+    'bg':                       main2,
+    'fg':                       '#BCBCBC',
+    'bd':                        '5',
+    'relief':                   'flat',
+    'highlightthickness':       '0',
+    'font':                     ("Segoe UI", 9),
+    'anchor':                   'nw',
+    'justify':                  'left',
+    }
+ 
+text_1 = {  
+    'bg':                       main2,
+    'fg':                       'white', 
+    'activebackground':         main2, 
+    'activeforeground':         'white',
+    'relief':                   'flat',
+    'border':                   '0',
+    'font':                     ("Segoe UI", 9)
+    }
+text_2 = {  
+    'bg':                       main2,
+    'fg':                       '#D0D0D0', 
+    'activebackground':         main2, 
+    'activeforeground':         'white',
+    'relief':                   'flat',
+    'border':                   '0',
+    'font':                     ("Segoe UI", 9)
+    }    
+text_3 = {  
+    'bg':                       main,
+    'fg':                       '#979797', 
+    'activebackground':         main, 
+    'activeforeground':         'white',
+    'relief':                   'flat',
+    'border':                   '0',
+    'font':                     ("Segoe UI", 9)
+    }    
+    
+
+
+option_slider_style = {  
+    'bg':                       main,
+    'activebackground':         main, 
+    'highlightcolor':           'white',
+    'highlightthickness':       '0',
+    'relief':                   'flat',
+    'sliderrelief':             'flat',                                
+    'border':                   '0',
+    'width':                    '3',
+    'troughcolor':              '#1F1F1F',
+    }  
+
+
+entry_3 = {  
+    'bg':                       '#1F1F1F', 
+    'fg':                       '#FFFFFF', 
+    'relief':                   'flat',
+    'border':                   '0',
+    'width':                    '5',
+    'justify':                  'c',    
+    'font':                     ("Segoe UI", 9),
+    'highlightthickness':        '1', 
+    'highlightbackground':        '#17181A', 
+    } 
+    
+entry_2 = {  
+    'bg':                       '#1F1F1F', 
+    'fg':                       '#FFFFFF', 
+    'relief':                   'flat',
+    'border':                   '0',
+    'highlightthickness':        '1', 
+    'highlightbackground':        '#17181A', 
+    'width':                    '5',
+    'justify':                  'l',    
+    'font':                     ("Segoe UI", 9)
+    }     
+
+text_selection_off_3 = {  
+    'bg':                       main, 
+    'fg':                       '#7A7A7A', 
+    'activebackground':         main, 
+    'activeforeground':         'white',
+    'relief':                   'flat',
+    'border':                   '0',
+    'font':                     ("Segoe UI", 10)
+    }
+text_selection_on_3 = {  
+    'bg':                       main, 
+    'fg':                       '#FFFFFF', 
+    'activebackground':         main, 
+    'activeforeground':         'white',
+    'relief':                   'flat',
+    'border':                   '0',
+    'font':                     ("Segoe UI", 10)
+    }    
+text_selection_off_2 = {  
+    'bg':                       main2, 
+    'fg':                       '#7A7A7A', 
+    'activebackground':         main2, 
+    'activeforeground':         'white',
+    'relief':                   'flat',
+    'border':                   '0',
+    'font':                     ("Segoe UI", 10)
+    }    
+text_selection_on_2 = {  
+    'bg':                       main2, 
+    'fg':                       '#FFFFFF', 
+    'activebackground':         main2, 
+    'activeforeground':         'white',
+    'relief':                   'flat',
+    'border':                   '0',
+    'font':                     ("Segoe UI", 10)
+    }       
+    
+
+parameter_switch_3 = {  
+    'bg':                       main,
+    'fg':                       '#FFFFFF', 
+    'activebackground':         main, 
+    'activeforeground':         'white',
+    'relief':                   'flat',
+    'border':                   '0',
+    'font':                     ("Segoe UI", 10)
+    }
+    
+
+
+    
+canvas_bg = {  
+    'bg':                       bg,
+    'relief':                   'flat',
+    'bd':                       '0',
+    'highlightthickness':       '0'
+    }    
+    
+icon = {    
+    'IconOn':                   './rope/media/OnState.png',
+    'IconOff':                  './rope/media/OffState.png',
+    }
+    
+
+frame_style_bg =      {  
+    'bg':               bg,
+    'relief':           'flat',
+    'bd':               '0'
+    }      
+     
+button_3 =    {  
+    'bg':               main2,
+    'fg':               '#FFFFFF', 
+    'activebackground': main2, 
+    'activeforeground': 'white',
+    'relief':           'flat',
+    'border':           '0',
+    'font':             ("Segoe UI", 10)
+    }  
+button_2 =    {  
+    'bg':               main2, 
+    'fg':               '#FFFFFF', 
+    'activebackground': main2, 
+    'activeforeground': 'white',
+    'relief':           'flat',
+    'border':           '0',
+    'font':             ("Segoe UI", 10)
+    } 
+button_1 =    {  
+    'bg':               main2, 
+    'fg':               '#FFFFFF', 
+    'activebackground': main2, 
+    'activeforeground': 'white',
+    'relief':           'flat',
+    'border':           '0',
+    'font':             ("Segoe UI", 10)
+    } 
+    
+button_inactive =    {  
+    'bg':               main2, 
+    'fg':               '#FFFFFF', 
+    'activebackground': main2, 
+    'activeforeground': 'white',
+    'relief':           'flat',
+    'border':           '0',
+    'font':             ("Segoe UI", 10)
+    }     
+
+button_active =    {  
+    'bg':               main2, 
+    'fg':               '#FFFFFF', 
+    'activebackground': main2, 
+    'activeforeground': 'white',
+    'relief':           'flat',
+    'border':           '0',
+    'font':             ("Segoe UI", 10)
+    }    
+    
+    
+media_button_off_3= {
+    'bg':               main2,
+    'fg':               '#7A7A7A', 
+    'activebackground': main2, 
+    'activeforeground': 'white',
+    'relief':           'flat',
+    'border':           '0',
+    'font':             ("Segoe UI", 8)
+    }   
+    
+media_button_on_3= {
+    'bg':               '#4a57ee', 
+    'fg':               '#FFFFFF', 
+    'activebackground': '#4a57ee', 
+    'activeforeground': 'white',
+    'relief':           'flat',
+    'border':           '0',
+    'font':             ("Segoe UI", 8)
+    }       
+    
+ui_text_na_2 =    {  
+    'bg':               main,
+    'fg':               '#7A7A7A', 
+    'activebackground': main, 
+    'activeforeground': 'white',
+    'relief':           'flat',
+    'border':           '0',
+    'font':             ("Segoe UI", 9)
+    } 
+
+timeline_canvas = {  
+    'bg':                       main, 
+    'relief':                   'flat',
+    'bd':                       '0',
+    'highlightthickness':       '0'
+    }    
+
+donate_1 = {
+    'bg':               main, 
+    'fg':               '#7562ee', 
+    'relief':           'flat',
+    'border':           '0',
+    'font':             ("Segoe UI Semibold", 10),
+    'cursor':           "hand2",
+    }      
+    
+# Panes
+# 3:#28282E
+# 2:#212126
+# 1:#17181A
+
+# preview background: #1A1A1A
+
+# Num Fields, slider bg: #1F1F1F
+# slider ball: #919191
+# Borders:#090909
+# Text
+# On/off:#FFFFFF
+# labels: #D0D0D0
+# notActive: #7A7A7A
+# active:#FFFFFF
+
+# highlighted button: #B1B1B2
+# Button off: #828282
+
+# on:     #FFFFFF
+# hover:  #b1b1b2
+# off:    #828282

