更换文档检测模型

2024-08-27 14:42:45 +08:00
parent aea6f19951
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--- a/paddle_detection/configs/mot/pedestrian/README.md
+++ b/paddle_detection/configs/mot/pedestrian/README.md
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+README_cn.md
--- a/paddle_detection/configs/mot/pedestrian/README_cn.md
+++ b/paddle_detection/configs/mot/pedestrian/README_cn.md
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+[English](README.md) | 简体中文
+# 特色垂类跟踪模型
+
+## 大规模行人跟踪 (Pedestrian Tracking)
+
+行人跟踪的主要应用之一是交通监控。
+
+[PathTrack](https://www.trace.ethz.ch/publications/2017/pathtrack/index.html)包含720个视频序列，有着超过15000个行人的轨迹。包含了街景、舞蹈、体育运动、采访等各种场景的，大部分是移动摄像头拍摄场景。该数据集只有Pedestrian一类标注作为跟踪任务。
+
+[VisDrone](http://aiskyeye.com)是无人机视角拍摄的数据集，是以俯视视角为主。该数据集涵盖不同位置（取自中国数千个相距数千公里的14个不同城市）、不同环境（城市和乡村）、不同物体（行人、车辆、自行车等）和不同密度（稀疏和拥挤的场景）。[VisDrone2019-MOT](https://github.com/VisDrone/VisDrone-Dataset)包含56个视频序列用于训练，7个视频序列用于验证。此处针对VisDrone2019-MOT多目标跟踪数据集进行提取，抽取出类别为pedestrian和people的数据组合成一个大的Pedestrian类别。
+
+
+## 模型库
+
+### FairMOT在各个数据集val-set上Pedestrian类别的结果
+
+|    数据集      |  骨干网络   |  输入尺寸 |  MOTA  |  IDF1  |  FPS   |  下载链接 | 配置文件 |
+| :-------------| :-------- | :------- | :----: | :----: | :----: | :-----: |:------: |
+|  PathTrack    |   DLA-34  | 1088x608 |  44.9 |    59.3   |    -   |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_pathtrack.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_pathtrack.yml) |
+|  VisDrone     |   DLA-34  | 1088x608 |  49.2 |   63.1 |    -   | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_visdrone_pedestrian.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml) |
+|  VisDrone     | HRNetv2-W18| 1088x608 |  40.5 |   54.7 |    -   | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_pedestrian.pdparams) | [配置文件](./fairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_pedestrian.yml) |
+|  VisDrone     | HRNetv2-W18| 864x480 |  38.6 |   50.9 |    -   | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone_pedestrian.pdparams) | [配置文件](./fairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone_pedestrian.yml) |
+|  VisDrone     | HRNetv2-W18| 576x320 |  30.6 |   47.2 |    -   | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone_pedestrian.pdparams) | [配置文件](./fairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone_pedestrian.yml) |
+
+**注意:**
+ - FairMOT均使用DLA-34为骨干网络，4个GPU进行训练，每个GPU上batch size为6，训练30个epoch。
+
+
+## 数据集准备和处理
+
+### 1、数据集处理代码说明
+代码统一都在tools目录下
+```
+# visdrone
+tools/visdrone/visdrone2mot.py: 生成visdrone_pedestrian据集
+```
+
+### 2、visdrone_pedestrian数据集处理
+```
+# 复制tool/visdrone/visdrone2mot.py到数据集目录下
+# 生成visdrone_pedestrian MOT格式的数据，抽取类别classes=1,2 (pedestrian, people)
+<<--生成前目录-->>
+├── VisDrone2019-MOT-val
+│   ├── annotations
+│   ├── sequences
+│   ├── visdrone2mot.py
+<<--生成后目录-->>
+├── VisDrone2019-MOT-val
+│   ├── annotations
+│   ├── sequences
+│   ├── visdrone2mot.py
+│   ├── visdrone_pedestrian
+│   │   ├── images
+│   │   │   ├── train
+│   │   │   ├── val
+│   │   ├── labels_with_ids
+│   │   │   ├── train
+│   │   │   ├── val
+# 执行
+python visdrone2mot.py --transMot=True --data_name=visdrone_pedestrian --phase=val
+python visdrone2mot.py --transMot=True --data_name=visdrone_pedestrian --phase=train
+```
+
+## 快速开始
+
+### 1. 训练
+使用2个GPU通过如下命令一键式启动训练
+```bash
+python -m paddle.distributed.launch --log_dir=./fairmot_dla34_30e_1088x608_visdrone_pedestrian/ --gpus 0,1 tools/train.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml
+```
+
+### 2. 评估
+使用单张GPU通过如下命令一键式启动评估
+```bash
+# 使用PaddleDetection发布的权重
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_visdrone_pedestrian.pdparams
+
+# 使用训练保存的checkpoint
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml -o weights=output/fairmot_dla34_30e_1088x608_visdrone_pedestrian/model_final.pdparams
+```
+
+### 3. 预测
