Update app.py

app.py

@@ -13,6 +13,39 @@ def extract_zip(zip_file_path, extract_dir):
     with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
         zip_ref.extractall(extract_dir)
 
+# Function to recognize faces
+def recognize_faces(frame, names, embeddings, app):
+    # Perform face analysis on the frame
+    faces = app.get(frame)
+
+    # Process each detected face separately
+    for face in faces:
+        # Retrieve the embedding for the detected face
+        detected_embedding = face.normed_embedding
+
+        # Calculate similarity scores with known embeddings
+        scores = np.dot(detected_embedding, np.array(embeddings).T)
+        scores = np.clip(scores, 0., 1.)
+
+        # Find the index with the highest score
+        idx = np.argmax(scores)
+        max_score = scores[idx]
+
+        # Check if the maximum score is above a certain threshold (adjust as needed)
+        threshold = 0.7
+        if max_score >= threshold:
+            recognized_name = names[idx]
+        else:
+            recognized_name = "Unknown"
+
+        # Draw bounding box around the detected face
+        bbox = face.bbox.astype(int)
+        cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)
+        # Write recognized name within the bounding box
+        cv2.putText(frame, recognized_name, (bbox[0], bbox[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+    return frame
+
 # Function to get embeddings
 def get_embeddings(db_dir):
     app = FaceAnalysis(name='buffalo_l')
@@ -53,7 +86,7 @@ def delete_files(db_dir):
 def main():
     st.title("Face Recognition App")
     # Tabs
-    tabs = ["Embeddings", "Face Recognition in Image", "Face Recognition in Video", "Face Recognition through Webcam"]
+    tabs = ["Embeddings", "Face Recognition in Image", "Face Recognition in Video", "Webcam"]
     choice = st.sidebar.selectbox("Select Option", tabs)
 
     # Embeddings tab
@@ -84,6 +117,46 @@ def main():
             st.success("Files deleted successfully!")
 
     # Other tabs can be added similarly
+    if choice == "Webcam":
+        st.header("WEBCAM")
+        st.subheader("upload names and embeddings file")
+        uploaded_names = st.file_uploader("Upload names.npy", type="npy")
+        uploaded_embeddings = st.file_uploader("Upload embeddings.npy", type="npy")
+
+        if uploaded_names and uploaded_embeddings:
+            # Load names and embeddings
+            names = np.load(uploaded_names)
+            embeddings = np.load(uploaded_embeddings)
+
+            # Initialize FaceAnalysis app
+            app = FaceAnalysis(name='buffalo_l')
+            app.prepare(ctx_id=0, det_size=(640, 640))
+
+            # Start capturing video from webcam
+            cap = cv2.VideoCapture(0)
+
+            # Process each frame in real-time
+            while True:
+                # Capture frame-by-frame
+                ret, frame = cap.read()
+                if not ret:
+                    break
+
+                # Perform face recognition
+                frame = recognize_faces(frame, names, embeddings, app)
+
+                # Display the resulting frame
+                st.image(frame, channels="BGR", use_column_width=True)
+
+                # Break the loop if 'q' is pressed
+                if cv2.waitKey(1) & 0xFF == ord('q'):
+                    break
+
+            # Release the capture
+            cap.release()
+            cv2.destroyAllWindows()
+
+
 
 if __name__ == "__main__":
     main()