rope/VideoManager.py

@@ -0,0 +1,1242 @@
+import os
+import cv2
+import tkinter as tk
+from PIL import Image, ImageTk
+import threading
+import time
+import numpy as np
+from skimage import transform as trans
+import subprocess
+from math import floor, ceil
+import bisect
+import onnxruntime
+import torchvision
+from torchvision.transforms.functional import normalize #update to v2
+import torch
+from torchvision import transforms
+torchvision.disable_beta_transforms_warning()
+from torchvision.transforms import v2
+torch.set_grad_enabled(False)
+onnxruntime.set_default_logger_severity(4)
+
+import inspect #print(inspect.currentframe().f_back.f_code.co_name, 'resize_image')
+
+device = 'cuda'
+
+lock=threading.Lock()
+
+class VideoManager():  
+    def __init__(self, models ):
+        self.models = models
+        # Model related
+        self.swapper_model = []             # insightface swapper model
+        # self.faceapp_model = []             # insight faceapp model
+        self.input_names = []               # names of the inswapper.onnx inputs
+        self.input_size = []                # size of the inswapper.onnx inputs
+
+        self.output_names = []              # names of the inswapper.onnx outputs    
+        self.arcface_dst = np.array( [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], [41.5493, 92.3655], [70.7299, 92.2041]], dtype=np.float32)     
+
+        self.video_file = []
+
+        self.FFHQ_kps = np.array([[ 192.98138, 239.94708 ], [ 318.90277, 240.1936 ], [ 256.63416, 314.01935 ], [ 201.26117, 371.41043 ], [ 313.08905, 371.15118 ] ])
+        
+     
+        
+        #Video related
+        self.capture = []                   # cv2 video
+        self.is_video_loaded = False        # flag for video loaded state    
+        self.video_frame_total = None       # length of currently loaded video
+        self.play = False                   # flag for the play button toggle
+        self.current_frame = 0              # the current frame of the video
+        self.create_video = False
+        self.output_video = []       
+        self.file_name = []       
+
+        
+        # Play related
+        # self.set_read_threads = []          # Name of threaded function
+        self.frame_timer = 0.0      # used to set the framerate during playing
+        
+        # Queues
+        self.action_q = []                  # queue for sending to the coordinator
+        self.frame_q = []                   # queue for frames that are ready for coordinator
+
+        self.r_frame_q = []                 # queue for frames that are requested by the GUI
+        self.read_video_frame_q = []
+        
+        # swapping related
+        # self.source_embedding = []          # array with indexed source embeddings
+
+        self.found_faces = []   # array that maps the found faces to source faces    
+
+        self.parameters = []
+
+
+        self.target_video = []
+
+        self.fps = 1.0
+        self.temp_file = []
+
+
+        self.clip_session = []
+
+        self.start_time = []
+        self.record = False
+        self.output = []
+        self.image = []
+
+        self.saved_video_path = []
+        self.sp = []
+        self.timer = []
+        self.fps_average = []
+        self.total_thread_time = 0.0
+        
+        self.start_play_time = []
+        self.start_play_frame = []
+        
+        self.rec_thread = []
+        self.markers = []
+        self.is_image_loaded = False
+        self.stop_marker = -1
+        self.perf_test = False
+
+        self.control = []
+
+        
+
+
+
+        self.process_q =    {
+                            "Thread":                   [],
+                            "FrameNumber":              [],
+                            "ProcessedFrame":           [],
+                            "Status":                   'clear',
+                            "ThreadTime":               []
+                            }   
+        self.process_qs = []
+        self.rec_q =    {
+                            "Thread":                   [],
+                            "FrameNumber":              [],
+                            "Status":                   'clear'
+                            }   
+        self.rec_qs = []
+
+    def assign_found_faces(self, found_faces):
+        self.found_faces = found_faces
+
+
+    def load_target_video( self, file ):
+        # If we already have a video loaded, release it
+        if self.capture:
+            self.capture.release()
+            
+        # Open file   
+        self.video_file = file
+        self.capture = cv2.VideoCapture(file)
+        self.fps = self.capture.get(cv2.CAP_PROP_FPS)
+        
+        if not self.capture.isOpened():
+            print("Cannot open file: ", file)
+            
+        else:
+            self.target_video = file
+            self.is_video_loaded = True
+            self.is_image_loaded = False
+            self.video_frame_total = int(self.capture.get(cv2.CAP_PROP_FRAME_COUNT))
+            self.play = False 
+            self.current_frame = 0
+            self.frame_timer = time.time()
+            self.frame_q = []            
+            self.r_frame_q = []             
+            self.found_faces = []
+            self.add_action("set_slider_length",self.video_frame_total-1)
+
+        self.capture.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)        
+        success, image = self.capture.read() 
+        
+        if success:
+            crop = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # RGB
+            temp = [crop, False]
+            self.r_frame_q.append(temp)
+            self.capture.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
+    
+    def load_target_image(self, file):
+        if self.capture:
+            self.capture.release()
+        self.is_video_loaded = False
+        self.play = False 
+        self.frame_q = []            
+        self.r_frame_q = [] 
+        self.found_faces = []
+        self.image = cv2.imread(file) # BGR
+        self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB) # RGB
+        temp = [self.image, False]
+        self.frame_q.append(temp)
+
+        self.is_image_loaded = True
+
+    
+    ## Action queue
+    def add_action(self, action, param):
+        # print(inspect.currentframe().f_back.f_code.co_name, '->add_action: '+action)
+        temp = [action, param]
+        self.action_q.append(temp)    
+    
+    def get_action_length(self):
+        return len(self.action_q)
+
+    def get_action(self):
+        action = self.action_q[0]
+        self.action_q.pop(0)
+        return action
+     
+    ## Queues for the Coordinator
+    def get_frame(self):
+        frame = self.frame_q[0]
+        self.frame_q.pop(0)
+        return frame
+    
+    def get_frame_length(self):
+        return len(self.frame_q)  
+        
+    def get_requested_frame(self):
+        frame = self.r_frame_q[0]
+        self.r_frame_q.pop(0)
+        return frame
+    
+    def get_requested_frame_length(self):
+        return len(self.r_frame_q)          
+    
+
+    def get_requested_video_frame(self, frame, marker=True):  
+        temp = []
+        if self.is_video_loaded:
+        
+            if self.play == True:            
+                self.play_video("stop")
+                self.process_qs = []
+                
+            self.current_frame = int(frame)
+
+            self.capture.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
+            success, target_image = self.capture.read() #BGR
+
+            if success:
+                target_image = cv2.cvtColor(target_image, cv2.COLOR_BGR2RGB) #RGB 
+                if not self.control['SwapFacesButton']:   
+                    temp = [target_image, self.current_frame] #temp = RGB
+                else:
+                    temp = [self.swap_video(target_image, self.current_frame, marker), self.current_frame] # temp = RGB
+
+                self.r_frame_q.append(temp)  
+        
+        elif self.is_image_loaded:
+            if not self.control['SwapFacesButton']:
+                temp = [self.image, self.current_frame] # image = RGB
+        
+            else:  
+                temp = [self.swap_video(self.image, self.current_frame, False), self.current_frame] # image = RGB
+            
+            self.r_frame_q.append(temp)  
+
+
+    def find_lowest_frame(self, queues):
+        min_frame=999999999
+        index=-1
+        
+        for idx, thread in enumerate(queues):
+            frame = thread['FrameNumber']
+            if frame != []:
+                if frame < min_frame:
+                    min_frame = frame
+                    index=idx
+        return index, min_frame
+
+
+    def play_video(self, command):
+        # print(inspect.currentframe().f_back.f_code.co_name, '->play_video: ')
+        if command == "play":
+            # Initialization
+            self.play = True
+            self.fps_average = []            
+            self.process_qs = []
+            self.capture.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
+            self.frame_timer = time.time()
+            
+            # Create reusable queue based on number of threads
+            for i in range(self.parameters['ThreadsSlider']):
+                    new_process_q = self.process_q.copy()
+                    self.process_qs.append(new_process_q)
+                    
+            
+            # Start up audio if requested
+            if self.control['AudioButton']:  
+                seek_time = (self.current_frame)/self.fps
+                args =  ["ffplay", 
+                        '-vn', 
+                        '-ss', str(seek_time),
+                        '-nodisp',
+                        '-stats',
+                        '-loglevel',  'quiet', 
+                        '-sync',  'audio',
+                        self.video_file]
+ 
+                
+                self.audio_sp = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+
+                # Parse the console to find where the audio started
+                while True:
+                    temp = self.audio_sp.stdout.read(69)    
+                    if temp[:7] != b'    nan':
+                        sought_time = float(temp[:7])
+                        self.current_frame = int(self.fps*sought_time)
+                        
+                        self.capture.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
+
+                        break
+
+
+#'    nan    :  0.000
+#'   1.25 M-A:  0.000 fd=   0 aq=   12KB vq=    0KB sq=    0B f=0/0' 
+                
+
+        elif command == "stop":
+            self.play = False
+            self.add_action("stop_play", True)
+            
+            index, min_frame = self.find_lowest_frame(self.process_qs)
+            
+            if index != -1:
+                self.current_frame = min_frame-1   
+            
+            if self.control['AudioButton']:    
+                self.audio_sp.terminate()
+
+            torch.cuda.empty_cache()
+                
+        elif command=='stop_from_gui':
+            self.play = False
+
+            # Find the lowest frame in the current render queue and set the current frame to the one before it
+            index, min_frame = self.find_lowest_frame(self.process_qs)
+            if index != -1:
+                self.current_frame = min_frame-1   
+            
+            if self.control['AudioButton']:    
+                self.audio_sp.terminate()
+
+            torch.cuda.empty_cache()
+
+        elif command == "record":
+            self.record = True
+            self.play = True
+            self.total_thread_time = 0.0
+            self.process_qs = []
+            self.capture.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
+            
+            for i in range(self.parameters['ThreadsSlider']):
+                    new_process_q = self.process_q.copy()
+                    self.process_qs.append(new_process_q)
+
+           # Initialize
+            self.timer = time.time()
+            frame_width = int(self.capture.get(3))
+            frame_width = int(self.capture.get(3))
+            frame_height = int(self.capture.get(4))
+
+            self.start_time = float(self.capture.get(cv2.CAP_PROP_POS_FRAMES) / float(self.fps))            
+            
+            self.file_name = os.path.splitext(os.path.basename(self.target_video))
+            base_filename =  self.file_name[0]+"_"+str(time.time())[:10]
+            self.output = os.path.join(self.saved_video_path, base_filename)
+            self.temp_file = self.output+"_temp"+self.file_name[1]  
+            
+            if self.parameters['RecordTypeTextSel']=='FFMPEG':
+                args =  ["ffmpeg", 
+                        '-hide_banner',
+                        '-loglevel',    'error',
+                        "-an",       
+                        "-r",           str(self.fps),
+                        "-i",           "pipe:",
