Create app.py

app.py

@@ -0,0 +1,119 @@
+import cv2
+import streamlit as st
+import tempfile
+import torch
+from torchvision import transforms
+from mtcnn import MTCNN
+from skimage import hog
+import pickle
+import torch.nn as nn
+from torchvision.models import resnet50
+
+def preprocess_image_siamese(img):
+    transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor()
+    ])
+
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    return transform(img)
+
+def preprocess_image_svm(img):
+    img= cv2.resize(img, (224, 224))
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    return img
+
+def extract_hog_features(img):
+    hog_features = hog(img, orientations=9, 
+                      pixels_per_cell=(8, 8),
+                      cells_per_block=(2, 2))
+    return hog_features
+
+def get_face(img):
+    detector = MTCNN()
+    faces = detector.detect_faces(img)
+
+    x1,y1,w,h = faces[0]['box']
+    x1, y1 = abs(x1), abs(y1)
+    x2 = abs(x1+w)
+    y2 = abs(y1+h)
+
+    store_face = img[y1:y2,x1:x2]
+
+    return store_face
+
+def verify(img1, img2, model_type):
+    
+    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 and face2:
+
+        st.image([face1, face2], caption=["Image 1", "Image 2"], width=200)
+
+        # if model_type == "Siamese":
+        #     face1 = preprocess_image_siamese(face1)
+        #     face2 = preprocess_image_siamese(face2)
+            
+
+        #     # Get predictions
+        #     with torch.no_grad():
+        #         prediction = model(face1_tensor, face2_tensor)
+                
+        #         # Threshold decision
+        #         if prediction.item() > 0.5:
+        #             st.write("Matched")
+        #         else:
+        #             st.write("Not Matched")
+        #         st.write(f"Confidence: {prediction.item():.4f}")
+
+        if model_type == "HOG-SVM":
+            with open('svm_model.pkl', 'rb') as f:
+                svm = pickle.load(f)
+            with open('pca_model.pkl', 'rb') as f:
+                pca = pickle.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")
+
+def main():
+    st.title("Face Verification App")
+    
+    model_type = st.selectbox("Select Model", ["Siamese", "HOG-SVM"])
+    
+    uploaded_img1 = st.file_uploader("Upload Image 1", type=["jpg", "png"])
+    uploaded_img2 = st.file_uploader("Upload Image 2", type=["jpg", "png"])
+    
+    if uploaded_img1 and uploaded_img2:
+        if st.button("Verify Faces"):
+            verify(uploaded_img1, uploaded_img2, model_type)
+
+if __name__ == "__main__":
+    main()