pull/1012/head
Yash Bodade
commit
7b4fcd3eaf
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# Auto detect text files and perform LF normalization
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* text=auto
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|||
***[Remove this]The issue would be closed without notice and be considered spam if the template is not followed.***
|
||||
|
||||
**Describe the bug**
|
||||
A clear and concise description of what the bug is.
|
||||
|
||||
**Screenshots**
|
||||
If applicable, add screenshots to help explain your problem.
|
||||
|
||||
**Error Message**
|
||||
|
||||
`<The error message in terminal>`
|
||||
|
||||
**Desktop (please complete the following information):**
|
||||
- OS: [e.g. Windows]
|
||||
- Version [e.g. 22]
|
||||
- GPU
|
||||
- CPU
|
||||
|
||||
**Additional context**
|
||||
Add any other context about the problem here.
|
||||
|
||||
**Confirmation (Mandatory)**
|
||||
- [ ] I have followed the template
|
||||
- [ ] This is not a query about how to increase performance
|
||||
- [ ] I have checked the issues page, and this is not a duplicate
|
||||
|
||||
|
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|||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
*.pyc
|
||||
.idea
|
||||
.todo
|
||||
*.log
|
||||
*.backup
|
||||
tf_env/
|
||||
*.png
|
||||
*.mp4
|
||||
*.mkv
|
||||
|
||||
.tmp/
|
||||
temp/
|
||||
.venv/
|
||||
venv/
|
||||
env/
|
||||
workflow/
|
||||
gfpgan/
|
||||
models/inswapper_128.onnx
|
||||
models/GFPGANv1.4.pth
|
||||
*.onnx
|
||||
models/DMDNet.pth
|
||||
faceswap/
|
||||
.vscode/
|
||||
switch_states.json
|
||||
|
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|
|||
# Collaboration Guidelines and Codebase Quality Standards
|
||||
|
||||
To ensure smooth collaboration and maintain the high quality of our codebase, please adhere to the following guidelines:
|
||||
|
||||
## Branching Strategy
|
||||
|
||||
* **`premain`**:
|
||||
* Always push your changes to the `premain` branch initially.
|
||||
* This safeguards the `main` branch from unintentional disruptions.
|
||||
* All tests will be performed on the `premain` branch.
|
||||
* Changes will only be merged into `main` after several hours or days of rigorous testing.
|
||||
* **`experimental`**:
|
||||
* For large or potentially disruptive changes, use the `experimental` branch.
|
||||
* This allows for thorough discussion and review before considering a merge into `main`.
|
||||
|
||||
## Pre-Pull Request Checklist
|
||||
|
||||
Before creating a Pull Request (PR), ensure you have completed the following tests:
|
||||
|
||||
### Functionality
|
||||
|
||||
* **Realtime Faceswap**:
|
||||
* Test with face enhancer **enabled** and **disabled**.
|
||||
* **Map Faces**:
|
||||
* Test with both options (**enabled** and **disabled**).
|
||||
* **Camera Listing**:
|
||||
* Verify that all cameras are listed accurately.
|
||||
|
||||
### Stability
|
||||
|
||||
* **Realtime FPS**:
|
||||
* Confirm that there is no drop in real-time frames per second (FPS).
|
||||
* **Boot Time**:
|
||||
* Changes should not negatively impact the boot time of either the application or the real-time faceswap feature.
|
||||
* **GPU Overloading**:
|
||||
* Test for a minimum of 15 minutes to guarantee no GPU overloading, which could lead to crashes.
|
||||
* **App Performance**:
|
||||
* The application should remain responsive and not exhibit any lag.
|
||||
|
|
@ -0,0 +1,661 @@
|
|||
GNU AFFERO GENERAL PUBLIC LICENSE
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Version 3, 19 November 2007
|
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|
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Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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Everyone is permitted to copy and distribute verbatim copies
|
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of this license document, but changing it is not allowed.
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|
||||
Preamble
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||||
|
||||
The GNU Affero General Public License is a free, copyleft license for
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software and other kinds of works, specifically designed to ensure
|
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cooperation with the community in the case of network server software.
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|
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The licenses for most software and other practical works are designed
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to take away your freedom to share and change the works. By contrast,
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our General Public Licenses are intended to guarantee your freedom to
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share and change all versions of a program--to make sure it remains free
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software for all its users.
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|
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When we speak of free software, we are referring to freedom, not
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have the freedom to distribute copies of free software (and charge for
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Developers that use our General Public Licenses protect your rights
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A secondary benefit of defending all users' freedom is that
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The GNU Affero General Public License is designed specifically to
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|
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An older license, called the Affero General Public License and
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published by Affero, was designed to accomplish similar goals. This is
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a different license, not a version of the Affero GPL, but Affero has
|
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released a new version of the Affero GPL which permits relicensing under
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this license.
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||||
|
||||
The precise terms and conditions for copying, distribution and
|
||||
modification follow.
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||||
|
||||
TERMS AND CONDITIONS
|
||||
|
||||
0. Definitions.
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|
||||
"This License" refers to version 3 of the GNU Affero General Public License.
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"Copyright" also means copyright-like laws that apply to other kinds of
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"The Program" refers to any copyrightable work licensed under this
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To "convey" a work means any kind of propagation that enables other
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The Corresponding Source need not include anything that users
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The Corresponding Source for a work in source code form is that
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2. Basic Permissions.
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All rights granted under this License are granted for the term of
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You may make, run and propagate covered works that you do not
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Conveying under any other circumstances is permitted solely under
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No covered work shall be deemed part of an effective technological
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You may convey verbatim copies of the Program's source code as you
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keep intact all notices of the absence of any warranty; and give all
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You may charge any price or no price for each copy that you convey,
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You may convey a work based on the Program, or the modifications to
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produce it from the Program, in the form of source code under the
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a) The work must carry prominent notices stating that you modified
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it, and giving a relevant date.
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b) The work must carry prominent notices stating that it is
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released under this License and any conditions added under section
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7. This requirement modifies the requirement in section 4 to
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"keep intact all notices".
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c) You must license the entire work, as a whole, under this
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License to anyone who comes into possession of a copy. This
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License will therefore apply, along with any applicable section 7
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regardless of how they are packaged. This License gives no
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permission to license the work in any other way, but it does not
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invalidate such permission if you have separately received it.
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d) If the work has interactive user interfaces, each must display
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Appropriate Legal Notices; however, if the Program has interactive
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A compilation of a covered work with other separate and independent
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6. Conveying Non-Source Forms.
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You may convey a covered work in object code form under the terms
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you inform other peers where the object code and Corresponding
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Source of the work are being offered to the general public at no
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A separable portion of the object code, whose source code is excluded
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A "User Product" is either (1) a "consumer product", which means any
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"Installation Information" for a User Product means any methods,
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If you convey an object code work under this section in, or with, or
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|
||||
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||||
"Additional permissions" are terms that supplement the terms of this
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||||
Notices displayed by works containing it; or
|
||||
|
||||
c) Prohibiting misrepresentation of the origin of that material, or
|
||||
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|
||||
reasonable ways as different from the original version; or
|
||||
|
||||
d) Limiting the use for publicity purposes of names of licensors or
|
||||
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|
||||
|
||||
e) Declining to grant rights under trademark law for use of some
|
||||
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|
||||
|
||||
f) Requiring indemnification of licensors and authors of that
|
||||
material by anyone who conveys the material (or modified versions of
|
||||
it) with contractual assumptions of liability to the recipient, for
|
||||
any liability that these contractual assumptions directly impose on
|
||||
those licensors and authors.
|
||||
|
||||
All other non-permissive additional terms are considered "further
|
||||
restrictions" within the meaning of section 10. If the Program as you
|
||||
received it, or any part of it, contains a notice stating that it is
|
||||
governed by this License along with a term that is a further
|
||||
restriction, you may remove that term. If a license document contains
|
||||
a further restriction but permits relicensing or conveying under this
|
||||
License, you may add to a covered work material governed by the terms
|
||||
of that license document, provided that the further restriction does
|
||||
not survive such relicensing or conveying.
|
||||
|
||||
If you add terms to a covered work in accord with this section, you
|
||||
must place, in the relevant source files, a statement of the
|
||||
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|
||||
where to find the applicable terms.
|
||||
|
||||
Additional terms, permissive or non-permissive, may be stated in the
|
||||
form of a separately written license, or stated as exceptions;
|
||||
the above requirements apply either way.
|
||||
|
||||
8. Termination.
|
||||
|
||||
You may not propagate or modify a covered work except as expressly
|
||||
provided under this License. Any attempt otherwise to propagate or
|
||||
modify it is void, and will automatically terminate your rights under
|
||||
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|
||||
paragraph of section 11).
|
||||
|
||||
However, if you cease all violation of this License, then your
|
||||
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|
||||
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|
||||
finally terminates your license, and (b) permanently, if the copyright
|
||||
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|
||||
prior to 60 days after the cessation.
|
||||
|
||||
Moreover, your license from a particular copyright holder is
|
||||
reinstated permanently if the copyright holder notifies you of the
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
Termination of your rights under this section does not terminate the
|
||||
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|
||||
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|
||||
reinstated, you do not qualify to receive new licenses for the same
|
||||
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|
||||
|
||||
9. Acceptance Not Required for Having Copies.
|
||||
|
||||
You are not required to accept this License in order to receive or
|
||||
run a copy of the Program. Ancillary propagation of a covered work
|
||||
occurring solely as a consequence of using peer-to-peer transmission
|
||||
to receive a copy likewise does not require acceptance. However,
|
||||
nothing other than this License grants you permission to propagate or
|
||||
modify any covered work. These actions infringe copyright if you do
|
||||
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|
||||
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|
||||
|
||||
10. Automatic Licensing of Downstream Recipients.
|
||||
|
||||
Each time you convey a covered work, the recipient automatically
|
||||
receives a license from the original licensors, to run, modify and
|
||||
propagate that work, subject to this License. You are not responsible
|
||||
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|
||||
|
||||
An "entity transaction" is a transaction transferring control of an
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
You may not impose any further restrictions on the exercise of the
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
11. Patents.
|
||||
|
||||
A "contributor" is a copyright holder who authorizes use under this
|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
but do not include claims that would be infringed only as a
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||
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|
||||
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|
||||
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|
||||
|
||||
In the following three paragraphs, a "patent license" is any express
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
If you convey a covered work, knowingly relying on a patent license,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
license to downstream recipients. "Knowingly relying" means you have
|
||||
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|
||||
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|
||||
in a country, would infringe one or more identifiable patents in that
|
||||
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|
||||
|
||||
If, pursuant to or in connection with a single transaction or
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
A patent license is "discriminatory" if it does not include within
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
or that patent license was granted, prior to 28 March 2007.
|
||||
|
||||
Nothing in this License shall be construed as excluding or limiting
|
||||
any implied license or other defenses to infringement that may
|
||||
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|
||||
|
||||
12. No Surrender of Others' Freedom.
|
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
13. Remote Network Interaction; Use with the GNU General Public License.
|
||||
|
||||
Notwithstanding any other provision of this License, if you modify the
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
Notwithstanding any other provision of this License, you have
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
14. Revised Versions of this License.
|
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of
|
||||
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|
||||
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|
||||
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|
||||
|
||||
Each version is given a distinguishing version number. If the
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
Foundation. If the Program does not specify a version number of the
|
||||
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|
||||
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|
||||
|
||||
If the Program specifies that a proxy can decide which future
|
||||
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|
||||
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|
||||
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|
||||
|
||||
Later license versions may give you additional or different
|
||||
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|
||||
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|
||||
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|
||||
|
||||
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
|
||||
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|
||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||
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|
||||
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|
||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
||||
|
||||
16. Limitation of Liability.
|
||||
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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
|
||||
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|
||||
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|
||||
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 Affero 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 Affero General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU Affero 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 your software can interact with users remotely through a computer
|
||||
network, you should also make sure that it provides a way for users to
|
||||
get its source. For example, if your program is a web application, its
|
||||
interface could display a "Source" link that leads users to an archive
|
||||
of the code. There are many ways you could offer source, and different
|
||||
solutions will be better for different programs; see section 13 for the
|
||||
specific requirements.
|
||||
|
||||
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 AGPL, see
|
||||
<https://www.gnu.org/licenses/>.
|
||||
|
|
@ -0,0 +1,333 @@
|
|||
<h1 align="center">Deep-Live-Cam</h1>
|
||||
|
||||
<p align="center">
|
||||
Real-time face swap and video deepfake with a single click and only a single image.