+使用单个GPU通过如下命令预测一个视频，并保存为视频
+```bash
+# 预测一个视频
+CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_visdrone_pedestrian.pdparams --video_file={your video name}.mp4  --save_videos
+```
+**注意:**
+ - 请先确保已经安装了[ffmpeg](https://ffmpeg.org/ffmpeg.html), Linux(Ubuntu)平台可以直接用以下命令安装：`apt-get update && apt-get install -y ffmpeg`。
+
+### 4. 导出预测模型
+```bash
+CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_visdrone_pedestrian.pdparams
+```
+
+### 5. 用导出的模型基于Python去预测
+```bash
+python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608_visdrone_pedestrian --video_file={your video name}.mp4 --device=GPU --save_mot_txts
+```
+**注意:**
+ - 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。
+ - 跟踪结果txt文件每行信息是`frame,id,x1,y1,w,h,score,-1,-1,-1`。
+
+## 引用
+```
+@article{zhang2020fair,
+  title={FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking},
+  author={Zhang, Yifu and Wang, Chunyu and Wang, Xinggang and Zeng, Wenjun and Liu, Wenyu},
+  journal={arXiv preprint arXiv:2004.01888},
+  year={2020}
+}
+
+@INPROCEEDINGS{8237302,
+author={S. {Manen} and M. {Gygli} and D. {Dai} and L. V. {Gool}},
+booktitle={2017 IEEE International Conference on Computer Vision (ICCV)},
+title={PathTrack: Fast Trajectory Annotation with Path Supervision},
+year={2017},
+volume={},
+number={},
+pages={290-299},
+doi={10.1109/ICCV.2017.40},
+ISSN={2380-7504},
+month={Oct},}
+
+@ARTICLE{9573394,
+  author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
+  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  title={Detection and Tracking Meet Drones Challenge},
+  year={2021},
+  volume={},
+  number={},
+  pages={1-1},
+  doi={10.1109/TPAMI.2021.3119563}
+}
+```
--- a/paddle_detection/configs/mot/pedestrian/fairmot_dla34_30e_1088x608_pathtrack.yml
+++ b/paddle_detection/configs/mot/pedestrian/fairmot_dla34_30e_1088x608_pathtrack.yml
@@ -0,0 +1,26 @@
+_BASE_: [
+  '../fairmot/fairmot_dla34_30e_1088x608.yml'
+]
+
+weights: output/fairmot_dla34_30e_1088x608_pathtrack/model_final
+
+# for MOT training
+TrainDataset:
+  !MOTDataSet
+    dataset_dir: dataset/mot
+    image_lists: ['pathtrack.train']
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
+
+# for MOT evaluation
+# If you want to change the MOT evaluation dataset, please modify 'data_root'
+EvalMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    data_root: pathtrack/images/test
+    keep_ori_im: False # set True if save visualization images or video, or used in DeepSORT
+
+# for MOT video inference
+TestMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    keep_ori_im: True # set True if save visualization images or video
--- a/paddle_detection/configs/mot/pedestrian/fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml
+++ b/paddle_detection/configs/mot/pedestrian/fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml
@@ -0,0 +1,26 @@
+_BASE_: [
+  '../fairmot/fairmot_dla34_30e_1088x608.yml'
+]
+
+weights: output/fairmot_dla34_30e_1088x608_visdrone_pedestrian/model_final
+
+# for MOT training
+TrainDataset:
+  !MOTDataSet
+    dataset_dir: dataset/mot
+    image_lists: ['visdrone_pedestrian.train']
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
+
+# for MOT evaluation
+# If you want to change the MOT evaluation dataset, please modify 'data_root'
+EvalMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    data_root: visdrone_pedestrian/images/val
+    keep_ori_im: False # set True if save visualization images or video, or used in DeepSORT
+
+# for MOT video inference
+TestMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    keep_ori_im: True # set True if save visualization images or video
--- a/paddle_detection/configs/mot/pedestrian/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_pedestrian.yml
+++ b/paddle_detection/configs/mot/pedestrian/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_pedestrian.yml
