| import os |
| import cv2 |
| import json |
| import numpy as np |
| from PIL import Image |
| import torchvision.transforms as transforms |
|
|
| def load_json(path): |
| with open(path, 'r', encoding='utf-8') as f: |
| return json.load(f) |
|
|
| def load_video_info(json_path, metric): |
| video_info = load_json(json_path) |
| video_list = [] |
| |
| for item in video_info: |
| video_list.append({ |
| 'id': item['id'], |
| 'src_video_name': item['src_video_name'], |
| 'category': item['category'], |
| 'subcategory': item['subcategory'], |
| 'source_prompt': item['source_prompt'], |
| 'edit_prompt': item['edit_prompt'], |
| 'target_prompt': item['target_prompt'] |
| }) |
| |
| return video_list |
|
|
| def load_frames_from_folder(frame_folder_path): |
| if not os.path.exists(frame_folder_path): |
| raise FileNotFoundError(f"Frame folder not found: {frame_folder_path}") |
| |
| frame_files = sorted([f for f in os.listdir(frame_folder_path) |
| if f.lower().endswith(('.png', '.jpg', '.jpeg'))]) |
| |
| if not frame_files: |
| raise ValueError(f"No image files found in {frame_folder_path}") |
| |
| frames = [] |
| for frame_file in frame_files: |
| frame_path = os.path.join(frame_folder_path, frame_file) |
| try: |
| frame = Image.open(frame_path).convert('RGB') |
| frames.append(frame) |
| except Exception as e: |
| logger.warning(f"Could not load frame {frame_path}: {e}") |
| |
| if not frames: |
| raise ValueError(f"No valid frames loaded from {frame_folder_path}") |
| |
| return frames |
|
|
| def get_frames_from_folder(frame_folder_path): |
| if not os.path.exists(frame_folder_path): |
| raise FileNotFoundError(f"Frame folder not found: {frame_folder_path}") |
| |
| frame_files = sorted([f for f in os.listdir(frame_folder_path) |
| if f.lower().endswith(('.png', '.jpg', '.jpeg'))]) |
| |
| if not frame_files: |
| raise ValueError(f"No image files found in {frame_folder_path}") |
| |
| frames = [] |
| for frame_file in frame_files: |
| frame_path = os.path.join(frame_folder_path, frame_file) |
| frame = cv2.imread(frame_path) |
| if frame is not None: |
| frames.append(frame) |
| else: |
| logger.warning(f"Could not load frame: {frame_path}") |
| |
| if not frames: |
| raise ValueError(f"No valid frames loaded from {frame_folder_path}") |
| |
| return frames |
|
|
| def get_frames_from_video(video_path): |
| frames = [] |
| video = cv2.VideoCapture(video_path) |
| |
| if not video.isOpened(): |
| raise ValueError(f"Could not open video: {video_path}") |
| |
| while video.isOpened(): |
| success, frame = video.read() |
| if success: |
| frames.append(frame) |
| else: |
| break |
| |
| video.release() |
| |
| if not frames: |
| raise ValueError(f"No frames extracted from video: {video_path}") |
| |
| return frames |
|
|
| def dino_transform_Image(size=224): |
| transform = transforms.Compose([ |
| transforms.Resize((size, size)), |
| transforms.ToTensor(), |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], |
| std=[0.229, 0.224, 0.225]) |
| ]) |
| return transform |
|
|
| def load_dino_model(device): |
| import torch |
| |
| model = torch.hub.load('facebookresearch/dino:main', 'dino_vits16', pretrained=True) |
| model.eval() |
| model.to(device) |
| |
| return model |