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://trendshift.io/repositories/11395" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11395" alt="hacksider%2FDeep-Live-Cam | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<img src="media/demo.gif" alt="Demo GIF" width="800">
|
||||
</p>
|
||||
|
||||
## Disclaimer
|
||||
|
||||
This deepfake software is designed to be a productive tool for the AI-generated media industry. It can assist artists in animating custom characters, creating engaging content, and even using models for clothing design.
|
||||
|
||||
We are aware of the potential for unethical applications and are committed to preventative measures. A built-in check prevents the program from processing inappropriate media (nudity, graphic content, sensitive material like war footage, etc.). We will continue to develop this project responsibly, adhering to the law and ethics. We may shut down the project or add watermarks if legally required.
|
||||
|
||||
- Ethical Use: Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online.
|
||||
|
||||
- Content Restrictions: The software includes built-in checks to prevent processing inappropriate media, such as nudity, graphic content, or sensitive material.
|
||||
|
||||
- Legal Compliance: We adhere to all relevant laws and ethical guidelines. If legally required, we may shut down the project or add watermarks to the output.
|
||||
|
||||
- User Responsibility: We are not responsible for end-user actions. Users must ensure their use of the software aligns with ethical standards and legal requirements.
|
||||
|
||||
By using this software, you agree to these terms and commit to using it in a manner that respects the rights and dignity of others.
|
||||
|
||||
Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online. We are not responsible for end-user actions.
|
||||
|
||||
|
||||
## Quick Start - Pre-built (Windows / Nvidia)
|
||||
|
||||
<a href="https://hacksider.gumroad.com/l/vccdmm"> <img src="https://github.com/user-attachments/assets/7d993b32-e3e8-4cd3-bbfb-a549152ebdd5" width="285" height="77" />
|
||||
|
||||
##### This is the fastest build you can get if you have a discrete NVIDIA GPU.
|
||||
|
||||
###### These Pre-builts are perfect for non-technical users or those who don’t have time to, or can't manually install all the requirements. Just a heads-up: this is an open-source project, so you can also install it manually.
|
||||
|
||||
## TLDR; Live Deepfake in just 3 Clicks
|
||||
|
||||
1. Select a face
|
||||
2. Select which camera to use
|
||||
3. Press live!
|
||||
|
||||
## Features & Uses - Everything is in real-time
|
||||
|
||||
### Mouth Mask
|
||||
|
||||
**Retain your original mouth for accurate movement using Mouth Mask**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/ludwig.gif" alt="resizable-gif">
|
||||
</p>
|
||||
|
||||
### Face Mapping
|
||||
|
||||
**Use different faces on multiple subjects simultaneously**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/streamers.gif" alt="face_mapping_source">
|
||||
</p>
|
||||
|
||||
### Your Movie, Your Face
|
||||
|
||||
**Watch movies with any face in real-time**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/movie.gif" alt="movie">
|
||||
</p>
|
||||
|
||||
### Live Show
|
||||
|
||||
**Run Live shows and performances**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/live_show.gif" alt="show">
|
||||
</p>
|
||||
|
||||
### Memes
|
||||
|
||||
**Create Your Most Viral Meme Yet**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/meme.gif" alt="show" width="450">
|
||||
<br>
|
||||
<sub>Created using Many Faces feature in Deep-Live-Cam</sub>
|
||||
</p>
|
||||
|
||||
### Omegle
|
||||
|
||||
**Surprise people on Omegle**
|
||||
|
||||
<p align="center">
|
||||
<video src="https://github.com/user-attachments/assets/2e9b9b82-fa04-4b70-9f56-b1f68e7672d0" width="450" controls></video>
|
||||
</p>
|
||||
|
||||
## Installation (Manual)
|
||||
|
||||
**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the prebuilt version.**
|
||||
|
||||
<details>
|
||||
<summary>Click to see the process</summary>
|
||||
|
||||
### Installation
|
||||
|
||||
This is more likely to work on your computer but will be slower as it utilizes the CPU.
|
||||
|
||||
**1. Set up Your Platform**
|
||||
|
||||
- Python (3.10 recommended)
|
||||
- pip
|
||||
- git
|
||||
- [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) - ```iex (irm ffmpeg.tc.ht)```
|
||||
- [Visual Studio 2022 Runtimes (Windows)](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
|
||||
|
||||
**2. Clone the Repository**
|
||||
|
||||
```bash
|
||||
https://github.com/hacksider/Deep-Live-Cam.git
|
||||
```
|
||||
|
||||
**3. Download the Models**
|
||||
|
||||
1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.pth)
|
||||
2. [inswapper\_128\_fp16.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx)
|
||||
|
||||
Place these files in the "**models**" folder.
|
||||
|
||||
**4. Install Dependencies**
|
||||
|
||||
We highly recommend using a `venv` to avoid issues.
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
**For macOS:** Install or upgrade the `python-tk` package:
|
||||
|
||||
```bash
|
||||
brew install python-tk@3.10
|
||||
```
|
||||
|
||||
**Run:** If you don't have a GPU, you can run Deep-Live-Cam using `python run.py`. Note that initial execution will download models (~300MB).
|
||||
|
||||
### GPU Acceleration
|
||||
|
||||
**CUDA Execution Provider (Nvidia)**
|
||||
|
||||
1. Install [CUDA Toolkit 11.8.0](https://developer.nvidia.com/cuda-11-8-0-download-archive)
|
||||
2. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-gpu
|
||||
pip install onnxruntime-gpu==1.16.3
|
||||
```
|
||||
|
||||
3. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider cuda
|
||||
```
|
||||
|
||||
**CoreML Execution Provider (Apple Silicon)**
|
||||
|
||||
1. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-silicon
|
||||
pip install onnxruntime-silicon==1.13.1
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider coreml
|
||||
```
|
||||
|
||||
**CoreML Execution Provider (Apple Legacy)**
|
||||
|
||||
1. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-coreml
|
||||
pip install onnxruntime-coreml==1.13.1
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider coreml
|
||||
```
|
||||
|
||||
**DirectML Execution Provider (Windows)**
|
||||
|
||||
1. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-directml
|
||||
pip install onnxruntime-directml==1.15.1
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider directml
|
||||
```
|
||||
|
||||
**OpenVINO™ Execution Provider (Intel)**
|
||||
|
||||
1. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-openvino
|
||||
pip install onnxruntime-openvino==1.15.0
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider openvino
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## Usage
|
||||
|
||||
**1. Image/Video Mode**
|
||||
|
||||
- Execute `python run.py`.
|
||||
- Choose a source face image and a target image/video.
|
||||
- Click "Start".
|
||||
- The output will be saved in a directory named after the target video.
|
||||
|
||||
**2. Webcam Mode**
|
||||
|
||||
- Execute `python run.py`.
|
||||
- Select a source face image.
|
||||
- Click "Live".
|
||||
- Wait for the preview to appear (10-30 seconds).
|
||||
- Use a screen capture tool like OBS to stream.
|
||||
- To change the face, select a new source image.
|
||||
|
||||
## Tips and Tricks
|
||||
|
||||
Check out these helpful guides to get the most out of Deep-Live-Cam:
|
||||
|
||||
- [Unlocking the Secrets to the Perfect Deepfake Image](https://deeplivecam.net/index.php/blog/tips-and-tricks/unlocking-the-secrets-to-the-perfect-deepfake-image) - Learn how to create the best deepfake with full head coverage
|
||||
- [Video Call with DeepLiveCam](https://deeplivecam.net/index.php/blog/tips-and-tricks/video-call-with-deeplivecam) - Make your meetings livelier by using DeepLiveCam with OBS and meeting software
|
||||
- [Have a Special Guest!](https://deeplivecam.net/index.php/blog/tips-and-tricks/have-a-special-guest) - Tutorial on how to use face mapping to add special guests to your stream
|
||||
- [Watch Deepfake Movies in Realtime](https://deeplivecam.net/index.php/blog/tips-and-tricks/watch-deepfake-movies-in-realtime) - See yourself star in any video without processing the video
|
||||
- [Better Quality without Sacrificing Speed](https://deeplivecam.net/index.php/blog/tips-and-tricks/better-quality-without-sacrificing-speed) - Tips for achieving better results without impacting performance
|
||||
- [Instant Vtuber!](https://deeplivecam.net/index.php/blog/tips-and-tricks/instant-vtuber) - Create a new persona/vtuber easily using Metahuman Creator
|
||||
|
||||
Visit our [official blog](https://deeplivecam.net/index.php/blog/tips-and-tricks) for more tips and tutorials.
|
||||
|
||||
## Command Line Arguments (Unmaintained)
|
||||
|
||||
```
|
||||
options:
|
||||
-h, --help show this help message and exit
|
||||
-s SOURCE_PATH, --source SOURCE_PATH select a source image
|
||||
-t TARGET_PATH, --target TARGET_PATH select a target image or video
|
||||
-o OUTPUT_PATH, --output OUTPUT_PATH select output file or directory
|
||||
--frame-processor FRAME_PROCESSOR [FRAME_PROCESSOR ...] frame processors (choices: face_swapper, face_enhancer, ...)
|
||||
--keep-fps keep original fps
|
||||
--keep-audio keep original audio
|
||||
--keep-frames keep temporary frames
|
||||
--many-faces process every face
|
||||
--map-faces map source target faces
|
||||
--mouth-mask mask the mouth region
|
||||
--video-encoder {libx264,libx265,libvpx-vp9} adjust output video encoder
|
||||
--video-quality [0-51] adjust output video quality
|
||||
--live-mirror the live camera display as you see it in the front-facing camera frame
|
||||
--live-resizable the live camera frame is resizable
|
||||
--max-memory MAX_MEMORY maximum amount of RAM in GB
|
||||
--execution-provider {cpu} [{cpu} ...] available execution provider (choices: cpu, ...)
|
||||
--execution-threads EXECUTION_THREADS number of execution threads
|
||||
-v, --version show program's version number and exit
|
||||
```
|
||||
|
||||
Looking for a CLI mode? Using the -s/--source argument will make the run program in cli mode.
|
||||
|
||||
## Press
|
||||
|
||||
**We are always open to criticism and are ready to improve, that's why we didn't cherry-pick anything.**
|
||||
|
||||
- [*"Deep-Live-Cam goes viral, allowing anyone to become a digital doppelganger"*](https://arstechnica.com/information-technology/2024/08/new-ai-tool-enables-real-time-face-swapping-on-webcams-raising-fraud-concerns/) - Ars Technica
|
||||
- [*"Thanks Deep Live Cam, shapeshifters are among us now"*](https://dataconomy.com/2024/08/15/what-is-deep-live-cam-github-deepfake/) - Dataconomy
|
||||
- [*"This free AI tool lets you become anyone during video-calls"*](https://www.newsbytesapp.com/news/science/deep-live-cam-ai-impersonation-tool-goes-viral/story) - NewsBytes
|
||||
- [*"OK, this viral AI live stream software is truly terrifying"*](https://www.creativebloq.com/ai/ok-this-viral-ai-live-stream-software-is-truly-terrifying) - Creative Bloq
|
||||
- [*"Deepfake AI Tool Lets You Become Anyone in a Video Call With Single Photo"*](https://petapixel.com/2024/08/14/deep-live-cam-deepfake-ai-tool-lets-you-become-anyone-in-a-video-call-with-single-photo-mark-zuckerberg-jd-vance-elon-musk/) - PetaPixel
|
||||
- [*"Deep-Live-Cam Uses AI to Transform Your Face in Real-Time, Celebrities Included"*](https://www.techeblog.com/deep-live-cam-ai-transform-face/) - TechEBlog
|
||||
- [*"An AI tool that "makes you look like anyone" during a video call is going viral online"*](https://telegrafi.com/en/a-tool-that-makes-you-look-like-anyone-during-a-video-call-is-going-viral-on-the-Internet/) - Telegrafi
|
||||
- [*"This Deepfake Tool Turning Images Into Livestreams is Topping the GitHub Charts"*](https://decrypt.co/244565/this-deepfake-tool-turning-images-into-livestreams-is-topping-the-github-charts) - Emerge
|
||||
- [*"New Real-Time Face-Swapping AI Allows Anyone to Mimic Famous Faces"*](https://www.digitalmusicnews.com/2024/08/15/face-swapping-ai-real-time-mimic/) - Digital Music News
|
||||
- [*"This real-time webcam deepfake tool raises alarms about the future of identity theft"*](https://www.diyphotography.net/this-real-time-webcam-deepfake-tool-raises-alarms-about-the-future-of-identity-theft/) - DIYPhotography
|
||||
- [*"That's Crazy, Oh God. That's Fucking Freaky Dude... That's So Wild Dude"*](https://www.youtube.com/watch?time_continue=1074&v=py4Tc-Y8BcY) - SomeOrdinaryGamers
|
||||
- [*"Alright look look look, now look chat, we can do any face we want to look like chat"*](https://www.youtube.com/live/mFsCe7AIxq8?feature=shared&t=2686) - IShowSpeed
|
||||
|
||||
## Credits
|
||||
|
||||
- [ffmpeg](https://ffmpeg.org/): for making video-related operations easy
|
||||
- [deepinsight](https://github.com/deepinsight): for their [insightface](https://github.com/deepinsight/insightface) project which provided a well-made library and models. Please be reminded that the [use of the model is for non-commercial research purposes only](https://github.com/deepinsight/insightface?tab=readme-ov-file#license).
|
||||
- [havok2-htwo](https://github.com/havok2-htwo): for sharing the code for webcam
|
||||
- [GosuDRM](https://github.com/GosuDRM): for the open version of roop
|
||||
- [pereiraroland26](https://github.com/pereiraroland26): Multiple faces support
|
||||
- [vic4key](https://github.com/vic4key): For supporting/contributing to this project
|
||||
- [kier007](https://github.com/kier007): for improving the user experience
|
||||
- [qitianai](https://github.com/qitianai): for multi-lingual support
|
||||
- and [all developers](https://github.com/hacksider/Deep-Live-Cam/graphs/contributors) behind libraries used in this project.