@@ -0,0 +1,26 @@
+_BASE_: [
+  '../fairmot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608.yml'
+]
+
+weights: output/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_pedestrian/model_final
+
+# for MOT training
+TrainDataset:
+  !MOTDataSet
+    dataset_dir: dataset/mot
+    image_lists: ['visdrone_pedestrian.train']
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
+
+# for MOT evaluation
+# If you want to change the MOT evaluation dataset, please modify 'data_root'
+EvalMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    data_root: visdrone_pedestrian/images/val
+    keep_ori_im: False # set True if save visualization images or video, or used in DeepSORT
+
+# for MOT video inference
+TestMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    keep_ori_im: True # set True if save visualization images or video
--- a/paddle_detection/configs/mot/pedestrian/fairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone_pedestrian.yml
+++ b/paddle_detection/configs/mot/pedestrian/fairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone_pedestrian.yml
@@ -0,0 +1,26 @@
+_BASE_: [
+  '../fairmot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.yml'
+]
+
+weights: output/fairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone_pedestrian/model_final
+
+# for MOT training
+TrainDataset:
+  !MOTDataSet
+    dataset_dir: dataset/mot
+    image_lists: ['visdrone_pedestrian.train']
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
+
+# for MOT evaluation
+# If you want to change the MOT evaluation dataset, please modify 'data_root'
+EvalMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    data_root: visdrone_pedestrian/images/val
+    keep_ori_im: False # set True if save visualization images or video, or used in DeepSORT
+
+# for MOT video inference
+TestMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    keep_ori_im: True # set True if save visualization images or video
--- a/paddle_detection/configs/mot/pedestrian/fairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone_pedestrian.yml
+++ b/paddle_detection/configs/mot/pedestrian/fairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone_pedestrian.yml
@@ -0,0 +1,26 @@
+_BASE_: [
+  '../fairmot/fairmot_hrnetv2_w18_dlafpn_30e_864x480.yml'
+]
+
+weights: output/fairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone_pedestrian/model_final
+
+# for MOT training
+TrainDataset:
+  !MOTDataSet
+    dataset_dir: dataset/mot
+    image_lists: ['visdrone_pedestrian.train']
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
+
+# for MOT evaluation
+# If you want to change the MOT evaluation dataset, please modify 'data_root'
+EvalMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    data_root: visdrone_pedestrian/images/val
+    keep_ori_im: False # set True if save visualization images or video, or used in DeepSORT
+
+# for MOT video inference
+TestMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    keep_ori_im: True # set True if save visualization images or video
--- a/paddle_detection/configs/mot/pedestrian/tools/visdrone/visdrone2mot.py
+++ b/paddle_detection/configs/mot/pedestrian/tools/visdrone/visdrone2mot.py
@@ -0,0 +1,299 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import glob
+import os
+import os.path as osp
+import cv2
+import argparse
+import numpy as np
+import random
+
+# The object category indicates the type of annotated object, 
+# (i.e., ignored regions(0), pedestrian(1), people(2), bicycle(3), car(4), van(5), truck(6), tricycle(7), awning-tricycle(8), bus(9), motor(10),others(11))
+
+# Extract single class or multi class
+isExtractMultiClass = False
+# These sequences are excluded because there are too few pedestrians
+exclude_seq = [
+    "uav0000117_02622_v", "uav0000182_00000_v", "uav0000268_05773_v",
+    "uav0000305_00000_v"
+]
+
+
+def mkdir_if_missing(d):