+                        # '-g',           '25',
+                        "-vf",          "format=yuvj420p",
+                        "-c:v",         "libx264",
+                        "-crf",         str(self.parameters['VideoQualSlider']),
+                        "-r",           str(self.fps),
+                        "-s",           str(frame_width)+"x"+str(frame_height),
+                        self.temp_file]  
+
+                self.sp = subprocess.Popen(args, stdin=subprocess.PIPE)
+            
+            elif self.parameters['RecordTypeTextSel']=='OPENCV':    
+                size = (frame_width, frame_height)
+                self.sp = cv2.VideoWriter(self.temp_file,  cv2.VideoWriter_fourcc(*'mp4v') , self.fps, size) 
+      
+    # @profile
+    def process(self):
+        process_qs_len = range(len(self.process_qs))
+
+        # Add threads to Queue
+        if self.play == True and self.is_video_loaded == True:
+            for item in self.process_qs:
+                if item['Status'] == 'clear' and self.current_frame < self.video_frame_total:
+                    item['Thread'] = threading.Thread(target=self.thread_video_read, args = [self.current_frame]).start()
+                    item['FrameNumber'] = self.current_frame
+                    item['Status'] = 'started'
+                    item['ThreadTime'] = time.time()
+
+                    self.current_frame += 1
+                    break
+          
+        else:
+            self.play = False
+
+        # Always be emptying the queues
+        time_diff = time.time() - self.frame_timer
+
+        if not self.record and time_diff >= 1.0/float(self.fps) and self.play:
+
+            index, min_frame = self.find_lowest_frame(self.process_qs)
+
+            if index != -1:
+                if self.process_qs[index]['Status'] == 'finished':
+                    temp = [self.process_qs[index]['ProcessedFrame'], self.process_qs[index]['FrameNumber']]
+                    self.frame_q.append(temp)
+
+                    # Report fps, other data
+                    self.fps_average.append(1.0/time_diff)
+                    if len(self.fps_average) >= floor(self.fps):
+                        fps = round(np.average(self.fps_average), 2)
+                        msg = "%s fps, %s process time" % (fps, round(self.process_qs[index]['ThreadTime'], 4))
+                        self.fps_average = []
+
+                    if self.process_qs[index]['FrameNumber'] >= self.video_frame_total-1 or self.process_qs[index]['FrameNumber'] == self.stop_marker:
+                        self.play_video('stop')
+                        
+                    self.process_qs[index]['Status'] = 'clear'
+                    self.process_qs[index]['Thread'] = []
+                    self.process_qs[index]['FrameNumber'] = []
+                    self.process_qs[index]['ThreadTime'] = []
+                    self.frame_timer += 1.0/self.fps
+                    
+        elif self.record:
+           
+            index, min_frame = self.find_lowest_frame(self.process_qs)           
+            
+            if index != -1:
+
+                # If the swapper thread has finished generating a frame
+                if self.process_qs[index]['Status'] == 'finished':
+                    image = self.process_qs[index]['ProcessedFrame']  
+                    
+                    if self.parameters['RecordTypeTextSel']=='FFMPEG':
+                        pil_image = Image.fromarray(image)
+                        pil_image.save(self.sp.stdin, 'BMP')   
+                    
+                    elif self.parameters['RecordTypeTextSel']=='OPENCV':
+                        self.sp.write(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+                    temp = [image, self.process_qs[index]['FrameNumber']]
+                    self.frame_q.append(temp)
+
+                    # Close video and process
+                    if self.process_qs[index]['FrameNumber'] >= self.video_frame_total-1 or self.process_qs[index]['FrameNumber'] == self.stop_marker or self.play == False:
+                        self.play_video("stop")
+                        stop_time = float(self.capture.get(cv2.CAP_PROP_POS_FRAMES) / float(self.fps))
+                        if stop_time == 0:
+                            stop_time = float(self.video_frame_total) / float(self.fps)
+                        
+                        if self.parameters['RecordTypeTextSel']=='FFMPEG':
+                            self.sp.stdin.close()
+                            self.sp.wait()
+                        elif self.parameters['RecordTypeTextSel']=='OPENCV':    
+                            self.sp.release()
+
+                        orig_file = self.target_video
+                        final_file = self.output+self.file_name[1]
+                        print("adding audio...")    
+                        args = ["ffmpeg",
+                                '-hide_banner',
+                                '-loglevel',    'error',
+                                "-i", self.temp_file,
+                                "-ss", str(self.start_time), "-to", str(stop_time), "-i",  orig_file,
+                                "-c",  "copy", # may be c:v
+                                "-map", "0:v:0", "-map", "1:a:0?",
+                                "-shortest",
+                                final_file]
+                        
+                        four = subprocess.run(args)
+                        os.remove(self.temp_file)
+
+                        timef= time.time() - self.timer 
+                        self.record = False
+                        print('Video saved as:', final_file)
+                        msg = "Total time: %s s." % (round(timef,1))
+                        print(msg)
+
+                        
+                    self.total_thread_time = []
+                    self.process_qs[index]['Status'] = 'clear'
+                    self.process_qs[index]['FrameNumber'] = []
+                    self.process_qs[index]['Thread'] = []
+                    self.frame_timer = time.time()
+    # @profile
+    def thread_video_read(self, frame_number):  
+        with lock:
+            success, target_image = self.capture.read()
+
+        if success:
+            target_image = cv2.cvtColor(target_image, cv2.COLOR_BGR2RGB)
+            if not self.control['SwapFacesButton']:
+                temp = [target_image, frame_number]
+            
+            else:
+                temp = [self.swap_video(target_image, frame_number, True), frame_number]
+            
+            for item in self.process_qs:
+                if item['FrameNumber'] == frame_number:
+                    item['ProcessedFrame'] = temp[0]
+                    item['Status'] = 'finished'
+                    item['ThreadTime'] = time.time() - item['ThreadTime']
+                    break
+
+
+
+
+    # @profile
+    def swap_video(self, target_image, frame_number, use_markers):
+        # Grab a local copy of the parameters to prevent threading issues
+        parameters = self.parameters.copy()
+        control = self.control.copy()
+        
+        # Find out if the frame is in a marker zone and copy the parameters if true
+        if self.markers and use_markers:
+            temp=[]
+            for i in range(len(self.markers)):
+                temp.append(self.markers[i]['frame'])
+            idx = bisect.bisect(temp, frame_number)
+            
+            parameters = self.markers[idx-1]['parameters'].copy()
+        
+        # Load frame into VRAM
+        img = torch.from_numpy(target_image.astype('uint8')).to('cuda') #HxWxc
+        img = img.permute(2,0,1)#cxHxW        
+        
+        #Scale up frame if it is smaller than 512
+        img_x = img.size()[2]
+        img_y = img.size()[1]
+        
+        if img_x<512 and img_y<512:
+            # if x is smaller, set x to 512
+            if img_x <= img_y:
+                tscale = v2.Resize((int(512*img_y/img_x), 512), antialias=True)
+            else:
+                tscale = v2.Resize((512, int(512*img_x/img_y)), antialias=True)
+
+            img = tscale(img)
+            
+        elif img_x<512:
+            tscale = v2.Resize((int(512*img_y/img_x), 512), antialias=True)
+            img = tscale(img)
+        
+        elif img_y<512:
+            tscale = v2.Resize((512, int(512*img_x/img_y)), antialias=True)
+            img = tscale(img)    
+
+        # Rotate the frame
+        if parameters['OrientSwitch']:
+            img = v2.functional.rotate(img, angle=parameters['OrientSlider'], interpolation=v2.InterpolationMode.BILINEAR, expand=True)
+
+        # Find all faces in frame and return a list of 5-pt kpss
+        bboxes, kpss = self.func_w_test("detect", self.models.run_detect, img, parameters['DetectTypeTextSel'], max_num=20, score=parameters['DetectScoreSlider']/100.0, use_landmark_detection=parameters['LandmarksDetectionAdjSwitch'], landmark_detect_mode=parameters["LandmarksDetectTypeTextSel"], landmark_score=parameters["LandmarksDetectScoreSlider"]/100.0, from_points=parameters["LandmarksAlignModeFromPointsSwitch"])
+
+        # Get embeddings for all faces found in the frame
+        ret = []
+        for face_kps in kpss:
+            face_emb, _ = self.func_w_test('recognize',  self.models.run_recognize, img, face_kps)
+            ret.append([face_kps, face_emb])
+        
+        if ret:
+            # Loop through target faces to see if they match our found face embeddings
+            for fface in ret:
+                for found_face in self.found_faces:
+                    # sim between face in video and already found face
+                    sim = self.findCosineDistance(fface[1], found_face["Embedding"])
+                    # if the face[i] in the frame matches afound face[j] AND the found face is active (not []) 
+                    if sim>=float(parameters["ThresholdSlider"]) and found_face["SourceFaceAssignments"]:
+                        s_e = found_face["AssignedEmbedding"]
+                        # s_e = found_face['ptrdata']
+                        img = self.func_w_test("swap_video", self.swap_core, img, fface[0], s_e, parameters, control)
+                        # img = img.permute(2,0,1)
+                    
+            img = img.permute(1,2,0)
+            if not control['MaskViewButton'] and parameters['OrientSwitch']:
+                img = img.permute(2,0,1)
+                img = transforms.functional.rotate(img, angle=-parameters['OrientSlider'], expand=True)
+                img = img.permute(1,2,0)
+
+        else:
+            img = img.permute(1,2,0)
+            if parameters['OrientSwitch']:
+                img = img.permute(2,0,1)
+                img = v2.functional.rotate(img, angle=-parameters['OrientSlider'], interpolation=v2.InterpolationMode.BILINEAR, expand=True)
+                img = img.permute(1,2,0)
+        
+        if self.perf_test:
+            print('------------------------')  
+        
+        # Unscale small videos
+        if img_x <512 or img_y < 512:
+            tscale = v2.Resize((img_y, img_x), antialias=True)
+            img = img.permute(2,0,1)
+            img = tscale(img)
+            img = img.permute(1,2,0)
+            
+
+        img = img.cpu().numpy()  
+
+        if parameters["ShowLandmarksSwitch"]:
+            if ret:
+                if img_y <= 720:
+                    p = 1
+                else:
+                    p = 2
+
+                for face in ret:
+                    for kpoint in face[0]:
+                        for i in range(-1, p):
+                            for j in range(-1, p):
+                                try:
+                                    img[int(kpoint[1])+i][int(kpoint[0])+j][0] = 0
+                                    img[int(kpoint[1])+i][int(kpoint[0])+j][1] = 255
+                                    img[int(kpoint[1])+i][int(kpoint[0])+j][2] = 255
+                                except:
+                                    print("Key-points value {} exceed the image size {}.".format(kpoint, (img_x, img_y)))
+                                    continue
+
+        return img.astype(np.uint8)
+
+    def findCosineDistance(self, vector1, vector2):
+        vector1 = vector1.ravel()
+        vector2 = vector2.ravel()
+        cos_dist = 1.0 - np.dot(vector1, vector2)/(np.linalg.norm(vector1)*np.linalg.norm(vector2)) # 2..0
+
+        return 100.0-cos_dist*50.0
+        '''
+        vector1 = vector1.ravel()
+        vector2 = vector2.ravel()
+
+        return 1 - np.dot(vector1, vector2)/(np.linalg.norm(vector1)*np.linalg.norm(vector2))
+        '''
+
+    def func_w_test(self, name, func, *args, **argsv):
+        timing = time.time()
+        result = func(*args, **argsv)
+        if self.perf_test:
+            print(name, round(time.time()-timing, 5), 's')
+        return result
+
+    # @profile    
+    def swap_core(self, img, kps, s_e, parameters, control): # img = RGB