|
||||
- Footnote: Please be informed that the base author of the code is [s0md3v](https://github.com/s0md3v/roop)
|
||||
- All the wonderful users who helped make this project go viral by starring the repo ❤️
|
||||
|
||||
[](https://github.com/hacksider/Deep-Live-Cam/stargazers)
|
||||
|
||||
## Contributions
|
||||
|
||||
|
||||
|
||||
## Stars to the Moon 🚀
|
||||
|
||||
<a href="https://star-history.com/#hacksider/deep-live-cam&Date">
|
||||
<picture>
|
||||
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date&theme=dark" />
|
||||
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date" />
|
||||
<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date" />
|
||||
</picture>
|
||||
</a>
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
{
|
||||
"Source x Target Mapper": "Source x Target Mapper",
|
||||
"select an source image": "选择一个源图像",
|
||||
"Preview": "预览",
|
||||
"select an target image or video": "选择一个目标图像或视频",
|
||||
"save image output file": "保存图像输出文件",
|
||||
"save video output file": "保存视频输出文件",
|
||||
"select an target image": "选择一个目标图像",
|
||||
"source": "源",
|
||||
"Select a target": "选择一个目标",
|
||||
"Select a face": "选择一张脸",
|
||||
"Keep audio": "保留音频",
|
||||
"Face Enhancer": "面纹增强器",
|
||||
"Many faces": "多脸",
|
||||
"Show FPS": "显示帧率",
|
||||
"Keep fps": "保持帧率",
|
||||
"Keep frames": "保持帧数",
|
||||
"Fix Blueish Cam": "修复偏蓝的摄像头",
|
||||
"Mouth Mask": "口罩",
|
||||
"Show Mouth Mask Box": "显示口罩盒",
|
||||
"Start": "开始",
|
||||
"Live": "直播",
|
||||
"Destroy": "结束",
|
||||
"Map faces": "识别人脸",
|
||||
"Processing...": "处理中...",
|
||||
"Processing succeed!": "处理成功!",
|
||||
"Processing ignored!": "处理被忽略!",
|
||||
"Failed to start camera": "启动相机失败",
|
||||
"Please complete pop-up or close it.": "请先完成弹出窗口或者关闭它",
|
||||
"Getting unique faces": "获取独特面部",
|
||||
"Please select a source image first": "请先选择一个源图像",
|
||||
"No faces found in target": "目标图像中没有人脸",
|
||||
"Add": "添加",
|
||||
"Clear": "清除",
|
||||
"Submit": "确认",
|
||||
"Select source image": "请选取源图像",
|
||||
"Select target image": "请选取目标图像",
|
||||
"Please provide mapping!": "请提供映射",
|
||||
"Atleast 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
|
||||
"At least 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
|
||||
"Face could not be detected in last upload!": "最近上传的图像中没有检测到人脸!",
|
||||
"Select Camera:": "选择摄像头",
|
||||
"All mappings cleared!": "所有映射均已清除!",
|
||||
"Mappings successfully submitted!": "成功提交映射！",
|
||||
"Source x Target Mapper is already open.": "源 x 目标映射器已打开。"
|
||||
}
|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
After Width: | Height: | Size: 13 MiB |
|
|
@ -0,0 +1,4 @@
|
|||
just put the models in this folder -
|
||||
|
||||
https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx?download=true
|
||||
https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth
|
||||
|
|
@ -0,0 +1,32 @@
|
|||
from typing import Any
|
||||
import cv2
|
||||
import modules.globals # Import the globals to check the color correction toggle
|
||||
|
||||
|
||||
def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
|
||||
capture = cv2.VideoCapture(video_path)
|
||||
|
||||
# Set MJPEG format to ensure correct color space handling
|
||||
capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
|
||||
|
||||
# Only force RGB conversion if color correction is enabled
|
||||
if modules.globals.color_correction:
|
||||
capture.set(cv2.CAP_PROP_CONVERT_RGB, 1)
|
||||
|
||||
frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
|
||||
capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
|
||||
has_frame, frame = capture.read()
|
||||
|
||||
if has_frame and modules.globals.color_correction:
|
||||
# Convert the frame color if necessary
|
||||
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
capture.release()
|
||||
return frame if has_frame else None
|
||||
|
||||
|
||||
def get_video_frame_total(video_path: str) -> int:
|
||||
capture = cv2.VideoCapture(video_path)
|
||||
video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
capture.release()
|
||||
return video_frame_total
|
||||
|
|
@ -0,0 +1,32 @@
|
|||
import numpy as np
|
||||
from sklearn.cluster import KMeans
|
||||
from sklearn.metrics import silhouette_score
|
||||
from typing import Any
|
||||
|
||||
|
||||
def find_cluster_centroids(embeddings, max_k=10) -> Any:
|
||||
inertia = []
|
||||
cluster_centroids = []
|
||||
K = range(1, max_k+1)
|
||||
|
||||
for k in K:
|
||||
kmeans = KMeans(n_clusters=k, random_state=0)
|
||||
kmeans.fit(embeddings)
|
||||
inertia.append(kmeans.inertia_)
|
||||
cluster_centroids.append({"k": k, "centroids": kmeans.cluster_centers_})
|
||||
|
||||
diffs = [inertia[i] - inertia[i+1] for i in range(len(inertia)-1)]
|
||||
optimal_centroids = cluster_centroids[diffs.index(max(diffs)) + 1]['centroids']
|
||||
|
||||
return optimal_centroids
|
||||
|
||||
def find_closest_centroid(centroids: list, normed_face_embedding) -> list:
|
||||
try:
|
||||
centroids = np.array(centroids)
|
||||
normed_face_embedding = np.array(normed_face_embedding)
|
||||
similarities = np.dot(centroids, normed_face_embedding)
|
||||
closest_centroid_index = np.argmax(similarities)
|
||||
|
||||
return closest_centroid_index, centroids[closest_centroid_index]
|
||||
except ValueError:
|
||||
return None
|
||||
|
|
@ -0,0 +1,259 @@
|
|||
import os
|
||||
import sys
|
||||
# single thread doubles cuda performance - needs to be set before torch import
|
||||
if any(arg.startswith('--execution-provider') for arg in sys.argv):
|
||||
os.environ['OMP_NUM_THREADS'] = '1'
|
||||
# reduce tensorflow log level
|
||||
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
|
||||
import warnings
|
||||
from typing import List
|
||||
import platform
|
||||
import signal
|
||||
import shutil
|
||||
import argparse
|
||||
import torch
|
||||
import onnxruntime
|
||||
import tensorflow
|
||||
|
||||
import modules.globals
|
||||
import modules.metadata
|
||||
import modules.ui as ui
|
||||
from modules.processors.frame.core import get_frame_processors_modules
|
||||
from modules.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
|
||||
|
||||
if 'ROCMExecutionProvider' in modules.globals.execution_providers:
|
||||
del torch
|
||||
|
||||
warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
|
||||
warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
|
||||
|
||||
|
||||
def parse_args() -> None:
|
||||
signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
|
||||
program = argparse.ArgumentParser()
|
||||
program.add_argument('-s', '--source', help='select an source image', dest='source_path')
|
||||
program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
|
||||
program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
|
||||
program.add_argument('--frame-processor', help='pipeline of frame processors', dest='frame_processor', default=['face_swapper'], choices=['face_swapper', 'face_enhancer'], nargs='+')
|
||||
program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)
|
||||
program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
|
||||
program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
|
||||
program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)
|
||||
program.add_argument('--nsfw-filter', help='filter the NSFW image or video', dest='nsfw_filter', action='store_true', default=False)
|
||||
program.add_argument('--map-faces', help='map source target faces', dest='map_faces', action='store_true', default=False)
|
||||
program.add_argument('--mouth-mask', help='mask the mouth region', dest='mouth_mask', action='store_true', default=False)
|
||||
program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
|
||||
program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')
|
||||
program.add_argument('-l', '--lang', help='Ui language', default="en")
|
||||
program.add_argument('--live-mirror', help='The live camera display as you see it in the front-facing camera frame', dest='live_mirror', action='store_true', default=False)
|
||||
program.add_argument('--live-resizable', help='The live camera frame is resizable', dest='live_resizable', action='store_true', default=False)
|
||||
program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
|
||||
program.add_argument('--execution-provider', help='execution provider', dest='execution_provider', default=['cpu'], choices=suggest_execution_providers(), nargs='+')
|
||||
program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())
|
||||
program.add_argument('-v', '--version', action='version', version=f'{modules.metadata.name} {modules.metadata.version}')
|
||||
|
||||
# register deprecated args
|
||||
program.add_argument('-f', '--face', help=argparse.SUPPRESS, dest='source_path_deprecated')
|
||||
program.add_argument('--cpu-cores', help=argparse.SUPPRESS, dest='cpu_cores_deprecated', type=int)
|
||||
program.add_argument('--gpu-vendor', help=argparse.SUPPRESS, dest='gpu_vendor_deprecated')
|
||||
program.add_argument('--gpu-threads', help=argparse.SUPPRESS, dest='gpu_threads_deprecated', type=int)
|
||||
|
||||
args = program.parse_args()
|
||||
|
||||
modules.globals.source_path = args.source_path
|
||||
modules.globals.target_path = args.target_path
|
||||
modules.globals.output_path = normalize_output_path(modules.globals.source_path, modules.globals.target_path, args.output_path)
|
||||
modules.globals.frame_processors = args.frame_processor
|
||||
modules.globals.headless = args.source_path or args.target_path or args.output_path
|
||||
modules.globals.keep_fps = args.keep_fps
|
||||
modules.globals.keep_audio = args.keep_audio
|
||||
modules.globals.keep_frames = args.keep_frames
|
||||
modules.globals.many_faces = args.many_faces
|
||||
modules.globals.mouth_mask = args.mouth_mask
|
||||
modules.globals.nsfw_filter = args.nsfw_filter
|
||||
modules.globals.map_faces = args.map_faces
|
||||
modules.globals.video_encoder = args.video_encoder
|
||||
modules.globals.video_quality = args.video_quality
|
||||
modules.globals.live_mirror = args.live_mirror
|
||||
modules.globals.live_resizable = args.live_resizable
|
||||
modules.globals.max_memory = args.max_memory
|
||||
modules.globals.execution_providers = decode_execution_providers(args.execution_provider)
|
||||
modules.globals.execution_threads = args.execution_threads
|
||||
modules.globals.lang = args.lang
|
||||
|
||||
#for ENHANCER tumbler:
|
||||
if 'face_enhancer' in args.frame_processor:
|
||||
modules.globals.fp_ui['face_enhancer'] = True
|
||||
else:
|
||||
modules.globals.fp_ui['face_enhancer'] = False
|
||||
|
||||
# translate deprecated args
|
||||
if args.source_path_deprecated:
|
||||
print('\033[33mArgument -f and --face are deprecated. Use -s and --source instead.\033[0m')
|
||||
modules.globals.source_path = args.source_path_deprecated
|
||||
modules.globals.output_path = normalize_output_path(args.source_path_deprecated, modules.globals.target_path, args.output_path)
|
||||
if args.cpu_cores_deprecated:
|
||||
print('\033[33mArgument --cpu-cores is deprecated. Use --execution-threads instead.\033[0m')
|
||||
modules.globals.execution_threads = args.cpu_cores_deprecated
|
||||
if args.gpu_vendor_deprecated == 'apple':
|
||||
print('\033[33mArgument --gpu-vendor apple is deprecated. Use --execution-provider coreml instead.\033[0m')
|
||||
modules.globals.execution_providers = decode_execution_providers(['coreml'])
|
||||
if args.gpu_vendor_deprecated == 'nvidia':
|
||||
print('\033[33mArgument --gpu-vendor nvidia is deprecated. Use --execution-provider cuda instead.\033[0m')
|
||||
modules.globals.execution_providers = decode_execution_providers(['cuda'])
|
||||
if args.gpu_vendor_deprecated == 'amd':
|
||||
print('\033[33mArgument --gpu-vendor amd is deprecated. Use --execution-provider cuda instead.\033[0m')
|
||||
modules.globals.execution_providers = decode_execution_providers(['rocm'])
|
||||
if args.gpu_threads_deprecated:
|
||||
print('\033[33mArgument --gpu-threads is deprecated. Use --execution-threads instead.\033[0m')
|
||||
modules.globals.execution_threads = args.gpu_threads_deprecated
|
||||
|
||||
|
||||
def encode_execution_providers(execution_providers: List[str]) -> List[str]:
|
||||
return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
|
||||
|
||||
|
||||
def decode_execution_providers(execution_providers: List[str]) -> List[str]:
|
||||
return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
|
||||
if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
|
||||
|
||||
|
||||
def suggest_max_memory() -> int:
|
||||
if platform.system().lower() == 'darwin':
|
||||
return 4
|
||||
return 16
|
||||
|
||||
|
||||
def suggest_execution_providers() -> List[str]:
|
||||
return encode_execution_providers(onnxruntime.get_available_providers())
|
||||
|
||||
|
||||
def suggest_execution_threads() -> int:
|
||||
if 'DmlExecutionProvider' in modules.globals.execution_providers:
|
||||
return 1
|
||||
if 'ROCMExecutionProvider' in modules.globals.execution_providers:
|
||||
return 1
|
||||
return 8
|
||||
|
||||
|
||||
def limit_resources() -> None:
|
||||
# prevent tensorflow memory leak
|
||||
gpus = tensorflow.config.experimental.list_physical_devices('GPU')
|
||||
for gpu in gpus:
|
||||
tensorflow.config.experimental.set_memory_growth(gpu, True)
|
||||
# limit memory usage
|
||||
if modules.globals.max_memory:
|
||||
memory = modules.globals.max_memory * 1024 ** 3
|
||||
if platform.system().lower() == 'darwin':
|
||||
memory = modules.globals.max_memory * 1024 ** 6
|
||||
if platform.system().lower() == 'windows':
|
||||
import ctypes
|
||||
kernel32 = ctypes.windll.kernel32
|
||||
kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
|
||||
else:
|
||||
import resource
|
||||
resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
|
||||
|
||||
|
||||
def release_resources() -> None:
|
||||
if 'CUDAExecutionProvider' in modules.globals.execution_providers:
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
if sys.version_info < (3, 9):
|
||||
update_status('Python version is not supported - please upgrade to 3.9 or higher.')
|
||||
return False
|
||||
if not shutil.which('ffmpeg'):
|
||||
update_status('ffmpeg is not installed.')
