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# HID Tutorial
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This is the official support for competition of [Human Identification at a Distance (HID)](http://hid2022.iapr-tc4.org/). We report our result of 68.7% using the baseline model and 80.0% with re-ranking. In order for participants to better start the first step, we provide a tutorial on how to use OpenGait for HID.
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## Preprocess the dataset
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Download the raw dataset from the [official link](http://hid2022.iapr-tc4.org/). You will get three compressed files, i.e. `train.tar`, `HID2022_test_gallery.zip` and `HID2022_test_probe.zip`.
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After unpacking these three files, run this command:
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```shell
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python misc/HID/pretreatment_HID.py --input_train_path="train" --input_gallery_path="HID2022_test_gallery" --input_probe_path="HID2022_test_probe" --output_path="HID-128-pkl"
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```
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## Train the dataset
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Modify the `dataset_root` in `./misc/HID/baseline_hid.yaml`, and then run this command:
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```shell
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CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./misc/HID/baseline_hid.yaml --phase train
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```
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You can also download the [trained model](https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_hid_model.zip) and place it in `output` after unzipping.
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## Get the submission file
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```shell
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CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./misc/HID/baseline_hid.yaml --phase test
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```
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The result will be generated in your working directory.
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## Submit the result
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Follow the steps in the [official submission guide](https://codalab.lisn.upsaclay.fr/competitions/2542#participate), you need rename the file to `submission.csv` and compress it to a zip file. Finally, you can upload the zip file to the [official submission link](https://codalab.lisn.upsaclay.fr/competitions/2542#participate-submit_results).
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import os
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import cv2
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import numpy as np
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import argparse
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import pickle
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from tqdm import tqdm
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parser = argparse.ArgumentParser(description='Test')
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parser.add_argument('--input_train_path', default='', type=str,
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help='Root path of train.')
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parser.add_argument('--input_gallery_path', default='', type=str,
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help='Root path of gallery.')
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parser.add_argument('--input_probe_path', default='', type=str,
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help='Root path of probe.')
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parser.add_argument('--output_path', default='', type=str,
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help='Root path for output.')
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opt = parser.parse_args()
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OUTPUT_PATH = opt.output_path
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print('Pretreatment Start.\n'
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'Input train path: {}\n'
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'Input gallery path: {}\n'
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'Input probe path: {}\n'
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'Output path: {}\n'.format(
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opt.input_train_path, opt.input_gallery_path, opt.input_probe_path, OUTPUT_PATH))
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INPUT_PATH = opt.input_train_path
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print("Walk the input train path")
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id_list = os.listdir(INPUT_PATH)
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id_list.sort()
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for _id in tqdm(id_list):
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seq_type = os.listdir(os.path.join(INPUT_PATH, _id))
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seq_type.sort()
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for _seq_type in seq_type:
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out_dir = os.path.join(OUTPUT_PATH, _id, _seq_type, "default")
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count_frame = 0
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all_imgs = []
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frame_list = sorted(os.listdir(
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os.path.join(INPUT_PATH, _id, _seq_type)))
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for _frame_name in frame_list:
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frame_path = os.path.join(
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INPUT_PATH, _id, _seq_type, _frame_name)
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img = cv2.imread(frame_path, cv2.IMREAD_GRAYSCALE)
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if img is not None:
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# Save the img
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all_imgs.append(img)
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count_frame += 1
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all_imgs = np.asarray(all_imgs)
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if count_frame > 0:
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os.makedirs(out_dir, exist_ok=True)
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all_imgs_pkl = os.path.join(out_dir, '{}.pkl'.format(_seq_type))
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pickle.dump(all_imgs, open(all_imgs_pkl, 'wb'))
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# Warn if the sequence contains less than 5 frames
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if count_frame < 5:
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print('Seq:{}-{}, less than 5 valid data.'.format(_id, _seq_type))
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print("Walk the input gallery path")
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INPUT_PATH = opt.input_gallery_path
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id_list = os.listdir(INPUT_PATH)
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id_list.sort()
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for _id in tqdm(id_list):
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seq_type = os.listdir(os.path.join(INPUT_PATH, _id))
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seq_type.sort()
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for _seq_type in seq_type:
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out_dir = os.path.join(OUTPUT_PATH, _id, _seq_type, "default")
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count_frame = 0
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all_imgs = []
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frame_list = sorted(os.listdir(
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os.path.join(INPUT_PATH, _id, _seq_type)))
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for _frame_name in frame_list:
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frame_path = os.path.join(
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INPUT_PATH, _id, _seq_type, _frame_name)
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img = cv2.imread(frame_path, cv2.IMREAD_GRAYSCALE)
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if img is not None:
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# Save the img
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all_imgs.append(img)
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count_frame += 1
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all_imgs = np.asarray(all_imgs)
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if count_frame > 0:
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os.makedirs(out_dir, exist_ok=True)
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all_imgs_pkl = os.path.join(out_dir, '{}.pkl'.format(_seq_type))
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pickle.dump(all_imgs, open(all_imgs_pkl, 'wb'))
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# Warn if the sequence contains less than 5 frames
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if count_frame < 5:
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print('Seq:{}-{}, less than 5 valid data.'.format(_id, _seq_type))
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print("Finish {}".format(_id))
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print("Walk the input probe path")
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INPUT_PATH = opt.input_probe_path
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seq_type = os.listdir(INPUT_PATH)
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seq_type.sort()
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_id = "probe"
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for _seq_type in tqdm(seq_type):
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out_dir = os.path.join(OUTPUT_PATH, _id, _seq_type, "default")
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count_frame = 0
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all_imgs = []
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frame_list = sorted(os.listdir(
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os.path.join(INPUT_PATH, _seq_type)))
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for _frame_name in frame_list:
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frame_path = os.path.join(
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INPUT_PATH, _seq_type, _frame_name)
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img = cv2.imread(frame_path, cv2.IMREAD_GRAYSCALE)
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if img is not None:
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# Save the img
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all_imgs.append(img)
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count_frame += 1
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all_imgs = np.asarray(all_imgs)
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if count_frame > 0:
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os.makedirs(out_dir, exist_ok=True)
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all_imgs_pkl = os.path.join(out_dir, '{}.pkl'.format(_seq_type))
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pickle.dump(all_imgs, open(all_imgs_pkl, 'wb'))
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# Warn if the sequence contains less than 5 frames
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if count_frame < 5:
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print('Seq:{}-{}, less than 5 valid data.'.format(_id, _seq_type))
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