15090929758

Committed 19 Oct 2024 05:10AM UTC coverage: 54.553%. First build

Build # 15090929758

Build Type

push

github

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web-flow

Commit Message

Merge pull request #228 from cosanlab/huggingface

WIP: Huggingface Integration

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702 of 1620 new or added lines in 46 files covered. (43.33%)

3409 of 6249 relevant lines covered (54.55%)

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22.22

/feat/emo_detectors/StatLearning/EmoSL_test.py

1	# Implements different statistical learning algorithms to classify Emotions
2	# Please see https://www.cl.cam.ac.uk/~mmam3/pub/FG2015.pdf for more details and reasons
3	# Currently support: SVM (as in the paper), RandomForest (new implementation).
4	import numpy as np	6✔
5
6
7	class EmoSVMClassifier:	6✔
8	def __init__(self, **kwargs) -> None:	6✔
NEW 9	self.weights_loaded = False	×
10
11	def load_weights(self, scaler_full=None, pca_model_full=None, classifiers=None):	6✔
NEW 12	self.scaler_full = scaler_full	×
NEW 13	self.pca_model_full = pca_model_full	×
NEW 14	self.classifiers = classifiers	×
NEW 15	self.weights_loaded = True	×
16
17	def pca_transform(self, frame, scaler, pca_model, landmarks):	6✔
NEW 18	if not self.weights_loaded:	×
NEW 19	raise ValueError("Need to load weights before running pca_transform")	×
20	else:
NEW 21	transformed_frame = pca_model.transform(scaler.transform(frame))	×
NEW 22	return np.concatenate((transformed_frame, landmarks), axis=1)	×
23
24	def detect_emo(self, frame, landmarks, **kwargs):	6✔
25	"""
26	Note that here frame is represented by hogs
27	"""
NEW 28	if not self.weights_loaded:	×
NEW 29	raise ValueError("Need to load weights before running detect_au")	×
30	else:
NEW 31	landmarks = np.concatenate(landmarks)	×
NEW 32	landmarks = landmarks.reshape(-1, landmarks.shape[1] * landmarks.shape[2])	×
33
NEW 34	pca_transformed_full = self.pca_transform(	×
35	frame, self.scaler_full, self.pca_model_full, landmarks
36	)
NEW 37	emo_columns = ["anger", "disgust", "fear", "happ", "sad", "sur", "neutral"]	×
38
NEW 39	pred_emo = []	×
NEW 40	for keys in emo_columns:	×
NEW 41	emo_pred = self.classifiers[keys].predict(pca_transformed_full)	×
NEW 42	pred_emo.append(emo_pred)	×
43
NEW 44	pred_emos = np.array(pred_emo).T	×
NEW 45	return pred_emos	×

cosanlab / py-feat / 15090929758

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