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def update_status(message: str, scope: str = 'DLC.CORE') -> None:
|
||||
print(f'[{scope}] {message}')
|
||||
if not modules.globals.headless:
|
||||
ui.update_status(message)
|
||||
|
||||
def start() -> None:
|
||||
for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
|
||||
if not frame_processor.pre_start():
|
||||
return
|
||||
update_status('Processing...')
|
||||
# process image to image
|
||||
if has_image_extension(modules.globals.target_path):
|
||||
if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
|
||||
return
|
||||
try:
|
||||
shutil.copy2(modules.globals.target_path, modules.globals.output_path)
|
||||
except Exception as e:
|
||||
print("Error copying file:", str(e))
|
||||
for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
|
||||
update_status('Progressing...', frame_processor.NAME)
|
||||
frame_processor.process_image(modules.globals.source_path, modules.globals.output_path, modules.globals.output_path)
|
||||
release_resources()
|
||||
if is_image(modules.globals.target_path):
|
||||
update_status('Processing to image succeed!')
|
||||
else:
|
||||
update_status('Processing to image failed!')
|
||||
return
|
||||
# process image to videos
|
||||
if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
|
||||
return
|
||||
|
||||
if not modules.globals.map_faces:
|
||||
update_status('Creating temp resources...')
|
||||
create_temp(modules.globals.target_path)
|
||||
update_status('Extracting frames...')
|
||||
extract_frames(modules.globals.target_path)
|
||||
|
||||
temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
|
||||
for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
|
||||
update_status('Progressing...', frame_processor.NAME)
|
||||
frame_processor.process_video(modules.globals.source_path, temp_frame_paths)
|
||||
release_resources()
|
||||
# handles fps
|
||||
if modules.globals.keep_fps:
|
||||
update_status('Detecting fps...')
|
||||
fps = detect_fps(modules.globals.target_path)
|
||||
update_status(f'Creating video with {fps} fps...')
|
||||
create_video(modules.globals.target_path, fps)
|
||||
else:
|
||||
update_status('Creating video with 30.0 fps...')
|
||||
create_video(modules.globals.target_path)
|
||||
# handle audio
|
||||
if modules.globals.keep_audio:
|
||||
if modules.globals.keep_fps:
|
||||
update_status('Restoring audio...')
|
||||
else:
|
||||
update_status('Restoring audio might cause issues as fps are not kept...')
|
||||
restore_audio(modules.globals.target_path, modules.globals.output_path)
|
||||
else:
|
||||
move_temp(modules.globals.target_path, modules.globals.output_path)
|
||||
# clean and validate
|
||||
clean_temp(modules.globals.target_path)
|
||||
if is_video(modules.globals.target_path):
|
||||
update_status('Processing to video succeed!')
|
||||
else:
|
||||
update_status('Processing to video failed!')
|
||||
|
||||
|
||||
def destroy(to_quit=True) -> None:
|
||||
if modules.globals.target_path:
|
||||
clean_temp(modules.globals.target_path)
|
||||
if to_quit: quit()
|
||||
|
||||
|
||||
def run() -> None:
|
||||
parse_args()
|
||||
if not pre_check():
|
||||
return
|
||||
for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
|
||||
if not frame_processor.pre_check():
|
||||
return
|
||||
limit_resources()
|
||||
if modules.globals.headless:
|
||||
start()
|
||||
else:
|
||||
window = ui.init(start, destroy, modules.globals.lang)
|
||||
window.mainloop()
|
||||
|
|
@ -0,0 +1,189 @@
|
|||
import os
|
||||
import shutil
|
||||
from typing import Any
|
||||
import insightface
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import modules.globals
|
||||
from tqdm import tqdm
|
||||
from modules.typing import Frame
|
||||
from modules.cluster_analysis import find_cluster_centroids, find_closest_centroid
|
||||
from modules.utilities import get_temp_directory_path, create_temp, extract_frames, clean_temp, get_temp_frame_paths
|
||||
from pathlib import Path
|
||||
|
||||
FACE_ANALYSER = None
|
||||
|
||||
|
||||
def get_face_analyser() -> Any:
|
||||
global FACE_ANALYSER
|
||||
|
||||
if FACE_ANALYSER is None:
|
||||
FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=modules.globals.execution_providers)
|
||||
FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
|
||||
return FACE_ANALYSER
|
||||
|
||||
|
||||
def get_one_face(frame: Frame) -> Any:
|
||||
face = get_face_analyser().get(frame)
|
||||
try:
|
||||
return min(face, key=lambda x: x.bbox[0])
|
||||
except ValueError:
|
||||
return None
|
||||
|
||||
|
||||
def get_many_faces(frame: Frame) -> Any:
|
||||
try:
|
||||
return get_face_analyser().get(frame)
|
||||
except IndexError:
|
||||
return None
|
||||
|
||||
def has_valid_map() -> bool:
|
||||
for map in modules.globals.source_target_map:
|
||||
if "source" in map and "target" in map:
|
||||
return True
|
||||
return False
|
||||
|
||||
def default_source_face() -> Any:
|
||||
for map in modules.globals.source_target_map:
|
||||
if "source" in map:
|
||||
return map['source']['face']
|
||||
return None
|
||||
|
||||
def simplify_maps() -> Any:
|
||||
centroids = []
|
||||
faces = []
|
||||
for map in modules.globals.source_target_map:
|
||||
if "source" in map and "target" in map:
|
||||
centroids.append(map['target']['face'].normed_embedding)
|
||||
faces.append(map['source']['face'])
|
||||
|
||||
modules.globals.simple_map = {'source_faces': faces, 'target_embeddings': centroids}
|
||||
return None
|
||||
|
||||
def add_blank_map() -> Any:
|
||||
try:
|
||||
max_id = -1
|
||||
if len(modules.globals.source_target_map) > 0:
|
||||
max_id = max(modules.globals.source_target_map, key=lambda x: x['id'])['id']
|
||||
|
||||
modules.globals.source_target_map.append({
|
||||
'id' : max_id + 1
|
||||
})
|
||||
except ValueError:
|
||||
return None
|
||||
|
||||
def get_unique_faces_from_target_image() -> Any:
|
||||
try:
|
||||
modules.globals.source_target_map = []
|
||||
target_frame = cv2.imread(modules.globals.target_path)
|
||||
many_faces = get_many_faces(target_frame)
|
||||
i = 0
|
||||
|
||||
for face in many_faces:
|
||||
x_min, y_min, x_max, y_max = face['bbox']
|
||||
modules.globals.source_target_map.append({
|
||||
'id' : i,
|
||||
'target' : {
|
||||
'cv2' : target_frame[int(y_min):int(y_max), int(x_min):int(x_max)],
|
||||
'face' : face
|
||||
}
|
||||
})
|
||||
i = i + 1
|
||||
except ValueError:
|
||||
return None
|
||||
|
||||
|
||||
def get_unique_faces_from_target_video() -> Any:
|
||||
try:
|
||||
modules.globals.source_target_map = []
|
||||
frame_face_embeddings = []
|
||||
face_embeddings = []
|
||||
|
||||
print('Creating temp resources...')
|
||||
clean_temp(modules.globals.target_path)
|
||||
create_temp(modules.globals.target_path)
|
||||
print('Extracting frames...')
|
||||
extract_frames(modules.globals.target_path)
|
||||
|
||||
temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
|
||||
|
||||
i = 0
|
||||
for temp_frame_path in tqdm(temp_frame_paths, desc="Extracting face embeddings from frames"):
|
||||
temp_frame = cv2.imread(temp_frame_path)
|
||||
many_faces = get_many_faces(temp_frame)
|
||||
|
||||
for face in many_faces:
|
||||
face_embeddings.append(face.normed_embedding)
|
||||
|
||||
frame_face_embeddings.append({'frame': i, 'faces': many_faces, 'location': temp_frame_path})
|
||||
i += 1
|
||||
|
||||
centroids = find_cluster_centroids(face_embeddings)
|
||||
|
||||
for frame in frame_face_embeddings:
|
||||
for face in frame['faces']:
|
||||
closest_centroid_index, _ = find_closest_centroid(centroids, face.normed_embedding)
|
||||
face['target_centroid'] = closest_centroid_index
|
||||
|
||||
for i in range(len(centroids)):
|
||||
modules.globals.source_target_map.append({
|
||||
'id' : i
|
||||
})
|
||||
|
||||
temp = []
|
||||
for frame in tqdm(frame_face_embeddings, desc=f"Mapping frame embeddings to centroids-{i}"):
|
||||
temp.append({'frame': frame['frame'], 'faces': [face for face in frame['faces'] if face['target_centroid'] == i], 'location': frame['location']})
|
||||
|
||||
modules.globals.source_target_map[i]['target_faces_in_frame'] = temp
|
||||
|
||||
# dump_faces(centroids, frame_face_embeddings)
|
||||
default_target_face()
|
||||
except ValueError:
|
||||
return None
|
||||
|
||||
|
||||
def default_target_face():
|
||||
for map in modules.globals.source_target_map:
|
||||
best_face = None
|
||||
best_frame = None
|
||||
for frame in map['target_faces_in_frame']:
|
||||
if len(frame['faces']) > 0:
|
||||
best_face = frame['faces'][0]
|
||||
best_frame = frame
|
||||
break
|
||||
|
||||
for frame in map['target_faces_in_frame']:
|
||||
for face in frame['faces']:
|
||||
if face['det_score'] > best_face['det_score']:
|
||||
best_face = face
|
||||
best_frame = frame
|
||||
|
||||
x_min, y_min, x_max, y_max = best_face['bbox']
|
||||
|
||||
target_frame = cv2.imread(best_frame['location'])
|
||||
map['target'] = {
|
||||
'cv2' : target_frame[int(y_min):int(y_max), int(x_min):int(x_max)],
|
||||
'face' : best_face
|
||||
}
|
||||
|
||||
|
||||
def dump_faces(centroids: Any, frame_face_embeddings: list):
|
||||
temp_directory_path = get_temp_directory_path(modules.globals.target_path)
|
||||
|
||||
for i in range(len(centroids)):
|
||||
if os.path.exists(temp_directory_path + f"/{i}") and os.path.isdir(temp_directory_path + f"/{i}"):
|
||||
shutil.rmtree(temp_directory_path + f"/{i}")
|
||||
Path(temp_directory_path + f"/{i}").mkdir(parents=True, exist_ok=True)
|
||||
|
||||
for frame in tqdm(frame_face_embeddings, desc=f"Copying faces to temp/./{i}"):
|
||||
temp_frame = cv2.imread(frame['location'])
|
||||
|
||||
j = 0
|
||||
for face in frame['faces']:
|
||||
if face['target_centroid'] == i:
|
||||
x_min, y_min, x_max, y_max = face['bbox']
|
||||
|
||||
if temp_frame[int(y_min):int(y_max), int(x_min):int(x_max)].size > 0:
|
||||
cv2.imwrite(temp_directory_path + f"/{i}/{frame['frame']}_{j}.png", temp_frame[int(y_min):int(y_max), int(x_min):int(x_max)])
|
||||
j += 1
|
||||
|
|
@ -0,0 +1,26 @@
|
|||
import json
|
||||
from pathlib import Path
|
||||
|
||||
class LanguageManager:
|
||||
def __init__(self, default_language="en"):
|
||||
self.current_language = default_language
|
||||
self.translations = {}
|
||||
self.load_language(default_language)
|
||||
|
||||
def load_language(self, language_code) -> bool:
|
||||
"""load language file"""
|
||||
if language_code == "en":
|
||||
return True
|
||||
try:
|
||||
file_path = Path(__file__).parent.parent / f"locales/{language_code}.json"
|
||||
with open(file_path, "r", encoding="utf-8") as file:
|
||||
self.translations = json.load(file)
|
||||
self.current_language = language_code
|
||||
return True
|
||||
except FileNotFoundError:
|
||||
print(f"Language file not found: {language_code}")
|
||||
return False
|
||||
|
||||
def _(self, key, default=None) -> str:
|
||||
"""get translate text"""
|
||||
return self.translations.get(key, default if default else key)
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
import os
|
||||
from typing import List, Dict, Any
|
||||
|
||||
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
WORKFLOW_DIR = os.path.join(ROOT_DIR, "workflow")
|
||||
|
||||
file_types = [
|
||||
("Image", ("*.png", "*.jpg", "*.jpeg", "*.gif", "*.bmp")),
|
||||
("Video", ("*.mp4", "*.mkv")),
|
||||
]
|
||||
|
||||
source_target_map = []
|
||||
simple_map = {}
|
||||
|
||||
source_path = None
|
||||
target_path = None
|
||||
output_path = None
|
||||
frame_processors: List[str] = []
|
||||
keep_fps = True
|
||||
keep_audio = True
|
||||
keep_frames = False
|
||||
many_faces = False
|
||||
map_faces = False
|
||||
color_correction = False # New global variable for color correction toggle
|
||||
nsfw_filter = False
|
||||
video_encoder = None
|
||||
video_quality = None
|
||||
live_mirror = False
|
||||
live_resizable = True
|
||||
max_memory = None
|
||||
execution_providers: List[str] = []
|
||||
execution_threads = None
|
||||
headless = None
|
||||
log_level = "error"
|
||||
fp_ui: Dict[str, bool] = {"face_enhancer": False}
|
||||
camera_input_combobox = None
|
||||
webcam_preview_running = False
|
||||
show_fps = False
|
||||
mouth_mask = False
|
||||
show_mouth_mask_box = False
|
||||
mask_feather_ratio = 8
|
||||
mask_down_size = 0.50
