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# Classical ML Emotion Detection with HOG, LBP, Gabor (NumPy 2.x Compatible - OpenCV Removed)
import numpy as np
from skimage.io import imread
from skimage.transform import resize
from skimage.feature import hog, local_binary_pattern
from skimage.filters import gabor
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
import os
from tqdm import tqdm
import joblib # Import joblib for saving models
# ----------------------
# Configuration
# ----------------------
IMG_SIZE = 48
DATASET_PATH = "./train" # Folder structure: ./dataset/<label>/<image>.jpg
EMOTIONS = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']
# ----------------------
# Feature Extraction Functions
# ----------------------
def extract_hog(img):
return hog(img, pixels_per_cell=(8, 8), cells_per_block=(2, 2))
def extract_lbp(img):
lbp = local_binary_pattern(img, P=8, R=1, method="uniform")
(hist, _) = np.histogram(lbp.ravel(), bins=np.arange(0, 59))
hist = hist.astype("float")
hist /= (hist.sum() + 1e-6)
return hist
def extract_gabor(img):
filt_real, _ = gabor(img, frequency=0.6)
return filt_real.ravel()[::64] # downsample to reduce dimensionality
# ----------------------
# Load Dataset and Extract Features
# ----------------------
def load_dataset():
data = []
labels = []
print("[INFO] Loading dataset and extracting features...")
for label in EMOTIONS:
folder = os.path.join(DATASET_PATH, label)
if not os.path.exists(folder):
continue
for file in tqdm(os.listdir(folder), desc=label):
path = os.path.join(folder, file)
try:
img = imread(path, as_gray=True)
img = resize(img, (IMG_SIZE, IMG_SIZE), anti_aliasing=True)
except Exception as e:
print(f"[WARNING] Skipped {file}: {e}")
continue
feat = []
feat.extend(extract_hog(img))
feat.extend(extract_lbp(img))
feat.extend(extract_gabor(img))
data.append(feat)
labels.append(EMOTIONS.index(label))
return np.array(data), np.array(labels)
# ----------------------
# Train & Evaluate Models
# ----------------------
def train_and_evaluate(X, y):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
classifiers = {
"SVM": SVC(kernel='linear'),
"Random Forest": RandomForestClassifier(n_estimators=100),
"k-NN": KNeighborsClassifier(n_neighbors=5),
"Logistic Regression": LogisticRegression(max_iter=1000)
}
for name, clf in classifiers.items():
print(f"\n[INFO] Training {name}...")
clf.fit(X_train, y_train)
preds = clf.predict(X_test)
print(f"\n[RESULTS] {name} Classification Report")
print(classification_report(y_test, preds, target_names=EMOTIONS))
# Save the trained model using joblib
model_filename = f'{name.lower().replace(" ", "_")}_model.joblib'
print(f"[INFO] Saving {name} model to {model_filename}...")
joblib.dump(clf, model_filename)
# ----------------------
# Run Full Pipeline
# ----------------------
if __name__ == "__main__":
X, y = load_dataset()
train_and_evaluate(X, y)
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