+        # 512 transforms
+        dst = self.arcface_dst * 4.0
+        dst[:,0] += 32.0
+        
+        # Change the ref points
+        if parameters['FaceAdjSwitch']:
+            dst[:,0] += parameters['KPSXSlider']
+            dst[:,1] += parameters['KPSYSlider']
+            dst[:,0] -= 255
+            dst[:,0] *= (1+parameters['KPSScaleSlider']/100)
+            dst[:,0] += 255
+            dst[:,1] -= 255
+            dst[:,1] *= (1+parameters['KPSScaleSlider']/100)
+            dst[:,1] += 255
+
+        tform = trans.SimilarityTransform()
+        tform.estimate(kps, dst) 
+
+        # Scaling Transforms
+        t512 = v2.Resize((512, 512), interpolation=v2.InterpolationMode.BILINEAR, antialias=False)
+        t256 = v2.Resize((256, 256), interpolation=v2.InterpolationMode.BILINEAR, antialias=False)
+        t128 = v2.Resize((128, 128), interpolation=v2.InterpolationMode.BILINEAR, antialias=False)
+
+        # Grab 512 face from image and create 256 and 128 copys
+        original_face_512 = v2.functional.affine(img, tform.rotation*57.2958, (tform.translation[0], tform.translation[1]) , tform.scale, 0, center = (0,0), interpolation=v2.InterpolationMode.BILINEAR )
+        original_face_512 = v2.functional.crop(original_face_512, 0,0, 512, 512)# 3, 512, 512
+        original_face_256 = t256(original_face_512)
+        original_face_128 = t128(original_face_256)
+
+        latent = torch.from_numpy(self.models.calc_swapper_latent(s_e)).float().to('cuda')
+
+        dim = 1
+        if parameters['SwapperTypeTextSel'] == '128':
+            dim = 1
+            input_face_affined = original_face_128
+        elif parameters['SwapperTypeTextSel'] == '256':
+            dim = 2
+            input_face_affined = original_face_256
+        elif parameters['SwapperTypeTextSel'] == '512':
+            dim = 4
+            input_face_affined = original_face_512
+
+        # Optional Scaling # change the thransform matrix
+        if parameters['FaceAdjSwitch']:
+            input_face_affined = v2.functional.affine(input_face_affined, 0, (0, 0), 1 + parameters['FaceScaleSlider'] / 100, 0, center=(dim*128-1, dim*128-1), interpolation=v2.InterpolationMode.BILINEAR)
+
+        itex = 1
+        if parameters['StrengthSwitch']:
+            itex = ceil(parameters['StrengthSlider'] / 100.)
+
+        output_size = int(128 * dim)
+        output = torch.zeros((output_size, output_size, 3), dtype=torch.float32, device='cuda')
+        input_face_affined = input_face_affined.permute(1, 2, 0)
+        input_face_affined = torch.div(input_face_affined, 255.0)
+
+        for k in range(itex):
+            for j in range(dim):
+                for i in range(dim):
+                    input_face_disc = input_face_affined[j::dim,i::dim]
+                    input_face_disc = input_face_disc.permute(2, 0, 1)
+                    input_face_disc = torch.unsqueeze(input_face_disc, 0).contiguous()
+
+                    swapper_output = torch.empty((1,3,128,128), dtype=torch.float32, device='cuda').contiguous()
+                    self.models.run_swapper(input_face_disc, latent, swapper_output)
+
+                    swapper_output = torch.squeeze(swapper_output)
+                    swapper_output = swapper_output.permute(1, 2, 0)
+
+
+                    output[j::dim, i::dim] = swapper_output.clone()
+            prev_face = input_face_affined.clone()
+            input_face_affined = output.clone()
+            output = torch.mul(output, 255)
+            output = torch.clamp(output, 0, 255)
+
+
+        output = output.permute(2, 0, 1)
+
+
+        swap = t512(output)
+
+        if parameters['StrengthSwitch']:
+            if itex == 0:
+                swap = original_face_512.clone()
+            else:
+                alpha = np.mod(parameters['StrengthSlider'], 100)*0.01
+                if alpha==0:
+                    alpha=1
+
+                # Blend the images
+                prev_face = torch.mul(prev_face, 255)
+                prev_face = torch.clamp(prev_face, 0, 255)
+                prev_face = prev_face.permute(2, 0, 1)
+                prev_face = t512(prev_face)
+                swap = torch.mul(swap, alpha)
+                prev_face = torch.mul(prev_face, 1-alpha)
+                swap = torch.add(swap, prev_face)
+
+
+
+
+            # swap = torch.squeeze(swap)
+            # swap = torch.mul(swap, 255)
+            # swap = torch.clamp(swap, 0, 255)
+            # # swap_128 = swap
+            # swap = t256(swap)
+            # swap = t512(swap)
+
+        
+        # Apply color corerctions
+        if parameters['ColorSwitch']:
+            # print(parameters['ColorGammaSlider'])
+            swap = torch.unsqueeze(swap,0)
+            swap = v2.functional.adjust_gamma(swap, parameters['ColorGammaSlider'], 1.0)
+            swap = torch.squeeze(swap)
+            swap = swap.permute(1, 2, 0).type(torch.float32)
+
+            del_color = torch.tensor([parameters['ColorRedSlider'], parameters['ColorGreenSlider'], parameters['ColorBlueSlider']], device=device)
+            swap += del_color
+            swap = torch.clamp(swap, min=0., max=255.)
+            swap = swap.permute(2, 0, 1).type(torch.uint8)        
+
+        # Create border mask
+        border_mask = torch.ones((128, 128), dtype=torch.float32, device=device)
+        border_mask = torch.unsqueeze(border_mask,0)
+        
+        # if parameters['BorderState']:
+        top = parameters['BorderTopSlider']
+        left = parameters['BorderSidesSlider']
+        right = 128-parameters['BorderSidesSlider']
+        bottom = 128-parameters['BorderBottomSlider']
+
+        border_mask[:, :top, :] = 0
+        border_mask[:, bottom:, :] = 0
+        border_mask[:, :, :left] = 0
+        border_mask[:, :, right:] = 0
+
+        gauss = transforms.GaussianBlur(parameters['BorderBlurSlider']*2+1, (parameters['BorderBlurSlider']+1)*0.2)
+        border_mask = gauss(border_mask)        
+
+        # Create image mask
+        swap_mask = torch.ones((128, 128), dtype=torch.float32, device=device)
+        swap_mask = torch.unsqueeze(swap_mask,0)    
+
+        # Face Diffing
+        if parameters["DiffSwitch"]:
+            mask = self.apply_fake_diff(swap, original_face_512, parameters["DiffSlider"])
+            # mask = t128(mask)
+            gauss = transforms.GaussianBlur(parameters['BlendSlider']*2+1, (parameters['BlendSlider']+1)*0.2)
+            mask = gauss(mask.type(torch.float32)) 
+            swap = swap*mask + original_face_512*(1-mask)
+ 
+        # Restorer
+        if parameters["RestorerSwitch"]: 
+            swap = self.func_w_test('Restorer', self.apply_restorer, swap, parameters)  
+        
+            
+        # Occluder
+        if parameters["OccluderSwitch"]:
+            mask = self.func_w_test('occluder', self.apply_occlusion , original_face_256, parameters["OccluderSlider"])
+            mask = t128(mask)  
+            swap_mask = torch.mul(swap_mask, mask)
+
+
+        if parameters["FaceParserSwitch"]:
+            mask = self.apply_face_parser(swap, parameters["FaceParserSlider"], parameters['MouthParserSlider'])
+            mask = t128(mask)
+            swap_mask = torch.mul(swap_mask, mask)            
+        
+        # CLIPs 
+        if parameters["CLIPSwitch"]:
+            with lock:
+                mask = self.func_w_test('CLIP', self.apply_CLIPs, original_face_512, parameters["CLIPTextEntry"], parameters["CLIPSlider"])
+            mask = cv2.resize(mask, (128,128))
+            mask = torch.from_numpy(mask).to('cuda')
+            swap_mask *= mask
+
+
+        # Add blur to swap_mask results
+        gauss = transforms.GaussianBlur(parameters['BlendSlider']*2+1, (parameters['BlendSlider']+1)*0.2)
+        swap_mask = gauss(swap_mask)  
+        
+
+        # Combine border and swap mask, scale, and apply to swap
+        swap_mask = torch.mul(swap_mask, border_mask)
+        swap_mask = t512(swap_mask)
+        swap = torch.mul(swap, swap_mask)
+
+        if not control['MaskViewButton']:
+            # Cslculate the area to be mergerd back to the original frame
+            IM512 = tform.inverse.params[0:2, :]
+            corners = np.array([[0,0], [0,511], [511, 0], [511, 511]])
+
+            x = (IM512[0][0]*corners[:,0] + IM512[0][1]*corners[:,1] + IM512[0][2])
+            y = (IM512[1][0]*corners[:,0] + IM512[1][1]*corners[:,1] + IM512[1][2])
+            
+            left = floor(np.min(x))
+            if left<0:
+                left=0
+            top = floor(np.min(y))
+            if top<0: 
+                top=0
+            right = ceil(np.max(x))
+            if right>img.shape[2]:
+                right=img.shape[2]            
+            bottom = ceil(np.max(y))
+            if bottom>img.shape[1]:
+                bottom=img.shape[1]   
+
+            # Untransform the swap
+            swap = v2.functional.pad(swap, (0,0,img.shape[2]-512, img.shape[1]-512))
+            swap = v2.functional.affine(swap, tform.inverse.rotation*57.2958, (tform.inverse.translation[0], tform.inverse.translation[1]), tform.inverse.scale, 0,interpolation=v2.InterpolationMode.BILINEAR, center = (0,0) )
+            swap = swap[0:3, top:bottom, left:right]
+            swap = swap.permute(1, 2, 0)
+            
+            # Untransform the swap mask
+            swap_mask = v2.functional.pad(swap_mask, (0,0,img.shape[2]-512, img.shape[1]-512))
+            swap_mask = v2.functional.affine(swap_mask, tform.inverse.rotation*57.2958, (tform.inverse.translation[0], tform.inverse.translation[1]), tform.inverse.scale, 0, interpolation=v2.InterpolationMode.BILINEAR, center = (0,0) )
+            swap_mask = swap_mask[0:1, top:bottom, left:right]                        
+            swap_mask = swap_mask.permute(1, 2, 0)
+            swap_mask = torch.sub(1, swap_mask) 
+
+            # Apply the mask to the original image areas
+            img_crop = img[0:3, top:bottom, left:right]
+            img_crop = img_crop.permute(1,2,0)            
+            img_crop = torch.mul(swap_mask,img_crop)
+            
+            #Add the cropped areas and place them back into the original image
+            swap = torch.add(swap, img_crop)
+            swap = swap.type(torch.uint8)
+            swap = swap.permute(2,0,1)
+            img[0:3, top:bottom, left:right] = swap  
+
+        else:
+            # Invert swap mask
+            swap_mask = torch.sub(1, swap_mask)
+            
+            # Combine preswapped face with swap
+            original_face_512 = torch.mul(swap_mask, original_face_512)
+            original_face_512 = torch.add(swap, original_face_512)            
+            original_face_512 = original_face_512.type(torch.uint8)
+            original_face_512 = original_face_512.permute(1, 2, 0)
+
+            # Uninvert and create image from swap mask
+            swap_mask = torch.sub(1, swap_mask) 
+            swap_mask = torch.cat((swap_mask,swap_mask,swap_mask),0)
+            swap_mask = swap_mask.permute(1, 2, 0)
+
+            # Place them side by side
+            img = torch.hstack([original_face_512, swap_mask*255])
+            img = img.permute(2,0,1)
+
+        return img
+        
+    # @profile    
+    def apply_occlusion(self, img, amount):        
+        img = torch.div(img, 255)
+        img = torch.unsqueeze(img, 0)
+        outpred = torch.ones((256,256), dtype=torch.float32, device=device).contiguous()
+        
+        self.models.run_occluder(img, outpred)        
+                
+        outpred = torch.squeeze(outpred)
+        outpred = (outpred > 0)
+        outpred = torch.unsqueeze(outpred, 0).type(torch.float32)
+        
+        if amount >0:                   
+            kernel = torch.ones((1,1,3,3), dtype=torch.float32, device=device)
+
+            for i in range(int(amount)):
+                outpred = torch.nn.functional.conv2d(outpred, kernel, padding=(1, 1))       
+                outpred = torch.clamp(outpred, 0, 1)
+            
+            outpred = torch.squeeze(outpred)
+            
+        if amount <0:      
+            outpred = torch.neg(outpred)
+            outpred = torch.add(outpred, 1)
+            kernel = torch.ones((1,1,3,3), dtype=torch.float32, device=device)
+
+            for i in range(int(-amount)):
+                outpred = torch.nn.functional.conv2d(outpred, kernel, padding=(1, 1))       
+                outpred = torch.clamp(outpred, 0, 1)
+            
+            outpred = torch.squeeze(outpred)
+            outpred = torch.neg(outpred)
+            outpred = torch.add(outpred, 1)
+            
+        outpred = torch.reshape(outpred, (1, 256, 256)) 
+        return outpred         
+    
+      
+    def apply_CLIPs(self, img, CLIPText, CLIPAmount):
+        clip_mask = np.ones((352, 352))
+        img = img.permute(1,2,0)
+        img = img.cpu().numpy()
+        # img = img.to(torch.float)
+        # img = img.permute(1,2,0)
+        transform = transforms.Compose([transforms.ToTensor(),
