Vid_1158.mp4 (2026)

# More complex visual feature extraction def extract_visual_features(frames): model = models.resnet50(pretrained=True) model.fc = torch.nn.Identity() # Remove the final classification layer device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model.to(device) model.eval()

# Load the video def load_video(video_path): cap = cv2.VideoCapture(video_path) frames = [] while cap.isOpened(): ret, frame = cap.read() if not ret: break # Convert to RGB frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frames.append(frame) cap.release() return frames vid_1158.mp4

# Simple Feature Extraction def extract_simple_features(video_path): cap = cv2.VideoCapture(video_path) duration = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = cap.get(cv2.CAP_PROP_FPS) cap.release() features = { "file_name": video_path, "duration": duration, "resolution": (width, height), "fps": fps } return features "fps": fps } return features