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# quality/temporal_flickering.py
import os
import numpy as np
import cv2
import torch
from tqdm import tqdm
import logging
from ivebench_utils import load_video_info, get_frames_from_folder, get_frames_from_video
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def calculate_mae(img1, img2):
if img1.shape != img2.shape:
logger.warning("Images don't have the same shape.")
return 0.0
mae = np.mean(cv2.absdiff(np.array(img1, dtype=np.float32), np.array(img2, dtype=np.float32)))
return float(mae)
def mae_sequence(frames):
maes = []
for i in range(len(frames) - 1):
mae = calculate_mae(frames[i], frames[i + 1])
maes.append(mae)
return maes
def calculate_flickering_score(frames):
if len(frames) < 2:
logger.warning("Need at least 2 frames to calculate flickering")
return 0.0
mae_scores = mae_sequence(frames)
avg_mae = sum(mae_scores) / len(mae_scores)
flickering_score = (255.0 - avg_mae) / 255.0
flickering_score = max(0.0, min(1.0, flickering_score))
return float(flickering_score)
def temporal_flickering_single_video(video_info, target_videos_path, use_frames=True):
video_name = video_info['src_video_name']
video_id = video_info['id']
try:
if use_frames:
video_name_without_ext = os.path.splitext(video_name)[0]
target_frame_folder = os.path.join(target_videos_path, video_name_without_ext)
frames = get_frames_from_folder(target_frame_folder)
else:
target_video_path = os.path.join(target_videos_path, video_name)
frames = get_frames_from_video(target_video_path)
score = calculate_flickering_score(frames)
return {
'video_id': int(video_id),
'video_name': str(video_name),
'video_results': float(score),
'frame_count': int(len(frames)),
'category': str(video_info['category']),
'subcategory': str(video_info['subcategory'])
}
except Exception as e:
logger.error(f"Error processing video {video_name}: {str(e)}")
return {
'video_id': int(video_id),
'video_name': str(video_name),
'video_results': 0.0,
'error': str(e)
}
def temporal_flickering(video_info_list, target_videos_path, use_frames=True):
scores = []
video_results = []
logger.info(f"Processing {len(video_info_list)} videos for temporal flickering evaluation")
for video_info in tqdm(video_info_list, desc="Evaluating temporal flickering"):
result = temporal_flickering_single_video(video_info, target_videos_path, use_frames)
video_results.append(result)
if 'error' not in result:
scores.append(result['video_results'])
logger.debug(f"Video {result['video_name']}: flickering score = {result['video_results']:.4f}")
if scores:
avg_score = sum(scores) / len(scores)
else:
avg_score = 0.0
logger.warning("No valid video scores calculated")
logger.info(f"Overall temporal flickering score: {avg_score:.4f}")
return float(avg_score), video_results
def compute_temporal_flickering(json_dir, device, source_videos_path=None, target_videos_path=None,
use_frames=True, **kwargs):
try:
video_info_list = load_video_info(json_dir, 'temporal_flickering')
logger.info(f"Loaded {len(video_info_list)} video entries")
if target_videos_path is None:
raise ValueError("target_videos_path is required for temporal flickering evaluation")
if not os.path.exists(target_videos_path):
raise FileNotFoundError(f"Target videos path not found: {target_videos_path}")
overall_score, video_results = temporal_flickering(
video_info_list, target_videos_path, use_frames
)
logger.info(f"Temporal flickering evaluation completed. Overall score: {overall_score:.4f}")
return overall_score, video_results
except Exception as e:
logger.error(f"Error in compute_temporal_flickering: {str(e)}")
return 0.0, []