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import os
import cv2
import pdb
import glob
import pandas
import pickle
import argparse
import numpy as np
from tqdm import tqdm
from functools import partial
def csv2dict_phoneix2014(anno_path, dataset_type, extra_info=None):
anno_path = f"{anno_path}/{extra_info.format(dataset_type)}"
inputs_list = pandas.read_csv(anno_path)
inputs_list = (inputs_list.to_dict()['name|video|start|end|speaker|orth|translation'].values())
info_dict = dict()
info_dict['prefix'] = anno_path.rsplit("/", 3)[0] + "/features/fullFrame-210x260px"
print(f"Generate information dict from {anno_path} for {dataset_type}")
for file_idx, file_info in tqdm(enumerate(inputs_list), total=len(inputs_list)):
fileid, folder, _, _, signer, label, _ = file_info.split("|")
num_frames = len(glob.glob(f"{info_dict['prefix']}/{dataset_type}/{folder}"))
info_dict[file_idx] = {
'fileid': fileid,
'folder': f"{dataset_type}/{fileid}/*.png",
'signer': signer,
'label': label,
'num_frames': num_frames,
'original_info': (file_info, file_idx, fileid),
}
return info_dict
def csv2dict_csldaily(anno_path, dataset_type, dataset=None, dataset_split=None):
info_dict = dict()
info_dict['prefix'] = anno_path + "/sentence/frames_512x512"
print(f"Generate information dict from {anno_path} for {dataset_type}")
actual_file_idx = 0
for file_idx, file_info in tqdm(enumerate(dataset['info']), total=len(dataset['info'])):
name = file_info['name']
if name not in dataset_split or dataset_split[name] != dataset_type:
continue
num_frames = len(glob.glob(f"{info_dict['prefix']}/{file_info['name']}/*.jpg"))
assert num_frames == file_info['length']
info_dict[actual_file_idx] = {
'fileid': file_info['name'],
'folder': file_info['name'] + "/*.jpg",
'signer': file_info['signer'],
'label': ' '.join(file_info['label_gloss']),
'num_frames': num_frames,
'original_info': (file_info, actual_file_idx, file_info['name']),
}
actual_file_idx += 1
return info_dict
def csv2dict_dgs(anno_path, dataset_type):
gloss_list = open(anno_path + "/%s.bpe.gloss" % dataset_type, 'r').readlines()
img_list = open(anno_path + "/%s.img" % dataset_type, 'r').readlines()
info_dict = dict()
info_dict['prefix'] = None
print(f"Generate information dict from {anno_path} for {dataset_type}")
for file_idx, file_info in tqdm(enumerate(zip(gloss_list, img_list)), total=len(gloss_list)):
gloss, img = file_info
gloss = gloss.strip()
img = img.strip()
if gloss == "":
gloss = "Unknown"
num_frames = int(cv2.VideoCapture(img).get(cv2.CAP_PROP_FRAME_COUNT))
info_dict[file_idx] = {
'fileid': os.path.basename(img),
'folder': img,
'signer': 'Unknown',
'label': gloss,
'num_frames': num_frames,
'original_info': (file_info, file_idx, os.path.basename(img)),
}
return info_dict
def generate_gt_stm(info, save_path):
with open(save_path, "w") as f:
for k, v in info.items():
if not isinstance(k, int):
continue
f.writelines(f"{v['fileid']} 1 {v['signer']} 0.0 1.79769e+308 {v['label']}\n")
def sign_dict_update(total_dict, info):
for k, v in info.items():
if not isinstance(k, int):
continue
split_label = v['label'].split()
for gloss in split_label:
if gloss not in total_dict.keys():
total_dict[gloss] = 1
else:
total_dict[gloss] += 1
return total_dict
def load_csldaily_split(split_file):
dataset_split = {}
with open(split_file, 'r') as reader:
reader.readline() # skip header
for sample in reader:
name, md = sample.strip().split('|')
dataset_split[name] = md
return dataset_split
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Data process for Visual Alignment Constraint for Continuous Sign Language Recognition.')
parser.add_argument('--dataset', type=str, default='phoenix2014', choices=['phoenix2014', 'csldaily', 'dgs3-t'],
help='save prefix')
parser.add_argument('--dataset-root', type=str, default='dataset/phoenix2014',
help='path to the dataset')
args = parser.parse_args()
args.dataset_root = os.path.abspath(args.dataset_root)
csv2dict = None
if args.dataset == 'phoenix2014':
csv2dict = partial(csv2dict_phoneix2014,
extra_info="annotations/manual/PHOENIX-2014-T.{}.corpus.csv")
elif args.dataset == 'csldaily':
# load dataset split: train/dev/test
dataset_split = load_csldaily_split(f"{args.dataset_root}/sentence_label/split_1.txt")
# load dataset info
with open(f"{args.dataset_root}/sentence_label/csl2020ct_v2.pkl", 'rb') as f:
dataset = pickle.load(f)
csv2dict = partial(csv2dict_csldaily, dataset=dataset, dataset_split=dataset_split)
elif args.dataset == 'dgs3-t':
csv2dict = csv2dict_dgs
else:
raise ValueError(f"Invalid dataset {args.dataset}")
mode = ["dev", "test", "train"]
sign_dict = dict()
if not os.path.exists(f"./{args.dataset}"):
os.makedirs(f"./{args.dataset}")
for md in mode:
# generate information dict
information = csv2dict(f"{args.dataset_root}", dataset_type=md)
np.save(f"./{args.dataset}/{md}_info.npy", information)
# update the total gloss dict
sign_dict_update(sign_dict, information)
# generate groudtruth stm for evaluation
generate_gt_stm(information, f"./{args.dataset}/{args.dataset}-groundtruth-{md}.stm")
sign_dict = sorted(sign_dict.items(), key=lambda d: d[0])
save_dict = {}
for idx, (key, value) in enumerate(sign_dict):
save_dict[key] = [idx + 1, value]
np.save(f"./{args.dataset}/gloss_dict.npy", save_dict)
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