|
||||
mask_size = 1
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
name = 'Deep-Live-Cam'
|
||||
version = '1.8'
|
||||
edition = 'GitHub Edition'
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
import numpy
|
||||
import opennsfw2
|
||||
from PIL import Image
|
||||
import cv2 # Add OpenCV import
|
||||
import modules.globals # Import globals to access the color correction toggle
|
||||
|
||||
from modules.typing import Frame
|
||||
|
||||
MAX_PROBABILITY = 0.85
|
||||
|
||||
# Preload the model once for efficiency
|
||||
model = None
|
||||
|
||||
def predict_frame(target_frame: Frame) -> bool:
|
||||
# Convert the frame to RGB before processing if color correction is enabled
|
||||
if modules.globals.color_correction:
|
||||
target_frame = cv2.cvtColor(target_frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
image = Image.fromarray(target_frame)
|
||||
image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)
|
||||
global model
|
||||
if model is None:
|
||||
model = opennsfw2.make_open_nsfw_model()
|
||||
|
||||
views = numpy.expand_dims(image, axis=0)
|
||||
_, probability = model.predict(views)[0]
|
||||
return probability > MAX_PROBABILITY
|
||||
|
||||
|
||||
def predict_image(target_path: str) -> bool:
|
||||
return opennsfw2.predict_image(target_path) > MAX_PROBABILITY
|
||||
|
||||
|
||||
def predict_video(target_path: str) -> bool:
|
||||
_, probabilities = opennsfw2.predict_video_frames(video_path=target_path, frame_interval=100)
|
||||
return any(probability > MAX_PROBABILITY for probability in probabilities)
|
||||
|
|
@ -0,0 +1,73 @@
|
|||
import sys
|
||||
import importlib
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from types import ModuleType
|
||||
from typing import Any, List, Callable
|
||||
from tqdm import tqdm
|
||||
|
||||
import modules
|
||||
import modules.globals
|
||||
|
||||
FRAME_PROCESSORS_MODULES: List[ModuleType] = []
|
||||
FRAME_PROCESSORS_INTERFACE = [
|
||||
'pre_check',
|
||||
'pre_start',
|
||||
'process_frame',
|
||||
'process_image',
|
||||
'process_video'
|
||||
]
|
||||
|
||||
|
||||
def load_frame_processor_module(frame_processor: str) -> Any:
|
||||
try:
|
||||
frame_processor_module = importlib.import_module(f'modules.processors.frame.{frame_processor}')
|
||||
for method_name in FRAME_PROCESSORS_INTERFACE:
|
||||
if not hasattr(frame_processor_module, method_name):
|
||||
sys.exit()
|
||||
except ImportError:
|
||||
print(f"Frame processor {frame_processor} not found")
|
||||
sys.exit()
|
||||
return frame_processor_module
|
||||
|
||||
|
||||
def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType]:
|
||||
global FRAME_PROCESSORS_MODULES
|
||||
|
||||
if not FRAME_PROCESSORS_MODULES:
|
||||
for frame_processor in frame_processors:
|
||||
frame_processor_module = load_frame_processor_module(frame_processor)
|
||||
FRAME_PROCESSORS_MODULES.append(frame_processor_module)
|
||||
set_frame_processors_modules_from_ui(frame_processors)
|
||||
return FRAME_PROCESSORS_MODULES
|
||||
|
||||
def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
|
||||
global FRAME_PROCESSORS_MODULES
|
||||
for frame_processor, state in modules.globals.fp_ui.items():
|
||||
if state == True and frame_processor not in frame_processors:
|
||||
frame_processor_module = load_frame_processor_module(frame_processor)
|
||||
FRAME_PROCESSORS_MODULES.append(frame_processor_module)
|
||||
modules.globals.frame_processors.append(frame_processor)
|
||||
if state == False:
|
||||
try:
|
||||
frame_processor_module = load_frame_processor_module(frame_processor)
|
||||
FRAME_PROCESSORS_MODULES.remove(frame_processor_module)
|
||||
modules.globals.frame_processors.remove(frame_processor)
|
||||
except:
|
||||
pass
|
||||
|
||||
def multi_process_frame(source_path: str, temp_frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None], progress: Any = None) -> None:
|
||||
with ThreadPoolExecutor(max_workers=modules.globals.execution_threads) as executor:
|
||||
futures = []
|
||||
for path in temp_frame_paths:
|
||||
future = executor.submit(process_frames, source_path, [path], progress)
|
||||
futures.append(future)
|
||||
for future in futures:
|
||||
future.result()
|
||||
|
||||
|
||||
def process_video(source_path: str, frame_paths: list[str], process_frames: Callable[[str, List[str], Any], None]) -> None:
|
||||
progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
|
||||
total = len(frame_paths)
|
||||
with tqdm(total=total, desc='Processing', unit='frame', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
|
||||
progress.set_postfix({'execution_providers': modules.globals.execution_providers, 'execution_threads': modules.globals.execution_threads, 'max_memory': modules.globals.max_memory})
|
||||
multi_process_frame(source_path, frame_paths, process_frames, progress)
|
||||
|
|
@ -0,0 +1,109 @@
|
|||
from typing import Any, List
|
||||
import cv2
|
||||
import threading
|
||||
import gfpgan
|
||||
import os
|
||||
|
||||
import modules.globals
|
||||
import modules.processors.frame.core
|
||||
from modules.core import update_status
|
||||
from modules.face_analyser import get_one_face
|
||||
from modules.typing import Frame, Face
|
||||
import platform
|
||||
import torch
|
||||
from modules.utilities import (
|
||||
conditional_download,
|
||||
is_image,
|
||||
is_video,
|
||||
)
|
||||
|
||||
FACE_ENHANCER = None
|
||||
THREAD_SEMAPHORE = threading.Semaphore()
|
||||
THREAD_LOCK = threading.Lock()
|
||||
NAME = "DLC.FACE-ENHANCER"
|
||||
|
||||
abs_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
models_dir = os.path.join(
|
||||
os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
|
||||
)
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
download_directory_path = models_dir
|
||||
conditional_download(
|
||||
download_directory_path,
|
||||
[
|
||||
"https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth"
|
||||
],
|
||||
)
|
||||
return True
|
||||
|
||||
|
||||
def pre_start() -> bool:
|
||||
if not is_image(modules.globals.target_path) and not is_video(
|
||||
modules.globals.target_path
|
||||
):
|
||||
update_status("Select an image or video for target path.", NAME)
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def get_face_enhancer() -> Any:
|
||||
global FACE_ENHANCER
|
||||
|
||||
with THREAD_LOCK:
|
||||
if FACE_ENHANCER is None:
|
||||
model_path = os.path.join(models_dir, "GFPGANv1.4.pth")
|
||||
|
||||
match platform.system():
|
||||
case "Darwin": # Mac OS
|
||||
if torch.backends.mps.is_available():
|
||||
mps_device = torch.device("mps")
|
||||
FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=mps_device) # type: ignore[attr-defined]
|
||||
else:
|
||||
FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1) # type: ignore[attr-defined]
|
||||
case _: # Other OS
|
||||
FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1) # type: ignore[attr-defined]
|
||||
|
||||
return FACE_ENHANCER
|
||||
|
||||
|
||||
def enhance_face(temp_frame: Frame) -> Frame:
|
||||
with THREAD_SEMAPHORE:
|
||||
_, _, temp_frame = get_face_enhancer().enhance(temp_frame, paste_back=True)
|
||||
return temp_frame
|
||||
|
||||
|
||||
def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
|
||||
target_face = get_one_face(temp_frame)
|
||||
if target_face:
|
||||
temp_frame = enhance_face(temp_frame)
|
||||
return temp_frame
|
||||
|
||||
|
||||
def process_frames(
|
||||
source_path: str, temp_frame_paths: List[str], progress: Any = None
|
||||
) -> None:
|
||||
for temp_frame_path in temp_frame_paths:
|
||||
temp_frame = cv2.imread(temp_frame_path)
|
||||
result = process_frame(None, temp_frame)
|
||||
cv2.imwrite(temp_frame_path, result)
|
||||
if progress:
|
||||
progress.update(1)
|
||||
|
||||
|
||||
def process_image(source_path: str, target_path: str, output_path: str) -> None:
|
||||
target_frame = cv2.imread(target_path)
|
||||
result = process_frame(None, target_frame)
|
||||
cv2.imwrite(output_path, result)
|
||||
|
||||
|
||||
def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
|
||||
modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
|
||||
|
||||
|
||||
def process_frame_v2(temp_frame: Frame) -> Frame:
|
||||
target_face = get_one_face(temp_frame)
|
||||
if target_face:
|
||||
temp_frame = enhance_face(temp_frame)
|
||||
return temp_frame
|
||||
|
|
@ -0,0 +1,622 @@
|
|||
from typing import Any, List
|
||||
import cv2
|
||||
import insightface
|
||||
import threading
|
||||
import numpy as np
|
||||
import modules.globals
|
||||
import logging
|
||||
import modules.processors.frame.core
|
||||
from modules.core import update_status
|
||||
from modules.face_analyser import get_one_face, get_many_faces, default_source_face
|
||||
from modules.typing import Face, Frame
|
||||
from modules.utilities import (
|
||||
conditional_download,
|
||||
is_image,
|
||||
is_video,
|
||||
)
|
||||
from modules.cluster_analysis import find_closest_centroid
|
||||
import os
|
||||
|
||||
FACE_SWAPPER = None
|
||||
THREAD_LOCK = threading.Lock()
|
||||
NAME = "DLC.FACE-SWAPPER"
|
||||
|
||||
abs_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
models_dir = os.path.join(
|
||||
os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
|
||||
)
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
download_directory_path = abs_dir
|
||||
conditional_download(
|
||||
download_directory_path,
|
||||
[
|
||||
"https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx"
|
||||
],
|
||||
)
|
||||
return True
|
||||
|
||||
|
||||
def pre_start() -> bool:
|
||||
if not modules.globals.map_faces and not is_image(modules.globals.source_path):
|
||||
update_status("Select an image for source path.", NAME)
|
||||
return False
|
||||
elif not modules.globals.map_faces and not get_one_face(
|
||||
cv2.imread(modules.globals.source_path)
|
||||
):
|
||||
update_status("No face in source path detected.", NAME)
|
||||
return False
|
||||
if not is_image(modules.globals.target_path) and not is_video(
|
||||
modules.globals.target_path
|
||||
):
|
||||
update_status("Select an image or video for target path.", NAME)
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def get_face_swapper() -> Any:
|
||||
global FACE_SWAPPER
|
||||
|
||||
with THREAD_LOCK:
|
||||
if FACE_SWAPPER is None:
|
||||
model_path = os.path.join(models_dir, "inswapper_128_fp16.onnx")
|
||||
FACE_SWAPPER = insightface.model_zoo.get_model(
|
||||
model_path, providers=modules.globals.execution_providers
|
||||
)
|
||||
return FACE_SWAPPER
|
||||
|
||||
|
||||
def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
|
||||
face_swapper = get_face_swapper()
|
||||
|
||||
# Apply the face swap
|
||||
swapped_frame = face_swapper.get(
|
||||
temp_frame, target_face, source_face, paste_back=True
|
||||
)
|
||||
|
||||
if modules.globals.mouth_mask:
|
||||
# Create a mask for the target face
|
||||
face_mask = create_face_mask(target_face, temp_frame)
|
||||
|
||||
# Create the mouth mask
|
||||
mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = (
|
||||
create_lower_mouth_mask(target_face, temp_frame)
|
||||
)
|
||||
|
||||
# Apply the mouth area
|
||||
swapped_frame = apply_mouth_area(
|
||||
swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
|
||||
)
|
||||
|
||||
if modules.globals.show_mouth_mask_box:
|
||||
mouth_mask_data = (mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon)
|
||||
swapped_frame = draw_mouth_mask_visualization(
|
||||
swapped_frame, target_face, mouth_mask_data
|
||||
)
|
||||
|
||||
return swapped_frame
|
||||
|
||||
|
||||
def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
|
||||
if modules.globals.color_correction:
|
||||
temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
if modules.globals.many_faces:
|
||||
many_faces = get_many_faces(temp_frame)
|
||||
if many_faces:
|
||||
for target_face in many_faces:
|
||||
if source_face and target_face:
|
||||
temp_frame = swap_face(source_face, target_face, temp_frame)
|
||||
else:
|
||||
print("Face detection failed for target/source.")
|
||||
else:
|
||||
target_face = get_one_face(temp_frame)
|
||||
if target_face and source_face:
|
||||
temp_frame = swap_face(source_face, target_face, temp_frame)
|
||||
else:
|
||||
logging.error("Face detection failed for target or source.")
