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---
license: mit
library_name: py-feat
pipeline_tag: image-feature-extraction
---
# svm_emo
## Model Description
svm_emo combines histogram of oriented gradient feature extraction with a linear support vector machine to predict emotional face expressions from single frame images.
## Model Details
- **Model Type**: Support Vector Machine (SVM)
- **Framework**: sklearn
## Model Sources
- **Repository**: [GitHub Repository](https://github.com/cosanlab/py-feat)
- **Paper**: [Py-feat: Python facial expression analysis toolbox](https://link.springer.com/article/10.1007/s42761-023-00191-4)
## Citation
If you use the svm_emo model in your research or application, please cite the following paper:
Cheong, J.H., Jolly, E., Xie, T. et al. Py-Feat: Python Facial Expression Analysis Toolbox. Affec Sci 4, 781–796 (2023). https://doi.org/10.1007/s42761-023-00191-4
```
@article{cheong2023py,
title={Py-feat: Python facial expression analysis toolbox},
author={Cheong, Jin Hyun and Jolly, Eshin and Xie, Tiankang and Byrne, Sophie and Kenney, Matthew and Chang, Luke J},
journal={Affective Science},
volume={4},
number={4},
pages={781--796},
year={2023},
publisher={Springer}
}
```
## Example Useage
```python
import numpy as np
from skops.io import dump, load, get_untrusted_types
from huggingface_hub import hf_hub_download
class EmoSVMClassifier:
def __init__(self, **kwargs) -> None:
self.weights_loaded = False
def load_weights(self, scaler_full=None, pca_model_full=None, classifiers=None):
self.scaler_full = scaler_full
self.pca_model_full = pca_model_full
self.classifiers = classifiers
self.weights_loaded = True
def pca_transform(self, frame, scaler, pca_model, landmarks):
if not self.weights_loaded:
raise ValueError('Need to load weights before running pca_transform')
else:
transformed_frame = pca_model.transform(scaler.transform(frame))
return np.concatenate((transformed_frame, landmarks), axis=1)
def detect_emo(self, frame, landmarks, **kwargs):
"""
Note that here frame is represented by hogs
"""
if not self.weights_loaded:
raise ValueError('Need to load weights before running detect_au')
else:
landmarks = np.concatenate(landmarks)
landmarks = landmarks.reshape(-1, landmarks.shape[1] * landmarks.shape[2])
pca_transformed_full = self.pca_transform(frame, self.scaler_full, self.pca_model_full, landmarks)
emo_columns = ["anger", "disgust", "fear", "happ", "sad", "sur", "neutral"]
pred_emo = []
for keys in emo_columns:
emo_pred = self.classifiers[keys].predict(pca_transformed_full)
pred_emo.append(emo_pred)
pred_emos = np.array(pred_emo).T
return pred_emos
# Load model and weights
emotion_model = EmoSVMClassifier()
model_path = hf_hub_download(repo_id="py-feat/svm_emo", filename="svm_emo_classifier.skops")
unknown_types = get_untrusted_types(file=model_path)
loaded_model = load(model_path, trusted=unknown_types)
emotion_model.load_weights(scaler_full=loaded_model.scaler_full,
pca_model_full=loaded_model.pca_model_full,
classifiers=loaded_model.classifiers)
# Test model
frame = "path/to/your/test_image.jpg" # Replace with your loaded image
landmarks = np.array([...]) # Replace with your landmarks data
pred = emotion_model.detect_emo(frame, landmarks)
print(pred)
```
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