+                                        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), 
+                                        transforms.Resize((352, 352))])
+        CLIPimg = transform(img).unsqueeze(0)
+        
+        if CLIPText != "":
+            prompts = CLIPText.split(',')
+
+            with torch.no_grad():
+                preds = self.clip_session(CLIPimg.repeat(len(prompts),1,1,1), prompts)[0]
+                # preds = self.clip_session(CLIPimg,  maskimg, True)[0]
+
+            clip_mask = 1 - torch.sigmoid(preds[0][0])
+            for i in range(len(prompts)-1):
+                clip_mask *= 1-torch.sigmoid(preds[i+1][0])
+            clip_mask = clip_mask.data.cpu().numpy()
+            
+            thresh = CLIPAmount/100.0
+            clip_mask[clip_mask>thresh] = 1.0
+            clip_mask[clip_mask<=thresh] = 0.0
+        return clip_mask    
+        
+    # @profile
+    def apply_face_parser(self, img, FaceAmount, MouthAmount):
+
+        # atts = [1 'skin', 2 'l_brow', 3 'r_brow', 4 'l_eye', 5 'r_eye', 6 'eye_g', 7 'l_ear', 8 'r_ear', 9 'ear_r', 10 'nose', 11 'mouth', 12 'u_lip', 13 'l_lip', 14 'neck', 15 'neck_l', 16 'cloth', 17 'hair', 18 'hat']
+       
+        outpred = torch.ones((512,512), dtype=torch.float32, device='cuda').contiguous()
+        
+
+        img = torch.div(img, 255)
+        img = v2.functional.normalize(img, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+        img = torch.reshape(img, (1, 3, 512, 512))
+        outpred = torch.empty((1,19,512,512), dtype=torch.float32, device='cuda').contiguous()
+
+        self.models.run_faceparser(img, outpred)
+
+        outpred = torch.squeeze(outpred)
+        outpred = torch.argmax(outpred, 0)
+
+        # Mouth Parse
+        if MouthAmount <0:
+            mouth_idxs = torch.tensor([11], device='cuda')
+            iters = int(-MouthAmount)
+
+            mouth_parse = torch.isin(outpred, mouth_idxs)
+            mouth_parse = torch.clamp(~mouth_parse, 0, 1).type(torch.float32)
+            mouth_parse = torch.reshape(mouth_parse, (1, 1, 512, 512))
+            mouth_parse = torch.neg(mouth_parse)
+            mouth_parse = torch.add(mouth_parse, 1)
+
+            kernel = torch.ones((1, 1, 3, 3), dtype=torch.float32,
+                                device='cuda')
+
+            for i in range(iters):
+                mouth_parse = torch.nn.functional.conv2d(mouth_parse, kernel,
+                                                         padding=(1, 1))
+                mouth_parse = torch.clamp(mouth_parse, 0, 1)
+
+            mouth_parse = torch.squeeze(mouth_parse)
+            mouth_parse = torch.neg(mouth_parse)
+            mouth_parse = torch.add(mouth_parse, 1)
+            mouth_parse = torch.reshape(mouth_parse, (1, 512, 512))
+
+        elif MouthAmount >0:
+            mouth_idxs = torch.tensor([11,12,13], device='cuda')
+            iters = int(MouthAmount)
+
+            mouth_parse = torch.isin(outpred, mouth_idxs)
+            mouth_parse = torch.clamp(~mouth_parse, 0, 1).type(torch.float32)
+            mouth_parse = torch.reshape(mouth_parse, (1,1,512,512))
+            mouth_parse = torch.neg(mouth_parse)
+            mouth_parse = torch.add(mouth_parse, 1)
+
+            kernel = torch.ones((1,1,3,3), dtype=torch.float32, device='cuda')
+
+            for i in range(iters):
+                mouth_parse = torch.nn.functional.conv2d(mouth_parse, kernel, padding=(1, 1))
+                mouth_parse = torch.clamp(mouth_parse, 0, 1)
+
+            mouth_parse = torch.squeeze(mouth_parse)
+            mouth_parse = torch.neg(mouth_parse)
+            mouth_parse = torch.add(mouth_parse, 1)
+            mouth_parse = torch.reshape(mouth_parse, (1, 512, 512))
+
+        else:
+            mouth_parse = torch.ones((1, 512, 512), dtype=torch.float32, device='cuda')
+
+        # BG Parse
+        bg_idxs = torch.tensor([0, 14, 15, 16, 17, 18], device=device)
+        bg_parse = torch.isin(outpred, bg_idxs)
+        bg_parse = torch.clamp(~bg_parse, 0, 1).type(torch.float32)
+        bg_parse = torch.reshape(bg_parse, (1, 1, 512, 512))
+
+        if FaceAmount > 0:
+            kernel = torch.ones((1, 1, 3, 3), dtype=torch.float32, device=device)
+
+            for i in range(int(FaceAmount)):
+                bg_parse = torch.nn.functional.conv2d(bg_parse, kernel, padding=(1, 1))
+                bg_parse = torch.clamp(bg_parse, 0, 1)
+
+            bg_parse = torch.squeeze(bg_parse)
+
+        elif FaceAmount < 0:
+            bg_parse = torch.neg(bg_parse)
+            bg_parse = torch.add(bg_parse, 1)
+
+            kernel = torch.ones((1, 1, 3, 3), dtype=torch.float32, device=device)
+
+            for i in range(int(-FaceAmount)):
+                bg_parse = torch.nn.functional.conv2d(bg_parse, kernel, padding=(1, 1))
+                bg_parse = torch.clamp(bg_parse, 0, 1)
+
+            bg_parse = torch.squeeze(bg_parse)
+            bg_parse = torch.neg(bg_parse)
+            bg_parse = torch.add(bg_parse, 1)
+            bg_parse = torch.reshape(bg_parse, (1, 512, 512))
+        else:
+            bg_parse = torch.ones((1,512,512), dtype=torch.float32, device='cuda')
+
+        out_parse = torch.mul(bg_parse, mouth_parse)
+
+        return out_parse
+
+    def apply_bg_face_parser(self, img, FaceParserAmount):
+
+        # atts = [1 'skin', 2 'l_brow', 3 'r_brow', 4 'l_eye', 5 'r_eye', 6 'eye_g', 7 'l_ear', 8 'r_ear', 9 'ear_r', 10 'nose', 11 'mouth', 12 'u_lip', 13 'l_lip', 14 'neck', 15 'neck_l', 16 'cloth', 17 'hair', 18 'hat']
+        # out = np.ones((512, 512), dtype=np.float32)  
+        
+        outpred = torch.ones((512,512), dtype=torch.float32, device='cuda').contiguous()
+
+        # turn mouth parser off at 0 so someone can just use the mouth parser
+        if FaceParserAmount != 0:
+            img = torch.div(img, 255)
+            img = v2.functional.normalize(img, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+            img = torch.reshape(img, (1, 3, 512, 512))      
+            outpred = torch.empty((1,19,512,512), dtype=torch.float32, device=device).contiguous()
+
+            self.models.run_faceparser(img, outpred)
+
+            outpred = torch.squeeze(outpred)
+            outpred = torch.argmax(outpred, 0)
+
+            test = torch.tensor([ 0, 14, 15, 16, 17, 18], device=device)
+            outpred = torch.isin(outpred, test)
+            outpred = torch.clamp(~outpred, 0, 1).type(torch.float32)
+            outpred = torch.reshape(outpred, (1,1,512,512))
+            
+            if FaceParserAmount >0:                   
+                kernel = torch.ones((1,1,3,3), dtype=torch.float32, device=device)
+
+                for i in range(int(FaceParserAmount)):
+                    outpred = torch.nn.functional.conv2d(outpred, kernel, padding=(1, 1))
+                    outpred = torch.clamp(outpred, 0, 1)
+                
+                outpred = torch.squeeze(outpred)
+                
+            if FaceParserAmount <0:      
+                outpred = torch.neg(outpred)
+                outpred = torch.add(outpred, 1)
+
+                kernel = torch.ones((1,1,3,3), dtype=torch.float32, device=device)
+
+                for i in range(int(-FaceParserAmount)):
+                    outpred = torch.nn.functional.conv2d(outpred, kernel, padding=(1, 1))
+                    outpred = torch.clamp(outpred, 0, 1)
+                
+                outpred = torch.squeeze(outpred)
+                outpred = torch.neg(outpred)
+                outpred = torch.add(outpred, 1)
+
+        outpred = torch.reshape(outpred, (1, 512, 512))
+        
+        return outpred
+    
+
+        
+    def apply_restorer(self, swapped_face_upscaled, parameters):     
+        temp = swapped_face_upscaled
+        t512 = v2.Resize((512, 512), antialias=False)
+        t256 = v2.Resize((256, 256), antialias=False)
+        t1024 = v2.Resize((1024, 1024), antialias=False)
+
+        # If using a separate detection mode
+        if parameters['RestorerDetTypeTextSel'] == 'Blend' or parameters['RestorerDetTypeTextSel'] == 'Reference':
+            if parameters['RestorerDetTypeTextSel'] == 'Blend':
+                # Set up Transformation
+                dst = self.arcface_dst * 4.0
+                dst[:,0] += 32.0        
+
+            elif parameters['RestorerDetTypeTextSel'] == 'Reference':
+                try:
+                    dst = self.models.resnet50(swapped_face_upscaled, score=parameters['DetectScoreSlider']/100.0) 
+                except:
+                    return swapped_face_upscaled       
+            
+            tform = trans.SimilarityTransform()
+            tform.estimate(dst, self.FFHQ_kps)
+
+            # Transform, scale, and normalize
+            temp = v2.functional.affine(swapped_face_upscaled, tform.rotation*57.2958, (tform.translation[0], tform.translation[1]) , tform.scale, 0, center = (0,0) )
+            temp = v2.functional.crop(temp, 0,0, 512, 512)        
+        
+        temp = torch.div(temp, 255)
+        temp = v2.functional.normalize(temp, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=False)
+        if parameters['RestorerTypeTextSel'] == 'GPEN256':
+            temp = t256(temp)
+        temp = torch.unsqueeze(temp, 0).contiguous()
+
+        # Bindings
+        outpred = torch.empty((1,3,512,512), dtype=torch.float32, device=device).contiguous()
+
+        if parameters['RestorerTypeTextSel'] == 'GFPGAN':
+            self.models.run_GFPGAN(temp, outpred)            
+            
+        elif parameters['RestorerTypeTextSel'] == 'CF':
+            self.models.run_codeformer(temp, outpred) 
+            
+        elif parameters['RestorerTypeTextSel'] == 'GPEN256':
+            outpred = torch.empty((1,3,256,256), dtype=torch.float32, device=device).contiguous()
+            self.models.run_GPEN_256(temp, outpred) 
+            
+        elif parameters['RestorerTypeTextSel'] == 'GPEN512':
+            self.models.run_GPEN_512(temp, outpred) 
+
+        elif parameters['RestorerTypeTextSel'] == 'GPEN1024':
+            temp = t1024(temp)
+            outpred = torch.empty((1, 3, 1024, 1024), dtype=torch.float32, device=device).contiguous()
+            self.models.run_GPEN_1024(temp, outpred)
+
+        # Format back to cxHxW @ 255
+        outpred = torch.squeeze(outpred)      
+        outpred = torch.clamp(outpred, -1, 1)
+        outpred = torch.add(outpred, 1)
+        outpred = torch.div(outpred, 2)
+        outpred = torch.mul(outpred, 255)
+        if parameters['RestorerTypeTextSel'] == 'GPEN256':
+            outpred = t512(outpred)
+        elif parameters['RestorerTypeTextSel'] == 'GPEN1024':
+            outpred = t512(outpred)
+        # Invert Transform
+        if parameters['RestorerDetTypeTextSel'] == 'Blend' or parameters['RestorerDetTypeTextSel'] == 'Reference':
+            outpred = v2.functional.affine(outpred, tform.inverse.rotation*57.2958, (tform.inverse.translation[0], tform.inverse.translation[1]), tform.inverse.scale, 0, interpolation=v2.InterpolationMode.BILINEAR, center = (0,0) )
+
+        # Blend
+        alpha = float(parameters["RestorerSlider"])/100.0  
+        outpred = torch.add(torch.mul(outpred, alpha), torch.mul(swapped_face_upscaled, 1-alpha))
+
+        return outpred        
+        
+    def apply_fake_diff(self, swapped_face, original_face, DiffAmount):
+        swapped_face = swapped_face.permute(1,2,0)
+        original_face = original_face.permute(1,2,0)
+
+        diff = swapped_face-original_face
+        diff = torch.abs(diff)
+        
+        # Find the diffrence between the swap and original, per channel
+        fthresh = DiffAmount*2.55
+        
+        # Bimodal
+        diff[diff<fthresh] = 0
+        diff[diff>=fthresh] = 1 
+        
+        # If any of the channels exceeded the threshhold, them add them to the mask
+        diff = torch.sum(diff, dim=2)
+        diff = torch.unsqueeze(diff, 2)
+        diff[diff>0] = 1
+        
+        diff = diff.permute(2,0,1)
+
+        return diff    
+    
+
+    
+    def clear_mem(self):
+        del self.swapper_model
+        del self.GFPGAN_model
+        del self.occluder_model
+        del self.face_parsing_model
+        del self.codeformer_model
+        del self.GPEN_256_model
+        del self.GPEN_512_model
+        del self.GPEN_1024_model
+        del self.resnet_model
+        del self.detection_model
+        del self.recognition_model
+        
+        self.swapper_model = []  
+        self.GFPGAN_model = []
+        self.occluder_model = []
+        self.face_parsing_model = []
+        self.codeformer_model = []
+        self.GPEN_256_model = []
+        self.GPEN_512_model = []
+        self.GPEN_1024_model = []
+        self.resnet_model = []
+        self.detection_model = []
+        self.recognition_model = []
+                
+        # test = swap.permute(1, 2, 0)
+        # test = test.cpu().numpy()
+        # cv2.imwrite('2.jpg', test) 

