File size: 2,428 Bytes
04fbff5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
import os
import json
import logging
import numpy as np
import clip
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
from vbench.utils import load_video, load_dimension_info, clip_transform
from tqdm import tqdm
def background_consistency(clip_model, preprocess, video_list, device, read_frame):
sim = 0.0
cnt = 0
video_results = []
image_transform = clip_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(preprocess(Image.open(tmp_path)))
images = torch.stack(images)
else:
images = load_video(video_path)
images = image_transform(images)
images = images.to(device)
image_features = clip_model.encode_image(images)
image_features = F.normalize(image_features, dim=-1, p=2)
for i in range(len(image_features)):
image_feature = image_features[i].unsqueeze(0)
if i == 0:
first_image_feature = image_feature
else:
sim_pre = max(0.0, F.cosine_similarity(former_image_feature, image_feature).item())
sim_fir = max(0.0, F.cosine_similarity(first_image_feature, image_feature).item())
cur_sim = (sim_pre + sim_fir) / 2
video_sim += cur_sim
cnt += 1
former_image_feature = image_feature
sim_per_image = video_sim / (len(image_features) - 1)
sim += video_sim
video_results.append({'video_path': video_path, 'video_results': sim_per_image})
sim_per_video = sim / (len(video_list) - 1)
sim_per_frame = sim / cnt
return sim_per_frame, video_results
def compute_background_consistency(json_dir, device, submodules_list):
vit_path, read_frame = submodules_list[0], submodules_list[1]
clip_model, preprocess = clip.load(vit_path, device=device)
video_list, _ = load_dimension_info(json_dir, dimension='background_consistency', lang='en')
all_results, video_results = background_consistency(clip_model, preprocess, video_list, device, read_frame)
return all_results, video_results
|