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