+    if not osp.exists(d):
+        os.makedirs(d)
+
+
+def genGtFile(seqPath, outPath, classes=[]):
+    id_idx = 0
+    old_idx = -1
+    with open(seqPath, 'r') as singleSeqFile:
+        motLine = []
+        allLines = singleSeqFile.readlines()
+        for line in allLines:
+            line = line.replace('\n', '')
+            line = line.split(',')
+            # exclude occlusion!='2'
+            if line[-1] != '2' and line[7] in classes:
+                if old_idx != int(line[1]):
+                    id_idx += 1
+                    old_idx = int(line[1])
+                newLine = line[0:6]
+                newLine[1] = str(id_idx)
+                newLine.append('1')
+                if (len(classes) > 1 and isExtractMultiClass):
+                    class_index = str(classes.index(line[7]) + 1)
+                    newLine.append(class_index)
+                else:
+                    newLine.append('1')  # use permanent class '1'
+                newLine.append('1')
+                motLine.append(newLine)
+        mkdir_if_missing(outPath)
+        gtFilePath = osp.join(outPath, 'gt.txt')
+        with open(gtFilePath, 'w') as gtFile:
+            motLine = list(map(lambda x: str.join(',', x), motLine))
+            motLineStr = str.join('\n', motLine)
+            gtFile.write(motLineStr)
+
+
+def genSeqInfo(img1Path, seqName):
+    imgPaths = glob.glob(img1Path + '/*.jpg')
+    seqLength = len(imgPaths)
+    if seqLength > 0:
+        image1 = cv2.imread(imgPaths[0])
+        imgHeight = image1.shape[0]
+        imgWidth = image1.shape[1]
+    else:
+        imgHeight = 0
+        imgWidth = 0
+    seqInfoStr = f'''[Sequence]\nname={seqName}\nimDir=img1\nframeRate=30\nseqLength={seqLength}\nimWidth={imgWidth}\nimHeight={imgHeight}\nimExt=.jpg'''
+    seqInfoPath = img1Path.replace('/img1', '')
+    with open(seqInfoPath + '/seqinfo.ini', 'w') as seqFile:
+        seqFile.write(seqInfoStr)
+
+
+def copyImg(img1Path, gtTxtPath, outputFileName):
+    with open(gtTxtPath, 'r') as gtFile:
+        allLines = gtFile.readlines()
+        imgList = []
+        for line in allLines:
+            imgIdx = int(line.split(',')[0])
+            if imgIdx not in imgList:
+                imgList.append(imgIdx)
+                seqName = gtTxtPath.replace('./{}/'.format(outputFileName),
+                                            '').replace('/gt/gt.txt', '')
+                sourceImgPath = osp.join('./sequences', seqName,
+                                         '{:07d}.jpg'.format(imgIdx))
+                os.system(f'cp {sourceImgPath} {img1Path}')
+
+
+def genMotLabels(datasetPath, outputFileName, classes=['2']):
+    mkdir_if_missing(osp.join(datasetPath, outputFileName))
+    annotationsPath = osp.join(datasetPath, 'annotations')
+    annotationsList = glob.glob(osp.join(annotationsPath, '*.txt'))
+    for annotationPath in annotationsList:
+        seqName = annotationPath.split('/')[-1].replace('.txt', '')
+        if seqName in exclude_seq:
+            continue
+        mkdir_if_missing(osp.join(datasetPath, outputFileName, seqName, 'gt'))
+        mkdir_if_missing(osp.join(datasetPath, outputFileName, seqName, 'img1'))
+        genGtFile(annotationPath,
+                  osp.join(datasetPath, outputFileName, seqName, 'gt'), classes)
+        img1Path = osp.join(datasetPath, outputFileName, seqName, 'img1')
+        gtTxtPath = osp.join(datasetPath, outputFileName, seqName, 'gt/gt.txt')
+        copyImg(img1Path, gtTxtPath, outputFileName)
+        genSeqInfo(img1Path, seqName)
+
+
+def deleteFileWhichImg1IsEmpty(mot16Path, dataType='train'):
+    path = mot16Path
+    data_images_train = osp.join(path, 'images', f'{dataType}')
+    data_images_train_seqs = glob.glob(data_images_train + '/*')
+    if (len(data_images_train_seqs) == 0):
+        print('dataset is empty!')
