crosstyan/OpenGait
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Reconstruct the directory structure

Browse Source
This commit is contained in:
darkliang
2022-04-12 13:44:07 +08:00
parent 213b3a658f
commit 28f50410a5
32 changed files with 162 additions and 45 deletions
Download Patch File Download Diff File Expand all files Collapse all files
datasets/GREW/GREW.json
+1
View File
File diff suppressed because one or more lines are too long
datasets/GREW/README.md
+77
View File
@@ -0,0 +1,77 @@
# GREW Tutorial
<!-- ![](http://hid2022.iapr-tc4.org/wp-content/uploads/sites/7/2022/03/%E5%9B%BE%E7%89%871-2.png) -->
This is for [GREW-Benchmark](https://github.com/GREW-Benchmark/GREW-Benchmark). We report our result of 48% using the baseline model. In order for participants to better start the first step, we provide a tutorial on how to use OpenGait for GREW.
## Preprocess the dataset
Download the raw dataset from the [official link](https://www.grew-benchmark.org/download.html). You will get three compressed files, i.e. `train.zip`, `test.zip` and `distractor.zip`.
Step 1: Unzip train and test:
```shell
unzip -P password train.zip (password is the obtained password)
tar -xzvf train.tgz
cd train
ls *.tgz | xargs -n1 tar xzvf
```
```shell
unzip -P password test.zip (password is the obtained password)
tar -xzvf test.tgz
cd test & cd gallery
ls *.tgz | xargs -n1 tar xzvf
cd .. & cd probe
ls *.tgz | xargs -n1 tar xzvf
```
After unpacking these compressed files, run this command:
Step2 : To rearrange directory of GREW dataset, turning to id-type-view structure, Run
```
python datasets/GREW/rearrange_GREW.py --input_path Path_of_GREW-raw --output_path Path_of_GREW-rearranged
```
Step3: Transforming images to pickle file, run
```
python datasets/pretreatment.py --input_path Path_of_GREW-rearranged --output_path Path_of_GREW-pkl
```
Then you will see the structure like:
- Processed
```
GREW-pkl
├── 00001train (subject in training set)
├── 00
├── 4XPn5Z28
├── 4XPn5Z28.pkl
├──5TXe8svE
├── 5TXe8svE.pkl
......
├── 00001 (subject in testing set)
├── 01
├── 79XJefi8
├── 79XJefi8.pkl
├── 02
├── t16VLaQf
├── t16VLaQf.pkl
├── probe
├── etaGVnWf
├── etaGVnWf.pkl
├── eT1EXpgZ
├── eT1EXpgZ.pkl
...
...
```
## Train the dataset
Modify the `dataset_root` in `./config/baseline/baseline_GREW.yaml`, and then run this command:
```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./config/baseline/baseline_GREW.yaml --phase train
```
## Get the submission file
```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./config/baseline/baseline_GREW.yaml --phase test
```
The result will be generated in your working directory, you must rename and compress it as the requirements before submitting.
## Evaluation locally
While the original grew treat both seq_01 and seq_02 as gallery, but there is no ground truth for probe. Therefore, it is nessesary to upload the submission file on grew competitation. We seperate test set to: seq_01 as gallery, seq_02 as probe. Then you can modify `eval_func` in the `./config/baseline/baseline_GREW.yaml` to `identification_real_scene`, you can obtain result localy like setting of OUMVLP.
datasets/GREW/rearrange_GREW.py
+89
View File
@@ -0,0 +1,89 @@
import argparse
import os
import shutil
from pathlib import Path
from tqdm import tqdm
TOTAL_Test = 24000
TOTAL_Train = 20000
def rearrange_train(train_path: Path, output_path: Path) -> None:
progress = tqdm(total=TOTAL_Train)
for sid in train_path.iterdir():
if not sid.is_dir():
continue
for sub_seq in sid.iterdir():
if not sub_seq.is_dir():
continue
for subfile in os.listdir(sub_seq):
src = os.path.join(train_path, sid.name, sub_seq.name)
dst = os.path.join(output_path, sid.name+'train', '00', sub_seq.name)
os.makedirs(dst,exist_ok=True)
if subfile not in os.listdir(dst) and subfile.endswith('.png'):
os.symlink(os.path.join(src, subfile),
os.path.join(dst, subfile))
progress.update(1)
def rearrange_test(test_path: Path, output_path: Path) -> None:
# for gallery
gallery = Path(os.path.join(test_path, 'gallery'))
probe = Path(os.path.join(test_path, 'probe'))
progress = tqdm(total=TOTAL_Test)
for sid in gallery.iterdir():
if not sid.is_dir():
continue
cnt = 1
for sub_seq in sid.iterdir():
if not sub_seq.is_dir():
continue
for subfile in sorted(os.listdir(sub_seq)):
src = os.path.join(gallery, sid.name, sub_seq.name)
dst = os.path.join(output_path, sid.name, '%02d'%cnt, sub_seq.name)
os.makedirs(dst,exist_ok=True)
if subfile not in os.listdir(dst) and subfile.endswith('.png'):
os.symlink(os.path.join(src, subfile),
os.path.join(dst, subfile))
cnt += 1
progress.update(1)
# for probe
for sub_seq in probe.iterdir():
if not sub_seq.is_dir():
continue
for subfile in os.listdir(sub_seq):
src = os.path.join(probe, sub_seq.name)
dst = os.path.join(output_path, 'probe', '03', sub_seq.name)
os.makedirs(dst,exist_ok=True)
if subfile not in os.listdir(dst) and subfile.endswith('.png'):
os.symlink(os.path.join(src, subfile),
os.path.join(dst, subfile))
progress.update(1)
def rearrange_GREW(input_path: Path, output_path: Path) -> None:
os.makedirs(output_path, exist_ok=True)
for folder in input_path.iterdir():
if not folder.is_dir():
continue
print(f'Rearranging {folder}')
if folder.name == 'train':
rearrange_train(folder,output_path)
if folder.name == 'test':
rearrange_test(folder, output_path)
if folder.name == 'distractor':
pass
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='GREW rearrange tool')
parser.add_argument('-i', '--input_path', required=True, type=str,
help='Root path of raw dataset.')
parser.add_argument('-o', '--output_path', default='GREW_rearranged', type=str,
help='Root path for output.')
args = parser.parse_args()
input_path = Path(args.input_path).resolve()
output_path = Path(args.output_path).resolve()
rearrange_GREW(input_path, output_path)