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| import numpy as np | |
| from tqdm import tqdm | |
| import cv2 | |
| from vbench.utils import load_dimension_info | |
| def get_frames(video_path): | |
| frames = [] | |
| video = cv2.VideoCapture(video_path) | |
| while video.isOpened(): | |
| success, frame = video.read() | |
| if success: | |
| frames.append(frame) | |
| else: | |
| break | |
| video.release() | |
| assert frames != [] | |
| return frames | |
| def mae_seq(frames): | |
| ssds = [] | |
| for i in range(len(frames)-1): | |
| ssds.append(calculate_mae(frames[i], frames[i+1])) | |
| return np.array(ssds) | |
| def calculate_mae(img1, img2): | |
| """Computing the mean absolute error (MAE) between two images.""" | |
| if img1.shape != img2.shape: | |
| print("Images don't have the same shape.") | |
| return | |
| return np.mean(cv2.absdiff(np.array(img1, dtype=np.float32), np.array(img2, dtype=np.float32))) | |
| def cal_score(video_path): | |
| """please ensure the video is static""" | |
| frames = get_frames(video_path) | |
| score_seq = mae_seq(frames) | |
| return (255.0 - np.mean(score_seq).item())/255.0 | |
| def temporal_flickering(video_list): | |
| sim = [] | |
| video_results = [] | |
| for video_path in tqdm(video_list): | |
| try: | |
| score_per_video = cal_score(video_path) | |
| except AssertionError: | |
| continue | |
| video_results.append({'video_path': video_path, 'video_results': score_per_video}) | |
| sim.append(score_per_video) | |
| avg_score = np.mean(sim) | |
| return avg_score, video_results | |
| def compute_temporal_flickering(json_dir, device, submodules_list): | |
| video_list, _ = load_dimension_info(json_dir, dimension='temporal_flickering', lang='en') | |
| all_results, video_results = temporal_flickering(video_list) | |
| return all_results, video_results | |