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Sample images were added to the README

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Emiliana 2020-05-21 18:25:43 +02:00 • committed by GitHub
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@ -77,7 +77,7 @@ To save both the trained model and the captured training data, type in a filenam
# Models # Models
Four pretrained models are included. Using the `--model` switch, it is possible to select them for tracking. The given fps values are for running the model on a single face video on a single CPU core. Lowering the frame rate would reduce CPU usage by a corresponding degree. Four pretrained face landmark models are included. Using the `--model` switch, it is possible to select them for tracking. The given fps values are for running the model on a single face video on a single CPU core. Lowering the frame rate would reduce CPU usage by a corresponding degree.
* Model **0**: This is a very fast, low accuracy model. (68fps) * Model **0**: This is a very fast, low accuracy model. (68fps)
* Model **1**: This is a slightly slower model with better accuracy. (59fps) * Model **1**: This is a slightly slower model with better accuracy. (59fps)
@ -86,6 +86,24 @@ Four pretrained models are included. Using the `--model` switch, it is possible
FPS measurements are from running on one core of my CPU. FPS measurements are from running on one core of my CPU.
Pytorch weights for use with `model.py` can be found [here](https://mega.nz/file/vvYXlYQT#h7FpEg4tmOCJNxjpsDEw0JomJIkVGKwrt4OUV0RNDDU).
# Results
## Landmarks
![Results1.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/Results1.png)
![Results2.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/Results2.png)
More samples: [Results3.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/Results3.png), [Results4.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/Results4.png)
## Face detection
The landmark model is quite robust with respect to the size and orientation of the faces, so the custom face detection model gets away with rougher bounding boxes than other approaches. It has a favorable speed to accuracy ratio for the purposes of this project.
![EmiFace.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/EmiFace.png)
# Release builds # Release builds
The builds in the release section of this repository contain a `facetracker.exe` inside a `Binary` folder that was built using `pyinstaller` and contains all required dependencies. The builds in the release section of this repository contain a `facetracker.exe` inside a `Binary` folder that was built using `pyinstaller` and contains all required dependencies.