Update app.py

app.py

@@ -52,49 +52,46 @@ if file_name is not None:
 
     imgS = cv2.resize(image, (0, 0), None, 0.25, 0.25)
     imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
-    facesCurFrame = face_recognition.face_locations(imgS)
-    
+    facesCurFrame   = face_recognition.face_locations(imgS)
+    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
+
     # List to store recognized names for all faces in the image
     recognized_names = []
 
-    # Iterate over each face location
-    for faceLoc in facesCurFrame:
-        # Extract the coordinates of the face location
-        top, right, bottom, left = faceLoc
-        
-        # Crop the face region from the image
-        face_img = imgS[top:bottom, left:right]
-
-        # Encode the face
-        face_encoding = face_recognition.face_encodings(face_img)[0]
-
-        # Compare the face encoding with known face encodings
-        matches = face_recognition.compare_faces(encodeListknown, face_encoding)
-        
-        # Initialize name as Unknown
-        name = "Unknown"
-        
-        # Check if there's a match with known faces
-        if True in matches:
-            matchIndex = matches.index(True)
-            name = classnames[matchIndex].upper()
-        
-        # Append recognized name to the list
-        recognized_names.append(name)
+    # Checking if faces are detected
+    if len(encodesCurFrame) > 0:
+        for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
+            # Assuming that encodeListknown is defined and populated in your code
+            matches = face_recognition.compare_faces(encodeListknown, encodeFace)
+            faceDis = face_recognition.face_distance(encodeListknown, encodeFace)
+            
+            # Initialize name as Unknown
+            name = "Unknown"
         
-        # Draw rectangle around the face
-        cv2.rectangle(image, (left*4, top*4), (right*4, bottom*4), (0, 255, 0), 2)
-        cv2.putText(image, name, (left*4 + 6, bottom*4 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
+            # Check if there's a match with known faces
+            if True in matches:
+                matchIndex = np.argmin(faceDis)
+                name = classnames[matchIndex].upper()
+                
+            # Append recognized name to the list
+            recognized_names.append(name)
         
-        # Store attendance in SQLite database
-        add_attendance(name)
+            # Draw rectangle around the face
+            y1, x2, y2, x1 = faceLoc
+            y1, x2, y2, x1 = (y1 * 4), (x2 * 4), (y2 * 4) ,(x1 * 4)
+            image = image.copy()
+            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
+            
+            # Store attendance in SQLite database
+            add_attendance(name)
             
-    # Display the image with recognized faces
-    st.image(image, use_column_width=True, output_format="PNG")
+        # Display the image with recognized faces
+        st.image(image, use_column_width=True, output_format="PNG")
 
-    # Display recognized names
-    st.write("Recognized Names:")
-    for i, name in enumerate(recognized_names):
-        st.write(f"Face {i+1}: {name}")
+        # Display recognized names
+        st.write("Recognized Names:")
+        for i, name in enumerate(recognized_names):
+            st.write(f"Face {i+1}: {name}")
     else:
         st.warning("No faces detected in the image. Face recognition failed.")