xgb_au
Model Description
xgb_au combines histogram of oriented gradient feature extraction with gradient boosting to predict facial action units from single frame images.
Model Details
- Model Type: Gradient Boosting (XGB)
- Framework: sklearn
Model Sources
- Repository: GitHub Repository
- Paper: Py-feat: Python facial expression analysis toolbox
Citation
If you use the svm_au 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
import numpy as np
from skops.io import dump, load, get_untrusted_types
from huggingface_hub import hf_hub_download
class XGBClassifier:
def __init__(self) -> None:
self.au_keys = [
"AU1", "AU2", "AU4", "AU5", "AU6", "AU7", "AU9", "AU10", "AU11", "AU12",
"AU14", "AU15", "AU17", "AU20", "AU23", "AU24", "AU25", "AU26", "AU28", "AU43"
]
self.weights_loaded = False
def load_weights(self, scaler_upper=None, pca_model_upper=None, scaler_lower=None, pca_model_lower=None, scaler_full=None, pca_model_full=None, classifiers=None):
self.scaler_upper = scaler_upper
self.pca_model_upper = pca_model_upper
self.scaler_lower = scaler_lower
self.pca_model_lower = pca_model_lower
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_au(self, frame, landmarks):
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_upper = self.pca_transform(frame, self.scaler_upper, self.pca_model_upper, landmarks)
pca_transformed_lower = self.pca_transform(frame, self.scaler_lower, self.pca_model_lower, landmarks)
pca_transformed_full = self.pca_transform(frame, self.scaler_full, self.pca_model_full, landmarks)
pred_aus = []
for key in self.au_keys:
classifier = self.classifiers[key]
if key in ["AU1", "AU2", "AU7"]:
au_pred = classifier.predict_proba(pca_transformed_upper)[:, 1]
elif key in ["AU11", "AU14", "AU17", "AU23", "AU24", "AU26"]:
au_pred = classifier.predict_proba(pca_transformed_lower)[:, 1]
else:
au_pred = classifier.predict_proba(pca_transformed_full)[:, 1]
pred_aus.append(au_pred)
return np.array(pred_aus).T def __init__(self) -> None:
self.weights_loaded = False
def load_weights(self, scaler_upper=None, pca_model_upper=None, scaler_lower=None, pca_model_lower=None, scaler_full=None, pca_model_full=None, classifiers=None):
self.scaler_upper = scaler_upper
self.pca_model_upper = pca_model_upper
self.scaler_lower = scaler_lower
self.pca_model_lower = pca_model_lower
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_au(self, frame, landmarks):
"""
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_upper = self.pca_transform(frame, self.scaler_upper, self.pca_model_upper, landmarks)
pca_transformed_lower = self.pca_transform(frame, self.scaler_lower, self.pca_model_lower, landmarks)
pca_transformed_full = self.pca_transform(frame, self.scaler_full, self.pca_model_full, landmarks)
aus_list = sorted(self.classifiers.keys(), key=lambda x: int(x[2::]))
pred_aus = []
for keys in aus_list:
if keys in ["AU1", "AU4", "AU6"]:
au_pred = self.classifiers[keys].predict(pca_transformed_upper)
elif keys in ["AU11", "AU12", "AU17"]:
au_pred = self.classifiers[keys].predict(pca_transformed_lower)
elif keys in [
"AU2",
"AU5",
"AU7",
"AU9",
"AU10",
"AU14",
"AU15",
"AU20",
"AU23",
"AU24",
"AU25",
"AU26",
"AU28",
"AU43",
]:
au_pred = self.classifiers[keys].predict(pca_transformed_full)
else:
raise ValueError("unknown AU detected")
pred_aus.append(au_pred)
pred_aus = np.array(pred_aus).T
return pred_aus
# Load model and weights
au_model = XGBClassifier()
model_path = hf_hub_download(repo_id="py-feat/xgb_au", filename="xgb_au_classifier.skops")
unknown_types = get_untrusted_types(file=model_path)
loaded_model = load(model_path, trusted=unknown_types)
au_model.load_weights(scaler_upper = loaded_model.scaler_upper,
pca_model_upper = loaded_model.pca_model_upper,
scaler_lower = loaded_model.scaler_lower,
pca_model_lower = 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 = au_model.detect_au(frame, landmarks)
print(pred)
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The HF Inference API does not support image-feature-extraction models for py-feat library.