rope/external/cliplib/__init__.py

@@ -0,0 +1 @@
+from .clip import *

rope/external/cliplib/bpe_simple_vocab_16e6.txt.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
+size 1356917

rope/external/cliplib/clip.py

@@ -0,0 +1,245 @@
+import hashlib
+import os
+import urllib
+import warnings
+from typing import Any, Union, List
+from pkg_resources import packaging
+
+import torch
+from PIL import Image
+from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
+from tqdm import tqdm
+
+from .model import build_model
+from .simple_tokenizer import SimpleTokenizer as _Tokenizer
+
+try:
+    from torchvision.transforms import InterpolationMode
+    BICUBIC = InterpolationMode.BICUBIC
+except ImportError:
+    BICUBIC = Image.BICUBIC
+
+
+if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
+    warnings.warn("PyTorch version 1.7.1 or higher is recommended")
+
+
+__all__ = ["available_models", "load", "tokenize"]
+_tokenizer = _Tokenizer()
+
+_MODELS = {
+    "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
+    "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
+    "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
+    "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
+    "RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt",
+    "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
+    "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
+    "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
+    "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
+}
+
+
+def _download(url: str, root: str):
+    os.makedirs(root, exist_ok=True)
+    filename = os.path.basename(url)
+
+    expected_sha256 = url.split("/")[-2]
+    download_target = os.path.join(root, filename)
+
+    if os.path.exists(download_target) and not os.path.isfile(download_target):
+        raise RuntimeError(f"{download_target} exists and is not a regular file")
+
+    if os.path.isfile(download_target):
+        if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
+            return download_target
+        else:
+            warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
+
+    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
+        with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
+            while True:
+                buffer = source.read(8192)
+                if not buffer:
+                    break
+
+                output.write(buffer)
+                loop.update(len(buffer))
+
+    if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
+        raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match")
+
+    return download_target
+
+
+def _convert_image_to_rgb(image):
+    return image.convert("RGB")
+
+
+def _transform(n_px):
+    return Compose([
+        Resize(n_px, interpolation=BICUBIC),
+        CenterCrop(n_px),
+        _convert_image_to_rgb,
+        ToTensor(),
+        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
+    ])
+
+
+def available_models() -> List[str]:
+    """Returns the names of available CLIP models"""
+    return list(_MODELS.keys())
+
+
+def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None):
+    """Load a CLIP model
+
+    Parameters
+    ----------
+    name : str
+        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
+
+    device : Union[str, torch.device]
+        The device to put the loaded model
+
+    jit : bool
+        Whether to load the optimized JIT model or more hackable non-JIT model (default).
+
+    download_root: str
+        path to download the model files; by default, it uses "~/.cache/clip"
+
+    Returns
+    -------
+    model : torch.nn.Module
+        The CLIP model
+
+    preprocess : Callable[[PIL.Image], torch.Tensor]
+        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
+    """
+    if name in _MODELS:
+        model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
+    elif os.path.isfile(name):
+        model_path = name
+    else:
+        raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
+
+    with open(model_path, 'rb') as opened_file:
+        try:
+            # loading JIT archive
+            model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval()
+            state_dict = None
+        except RuntimeError:
+            # loading saved state dict
+            if jit:
+                warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
+                jit = False
+            state_dict = torch.load(opened_file, map_location="cpu")
+
+    if not jit:
+        model = build_model(state_dict or model.state_dict()).to(device)
+        if str(device) == "cpu":
+            model.float()
+        return model, _transform(model.visual.input_resolution)
+
+    # patch the device names
+    device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
+    device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
+
+    def _node_get(node: torch._C.Node, key: str):
+        """Gets attributes of a node which is polymorphic over return type.
+        
+        From https://github.com/pytorch/pytorch/pull/82628
+        """
+        sel = node.kindOf(key)
+        return getattr(node, sel)(key)
+
+    def patch_device(module):
+        try:
+            graphs = [module.graph] if hasattr(module, "graph") else []
+        except RuntimeError:
+            graphs = []
+
+        if hasattr(module, "forward1"):
+            graphs.append(module.forward1.graph)
+
+        for graph in graphs:
+            for node in graph.findAllNodes("prim::Constant"):
+                if "value" in node.attributeNames() and str(_node_get(node, "value")).startswith("cuda"):
+                    node.copyAttributes(device_node)
+
+    model.apply(patch_device)
+    patch_device(model.encode_image)
+    patch_device(model.encode_text)
+
+    # patch dtype to float32 on CPU
+    if str(device) == "cpu":
+        float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
+        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
+        float_node = float_input.node()
+
+        def patch_float(module):
+            try:
+                graphs = [module.graph] if hasattr(module, "graph") else []
+            except RuntimeError:
+                graphs = []
+
+            if hasattr(module, "forward1"):
+                graphs.append(module.forward1.graph)
+
+            for graph in graphs:
+                for node in graph.findAllNodes("aten::to"):
+                    inputs = list(node.inputs())
+                    for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
+                        if _node_get(inputs[i].node(), "value") == 5:
+                            inputs[i].node().copyAttributes(float_node)
+
+        model.apply(patch_float)
+        patch_float(model.encode_image)
+        patch_float(model.encode_text)
+
+        model.float()
+
+    return model, _transform(model.input_resolution.item())
+
+
+def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> Union[torch.IntTensor, torch.LongTensor]:
+    """
+    Returns the tokenized representation of given input string(s)
+
+    Parameters
+    ----------
+    texts : Union[str, List[str]]
+        An input string or a list of input strings to tokenize
+
+    context_length : int
+        The context length to use; all CLIP models use 77 as the context length
+
+    truncate: bool
+        Whether to truncate the text in case its encoding is longer than the context length
+
+    Returns
+    -------
+    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
+    We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
+    """
+    if isinstance(texts, str):
+        texts = [texts]
+
+    sot_token = _tokenizer.encoder["<|startoftext|>"]
+    eot_token = _tokenizer.encoder["<|endoftext|>"]
+    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
+    if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
+        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
+    else:
+        result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)
+
+    for i, tokens in enumerate(all_tokens):
+        if len(tokens) > context_length:
+            if truncate:
+                tokens = tokens[:context_length]
+                tokens[-1] = eot_token
+            else:
+                raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
+        result[i, :len(tokens)] = torch.tensor(tokens)
+
+    return result

rope/external/cliplib/model.py

@@ -0,0 +1,436 @@
+from collections import OrderedDict
+from typing import Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1):
+        super().__init__()
+
+        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
+        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu1 = nn.ReLU(inplace=True)
+
+        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.relu2 = nn.ReLU(inplace=True)
+
+        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
+
+        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.relu3 = nn.ReLU(inplace=True)
+
+        self.downsample = None
+        self.stride = stride
+
+        if stride > 1 or inplanes != planes * Bottleneck.expansion:
+            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
+            self.downsample = nn.Sequential(OrderedDict([
+                ("-1", nn.AvgPool2d(stride)),
+                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
+                ("1", nn.BatchNorm2d(planes * self.expansion))
+            ]))
+
+    def forward(self, x: torch.Tensor):
+        identity = x
+
+        out = self.relu1(self.bn1(self.conv1(x)))
+        out = self.relu2(self.bn2(self.conv2(out)))
+        out = self.avgpool(out)
+        out = self.bn3(self.conv3(out))
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu3(out)
+        return out
+
+
+class AttentionPool2d(nn.Module):
+    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
+        self.k_proj = nn.Linear(embed_dim, embed_dim)
+        self.q_proj = nn.Linear(embed_dim, embed_dim)
+        self.v_proj = nn.Linear(embed_dim, embed_dim)
+        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
+        self.num_heads = num_heads
+
+    def forward(self, x):
+        x = x.flatten(start_dim=2).permute(2, 0, 1)  # NCHW -> (HW)NC
+        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
+        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
+        x, _ = F.multi_head_attention_forward(
+            query=x[:1], key=x, value=x,
+            embed_dim_to_check=x.shape[-1],
+            num_heads=self.num_heads,
+            q_proj_weight=self.q_proj.weight,
+            k_proj_weight=self.k_proj.weight,
+            v_proj_weight=self.v_proj.weight,
+            in_proj_weight=None,
+            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
+            bias_k=None,
+            bias_v=None,
+            add_zero_attn=False,
+            dropout_p=0,
+            out_proj_weight=self.c_proj.weight,
+            out_proj_bias=self.c_proj.bias,
+            use_separate_proj_weight=True,
+            training=self.training,
+            need_weights=False
+        )
+        return x.squeeze(0)
+
+
+class ModifiedResNet(nn.Module):
+    """
+    A ResNet class that is similar to torchvision's but contains the following changes:
+    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
+    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
+    - The final pooling layer is a QKV attention instead of an average pool
+    """
+
+    def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
+        super().__init__()
+        self.output_dim = output_dim
+        self.input_resolution = input_resolution
+
+        # the 3-layer stem
+        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(width // 2)
+        self.relu1 = nn.ReLU(inplace=True)
+        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(width // 2)
+        self.relu2 = nn.ReLU(inplace=True)
+        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(width)
+        self.relu3 = nn.ReLU(inplace=True)
+        self.avgpool = nn.AvgPool2d(2)
+
+        # residual layers
+        self._inplanes = width  # this is a *mutable* variable used during construction
+        self.layer1 = self._make_layer(width, layers[0])
+        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
+        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
+        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
+
+        embed_dim = width * 32  # the ResNet feature dimension
+        self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)
+
+    def _make_layer(self, planes, blocks, stride=1):
+        layers = [Bottleneck(self._inplanes, planes, stride)]
+
+        self._inplanes = planes * Bottleneck.expansion
+        for _ in range(1, blocks):
+            layers.append(Bottleneck(self._inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        def stem(x):
+            x = self.relu1(self.bn1(self.conv1(x)))
+            x = self.relu2(self.bn2(self.conv2(x)))
+            x = self.relu3(self.bn3(self.conv3(x)))
+            x = self.avgpool(x)
+            return x
+
+        x = x.type(self.conv1.weight.dtype)
+        x = stem(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.attnpool(x)
+
+        return x
+
+
+class LayerNorm(nn.LayerNorm):
+    """Subclass torch's LayerNorm to handle fp16."""
+
+    def forward(self, x: torch.Tensor):
+        orig_type = x.dtype
+        ret = super().forward(x.type(torch.float32))
+        return ret.type(orig_type)
+
+
+class QuickGELU(nn.Module):
+    def forward(self, x: torch.Tensor):
+        return x * torch.sigmoid(1.702 * x)
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
+        super().__init__()
+
+        self.attn = nn.MultiheadAttention(d_model, n_head)
+        self.ln_1 = LayerNorm(d_model)
+        self.mlp = nn.Sequential(OrderedDict([
+            ("c_fc", nn.Linear(d_model, d_model * 4)),
+            ("gelu", QuickGELU()),
+            ("c_proj", nn.Linear(d_model * 4, d_model))
+        ]))
+        self.ln_2 = LayerNorm(d_model)
+        self.attn_mask = attn_mask
+
+    def attention(self, x: torch.Tensor):
+        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
+        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
+
+    def forward(self, x: torch.Tensor):
+        x = x + self.attention(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class Transformer(nn.Module):
+    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
+        super().__init__()
+        self.width = width
+        self.layers = layers
+        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
+
+    def forward(self, x: torch.Tensor):
+        return self.resblocks(x)
+
+
+class VisionTransformer(nn.Module):
+    def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.output_dim = output_dim
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
+
+        scale = width ** -0.5
+        self.class_embedding = nn.Parameter(scale * torch.randn(width))
+        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
+        self.ln_pre = LayerNorm(width)
+
+        self.transformer = Transformer(width, layers, heads)
+
+        self.ln_post = LayerNorm(width)
+        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
+
+    def forward(self, x: torch.Tensor):
+        x = self.conv1(x)  # shape = [*, width, grid, grid]
+        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
+        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
+        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
+        x = x + self.positional_embedding.to(x.dtype)
+        x = self.ln_pre(x)
+
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.transformer(x)
+        x = x.permute(1, 0, 2)  # LND -> NLD
+
+        x = self.ln_post(x[:, 0, :])
+
+        if self.proj is not None:
+            x = x @ self.proj
+
+        return x
+
+
+class CLIP(nn.Module):
+    def __init__(self,
+                 embed_dim: int,
+                 # vision
+                 image_resolution: int,
+                 vision_layers: Union[Tuple[int, int, int, int], int],
+                 vision_width: int,
+                 vision_patch_size: int,
+                 # text
+                 context_length: int,
+                 vocab_size: int,
+                 transformer_width: int,
+                 transformer_heads: int,
+                 transformer_layers: int
+                 ):
+        super().__init__()
+
+        self.context_length = context_length
+
+        if isinstance(vision_layers, (tuple, list)):
+            vision_heads = vision_width * 32 // 64
+            self.visual = ModifiedResNet(
+                layers=vision_layers,
+                output_dim=embed_dim,
+                heads=vision_heads,
+                input_resolution=image_resolution,
+                width=vision_width
+            )
+        else:
+            vision_heads = vision_width // 64
+            self.visual = VisionTransformer(
+                input_resolution=image_resolution,
+                patch_size=vision_patch_size,
+                width=vision_width,
+                layers=vision_layers,
+                heads=vision_heads,
+                output_dim=embed_dim
+            )
+
+        self.transformer = Transformer(
+            width=transformer_width,
+            layers=transformer_layers,
+            heads=transformer_heads,
+            attn_mask=self.build_attention_mask()
+        )
+
+        self.vocab_size = vocab_size
+        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
+        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
+        self.ln_final = LayerNorm(transformer_width)
+
+        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
+        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
+
+        self.initialize_parameters()
+
+    def initialize_parameters(self):
+        nn.init.normal_(self.token_embedding.weight, std=0.02)
+        nn.init.normal_(self.positional_embedding, std=0.01)
+
+        if isinstance(self.visual, ModifiedResNet):
+            if self.visual.attnpool is not None:
+                std = self.visual.attnpool.c_proj.in_features ** -0.5
+                nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
+                nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
+                nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
+                nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
+
+            for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
+                for name, param in resnet_block.named_parameters():
+                    if name.endswith("bn3.weight"):
+                        nn.init.zeros_(param)
+
+        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
+        attn_std = self.transformer.width ** -0.5
+        fc_std = (2 * self.transformer.width) ** -0.5
+        for block in self.transformer.resblocks:
+            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
+            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
+            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
+            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
+
+        if self.text_projection is not None:
+            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
+
+    def build_attention_mask(self):
+        # lazily create causal attention mask, with full attention between the vision tokens
+        # pytorch uses additive attention mask; fill with -inf
+        mask = torch.empty(self.context_length, self.context_length)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)  # zero out the lower diagonal
+        return mask
+
+    @property
+    def dtype(self):
+        return self.visual.conv1.weight.dtype
+
+    def encode_image(self, image):
+        return self.visual(image.type(self.dtype))
+
+    def encode_text(self, text):
+        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
+
+        x = x + self.positional_embedding.type(self.dtype)
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.transformer(x)
+        x = x.permute(1, 0, 2)  # LND -> NLD
+        x = self.ln_final(x).type(self.dtype)
+
+        # x.shape = [batch_size, n_ctx, transformer.width]
+        # take features from the eot embedding (eot_token is the highest number in each sequence)
+        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
+
+        return x
+
+    def forward(self, image, text):
+        image_features = self.encode_image(image)
+        text_features = self.encode_text(text)
+
+        # normalized features
+        image_features = image_features / image_features.norm(dim=1, keepdim=True)
+        text_features = text_features / text_features.norm(dim=1, keepdim=True)
+
+        # cosine similarity as logits
+        logit_scale = self.logit_scale.exp()
+        logits_per_image = logit_scale * image_features @ text_features.t()
+        logits_per_text = logits_per_image.t()
+
+        # shape = [global_batch_size, global_batch_size]
+        return logits_per_image, logits_per_text
+
+
+def convert_weights(model: nn.Module):
+    """Convert applicable model parameters to fp16"""
+
+    def _convert_weights_to_fp16(l):
+        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
+            l.weight.data = l.weight.data.half()
+            if l.bias is not None:
+                l.bias.data = l.bias.data.half()
+
+        if isinstance(l, nn.MultiheadAttention):
+            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
+                tensor = getattr(l, attr)
+                if tensor is not None:
+                    tensor.data = tensor.data.half()
+
+        for name in ["text_projection", "proj"]:
+            if hasattr(l, name):
+                attr = getattr(l, name)
+                if attr is not None:
+                    attr.data = attr.data.half()
+
+    model.apply(_convert_weights_to_fp16)
+
+
+def build_model(state_dict: dict):
+    vit = "visual.proj" in state_dict
+
+    if vit:
+        vision_width = state_dict["visual.conv1.weight"].shape[0]
+        vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
+        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
+        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
+        image_resolution = vision_patch_size * grid_size
+    else:
+        counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
+        vision_layers = tuple(counts)
+        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
+        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
+        vision_patch_size = None
+        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
+        image_resolution = output_width * 32
+
+    embed_dim = state_dict["text_projection"].shape[1]
+    context_length = state_dict["positional_embedding"].shape[0]
+    vocab_size = state_dict["token_embedding.weight"].shape[0]
+    transformer_width = state_dict["ln_final.weight"].shape[0]
+    transformer_heads = transformer_width // 64
+    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")))
+
+    model = CLIP(
+        embed_dim,
+        image_resolution, vision_layers, vision_width, vision_patch_size,
+        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
+    )
+
+    for key in ["input_resolution", "context_length", "vocab_size"]:
+        if key in state_dict:
+            del state_dict[key]
+
+    convert_weights(model)
+    model.load_state_dict(state_dict)
+    return model.eval()