+    for data_images_train_seq in data_images_train_seqs:
+        data_images_train_seq_img1 = osp.join(data_images_train_seq, 'img1')
+        if len(glob.glob(data_images_train_seq_img1 + '/*.jpg')) == 0:
+            print(f"os.system(rm -rf {data_images_train_seq})")
+            os.system(f'rm -rf {data_images_train_seq}')
+
+
+def formatMot16Path(dataPath, pathType='train'):
+    train_path = osp.join(dataPath, 'images', pathType)
+    mkdir_if_missing(train_path)
+    os.system(f'mv {dataPath}/* {train_path}')
+
+
+def VisualGt(dataPath, phase='train'):
+    seqList = sorted(glob.glob(osp.join(dataPath, 'images', phase) + '/*'))
+    seqIndex = random.randint(0, len(seqList) - 1)
+    seqPath = seqList[seqIndex]
+    gt_path = osp.join(seqPath, 'gt', 'gt.txt')
+    img_list_path = sorted(glob.glob(osp.join(seqPath, 'img1', '*.jpg')))
+    imgIndex = random.randint(0, len(img_list_path))
+    img_Path = img_list_path[imgIndex]
+    frame_value = int(img_Path.split('/')[-1].replace('.jpg', ''))
+    gt_value = np.loadtxt(gt_path, dtype=int, delimiter=',')
+    gt_value = gt_value[gt_value[:, 0] == frame_value]
+    get_list = gt_value.tolist()
+    img = cv2.imread(img_Path)
+    colors = [[255, 0, 0], [255, 255, 0], [255, 0, 255], [0, 255, 0],
+              [0, 255, 255], [0, 0, 255]]
+    for seq, _id, pl, pt, w, h, _, bbox_class, _ in get_list:
+        cv2.putText(img,
+                    str(bbox_class), (pl, pt), cv2.FONT_HERSHEY_PLAIN, 2,
+                    colors[bbox_class - 1])
+        cv2.rectangle(
+            img, (pl, pt), (pl + w, pt + h),
+            colors[bbox_class - 1],
+            thickness=2)
+    cv2.imwrite('testGt.jpg', img)
+
+
+def VisualDataset(datasetPath, phase='train', seqName='', frameId=1):
+    trainPath = osp.join(datasetPath, 'labels_with_ids', phase)
+    seq1Paths = osp.join(trainPath, seqName)
+    seq_img1_path = osp.join(seq1Paths, 'img1')
+    label_with_idPath = osp.join(seq_img1_path, '%07d' % frameId) + '.txt'
+    image_path = label_with_idPath.replace('labels_with_ids', 'images').replace(
+        '.txt', '.jpg')
+    seqInfoPath = str.join('/', image_path.split('/')[:-2])
+    seqInfoPath = seqInfoPath + '/seqinfo.ini'
+    seq_info = open(seqInfoPath).read()
+    width = int(seq_info[seq_info.find('imWidth=') + 8:seq_info.find(
+        '\nimHeight')])
+    height = int(seq_info[seq_info.find('imHeight=') + 9:seq_info.find(
+        '\nimExt')])
+
+    with open(label_with_idPath, 'r') as label:
+        allLines = label.readlines()
+        images = cv2.imread(image_path)
+        for line in allLines:
+            line = line.split(' ')
+            line = list(map(lambda x: float(x), line))
+            c1, c2, w, h = line[2:6]
+            x1 = c1 - w / 2
+            x2 = c2 - h / 2
+            x3 = c1 + w / 2
+            x4 = c2 + h / 2
+            cv2.rectangle(
+                images, (int(x1 * width), int(x2 * height)),
+                (int(x3 * width), int(x4 * height)), (255, 0, 0),
+                thickness=2)
+        cv2.imwrite('test.jpg', images)
+
+
+def gen_image_list(dataPath, datType):
+    inputPath = f'{dataPath}/images/{datType}'
+    pathList = glob.glob(inputPath + '/*')
+    pathList = sorted(pathList)
+    allImageList = []
+    for pathSingle in pathList:
+        imgList = sorted(glob.glob(osp.join(pathSingle, 'img1', '*.jpg')))
+        for imgPath in imgList:
+            allImageList.append(imgPath)
+    with open(f'{dataPath}.{datType}', 'w') as image_list_file:
+        allImageListStr = str.join('\n', allImageList)
