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| import cv2 | |
| import streamlit as st | |
| import tempfile | |
| import torch | |
| from torchvision import transforms | |
| from mtcnn import MTCNN | |
| from skimage.feature import hog | |
| import joblib | |
| import numpy as np | |
| # Preprocessing for Siamese Model | |
| def preprocess_image_siamese(img): | |
| transform = transforms.Compose([ | |
| transforms.Resize((224, 224)), | |
| transforms.ToTensor() | |
| ]) | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| return transform(img) | |
| # Preprocessing for SVM model (converting to grayscale) | |
| def preprocess_image_svm(img): | |
| img = cv2.resize(img, (224, 224)) | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| return img | |
| # Extract HOG Features | |
| def extract_hog_features(img): | |
| hog_features = hog(img, orientations=9, pixels_per_cell=(16, 16), cells_per_block=(4, 4)) | |
| return hog_features | |
| # Detect faces using MTCNN | |
| def get_face(img): | |
| detector = MTCNN() | |
| faces = detector.detect_faces(img) | |
| if faces: | |
| x1, y1, w, h = faces[0]['box'] | |
| x1, y1 = abs(x1), abs(y1) | |
| x2, y2 = x1 + w, y1 + h | |
| return img[y1:y2, x1:x2] | |
| return None | |
| # Function to verify face (either HOG-SVM or Siamese model) | |
| def verify(image, model, person): | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_image: | |
| temp_image.write(image.read()) | |
| temp_image_path = temp_image.name | |
| image = cv2.imread(temp_image_path) | |
| face = get_face(image) | |
| if face is not None: | |
| if model_type == "HOG-SVM": | |
| with open(f'./svm_{lower(person)}.pkl', 'rb') as f: | |
| svm = joblib.load(f) | |
| with open(f'./pca_{lower(person)}.pkl', 'rb') as f: | |
| pca = joblib.load(f) | |
| face = preprocess_image_svm(face) | |
| hog = extract_hog_features(face) | |
| hog_pca = pca.transform([hog]) | |
| pred = svm.predict(hog_pca) | |
| if pred == 1: | |
| st.write("Match") | |
| else: | |
| st.write("Not Match") | |
| else: | |
| st.write("Face not detected in one or both images") | |
| # Main function to handle Streamlit interaction | |
| def main(): | |
| st.title("Real-time Face Verification App") | |
| model = st.selectbox("Select Model", ["Siamese", "HOG-SVM"]) | |
| person = st.selectbox("Select Person", ["Theo"]) | |
| enable = st.checkbox("Enable camera") | |
| captured_image = st.camera_input("Take a picture", disabled=not enable) | |
| if captured_image: | |
| verify(captured_image, model, person) | |
| if __name__ == "__main__": | |
| main() | |