No project description provided
Project description
YOLO video detection in the TouchDesigner
Description
This project demonstrate how to use custom ML model in the TouchDesigner. The solution consists from two components:
- Script TOP in the TouchDesigner.
- External process to process data.
sequenceDiagram
participant T as TouchDesigner
participant S as Shared memory
participant P as Python external process
T->>S: Send frame to shared memory
S->>P: Read frame from shared memory
P->>S: Process frame and write to shared memory
S->>T: Read processed frame from shared memory
Requirements
- Python 3.9 or higher. It is more preferable to have same version as in the TouchDesigner.
How to run
Install prebuild package from PyPi
From source
Install dependencies
For GPU:
pip install -r. /requirements.gpu.txt
For CPU:
pip install -r. /requirements.cpu.txt
For development:
pip install -r ./requirements.dev.txt
Compile Cython extension
You need C compiler to compile extension.
Develop mode
pip install -e .
Binary distribution
To compile binary distribution:
python -m cibuildwheel --platform <your_platform>
Install binary distribution:
pip install ./dist/*.whl
Usecases
Video detection
Run:
python ./main.py -c <checkpoint_path> -i <video_path> -o <video_path>
With TouchDesigner
Run server processing:
python ./processing.py -p <path_to_model>
Open touch_designer.py
in the TouchDesigner as script TOP.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
File details
Details for the file touch_designer_yolo_detection-0.1.0-cp39-cp39-win_amd64.whl
.
File metadata
- Download URL: touch_designer_yolo_detection-0.1.0-cp39-cp39-win_amd64.whl
- Upload date:
- Size: 55.9 kB
- Tags: CPython 3.9, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 07f037e73fb3b8f0bbf6493ae4f5e673eb4eb8c4c71127f983f8c4696f9b6351 |
|
MD5 | 68c41d1ff404cd9c1cc803075109751e |
|
BLAKE2b-256 | b3d111d46d9a881078630ca09d97a31e42e527e4d670df58c6944f3b6099e8a5 |
File details
Details for the file touch_designer_yolo_detection-0.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
.
File metadata
- Download URL: touch_designer_yolo_detection-0.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
- Upload date:
- Size: 247.8 kB
- Tags: CPython 3.9, manylinux: glibc 2.17+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.17
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | b26a55fa9358f1fe9ac541a220ec8062ca4773c9764b4afbfa09e30eb716eba7 |
|
MD5 | d29fa55ac38d19269fcc22385a14e751 |
|
BLAKE2b-256 | 2b62640c68ec0a2fb4b2e423bf8d71ad7c283f74a83381135945a25d307eee5f |
File details
Details for the file touch_designer_yolo_detection-0.1.0-cp39-cp39-macosx_10_9_x86_64.whl
.
File metadata
- Download URL: touch_designer_yolo_detection-0.1.0-cp39-cp39-macosx_10_9_x86_64.whl
- Upload date:
- Size: 56.0 kB
- Tags: CPython 3.9, macOS 10.9+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.17
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8b64eb6a98fd75588d56135b74b586dc40525378809406adb8b400bdda567036 |
|
MD5 | 51e28bac843e100d5da46f5af6dffb66 |
|
BLAKE2b-256 | be6e28794e1f1f3ffee7732186756dea24de0b38457927fecd54d4ff48a8a0a4 |