|
||||
return temp_frame
|
||||
|
||||
|
||||
|
||||
def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
|
||||
if is_image(modules.globals.target_path):
|
||||
if modules.globals.many_faces:
|
||||
source_face = default_source_face()
|
||||
for map in modules.globals.source_target_map:
|
||||
target_face = map["target"]["face"]
|
||||
temp_frame = swap_face(source_face, target_face, temp_frame)
|
||||
|
||||
elif not modules.globals.many_faces:
|
||||
for map in modules.globals.source_target_map:
|
||||
if "source" in map:
|
||||
source_face = map["source"]["face"]
|
||||
target_face = map["target"]["face"]
|
||||
temp_frame = swap_face(source_face, target_face, temp_frame)
|
||||
|
||||
elif is_video(modules.globals.target_path):
|
||||
if modules.globals.many_faces:
|
||||
source_face = default_source_face()
|
||||
for map in modules.globals.source_target_map:
|
||||
target_frame = [
|
||||
f
|
||||
for f in map["target_faces_in_frame"]
|
||||
if f["location"] == temp_frame_path
|
||||
]
|
||||
|
||||
for frame in target_frame:
|
||||
for target_face in frame["faces"]:
|
||||
temp_frame = swap_face(source_face, target_face, temp_frame)
|
||||
|
||||
elif not modules.globals.many_faces:
|
||||
for map in modules.globals.source_target_map:
|
||||
if "source" in map:
|
||||
target_frame = [
|
||||
f
|
||||
for f in map["target_faces_in_frame"]
|
||||
if f["location"] == temp_frame_path
|
||||
]
|
||||
source_face = map["source"]["face"]
|
||||
|
||||
for frame in target_frame:
|
||||
for target_face in frame["faces"]:
|
||||
temp_frame = swap_face(source_face, target_face, temp_frame)
|
||||
|
||||
else:
|
||||
detected_faces = get_many_faces(temp_frame)
|
||||
if modules.globals.many_faces:
|
||||
if detected_faces:
|
||||
source_face = default_source_face()
|
||||
for target_face in detected_faces:
|
||||
temp_frame = swap_face(source_face, target_face, temp_frame)
|
||||
|
||||
elif not modules.globals.many_faces:
|
||||
if detected_faces:
|
||||
if len(detected_faces) <= len(
|
||||
modules.globals.simple_map["target_embeddings"]
|
||||
):
|
||||
for detected_face in detected_faces:
|
||||
closest_centroid_index, _ = find_closest_centroid(
|
||||
modules.globals.simple_map["target_embeddings"],
|
||||
detected_face.normed_embedding,
|
||||
)
|
||||
|
||||
temp_frame = swap_face(
|
||||
modules.globals.simple_map["source_faces"][
|
||||
closest_centroid_index
|
||||
],
|
||||
detected_face,
|
||||
temp_frame,
|
||||
)
|
||||
else:
|
||||
detected_faces_centroids = []
|
||||
for face in detected_faces:
|
||||
detected_faces_centroids.append(face.normed_embedding)
|
||||
i = 0
|
||||
for target_embedding in modules.globals.simple_map[
|
||||
"target_embeddings"
|
||||
]:
|
||||
closest_centroid_index, _ = find_closest_centroid(
|
||||
detected_faces_centroids, target_embedding
|
||||
)
|
||||
|
||||
temp_frame = swap_face(
|
||||
modules.globals.simple_map["source_faces"][i],
|
||||
detected_faces[closest_centroid_index],
|
||||
temp_frame,
|
||||
)
|
||||
i += 1
|
||||
return temp_frame
|
||||
|
||||
|
||||
def process_frames(
|
||||
source_path: str, temp_frame_paths: List[str], progress: Any = None
|
||||
) -> None:
|
||||
if not modules.globals.map_faces:
|
||||
source_face = get_one_face(cv2.imread(source_path))
|
||||
for temp_frame_path in temp_frame_paths:
|
||||
temp_frame = cv2.imread(temp_frame_path)
|
||||
try:
|
||||
result = process_frame(source_face, temp_frame)
|
||||
cv2.imwrite(temp_frame_path, result)
|
||||
except Exception as exception:
|
||||
print(exception)
|
||||
pass
|
||||
if progress:
|
||||
progress.update(1)
|
||||
else:
|
||||
for temp_frame_path in temp_frame_paths:
|
||||
temp_frame = cv2.imread(temp_frame_path)
|
||||
try:
|
||||
result = process_frame_v2(temp_frame, temp_frame_path)
|
||||
cv2.imwrite(temp_frame_path, result)
|
||||
except Exception as exception:
|
||||
print(exception)
|
||||
pass
|
||||
if progress:
|
||||
progress.update(1)
|
||||
|
||||
|
||||
def process_image(source_path: str, target_path: str, output_path: str) -> None:
|
||||
if not modules.globals.map_faces:
|
||||
source_face = get_one_face(cv2.imread(source_path))
|
||||
target_frame = cv2.imread(target_path)
|
||||
result = process_frame(source_face, target_frame)
|
||||
cv2.imwrite(output_path, result)
|
||||
else:
|
||||
if modules.globals.many_faces:
|
||||
update_status(
|
||||
"Many faces enabled. Using first source image. Progressing...", NAME
|
||||
)
|
||||
target_frame = cv2.imread(output_path)
|
||||
result = process_frame_v2(target_frame)
|
||||
cv2.imwrite(output_path, result)
|
||||
|
||||
|
||||
def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
|
||||
if modules.globals.map_faces and modules.globals.many_faces:
|
||||
update_status(
|
||||
"Many faces enabled. Using first source image. Progressing...", NAME
|
||||
)
|
||||
modules.processors.frame.core.process_video(
|
||||
source_path, temp_frame_paths, process_frames
|
||||
)
|
||||
|
||||
|
||||
def create_lower_mouth_mask(
|
||||
face: Face, frame: Frame
|
||||
) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
|
||||
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
|
||||
mouth_cutout = None
|
||||
landmarks = face.landmark_2d_106
|
||||
if landmarks is not None:
|
||||
# 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
|
||||
lower_lip_order = [
|
||||
65,
|
||||
66,
|
||||
62,
|
||||
70,
|
||||
69,
|
||||
18,
|
||||
19,
|
||||
20,
|
||||
21,
|
||||
22,
|
||||
23,
|
||||
24,
|
||||
0,
|
||||
8,
|
||||
7,
|
||||
6,
|
||||
5,
|
||||
4,
|
||||
3,
|
||||
2,
|
||||
65,
|
||||
]
|
||||
lower_lip_landmarks = landmarks[lower_lip_order].astype(
|
||||
np.float32
|
||||
) # Use float for precise calculations
|
||||
|
||||
# Calculate the center of the landmarks
|
||||
center = np.mean(lower_lip_landmarks, axis=0)
|
||||
|
||||
# Expand the landmarks outward
|
||||
expansion_factor = (
|
||||
1 + modules.globals.mask_down_size
|
||||
) # Adjust this for more or less expansion
|
||||
expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
|
||||
|
||||
# Extend the top lip part
|
||||
toplip_indices = [
|
||||
20,
|
||||
0,
|
||||
1,
|
||||
2,
|
||||
3,
|
||||
4,
|
||||
5,
|
||||
] # Indices for landmarks 2, 65, 66, 62, 70, 69, 18
|
||||
toplip_extension = (
|
||||
modules.globals.mask_size * 0.5
|
||||
) # Adjust this factor to control the extension
|
||||
for idx in toplip_indices:
|
||||
direction = expanded_landmarks[idx] - center
|
||||
direction = direction / np.linalg.norm(direction)
|
||||
expanded_landmarks[idx] += direction * toplip_extension
|
||||
|
||||
# Extend the bottom part (chin area)
|
||||
chin_indices = [
|
||||
11,
|
||||
12,
|
||||
13,
|
||||
14,
|
||||
15,
|
||||
16,
|
||||
] # Indices for landmarks 21, 22, 23, 24, 0, 8
|
||||
chin_extension = 2 * 0.2 # Adjust this factor to control the extension
|
||||
for idx in chin_indices:
|
||||
expanded_landmarks[idx][1] += (
|
||||
expanded_landmarks[idx][1] - center[1]
|
||||
) * chin_extension
|
||||
|
||||
# Convert back to integer coordinates
|
||||
expanded_landmarks = expanded_landmarks.astype(np.int32)
|
||||
|
||||
# Calculate bounding box for the expanded lower mouth
|
||||
min_x, min_y = np.min(expanded_landmarks, axis=0)
|
||||
max_x, max_y = np.max(expanded_landmarks, axis=0)
|
||||
|
||||
# Add some padding to the bounding box
|
||||
padding = int((max_x - min_x) * 0.1) # 10% padding
|
||||
min_x = max(0, min_x - padding)
|
||||
min_y = max(0, min_y - padding)
|
||||
max_x = min(frame.shape[1], max_x + padding)
|
||||
max_y = min(frame.shape[0], max_y + padding)
|
||||
|
||||
# Ensure the bounding box dimensions are valid
|
||||
if max_x <= min_x or max_y <= min_y:
|
||||
if (max_x - min_x) <= 1:
|
||||
max_x = min_x + 1
|
||||
if (max_y - min_y) <= 1:
|
||||
max_y = min_y + 1
|
||||
|
||||
# Create the mask
|
||||
mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
|
||||
cv2.fillPoly(mask_roi, [expanded_landmarks - [min_x, min_y]], 255)
|
||||
|
||||
# Apply Gaussian blur to soften the mask edges
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
|
||||
|
||||
# Place the mask ROI in the full-sized mask
|
||||
mask[min_y:max_y, min_x:max_x] = mask_roi
|
||||
|
||||
# Extract the masked area from the frame
|
||||
mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
|
||||
|
||||
# Return the expanded lower lip polygon in original frame coordinates
|
||||
lower_lip_polygon = expanded_landmarks
|
||||
|
||||
return mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon
|
||||
|
||||
|
||||
def draw_mouth_mask_visualization(
|
||||
frame: Frame, face: Face, mouth_mask_data: tuple
|
||||
) -> Frame:
|
||||
landmarks = face.landmark_2d_106
|
||||
if landmarks is not None and mouth_mask_data is not None:
|
||||
mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon = (
|
||||
mouth_mask_data
|
||||
)
|
||||
|
||||
vis_frame = frame.copy()
|
||||
|
||||
# Ensure coordinates are within frame bounds
|
||||
height, width = vis_frame.shape[:2]
|
||||
min_x, min_y = max(0, min_x), max(0, min_y)
|
||||
max_x, max_y = min(width, max_x), min(height, max_y)
|
||||
|
||||
# Adjust mask to match the region size
|
||||
mask_region = mask[0 : max_y - min_y, 0 : max_x - min_x]
|
||||
|
||||
# Remove the color mask overlay
|
||||
# color_mask = cv2.applyColorMap((mask_region * 255).astype(np.uint8), cv2.COLORMAP_JET)
|
||||
|
||||
# Ensure shapes match before blending
|
||||
vis_region = vis_frame[min_y:max_y, min_x:max_x]
|
||||
# Remove blending with color_mask
|
||||
# if vis_region.shape[:2] == color_mask.shape[:2]:
|
||||
# blended = cv2.addWeighted(vis_region, 0.7, color_mask, 0.3, 0)
|
||||
# vis_frame[min_y:max_y, min_x:max_x] = blended
|
||||
|
||||
# Draw the lower lip polygon
|
||||
cv2.polylines(vis_frame, [lower_lip_polygon], True, (0, 255, 0), 2)
|
||||
|
||||
# Remove the red box
|
||||
# cv2.rectangle(vis_frame, (min_x, min_y), (max_x, max_y), (0, 0, 255), 2)
|
||||
|
||||
# Visualize the feathered mask
|
||||
feather_amount = max(
|
||||
1,
|
||||
min(
|
||||
30,
|
||||
(max_x - min_x) // modules.globals.mask_feather_ratio,
|
||||
(max_y - min_y) // modules.globals.mask_feather_ratio,
|
||||
),
|
||||
)
|
||||
# Ensure kernel size is odd
|
||||
kernel_size = 2 * feather_amount + 1
|
||||
feathered_mask = cv2.GaussianBlur(
|
||||
mask_region.astype(float), (kernel_size, kernel_size), 0
|
||||
)
|
||||
feathered_mask = (feathered_mask / feathered_mask.max() * 255).astype(np.uint8)
|
||||
# Remove the feathered mask color overlay
|
||||
# color_feathered_mask = cv2.applyColorMap(feathered_mask, cv2.COLORMAP_VIRIDIS)
|
||||
|
||||
# Ensure shapes match before blending feathered mask
|
||||
# if vis_region.shape == color_feathered_mask.shape:
|
||||
# blended_feathered = cv2.addWeighted(vis_region, 0.7, color_feathered_mask, 0.3, 0)
|
||||
# vis_frame[min_y:max_y, min_x:max_x] = blended_feathered
|
||||
|
||||
# Add labels
|
||||
cv2.putText(
|
||||
vis_frame,
|
||||
"Lower Mouth Mask",
|
||||
(min_x, min_y - 10),
|
||||
cv2.FONT_HERSHEY_SIMPLEX,
|
||||
