| import os |
| import cv2 |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| import torchvision.transforms as transforms |
| from tqdm import tqdm |
| from PIL import Image |
| from dreamsim import dreamsim |
| from decord import VideoReader |
| from torchvision.io import write_video |
| from vbench.subject_consistency import compute_subject_consistency, subject_consistency |
| from vbench.utils import load_video, load_dimension_info, dino_transform, dino_transform_Image |
| from vbench2_beta_long.utils import reorganize_clips_results, create_video_from_first_frames, fuse_inclip_clip2clip |
| from vbench2_beta_long.utils import dreamsim_transform, dreamsim_transform_Image, dinov2_transform, dinov2_transform_Image |
| import logging |
| logging.basicConfig(level = logging.INFO,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s') |
| logger = logging.getLogger(__name__) |
|
|
| def compute_long_subject_consistency(json_dir, device, submodules_list, **kwargs): |
| |
| all_results, detailed_results = compute_subject_consistency(json_dir, device, submodules_list) |
|
|
| inclip_all_results, inclip_detailed_results, inclip_average_scores = reorganize_clips_results(detailed_results) |
| inclip_all_results = all_results |
|
|
|
|
| |
| |
| base_path_video = os.path.dirname(list(detailed_results[0].values())[0]).split("split_clip")[0] |
| long_video_path = os.path.join(base_path_video, "split_clip") |
| new_cat_video_path = os.path.join(base_path_video, 'subject_consistency_cat_firstframes_videos') |
| if not os.path.exists(new_cat_video_path): |
| os.makedirs(new_cat_video_path, exist_ok=True) |
| create_video_from_first_frames(long_video_path, new_cat_video_path, detailed_results) |
| else: |
| print(f"{new_cat_video_path} has already been created, please check the path") |
|
|
| |
| video_list = [] |
| for video_path in os.listdir(new_cat_video_path): |
| video_list.append(os.path.join(new_cat_video_path, video_path)) |
| |
| def _compute_subject_consistency(video_list, device, submodules_list, **kwargs): |
| if kwargs['sb_clip2clip_feat_extractor'] == 'dino': |
| dino_model = torch.hub.load(**submodules_list).to(device) |
| read_frame = submodules_list['read_frame'] |
| logger.info("Initialize DINO success") |
| all_results, video_results = subject_consistency(dino_model, video_list, device, read_frame) |
| elif kwargs['sb_clip2clip_feat_extractor'] == 'dinov2': |
| dinov2_dict = { |
| 'repo_or_dir': f'facebookresearch/dinov2', |
| 'model': 'dinov2_vitb14', |
| } |
| dinov2_model = torch.hub.load(**dinov2_dict).to(device) |
| read_frame = submodules_list['read_frame'] |
| logger.info("Initialize DINOv2 success") |
| all_results, video_results = subject_consistency_dinov2(dinov2_model, video_list, device, read_frame) |
|
|
| elif kwargs['sb_clip2clip_feat_extractor'] == 'dreamsim': |
| read_frame = submodules_list['read_frame'] |
| cache_dir = os.path.expanduser("~/.cache") |
| dreamsim_model, _ = dreamsim(pretrained=True, cache_dir=cache_dir) |
| all_results, video_results = subject_consistency_dreamsim(dreamsim_model, video_list, device, read_frame) |
| return all_results, video_results |
|
|
|
|
|
|
|
|
| clip2clip_all_results, clip2clip_detailed_results = _compute_subject_consistency(video_list, device, submodules_list, **kwargs) |
| dimension = 'subject_consistency' |
| fused_all_results, fused_detailed_results = fuse_inclip_clip2clip(inclip_all_results, clip2clip_all_results, inclip_average_scores, clip2clip_detailed_results, dimension, **kwargs) |
| |
| return fused_all_results, fused_detailed_results |
|
|
|
|
|
|
| def subject_consistency_dreamsim(model, video_list, device, read_frame): |
| sim = 0.0 |
| cnt = 0 |
| video_results = [] |
| if read_frame: |
| image_transform = dreamsim_transform_Image(224) |
| else: |
| image_transform = dreamsim_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))) |
| images = torch.stack(images) |
| else: |
| images = load_video(video_path) |
| images = image_transform(images) |
| |
|
|
| images = images.to(device) |
|
|
| for i in range(len(images)): |
| with torch.no_grad(): |
| image = images[i].unsqueeze(0) |
| image_features = model.embed(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.append({'video_path': video_path, 'video_results': sim_per_images}) |
| |
| sim_per_frame = sim / cnt if cnt != 0 else None |
| return sim_per_frame, video_results |
|
|
|
|
| def subject_consistency_dinov2(model, video_list, device, read_frame): |
| sim = 0.0 |
| cnt = 0 |
| video_results = [] |
| if read_frame: |
| image_transform = dinov2_transform_Image(224) |
| else: |
| image_transform = dinov2_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.append({'video_path': video_path, 'video_results': sim_per_images}) |
| |
| sim_per_frame = sim / cnt if cnt != 0 else None |
| return sim_per_frame, video_results |
