File size: 3,451 Bytes
8e29a6e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | 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 |