0.5,
|
||||
(255, 255, 255),
|
||||
1,
|
||||
)
|
||||
cv2.putText(
|
||||
vis_frame,
|
||||
"Feathered Mask",
|
||||
(min_x, max_y + 20),
|
||||
cv2.FONT_HERSHEY_SIMPLEX,
|
||||
0.5,
|
||||
(255, 255, 255),
|
||||
1,
|
||||
)
|
||||
|
||||
return vis_frame
|
||||
return frame
|
||||
|
||||
|
||||
def apply_mouth_area(
|
||||
frame: np.ndarray,
|
||||
mouth_cutout: np.ndarray,
|
||||
mouth_box: tuple,
|
||||
face_mask: np.ndarray,
|
||||
mouth_polygon: np.ndarray,
|
||||
) -> np.ndarray:
|
||||
min_x, min_y, max_x, max_y = mouth_box
|
||||
box_width = max_x - min_x
|
||||
box_height = max_y - min_y
|
||||
|
||||
if (
|
||||
mouth_cutout is None
|
||||
or box_width is None
|
||||
or box_height is None
|
||||
or face_mask is None
|
||||
or mouth_polygon is None
|
||||
):
|
||||
return frame
|
||||
|
||||
try:
|
||||
resized_mouth_cutout = cv2.resize(mouth_cutout, (box_width, box_height))
|
||||
roi = frame[min_y:max_y, min_x:max_x]
|
||||
|
||||
if roi.shape != resized_mouth_cutout.shape:
|
||||
resized_mouth_cutout = cv2.resize(
|
||||
resized_mouth_cutout, (roi.shape[1], roi.shape[0])
|
||||
)
|
||||
|
||||
color_corrected_mouth = apply_color_transfer(resized_mouth_cutout, roi)
|
||||
|
||||
# Use the provided mouth polygon to create the mask
|
||||
polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
|
||||
adjusted_polygon = mouth_polygon - [min_x, min_y]
|
||||
cv2.fillPoly(polygon_mask, [adjusted_polygon], 255)
|
||||
|
||||
# Apply feathering to the polygon mask
|
||||
feather_amount = min(
|
||||
30,
|
||||
box_width // modules.globals.mask_feather_ratio,
|
||||
box_height // modules.globals.mask_feather_ratio,
|
||||
)
|
||||
feathered_mask = cv2.GaussianBlur(
|
||||
polygon_mask.astype(float), (0, 0), feather_amount
|
||||
)
|
||||
feathered_mask = feathered_mask / feathered_mask.max()
|
||||
|
||||
face_mask_roi = face_mask[min_y:max_y, min_x:max_x]
|
||||
combined_mask = feathered_mask * (face_mask_roi / 255.0)
|
||||
|
||||
combined_mask = combined_mask[:, :, np.newaxis]
|
||||
blended = (
|
||||
color_corrected_mouth * combined_mask + roi * (1 - combined_mask)
|
||||
).astype(np.uint8)
|
||||
|
||||
# Apply face mask to blended result
|
||||
face_mask_3channel = (
|
||||
np.repeat(face_mask_roi[:, :, np.newaxis], 3, axis=2) / 255.0
|
||||
)
|
||||
final_blend = blended * face_mask_3channel + roi * (1 - face_mask_3channel)
|
||||
|
||||
frame[min_y:max_y, min_x:max_x] = final_blend.astype(np.uint8)
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
return frame
|
||||
|
||||
|
||||
def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
|
||||
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
|
||||
landmarks = face.landmark_2d_106
|
||||
if landmarks is not None:
|
||||
# Convert landmarks to int32
|
||||
landmarks = landmarks.astype(np.int32)
|
||||
|
||||
# Extract facial features
|
||||
right_side_face = landmarks[0:16]
|
||||
left_side_face = landmarks[17:32]
|
||||
right_eye = landmarks[33:42]
|
||||
right_eye_brow = landmarks[43:51]
|
||||
left_eye = landmarks[87:96]
|
||||
left_eye_brow = landmarks[97:105]
|
||||
|
||||
# Calculate forehead extension
|
||||
right_eyebrow_top = np.min(right_eye_brow[:, 1])
|
||||
left_eyebrow_top = np.min(left_eye_brow[:, 1])
|
||||
eyebrow_top = min(right_eyebrow_top, left_eyebrow_top)
|
||||
|
||||
face_top = np.min([right_side_face[0, 1], left_side_face[-1, 1]])
|
||||
forehead_height = face_top - eyebrow_top
|
||||
extended_forehead_height = int(forehead_height * 5.0) # Extend by 50%
|
||||
|
||||
# Create forehead points
|
||||
forehead_left = right_side_face[0].copy()
|
||||
forehead_right = left_side_face[-1].copy()
|
||||
forehead_left[1] -= extended_forehead_height
|
||||
forehead_right[1] -= extended_forehead_height
|
||||
|
||||
# Combine all points to create the face outline
|
||||
face_outline = np.vstack(
|
||||
[
|
||||
[forehead_left],
|
||||
right_side_face,
|
||||
left_side_face[
|
||||
::-1
|
||||
], # Reverse left side to create a continuous outline
|
||||
[forehead_right],
|
||||
]
|
||||
)
|
||||
|
||||
# Calculate padding
|
||||
padding = int(
|
||||
np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05
|
||||
) # 5% of face width
|
||||
|
||||
# Create a slightly larger convex hull for padding
|
||||
hull = cv2.convexHull(face_outline)
|
||||
hull_padded = []
|
||||
for point in hull:
|
||||
x, y = point[0]
|
||||
center = np.mean(face_outline, axis=0)
|
||||
direction = np.array([x, y]) - center
|
||||
direction = direction / np.linalg.norm(direction)
|
||||
padded_point = np.array([x, y]) + direction * padding
|
||||
hull_padded.append(padded_point)
|
||||
|
||||
hull_padded = np.array(hull_padded, dtype=np.int32)
|
||||
|
||||
# Fill the padded convex hull
|
||||
cv2.fillConvexPoly(mask, hull_padded, 255)
|
||||
|
||||
# Smooth the mask edges
|
||||
mask = cv2.GaussianBlur(mask, (5, 5), 3)
|
||||
|
||||
return mask
|
||||
|
||||
|
||||
def apply_color_transfer(source, target):
|
||||
"""
|
||||
Apply color transfer from target to source image
|
||||
"""
|
||||
source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32")
|
||||
target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32")
|
||||
|
||||
source_mean, source_std = cv2.meanStdDev(source)
|
||||
target_mean, target_std = cv2.meanStdDev(target)
|
||||
|
||||
# Reshape mean and std to be broadcastable
|
||||
source_mean = source_mean.reshape(1, 1, 3)
|
||||
source_std = source_std.reshape(1, 1, 3)
|
||||
target_mean = target_mean.reshape(1, 1, 3)
|
||||
target_std = target_std.reshape(1, 1, 3)
|
||||
|
||||
# Perform the color transfer
|
||||
source = (source - source_mean) * (target_std / source_std) + target_mean
|
||||
|
||||
return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
from typing import Any
|
||||
|
||||
from insightface.app.common import Face
|
||||
import numpy
|
||||
|
||||
Face = Face
|
||||
Frame = numpy.ndarray[Any, Any]
|
||||
|
|
@ -0,0 +1,158 @@
|
|||
{
|
||||
"CTk": {
|
||||
"fg_color": ["gray95", "gray10"]
|
||||
},
|
||||
"CTkToplevel": {
|
||||
"fg_color": ["gray95", "gray10"]
|
||||
},
|
||||
"CTkFrame": {
|
||||
"corner_radius": 0,
|
||||
"border_width": 0,
|
||||
"fg_color": ["gray90", "gray13"],
|
||||
"top_fg_color": ["gray85", "gray16"],
|
||||
"border_color": ["gray65", "gray28"]
|
||||
},
|
||||
"CTkButton": {
|
||||
"corner_radius": 0,
|
||||
"border_width": 0,
|
||||
"fg_color": ["#2aa666", "#1f538d"],
|
||||
"hover_color": ["#3cb666", "#14375e"],
|
||||
"border_color": ["#3e4a40", "#949A9F"],
|
||||
"text_color": ["#f3faf6", "#f3faf6"],
|
||||
"text_color_disabled": ["gray74", "gray60"]
|
||||
},
|
||||
"CTkLabel": {
|
||||
"corner_radius": 0,
|
||||
"fg_color": "transparent",
|
||||
"text_color": ["gray14", "gray84"]
|
||||
},
|
||||
"CTkEntry": {
|
||||
"corner_radius": 0,
|
||||
"border_width": 2,
|
||||
"fg_color": ["#F9F9FA", "#343638"],
|
||||
"border_color": ["#979DA2", "#565B5E"],
|
||||
"text_color": ["gray14", "gray84"],
|
||||
"placeholder_text_color": ["gray52", "gray62"]
|
||||
},
|
||||
"CTkCheckbox": {
|
||||
"corner_radius": 0,
|
||||
"border_width": 3,
|
||||
"fg_color": ["#2aa666", "#1f538d"],
|
||||
"border_color": ["#3e4a40", "#949A9F"],
|
||||
"hover_color": ["#3cb666", "#14375e"],
|
||||
"checkmark_color": ["#f3faf6", "gray90"],
|
||||
"text_color": ["gray14", "gray84"],
|
||||
"text_color_disabled": ["gray60", "gray45"]
|
||||
},
|
||||
"CTkSwitch": {
|
||||
"corner_radius": 1000,
|
||||
"border_width": 3,
|
||||
"button_length": 0,
|
||||
"fg_color": ["#939BA2", "#4A4D50"],
|
||||
"progress_color": ["#2aa666", "#1f538d"],
|
||||
"button_color": ["gray36", "#D5D9DE"],
|
||||
"button_hover_color": ["gray20", "gray100"],
|
||||
"text_color": ["gray14", "gray84"],
|
||||
"text_color_disabled": ["gray60", "gray45"]
|
||||
},
|
||||
"CTkRadiobutton": {
|
||||
"corner_radius": 1000,
|
||||
"border_width_checked": 6,
|
||||
"border_width_unchecked": 3,
|
||||
"fg_color": ["#2aa666", "#1f538d"],
|
||||
"border_color": ["#3e4a40", "#949A9F"],
|
||||
"hover_color": ["#3cb666", "#14375e"],
|
||||
"text_color": ["gray14", "gray84"],
|
||||
"text_color_disabled": ["gray60", "gray45"]
|
||||
},
|
||||
"CTkProgressBar": {
|
||||
"corner_radius": 1000,
|
||||
"border_width": 0,
|
||||
"fg_color": ["#939BA2", "#4A4D50"],
|
||||
"progress_color": ["#2aa666", "#1f538d"],
|
||||
"border_color": ["gray", "gray"]
|
||||
},
|
||||
"CTkSlider": {
|
||||
"corner_radius": 1000,
|
||||
"button_corner_radius": 1000,
|
||||
"border_width": 6,
|
||||
"button_length": 0,
|
||||
"fg_color": ["#939BA2", "#4A4D50"],
|
||||
"progress_color": ["gray40", "#AAB0B5"],
|
||||
"button_color": ["#2aa666", "#1f538d"],
|
||||
"button_hover_color": ["#3cb666", "#14375e"]
|
||||
},
|
||||
"CTkOptionMenu": {
|
||||
"corner_radius": 0,
|
||||
"fg_color": ["#2aa666", "#1f538d"],
|
||||
"button_color": ["#3cb666", "#14375e"],
|
||||
"button_hover_color": ["#234567", "#1e2c40"],
|
||||
"text_color": ["#f3faf6", "#f3faf6"],
|
||||
"text_color_disabled": ["gray74", "gray60"]
|
||||
},
|
||||
"CTkComboBox": {
|
||||
"corner_radius": 0,
|
||||
"border_width": 2,
|
||||
"fg_color": ["#F9F9FA", "#343638"],
|
||||
"border_color": ["#979DA2", "#565B5E"],
|
||||
"button_color": ["#979DA2", "#565B5E"],
|
||||
"button_hover_color": ["#6E7174", "#7A848D"],
|
||||
"text_color": ["gray14", "gray84"],
|
||||
"text_color_disabled": ["gray50", "gray45"]
|
||||
},
|
||||
"CTkScrollbar": {
|
||||
"corner_radius": 1000,
|
||||
"border_spacing": 4,
|
||||
"fg_color": "transparent",
|
||||
"button_color": ["gray55", "gray41"],
|
||||
"button_hover_color": ["gray40", "gray53"]
|
||||
},
|
||||
"CTkSegmentedButton": {
|
||||
"corner_radius": 0,
|
||||
"border_width": 2,
|
||||
"fg_color": ["#979DA2", "gray29"],
|
||||
"selected_color": ["#2aa666", "#1f538d"],
|
||||
"selected_hover_color": ["#3cb666", "#14375e"],
|
||||
"unselected_color": ["#979DA2", "gray29"],
|
||||
"unselected_hover_color": ["gray70", "gray41"],
|
||||
"text_color": ["#f3faf6", "#f3faf6"],
|
||||
"text_color_disabled": ["gray74", "gray60"]
|
||||
},
|
||||
"CTkTextbox": {
|
||||
"corner_radius": 0,
|
||||
"border_width": 0,
|
||||
"fg_color": ["gray100", "gray20"],
|
||||
"border_color": ["#979DA2", "#565B5E"],
|
||||
"text_color": ["gray14", "gray84"],
|
||||
"scrollbar_button_color": ["gray55", "gray41"],
|
||||
"scrollbar_button_hover_color": ["gray40", "gray53"]
|
||||
},
|
||||
"CTkScrollableFrame": {
|
||||
"label_fg_color": ["gray80", "gray21"]
|
||||
},
|
||||
"DropdownMenu": {
|
||||
"fg_color": ["gray90", "gray20"],
|
||||
"hover_color": ["gray75", "gray28"],
|
||||
"text_color": ["gray14", "gray84"]
|
||||
},
|
||||
"CTkFont": {
|
||||
"macOS": {
|
||||
"family": "Avenir",
|
||||
"size": 18,
|
||||
"weight": "normal"
|
||||
},
|
||||
"Windows": {
|
||||
"family": "Corbel",
|
||||
"size": 18,
|
||||
"weight": "normal"
|
||||
},
|
||||
"Linux": {
|
||||
"family": "Montserrat",
|
||||
"size": 18,
|
||||
"weight": "normal"
|
||||
}
|
||||
},
|
||||
"URL": {
|
||||
"text_color": ["gray74", "gray60"]
