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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 vbench.utils import load_video, load_dimension_info, dino_transform, dino_transform_Image
import logging
logging.basicConfig(level = logging.INFO,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
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 += video_sim
video_results.append({'video_path': video_path, 'video_results': video_sim})
sim_per_video = sim / (len(video_list) - 1)
sim_per_frame = sim / cnt
return sim_per_frame, 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']
logger.info("Initialize DINO success")
video_list, _ = load_dimension_info(json_dir, dimension='subject_consistency', lang='en')
all_results, video_results = subject_consistency(dino_model, video_list, device, read_frame)
return all_results, video_results
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