Update app.py

app.py

@@ -52,47 +52,49 @@ 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)
-    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
-
+    facesCurFrame = face_recognition.face_locations(imgS)
+    
     # List to store recognized names for all faces in the image
     recognized_names = []
 
-    # 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"
+    # Iterate over each face location
+    for faceLoc in facesCurFrame:
+        # Extract the coordinates of the face location
+        top, right, bottom, left = faceLoc
         
-            # 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)
+        # 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)
         
-            # Draw rectangle around the face
-        for faceLoc, name in zip(facesCurFrame, recognized_names):
-            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)
+        # 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)
+        
+        # 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)
+        
+        # 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.")