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,209 @@
|
|||
import glob
|
||||
import mimetypes
|
||||
import os
|
||||
import platform
|
||||
import shutil
|
||||
import ssl
|
||||
import subprocess
|
||||
import urllib
|
||||
from pathlib import Path
|
||||
from typing import List, Any
|
||||
from tqdm import tqdm
|
||||
|
||||
import modules.globals
|
||||
|
||||
TEMP_FILE = "temp.mp4"
|
||||
TEMP_DIRECTORY = "temp"
|
||||
|
||||
# monkey patch ssl for mac
|
||||
if platform.system().lower() == "darwin":
|
||||
ssl._create_default_https_context = ssl._create_unverified_context
|
||||
|
||||
|
||||
def run_ffmpeg(args: List[str]) -> bool:
|
||||
commands = [
|
||||
"ffmpeg",
|
||||
"-hide_banner",
|
||||
"-hwaccel",
|
||||
"auto",
|
||||
"-loglevel",
|
||||
modules.globals.log_level,
|
||||
]
|
||||
commands.extend(args)
|
||||
try:
|
||||
subprocess.check_output(commands, stderr=subprocess.STDOUT)
|
||||
return True
|
||||
except Exception:
|
||||
pass
|
||||
return False
|
||||
|
||||
|
||||
def detect_fps(target_path: str) -> float:
|
||||
command = [
|
||||
"ffprobe",
|
||||
"-v",
|
||||
"error",
|
||||
"-select_streams",
|
||||
"v:0",
|
||||
"-show_entries",
|
||||
"stream=r_frame_rate",
|
||||
"-of",
|
||||
"default=noprint_wrappers=1:nokey=1",
|
||||
target_path,
|
||||
]
|
||||
output = subprocess.check_output(command).decode().strip().split("/")
|
||||
try:
|
||||
numerator, denominator = map(int, output)
|
||||
return numerator / denominator
|
||||
except Exception:
|
||||
pass
|
||||
return 30.0
|
||||
|
||||
|
||||
def extract_frames(target_path: str) -> None:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
run_ffmpeg(
|
||||
[
|
||||
"-i",
|
||||
target_path,
|
||||
"-pix_fmt",
|
||||
"rgb24",
|
||||
os.path.join(temp_directory_path, "%04d.png"),
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
def create_video(target_path: str, fps: float = 30.0) -> None:
|
||||
temp_output_path = get_temp_output_path(target_path)
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
run_ffmpeg(
|
||||
[
|
||||
"-r",
|
||||
str(fps),
|
||||
"-i",
|
||||
os.path.join(temp_directory_path, "%04d.png"),
|
||||
"-c:v",
|
||||
modules.globals.video_encoder,
|
||||
"-crf",
|
||||
str(modules.globals.video_quality),
|
||||
"-pix_fmt",
|
||||
"yuv420p",
|
||||
"-vf",
|
||||
"colorspace=bt709:iall=bt601-6-625:fast=1",
|
||||
"-y",
|
||||
temp_output_path,
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
def restore_audio(target_path: str, output_path: str) -> None:
|
||||
temp_output_path = get_temp_output_path(target_path)
|
||||
done = run_ffmpeg(
|
||||
[
|
||||
"-i",
|
||||
temp_output_path,
|
||||
"-i",
|
||||
target_path,
|
||||
"-c:v",
|
||||
"copy",
|
||||
"-map",
|
||||
"0:v:0",
|
||||
"-map",
|
||||
"1:a:0",
|
||||
"-y",
|
||||
output_path,
|
||||
]
|
||||
)
|
||||
if not done:
|
||||
move_temp(target_path, output_path)
|
||||
|
||||
|
||||
def get_temp_frame_paths(target_path: str) -> List[str]:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
return glob.glob((os.path.join(glob.escape(temp_directory_path), "*.png")))
|
||||
|
||||
|
||||
def get_temp_directory_path(target_path: str) -> str:
|
||||
target_name, _ = os.path.splitext(os.path.basename(target_path))
|
||||
target_directory_path = os.path.dirname(target_path)
|
||||
return os.path.join(target_directory_path, TEMP_DIRECTORY, target_name)
|
||||
|
||||
|
||||
def get_temp_output_path(target_path: str) -> str:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
return os.path.join(temp_directory_path, TEMP_FILE)
|
||||
|
||||
|
||||
def normalize_output_path(source_path: str, target_path: str, output_path: str) -> Any:
|
||||
if source_path and target_path:
|
||||
source_name, _ = os.path.splitext(os.path.basename(source_path))
|
||||
target_name, target_extension = os.path.splitext(os.path.basename(target_path))
|
||||
if os.path.isdir(output_path):
|
||||
return os.path.join(
|
||||
output_path, source_name + "-" + target_name + target_extension
|
||||
)
|
||||
return output_path
|
||||
|
||||
|
||||
def create_temp(target_path: str) -> None:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
Path(temp_directory_path).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
def move_temp(target_path: str, output_path: str) -> None:
|
||||
temp_output_path = get_temp_output_path(target_path)
|
||||
if os.path.isfile(temp_output_path):
|
||||
if os.path.isfile(output_path):
|
||||
os.remove(output_path)
|
||||
shutil.move(temp_output_path, output_path)
|
||||
|
||||
|
||||
def clean_temp(target_path: str) -> None:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
parent_directory_path = os.path.dirname(temp_directory_path)
|
||||
if not modules.globals.keep_frames and os.path.isdir(temp_directory_path):
|
||||
shutil.rmtree(temp_directory_path)
|
||||
if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
|
||||
os.rmdir(parent_directory_path)
|
||||
|
||||
|
||||
def has_image_extension(image_path: str) -> bool:
|
||||
return image_path.lower().endswith(("png", "jpg", "jpeg"))
|
||||
|
||||
|
||||
def is_image(image_path: str) -> bool:
|
||||
if image_path and os.path.isfile(image_path):
|
||||
mimetype, _ = mimetypes.guess_type(image_path)
|
||||
return bool(mimetype and mimetype.startswith("image/"))
|
||||
return False
|
||||
|
||||
|
||||
def is_video(video_path: str) -> bool:
|
||||
if video_path and os.path.isfile(video_path):
|
||||
mimetype, _ = mimetypes.guess_type(video_path)
|
||||
return bool(mimetype and mimetype.startswith("video/"))
|
||||
return False
|
||||
|
||||
|
||||
def conditional_download(download_directory_path: str, urls: List[str]) -> None:
|
||||
if not os.path.exists(download_directory_path):
|
||||
os.makedirs(download_directory_path)
|
||||
for url in urls:
|
||||
download_file_path = os.path.join(
|
||||
download_directory_path, os.path.basename(url)
|
||||
)
|
||||
if not os.path.exists(download_file_path):
|
||||
request = urllib.request.urlopen(url) # type: ignore[attr-defined]
|
||||
total = int(request.headers.get("Content-Length", 0))
|
||||
with tqdm(
|
||||
total=total,
|
||||
desc="Downloading",
|
||||
unit="B",
|
||||
unit_scale=True,
|
||||
unit_divisor=1024,
|
||||
) as progress:
|
||||
urllib.request.urlretrieve(url, download_file_path, reporthook=lambda count, block_size, total_size: progress.update(block_size)) # type: ignore[attr-defined]
|
||||
|
||||
|
||||
def resolve_relative_path(path: str) -> str:
|
||||
return os.path.abspath(os.path.join(os.path.dirname(__file__), path))
|
||||
|
|
@ -0,0 +1,94 @@
|
|||
import cv2
|
||||
import numpy as np
|
||||
from typing import Optional, Tuple, Callable
|
||||
import platform
|
||||
import threading
|
||||
|
||||
# Only import Windows-specific library if on Windows
|
||||
if platform.system() == "Windows":
|
||||
from pygrabber.dshow_graph import FilterGraph
|
||||
|
||||
|
||||
class VideoCapturer:
|
||||
def __init__(self, device_index: int):
|
||||
self.device_index = device_index
|
||||
self.frame_callback = None
|
||||
self._current_frame = None
|
||||
self._frame_ready = threading.Event()
|
||||
self.is_running = False
|
||||
self.cap = None
|
||||
|
||||
# Initialize Windows-specific components if on Windows
|
||||
if platform.system() == "Windows":
|
||||
self.graph = FilterGraph()
|
||||
# Verify device exists
|
||||
devices = self.graph.get_input_devices()
|
||||
if self.device_index >= len(devices):
|
||||
raise ValueError(
|
||||
f"Invalid device index {device_index}. Available devices: {len(devices)}"
|
||||
)
|
||||
|
||||
def start(self, width: int = 960, height: int = 540, fps: int = 60) -> bool:
|
||||
"""Initialize and start video capture"""
|
||||
try:
|
||||
if platform.system() == "Windows":
|
||||
# Windows-specific capture methods
|
||||
capture_methods = [
|
||||
(self.device_index, cv2.CAP_DSHOW), # Try DirectShow first
|
||||
(self.device_index, cv2.CAP_ANY), # Then try default backend
|
||||
(-1, cv2.CAP_ANY), # Try -1 as fallback
|
||||
(0, cv2.CAP_ANY), # Finally try 0 without specific backend
|
||||
]
|
||||
|
||||
for dev_id, backend in capture_methods:
|
||||
try:
|
||||
self.cap = cv2.VideoCapture(dev_id, backend)
|
||||
if self.cap.isOpened():
|
||||
break
|
||||
self.cap.release()
|
||||
except Exception:
|
||||
continue
|
||||
else:
|
||||
# Unix-like systems (Linux/Mac) capture method
|
||||
self.cap = cv2.VideoCapture(self.device_index)
|
||||
|
||||
if not self.cap or not self.cap.isOpened():
|
||||
raise RuntimeError("Failed to open camera")
|
||||
|
||||
# Configure format
|
||||
self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
|
||||
self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
|
||||
self.cap.set(cv2.CAP_PROP_FPS, fps)
|
||||
|
||||
self.is_running = True
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"Failed to start capture: {str(e)}")
|
||||
if self.cap:
|
||||
self.cap.release()
|
||||
return False
|
||||
|
||||
def read(self) -> Tuple[bool, Optional[np.ndarray]]:
|
||||
"""Read a frame from the camera"""
|
||||
if not self.is_running or self.cap is None:
|
||||
return False, None
|
||||
|
||||
ret, frame = self.cap.read()
|
||||
if ret:
|
||||
self._current_frame = frame
|
||||
if self.frame_callback:
|
||||
self.frame_callback(frame)
|
||||
return True, frame
|
||||
return False, None
|
||||
|
||||
def release(self) -> None:
|
||||
"""Stop capture and release resources"""
|
||||
if self.is_running and self.cap is not None:
|
||||
self.cap.release()
|
||||
self.is_running = False
|
||||
self.cap = None
|
||||
|
||||
def set_frame_callback(self, callback: Callable[[np.ndarray], None]) -> None:
|
||||
"""Set callback for frame processing"""
|
||||
self.frame_callback = callback
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
[mypy]
|
||||
check_untyped_defs = True
|
||||
disallow_any_generics = True
|
||||
disallow_untyped_calls = True
|
||||
disallow_untyped_defs = True
|
||||
ignore_missing_imports = True
|
||||
strict_optional = False
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
--extra-index-url https://download.pytorch.org/whl/cu118
|
||||
|
||||
numpy>=1.23.5,<2
|
||||
typing-extensions>=4.8.0
|
||||
opencv-python==4.10.0.84
|
||||
cv2_enumerate_cameras==1.1.15
|
||||
onnx==1.16.0
|
||||
insightface==0.7.3
|
||||
psutil==5.9.8
|
||||
tk==0.1.0
|
||||
customtkinter==5.2.2
|
||||
pillow==11.1.0
|
||||
torch==2.5.1+cu118; sys_platform != 'darwin'
|
||||
torch==2.5.1; sys_platform == 'darwin'
|
||||
torchvision==0.20.1; sys_platform != 'darwin'
|
||||
torchvision==0.20.1; sys_platform == 'darwin'
|
||||
onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
|
||||
onnxruntime-gpu==1.16.3; sys_platform != 'darwin'
|
||||
tensorflow; sys_platform != 'darwin'
|
||||
opennsfw2==0.10.2
|
||||
protobuf==4.23.2
|
||||
tqdm==4.66.4
|
||||
gfpgan==1.3.8
|
||||
tkinterdnd2==0.4.2
|
||||
pygrabber==0.2
|
||||
|
|
@ -0,0 +1 @@
|
|||
python run.py --execution-provider cuda
|
||||
|
|
@ -0,0 +1 @@
|
|||
python run.py --execution-provider dml
|
||||
Loading…
Reference in New Issue