Create README.md

README.md

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+---
+license: mit
+library_name: sklearn
+---
+
+# 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)
+
+```
+
+