| --- |
| 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) |
| |
| ``` |
| |
| |
| |