rope/external/cliplib/simple_tokenizer.py

@@ -0,0 +1,132 @@
+import gzip
+import html
+import os
+from functools import lru_cache
+
+import ftfy
+import regex as re
+
+
+@lru_cache()
+def default_bpe():
+    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
+
+
+@lru_cache()
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a corresponding list of unicode strings.
+    The reversible bpe codes work on unicode strings.
+    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+    This is a signficant percentage of your normal, say, 32K bpe vocab.
+    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+    And avoids mapping to whitespace/control characters the bpe code barfs on.
+    """
+    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8+n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+    """Return set of symbol pairs in a word.
+    Word is represented as tuple of symbols (symbols being variable-length strings).
+    """
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+    return pairs
+
+
+def basic_clean(text):
+    text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+
+def whitespace_clean(text):
+    text = re.sub(r'\s+', ' ', text)
+    text = text.strip()
+    return text
+
+
+class SimpleTokenizer(object):
+    def __init__(self, bpe_path: str = default_bpe()):
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
+        merges = merges[1:49152-256-2+1]
+        merges = [tuple(merge.split()) for merge in merges]
+        vocab = list(bytes_to_unicode().values())
+        vocab = vocab + [v+'</w>' for v in vocab]
+        for merge in merges:
+            vocab.append(''.join(merge))
+        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
+        self.encoder = dict(zip(vocab, range(len(vocab))))
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.bpe_ranks = dict(zip(merges, range(len(merges))))
+        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
+        self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
+
+    def bpe(self, token):
+        if token in self.cache:
+            return self.cache[token]
+        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token+'</w>'
+
+        while True:
+            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                    new_word.extend(word[i:j])
+                    i = j
+                except:
+                    new_word.extend(word[i:])
+                    break
+
+                if word[i] == first and i < len(word)-1 and word[i+1] == second:
+                    new_word.append(first+second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = ' '.join(word)
+        self.cache[token] = word
+        return word
+
+    def encode(self, text):
+        bpe_tokens = []
+        text = whitespace_clean(basic_clean(text)).lower()
+        for token in re.findall(self.pat, text):
+            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
+            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
+        return bpe_tokens
+
+    def decode(self, tokens):
+        text = ''.join([self.decoder[token] for token in tokens])
+        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
+        return text

