Create app.py

app.py

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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(img1, img2, model_type, anchor_img):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_img1:
+        temp_img1.write(img1.read())
+        temp_img1_path = temp_img1.name
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_img2:
+        temp_img2.write(img2.read())
+        temp_img2_path = temp_img2.name
+
+    img1p = cv2.imread(temp_img1_path)
+    img2p = cv2.imread(temp_img2_path)
+
+    face1 = get_face(img1p)
+    face2 = get_face(img2p)
+
+    if face1 is not None and face2 is not None:
+        st.image([face1, face2], caption=["Image 1", "Image 2"], width=200)
+
+        if model_type == "HOG-SVM":
+            with open('./svm.pkl', 'rb') as f:
+                svm = joblib.load(f)
+            with open('./pca.pkl', 'rb') as f:
+                pca = joblib.load(f)
+
+            face1 = preprocess_image_svm(face1)
+            face2 = preprocess_image_svm(face2)
+
+            hog1 = extract_hog_features(face1)
+            hog2 = extract_hog_features(face2)
+
+            hog1_pca = pca.transform([hog1])
+            hog2_pca = pca.transform([hog2])
+
+            pred1 = svm.predict(hog1_pca)
+            pred2 = svm.predict(hog2_pca)
+
+            if pred1 == 1 and pred2 == 1:
+                st.write("Matched")
+            else:
+                st.write("Not Matched")
+    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_type = st.selectbox("Select Model", ["Siamese", "HOG-SVM"])
+    anchor_img = st.file_uploader("Select Anchor Image", type=["jpg", "png"])
+
+    if model_type == "Siamese":
+        # Implement Siamese model choice logic here if needed
+        st.write("Using Siamese Network")
+        
+    elif model_type == "HOG-SVM":
+        # Implement HOG-SVM logic here
+        st.write("Using HOG-SVM")
+
+    # Camera Input for Face Detection
+    run_detection = st.checkbox("Start Camera")
+    
+    if run_detection:
+        cap = cv2.VideoCapture(0)  # Start camera
+        
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                st.write("Failed to grab frame.")
+                break
+            
+            # Detect face in the current frame
+            face = get_face(frame)
+            if face is not None:
+                # Draw bounding box around detected face
+                x1, y1, x2, y2 = face[0], face[1], face[2], face[3]  # Update face coordinates
+                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                
+                # Show bounding box
+                st.image(frame, channels="BGR", use_column_width=True)
+            
+            # Stop camera when ESC is pressed
+            key = cv2.waitKey(1) & 0xFF
+            if key == 27:  # ESC key
+                break
+        
+        cap.release()
+        cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    main()