| import io |
| import os |
| import cv2 |
| import json |
| import numpy as np |
| from PIL import Image |
| from tqdm import tqdm |
|
|
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| import torchvision.transforms as transforms |
|
|
| from editboard.utils import load_video, load_dimension_info, dino_transform, dino_transform_Image |
|
|
|
|
| def subject_consistency(model, video_list, device, read_frame): |
| sim = 0.0 |
| cnt = 0 |
| video_results = {} |
| if read_frame: |
| image_transform = dino_transform_Image(224) |
| else: |
| image_transform = dino_transform(224) |
| for video_path in tqdm(video_list): |
| video_sim = 0.0 |
| if read_frame: |
| video_path = video_path[:-4].replace('videos', 'frames').replace(' ', '_') |
| tmp_paths = [os.path.join(video_path, f) for f in sorted(os.listdir(video_path))] |
| images = [] |
| for tmp_path in tmp_paths: |
| images.append(image_transform(Image.open(tmp_path))) |
| else: |
| images = load_video(video_path) |
| images = image_transform(images) |
| for i in range(len(images)): |
| with torch.no_grad(): |
| image = images[i].unsqueeze(0) |
| image = image.to(device) |
| image_features = model(image) |
| image_features = F.normalize(image_features, dim=-1, p=2) |
| if i == 0: |
| first_image_features = image_features |
| else: |
| sim_pre = max(0.0, F.cosine_similarity(former_image_features, image_features).item()) |
| sim_fir = max(0.0, F.cosine_similarity(first_image_features, image_features).item()) |
| cur_sim = (sim_pre + sim_fir) / 2 |
| video_sim += cur_sim |
| cnt += 1 |
| former_image_features = image_features |
| sim_per_images = video_sim / (len(images) - 1) |
| sim += video_sim |
| video_results[os.path.dirname(os.path.dirname(video_path))] = sim_per_images |
| return video_results |
|
|
|
|
| def compute_subject_consistency(json_dir, device, submodules_list): |
| dino_model = torch.hub.load(**submodules_list).to(device) |
| read_frame = submodules_list['read_frame'] |
| video_list = load_dimension_info(json_dir, dimension='subject_consistency') |
| video_results = subject_consistency(dino_model, video_list, device, read_frame) |
| return video_results |
|
|