rope/external/clipseg.py

@@ -0,0 +1,538 @@
+import math
+from os.path import basename, dirname, join, isfile
+import torch
+from torch import nn
+from torch.nn import functional as nnf
+from torch.nn.modules.activation import ReLU
+
+
+def get_prompt_list(prompt):
+    if prompt == 'plain':
+        return ['{}']    
+    elif prompt == 'fixed':
+        return ['a photo of a {}.']
+    elif prompt == 'shuffle':
+        return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
+    elif prompt == 'shuffle+':
+        return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
+                            'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
+                            'a bad photo of a {}.', 'a photo of the {}.']
+    else:
+        raise ValueError('Invalid value for prompt')        
+
+
+def forward_multihead_attention(x, b, with_aff=False, attn_mask=None):
+    """ 
+    Simplified version of multihead attention (taken from torch source code but without tons of if clauses). 
+    The mlp and layer norm come from CLIP.
+    x: input.
+    b: multihead attention module. 
+    """
+
+    x_ = b.ln_1(x)
+    q, k, v = nnf.linear(x_, b.attn.in_proj_weight, b.attn.in_proj_bias).chunk(3, dim=-1)
+    tgt_len, bsz, embed_dim = q.size()
+
+    head_dim = embed_dim // b.attn.num_heads
+    scaling = float(head_dim) ** -0.5
+
+    q = q.contiguous().view(tgt_len, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
+    k = k.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
+    v = v.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
+
+    q = q * scaling
+
+    attn_output_weights = torch.bmm(q, k.transpose(1, 2)) #  n_heads * batch_size, tokens^2, tokens^2
+    if attn_mask is not None:
+
+
+        attn_mask_type, attn_mask = attn_mask
+        n_heads = attn_output_weights.size(0) // attn_mask.size(0)
+        attn_mask = attn_mask.repeat(n_heads, 1)
+        
+        if attn_mask_type == 'cls_token':
+            # the mask only affects similarities compared to the readout-token.
+            attn_output_weights[:, 0, 1:] = attn_output_weights[:, 0, 1:] * attn_mask[None,...]
+            # attn_output_weights[:, 0, 0] = 0*attn_output_weights[:, 0, 0]
+
+        if attn_mask_type == 'all':
+            # print(attn_output_weights.shape, attn_mask[:, None].shape)
+            attn_output_weights[:, 1:, 1:] = attn_output_weights[:, 1:, 1:] * attn_mask[:, None]
+        
+    
+    attn_output_weights = torch.softmax(attn_output_weights, dim=-1)
+
+    attn_output = torch.bmm(attn_output_weights, v)
+    attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
+    attn_output = b.attn.out_proj(attn_output)
+
+    x = x + attn_output
+    x = x + b.mlp(b.ln_2(x))
+
+    if with_aff:
+        return x, attn_output_weights
+    else:
+        return x
+
+
+class CLIPDenseBase(nn.Module):
+
+    def __init__(self, version, reduce_cond, reduce_dim, prompt, n_tokens):
+        super().__init__()
+
+        from rope.external.cliplib import clip
+
+        # prec = torch.FloatTensor
+        self.clip_model, _ = clip.load(version, device='cpu', jit=False)
+        self.model = self.clip_model.visual
+
+        # if not None, scale conv weights such that we obtain n_tokens.
+        self.n_tokens = n_tokens
+
+        for p in self.clip_model.parameters():
+            p.requires_grad_(False)
+
+        # conditional
+        if reduce_cond is not None:
+            self.reduce_cond = nn.Linear(512, reduce_cond)
+            for p in self.reduce_cond.parameters():
+                p.requires_grad_(False)
+        else:
+            self.reduce_cond = None        
+
+        self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
+        self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
+        
+        self.reduce = nn.Linear(768, reduce_dim)
+
+        self.prompt_list = get_prompt_list(prompt)     
+
+        # precomputed prompts
+        import pickle
+        if isfile('precomputed_prompt_vectors.pickle'):
+            precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
+            self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}        
+        else:
+            self.precomputed_prompts = dict()
+    
+    def rescaled_pos_emb(self, new_size):
+        assert len(new_size) == 2
+
+        a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
+        b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
+        return torch.cat([self.model.positional_embedding[:1], b])
+
+    def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
+        
+
+        with torch.no_grad():
+
+            inp_size = x_inp.shape[2:]
+
+            if self.n_tokens is not None:
+                stride2 = x_inp.shape[2] // self.n_tokens
+                conv_weight2 = nnf.interpolate(self.model.conv1.weight, (stride2, stride2), mode='bilinear', align_corners=True)
+                x = nnf.conv2d(x_inp, conv_weight2, bias=self.model.conv1.bias, stride=stride2, dilation=self.model.conv1.dilation)
+            else:
+                x = self.model.conv1(x_inp)  # shape = [*, width, grid, grid]
+
+            x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
+            x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
+
+            x = torch.cat([self.model.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
+
+            standard_n_tokens = 50 if self.model.conv1.kernel_size[0] == 32 else 197
+
+            if x.shape[1] != standard_n_tokens:
+                new_shape = int(math.sqrt(x.shape[1]-1))
+                x = x + self.rescaled_pos_emb((new_shape, new_shape)).to(x.dtype)[None,:,:]
+            else:
+                x = x + self.model.positional_embedding.to(x.dtype)
+
+            x = self.model.ln_pre(x)
+
+            x = x.permute(1, 0, 2)  # NLD -> LND
+
+            activations, affinities = [], []
+            for i, res_block in enumerate(self.model.transformer.resblocks):
+                
+                if mask is not None:
+                    mask_layer, mask_type, mask_tensor = mask
+                    if mask_layer == i or mask_layer == 'all':
+                        # import ipdb; ipdb.set_trace()
+                        size = int(math.sqrt(x.shape[0] - 1))
+                        
+                        attn_mask = (mask_type, nnf.interpolate(mask_tensor.unsqueeze(1).float(), (size, size)).view(mask_tensor.shape[0], size * size))
+                        
+                    else:
+                        attn_mask = None
+                else:
+                    attn_mask = None
+
+                x, aff_per_head = forward_multihead_attention(x, res_block, with_aff=True, attn_mask=attn_mask)
+
+                if i in extract_layers:
+                    affinities += [aff_per_head]
+
+                    #if self.n_tokens is not None:
+                    #    activations += [nnf.interpolate(x, inp_size, mode='bilinear', align_corners=True)]
+                    #else:
+                    activations += [x]
+
+                if len(extract_layers) > 0 and i == max(extract_layers) and skip:
+                    print('early skip')
+                    break
+                
+            x = x.permute(1, 0, 2)  # LND -> NLD
+            x = self.model.ln_post(x[:, 0, :])
+
+            if self.model.proj is not None:
+                x = x @ self.model.proj
+
+            return x, activations, affinities
+
+    def sample_prompts(self, words, prompt_list=None):
+
+        prompt_list = prompt_list if prompt_list is not None else self.prompt_list
+
+        prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
+        prompts = [prompt_list[i] for i in prompt_indices]
+        return [promt.format(w) for promt, w in zip(prompts, words)]
+
+    def get_cond_vec(self, conditional, batch_size):
+        # compute conditional from a single string
+        if conditional is not None and type(conditional) == str:
+            cond = self.compute_conditional(conditional)
+            cond = cond.repeat(batch_size, 1)
+
+        # compute conditional from string list/tuple
+        elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
+            assert len(conditional) == batch_size
+            cond = self.compute_conditional(conditional)
+
+        # use conditional directly
+        elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
+            cond = conditional
+
+        # compute conditional from image
+        elif conditional is not None and type(conditional) == torch.Tensor:
+            with torch.no_grad():
+                cond, _, _ = self.visual_forward(conditional)
+        else:
+            raise ValueError('invalid conditional')
+        return cond   
+
+    def compute_conditional(self, conditional):
+        from rope.external.cliplib import clip
+
+        dev = next(self.parameters()).device
+
+        if type(conditional) in {list, tuple}:
+            text_tokens = clip.tokenize(conditional).to(dev)
+            cond = self.clip_model.encode_text(text_tokens)
+        else:
+            if conditional in self.precomputed_prompts:
+                cond = self.precomputed_prompts[conditional].float().to(dev)
+            else:
+                text_tokens = clip.tokenize([conditional]).to(dev)
+                cond = self.clip_model.encode_text(text_tokens)[0]
+        
+        if self.shift_vector is not None:
+            return cond + self.shift_vector
+        else:
+            return cond
+
+
+def clip_load_untrained(version):
+    assert version == 'ViT-B/16'
+    from clip.model import CLIP
+    from clip.clip import _MODELS, _download
+    model = torch.jit.load(_download(_MODELS['ViT-B/16'])).eval()
+    state_dict = model.state_dict()
+
+    vision_width = state_dict["visual.conv1.weight"].shape[0]
+    vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
+    vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
+    grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
+    image_resolution = vision_patch_size * grid_size
+    embed_dim = state_dict["text_projection"].shape[1]
+    context_length = state_dict["positional_embedding"].shape[0]
+    vocab_size = state_dict["token_embedding.weight"].shape[0]
+    transformer_width = state_dict["ln_final.weight"].shape[0]
+    transformer_heads = transformer_width // 64
+    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
+
+    return CLIP(embed_dim, image_resolution, vision_layers, vision_width, vision_patch_size, 
+        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)    
+
+
+class CLIPDensePredT(CLIPDenseBase):
+
+    def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed', 
+                 extra_blocks=0, reduce_cond=None, fix_shift=False,
+                 learn_trans_conv_only=False,  limit_to_clip_only=False, upsample=False, 
+                 add_calibration=False, rev_activations=False, trans_conv=None, n_tokens=None, complex_trans_conv=False):
+        
+        super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
+        # device = 'cpu'
+
+        self.extract_layers = extract_layers
+        self.cond_layer = cond_layer
+        self.limit_to_clip_only = limit_to_clip_only
+        self.process_cond = None
+        self.rev_activations = rev_activations
+        
+        depth = len(extract_layers)
+
+        if add_calibration:
+            self.calibration_conds = 1
+
+        self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
+
+        self.add_activation1 = True
+
+        self.version = version
+        
+        self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
+
+        if fix_shift:
+            # self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'clip_text_shift_vector.pth')), requires_grad=False)
+            self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'shift_text_to_vis.pth')), requires_grad=False)
+            # self.shift_vector = nn.Parameter(-1*torch.load(join(dirname(basename(__file__)), 'shift2.pth')), requires_grad=False)
+        else:
+            self.shift_vector = None
+
+        if trans_conv is None:
+            trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
+        else:
+            # explicitly define transposed conv kernel size
+            trans_conv_ks = (trans_conv, trans_conv)
+
+        if not complex_trans_conv:
+            self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
+        else:
+            assert trans_conv_ks[0] == trans_conv_ks[1]
+
+            tp_kernels = (trans_conv_ks[0] // 4, trans_conv_ks[0] // 4)
+
+            self.trans_conv = nn.Sequential(
+                nn.Conv2d(reduce_dim, reduce_dim, kernel_size=3, padding=1),
+                nn.ReLU(),
+                nn.ConvTranspose2d(reduce_dim, reduce_dim // 2, kernel_size=tp_kernels[0], stride=tp_kernels[0]),
+                nn.ReLU(),
+                nn.ConvTranspose2d(reduce_dim // 2, 1, kernel_size=tp_kernels[1], stride=tp_kernels[1]),               
+            )
+
+#        self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
+        
+        assert len(self.extract_layers) == depth
+
+        self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
+        self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
+        self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
+        
+        # refinement and trans conv
+
+        if learn_trans_conv_only:
+            for p in self.parameters():
+                p.requires_grad_(False)
+            
+            for p in self.trans_conv.parameters():
+                p.requires_grad_(True)
+
+        self.prompt_list = get_prompt_list(prompt)
+
+
+    def forward(self, inp_image, conditional=None, return_features=False, mask=None):
+
+        assert type(return_features) == bool
+
+        inp_image = inp_image.to(self.model.positional_embedding.device)
+
+        if mask is not None:
+            raise ValueError('mask not supported')
+
+        # x_inp = normalize(inp_image)
+        x_inp = inp_image
+
+        bs, dev = inp_image.shape[0], x_inp.device
+
+        cond = self.get_cond_vec(conditional, bs)
+
+        visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
+
+        activation1 = activations[0]
+        activations = activations[1:]
+
+        _activations = activations[::-1] if not self.rev_activations else activations
+
+        a = None
+        for i, (activation, block, reduce) in enumerate(zip(_activations, self.blocks, self.reduces)):
+            
+            if a is not None:
+                a = reduce(activation) + a
+            else:
+                a = reduce(activation)
+
+            if i == self.cond_layer:
+                if self.reduce_cond is not None:
+                    cond = self.reduce_cond(cond)
+                
+                a = self.film_mul(cond) * a + self.film_add(cond)
+
+            a = block(a)
+
+        for block in self.extra_blocks:
+            a = a + block(a)
+
+        a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
+
+        size = int(math.sqrt(a.shape[2]))
+
+        a = a.view(bs, a.shape[1], size, size)
+
+        a = self.trans_conv(a)
+
+        if self.n_tokens is not None:
+            a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear', align_corners=True) 
+
+        if self.upsample_proj is not None:
+            a = self.upsample_proj(a)
+            a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
+
+        if return_features:
+            return a, visual_q, cond, [activation1] + activations
+        else:
+            return a,
+
+
+
+class CLIPDensePredTMasked(CLIPDensePredT):
+
+    def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, 
+                 prompt='fixed', extra_blocks=0, reduce_cond=None, fix_shift=False, learn_trans_conv_only=False, 
+                 refine=None, limit_to_clip_only=False, upsample=False, add_calibration=False, n_tokens=None):
+
+        super().__init__(version=version, extract_layers=extract_layers, cond_layer=cond_layer, reduce_dim=reduce_dim, 
+                         n_heads=n_heads, prompt=prompt, extra_blocks=extra_blocks, reduce_cond=reduce_cond, 
+                         fix_shift=fix_shift, learn_trans_conv_only=learn_trans_conv_only,
+                         limit_to_clip_only=limit_to_clip_only, upsample=upsample, add_calibration=add_calibration,
+                         n_tokens=n_tokens)
+
+    def visual_forward_masked(self, img_s, seg_s):
+        return super().visual_forward(img_s, mask=('all', 'cls_token', seg_s))
+
+    def forward(self, img_q, cond_or_img_s, seg_s=None, return_features=False):
+
+        if seg_s is None:
+            cond = cond_or_img_s
+        else:
+            img_s = cond_or_img_s
+
+            with torch.no_grad():
+                cond, _, _ = self.visual_forward_masked(img_s, seg_s)
+
+        return super().forward(img_q, cond, return_features=return_features)
+
+
+
+class CLIPDenseBaseline(CLIPDenseBase):
+
+    def __init__(self, version='ViT-B/32', cond_layer=0, 
+                extract_layer=9, reduce_dim=128, reduce2_dim=None, prompt='fixed', 
+                 reduce_cond=None, limit_to_clip_only=False, n_tokens=None):
+        
+        super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
+        device = 'cpu'
+
+        # self.cond_layer = cond_layer
+        self.extract_layer = extract_layer
+        self.limit_to_clip_only = limit_to_clip_only
+        self.shift_vector = None
+
+        self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
+        
+        assert reduce2_dim is not None
+
+        self.reduce2 = nn.Sequential(
+            nn.Linear(reduce_dim, reduce2_dim),
+            nn.ReLU(),
+            nn.Linear(reduce2_dim, reduce_dim)
+        )
+        
+        trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
+        self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
+
+
+    def forward(self, inp_image, conditional=None, return_features=False):
+
+        inp_image = inp_image.to(self.model.positional_embedding.device)
+
+        # x_inp = normalize(inp_image)
+        x_inp = inp_image
+
+        bs, dev = inp_image.shape[0], x_inp.device
+
+        cond = self.get_cond_vec(conditional, bs)
+
+        visual_q, activations, affinities = self.visual_forward(x_inp, extract_layers=[self.extract_layer])
+
+        a = activations[0]
+        a = self.reduce(a)
+        a = self.film_mul(cond) * a + self.film_add(cond)
+
+        if self.reduce2 is not None:
+            a = self.reduce2(a)
+
+        # the original model would execute a transformer block here
+
+        a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
+
+        size = int(math.sqrt(a.shape[2]))
+
+        a = a.view(bs, a.shape[1], size, size)
+        a = self.trans_conv(a)
+
+        if return_features:
+            return a, visual_q, cond, activations
+        else:
+            return a,
+
+
+class CLIPSegMultiLabel(nn.Module):
+
+    def __init__(self, model) -> None:
+        super().__init__()
+
+        from third_party.JoEm.data_loader import get_seen_idx, get_unseen_idx, VOC
+
+        self.pascal_classes = VOC
+
+        from models.clipseg import CLIPDensePredT
+        from general_utils import load_model
+        # self.clipseg = load_model('rd64-vit16-neg0.2-phrasecut', strict=False)
+        self.clipseg = load_model(model, strict=False)
+        
+        self.clipseg.eval()
+
+    def forward(self, x):
+
+        bs = x.shape[0]
+        out = torch.ones(21, bs, 352, 352).to(x.device) * -10
+
+        for class_id, class_name in enumerate(self.pascal_classes):
+        
+            fac = 3 if class_name == 'background' else 1
+
+            with torch.no_grad():
+                pred = torch.sigmoid(self.clipseg(x, class_name)[0][:,0]) * fac
+
+            out[class_id] += pred
+
+
+        out = out.permute(1, 0, 2, 3)
+
+        return out
+
+        # construct output tensor
+                    

rope/external/resnet.py

@@ -0,0 +1,109 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.model_zoo as modelzoo
+
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    def __init__(self, in_chan, out_chan, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(in_chan, out_chan, stride)
+        self.bn1 = nn.BatchNorm2d(out_chan)
+        self.conv2 = conv3x3(out_chan, out_chan)
+        self.bn2 = nn.BatchNorm2d(out_chan)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = None
+        if in_chan != out_chan or stride != 1:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_chan, out_chan,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_chan),
+                )
+
+    def forward(self, x):
+        residual = self.conv1(x)
+        residual = F.relu(self.bn1(residual))
+        residual = self.conv2(residual)
+        residual = self.bn2(residual)
+
+        shortcut = x
+        if self.downsample is not None:
+            shortcut = self.downsample(x)
+
+        out = shortcut + residual
+        out = self.relu(out)
+        return out
+
+
+def create_layer_basic(in_chan, out_chan, bnum, stride=1):
+    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
+    for i in range(bnum-1):
+        layers.append(BasicBlock(out_chan, out_chan, stride=1))
+    return nn.Sequential(*layers)
+
+
+class Resnet18(nn.Module):
+    def __init__(self):
+        super(Resnet18, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
+        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
+        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
+        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(self.bn1(x))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        feat8 = self.layer2(x) # 1/8
+        feat16 = self.layer3(feat8) # 1/16
+        feat32 = self.layer4(feat16) # 1/32
+        return feat8, feat16, feat32
+
+    def init_weight(self):
+        state_dict = modelzoo.load_url(resnet18_url)
+        self_state_dict = self.state_dict()
+        for k, v in state_dict.items():
+            if 'fc' in k: continue
+            self_state_dict.update({k: v})
+        self.load_state_dict(self_state_dict)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module,  nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+if __name__ == "__main__":
+    net = Resnet18()
+    x = torch.randn(16, 3, 224, 224)
+    out = net(x)
+    print(out[0].size())
+    print(out[1].size())
+    print(out[2].size())
+    net.get_params()