Update utils.py

utils.py

@@ -37,64 +37,3 @@ def add_timecode_to_image_body(image, timecode):
     draw.text((10, 10), timecode, (255, 0, 0), font=font)
     return np.array(img_pil)
 
-def create_annotated_video(video_path, df, mse_embeddings, largest_cluster, output_path):
-    import cv2
-    import torch
-    from facenet_pytorch import MTCNN
-    import mediapipe as mp
-    import numpy as np
-    
-    video = cv2.VideoCapture(video_path)
-    fps = video.get(cv2.CAP_PROP_FPS)
-    width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
-    height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    mtcnn = MTCNN(keep_all=False, device=device, thresholds=[0.9, 0.9, 0.9], min_face_size=50)
-
-    mp_face_mesh = mp.solutions.face_mesh
-    mp_drawing = mp.solutions.drawing_utils
-    mp_drawing_styles = mp.solutions.drawing_styles
-    face_mesh = mp_face_mesh.FaceMesh(static_image_mode=False, max_num_faces=1, min_detection_confidence=0.5)
-
-    frame_number = 0
-    while True:
-        ret, frame = video.read()
-        if not ret:
-            break
-
-        # Detect face and draw bounding box
-        boxes, _ = mtcnn.detect(frame)
-        if boxes is not None and len(boxes) > 0:
-            box = boxes[0]
-            cv2.rectangle(frame, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 255, 0), 2)
-
-        # Draw facial landmarks
-        face_mesh_results = face_mesh.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-        if face_mesh_results.multi_face_landmarks:
-            for face_landmarks in face_mesh_results.multi_face_landmarks:
-                mp_drawing.draw_landmarks(
-                    image=frame,
-                    landmark_list=face_landmarks,
-                    connections=mp_face_mesh.FACEMESH_TESSELATION,
-                    landmark_drawing_spec=None,
-                    connection_drawing_spec=mp_drawing_styles.get_default_face_mesh_tesselation_style()
-                )
-
-        # Add MSE annotation
-        if frame_number in df['Frame'].values:
-            frame_index = np.where(df['Frame'].values == frame_number)[0][0]
-            if mse_embeddings.ndim == 1:
-                mse = mse_embeddings[frame_index]
-            else:
-                mse = mse_embeddings[frame_index, 0]  # Assuming MSE is in the first column if 2D
-            cv2.putText(frame, f"MSE: {mse:.4f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-
-        out.write(frame)
-        frame_number += 1
-
-    video.release()
-    out.release()
-    face_mesh.close()