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# Lib
import os
import glob
import json
import tqdm
import natsort
import random
from PIL import Image
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
import clip
from torchvision import models
from config import config
class loading_img(Dataset):
def __init__(self, img_list):
self.img_list = img_list
def __len__(self):
return len(self.img_list)
def __getitem__(self, idx):
return preprocess(Image.open(self.img_list[idx]))
# select frames
def select_frames(folder_list, preprocess, resnet18_pretrained):
for folder in folder_list:
img_list = natsort.natsorted(glob.glob(f"{folder}/*.jpg"))
img_feats = []
img_set = loading_img(img_list)
img_loader = DataLoader(img_set, batch_size=64, shuffle=False, num_workers=16)
for imgtensor in img_loader: img_feats.append(imgtensor)
img_feats = torch.concat(img_feats, dim=0).to(device)
with torch.no_grad():
featuremap = resnet18_pretrained(img_feats)
frame_num = featuremap.shape[0]
dist_list = []
for img_feat in featuremap: dist_list.append(torch.mean(torch.sqrt((featuremap-img_feat)**2), dim=-1))
dist_list = torch.concat(dist_list).reshape(frame_num, frame_num)
idx_list = [_ for _ in range(frame_num)]
loop_idx = 0
out_frames = []
output_results = []
while len(idx_list) > 5:
dist_idx = idx_list.pop(0)
data = dist_list[dist_idx, idx_list].softmax(dim=-1)
mu, std = torch.mean(data), torch.std(data)
pop_idx_list = torch.where(data < mu-std*(np.exp(1-loop_idx/config.divlam)))[0].detach().cpu().numpy()
result = list(np.array(idx_list)[pop_idx_list])
result.append(dist_idx)
output_results.append(result)
num_picks = 18
if len(result) > num_picks:
idx_result_list = sorted(random.sample(result, num_picks))
img_list = np.array(img_list)
idx_result_list = np.array(idx_result_list)
out_frames.extend(img_list[idx_result_list])
else:
idx_result_list = sorted(result)
img_list = np.array(img_list)
idx_result_list = np.array(idx_result_list)
out_frames.extend(img_list[idx_result_list])
loop_idx += 1
for pop_idx in reversed(pop_idx_list): idx_list.pop(pop_idx)
return out_frames, output_results
# Init
random.seed(10)
device = "cuda" if torch.cuda.is_available() else "cpu"
resnet18_pretrained = models.resnet18(pretrained=True).to(device)
resnet18_pretrained.fc = torch.nn.Identity()
resnet18_pretrained.avgpool = torch.nn.Identity()
resnet18_pretrained.eval()
model, preprocess = clip.load("ViT-B/32", device=device)
objs_acts = config.f_path
questions = config.q_path
questions = [json.loads(q) for q in open(os.path.expanduser(questions), "r")]
objs_acts = [json.loads(q) for q in open(os.path.expanduser(objs_acts), "r")]
answer_path = os.path.expanduser(config.a_path)
os.makedirs(os.path.dirname(answer_path), exist_ok=True)
ans_file = open(answer_path, "w")
output_results = []
for question in tqdm.tqdm(questions):
test_token = True
for objs_act in objs_acts:
if objs_act['q_uid'] == question['q_uid']:
question['Object'] = objs_act["Activity"]
question['Activity'] = objs_act["Activity"]
folder_list = glob.glob(f"{config.img_folder}/{question['q_uid']}/")
out_frames, output_result = select_frames(folder_list, preprocess, resnet18_pretrained)
output_results.append(output_result)
question['filepath'] = out_frames
ans_file.write(json.dumps(question) + "\n")
test_token = False
break |