+        image_list_file.write(allImageListStr)
+
+
+def gen_labels_mot(MOT_data, phase='train'):
+    seq_root = './{}/images/{}'.format(MOT_data, phase)
+    label_root = './{}/labels_with_ids/{}'.format(MOT_data, phase)
+    mkdir_if_missing(label_root)
+    seqs = [s for s in os.listdir(seq_root)]
+    print('seqs => ', seqs)
+    tid_curr = 0
+    tid_last = -1
+    for seq in seqs:
+        seq_info = open(osp.join(seq_root, seq, 'seqinfo.ini')).read()
+        seq_width = int(seq_info[seq_info.find('imWidth=') + 8:seq_info.find(
+            '\nimHeight')])
+        seq_height = int(seq_info[seq_info.find('imHeight=') + 9:seq_info.find(
+            '\nimExt')])
+
+        gt_txt = osp.join(seq_root, seq, 'gt', 'gt.txt')
+        gt = np.loadtxt(gt_txt, dtype=np.float64, delimiter=',')
+
+        seq_label_root = osp.join(label_root, seq, 'img1')
+        mkdir_if_missing(seq_label_root)
+
+        for fid, tid, x, y, w, h, mark, label, _ in gt:
+            # if mark == 0 or not label == 1: 
+            #     continue
+            fid = int(fid)
+            tid = int(tid)
+            if not tid == tid_last:
+                tid_curr += 1
+                tid_last = tid
+            x += w / 2
+            y += h / 2
+            label_fpath = osp.join(seq_label_root, '{:07d}.txt'.format(fid))
+            label_str = '0 {:d} {:.6f} {:.6f} {:.6f} {:.6f}\n'.format(
+                tid_curr, x / seq_width, y / seq_height, w / seq_width,
+                h / seq_height)
+            with open(label_fpath, 'a') as f:
+                f.write(label_str)
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(description='input method')
+    parser.add_argument("--transMot", type=bool, default=False)
+    parser.add_argument("--genMot", type=bool, default=False)
+    parser.add_argument("--formatMotPath", type=bool, default=False)
+    parser.add_argument("--deleteEmpty", type=bool, default=False)
+    parser.add_argument("--genLabelsMot", type=bool, default=False)
+    parser.add_argument("--genImageList", type=bool, default=False)
+    parser.add_argument("--visualImg", type=bool, default=False)
+    parser.add_argument("--visualGt", type=bool, default=False)
+    parser.add_argument("--data_name", type=str, default='visdrone_pedestrian')
+    parser.add_argument("--phase", type=str, default='train')
+    parser.add_argument(
+        "--classes", type=str, default='1,2')  # pedestrian and people
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_arguments()
+    classes = args.classes.split(',')
+    datasetPath = './'
+    dataName = args.data_name
+    phase = args.phase
+    if args.transMot:
+        genMotLabels(datasetPath, dataName, classes)
+        formatMot16Path(dataName, pathType=phase)
+        mot16Path = f'./{dataName}'
+        deleteFileWhichImg1IsEmpty(mot16Path, dataType=phase)
+        gen_labels_mot(dataName, phase=phase)
+        gen_image_list(dataName, phase)
+    if args.genMot:
+        genMotLabels(datasetPath, dataName, classes)
+    if args.formatMotPath:
+        formatMot16Path(dataName, pathType=phase)
+    if args.deleteEmpty:
+        mot16Path = f'./{dataName}'
+        deleteFileWhichImg1IsEmpty(mot16Path, dataType=phase)
+    if args.genLabelsMot:
+        gen_labels_mot(dataName, phase=phase)
+    if args.genImageList:
+        gen_image_list(dataName, phase)
+    if args.visualGt:
+        VisualGt(f'./{dataName}', phase)
+    if args.visualImg:
+        seqName = 'uav0000137_00458_v'
+        frameId = 43
+        VisualDataset(
+            f'./{dataName}', phase=phase, seqName=seqName, frameId=frameId)