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PaddleDetection
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未验证 提交 3c95b81d 编写于 作者: G George Ni 提交者： GitHub
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[MOT] add kitti metric (#3736) 

* add kitti metric

* clean kitti metric code

* fix kitticars doc

* remove kitti headtracking cfgs

* remove kitti headtracking cfgs, clean code

* fix datatype and ffmpeg, test=document_fix

* add soft link, test=document_fix

* add doc link, test=document_fix

* fix deploy image_list, test=document_fix
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configs/mot/README.md
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......@@ -6,6 +6,7 @@ English | [简体中文](README_cn.md)
- [Introduction](#Introduction)
- [Installation](#Installation)
- [Model Zoo](#Model_Zoo)
- [Feature Tracking Model](#Feature_Tracking_Model)
- [Dataset Preparation](#Dataset_Preparation)
- [Getting Start](#Getting_Start)
- [Citations](#Citations)
......@@ -131,6 +132,28 @@ If you use a stronger detection model, you can get better results. Each txt is t
FairMOT used 8 GPUs for training and mini-batch size as 6 on each GPU, and trained for 30 epoches.
## Feature Tracking Model
### 【Head Tracking](./headtracking21/README.md)
### FairMOT Results on HT-21 Training Set
| backbone | input shape | MOTA | IDF1 | IDS | FP | FN | FPS | download | config |
| :--------------| :------- | :----: | :----: | :---: | :----: | :---: | :------: | :----: |:----: |
| DLA-34 | 1088x608 | 67.2 | 70.4 | 9403 | 124840 | 255007 | - | [model](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml) |
### FairMOT Results on HT-21 Test Set
| backbone | input shape | MOTA | IDF1 | IDS | FP | FN | FPS | download | config |
| :--------------| :------- | :----: | :----: | :----: | :----: | :----: |:-------: | :----: | :----: |
| DLA-34 | 1088x608 | 58.2 | 61.3 | 13166 | 141872 | 197074 | - | [model](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml) |
### [Vehicle Tracking](./kitticars/README.md)
### FairMOT Results on KITTI tracking (2D bounding-boxes) Training Set (Car)
| backbone | input shape | MOTA | FPS | download | config |
| :--------------| :------- | :-----: | :-----: | :------: | :----: |
| DLA-34 | 1088x608 | 67.9 | - |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_kitticars.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/kitticars/fairmot_dla34_30e_1088x608_kitticars.yml) |
## Dataset Preparation
### MOT Dataset
......
configs/mot/README_cn.md
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......@@ -6,6 +6,7 @@
- [简介](#简介)
- [安装依赖](#安装依赖)
- [模型库](#模型库)
- [特色垂类跟踪模型](#特色垂类跟踪模型)
- [数据集准备](#数据集准备)
- [快速开始](#快速开始)
- [引用](#引用)
......@@ -131,6 +132,28 @@ wget https://dataset.bj.bcebos.com/mot/det_results_dir.zip
FairMOT使用8个GPU进行训练，每个GPU上batch size为6，训练30个epoch。
## 特色垂类跟踪模型
### 【人头跟踪（Head Tracking)](./headtracking21/README.md)
### FairMOT在HT-21 Training Set上结果
| 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 |
| :--------------| :------- | :----: | :----: | :---: | :----: | :---: | :------: | :----: |:----: |
| DLA-34 | 1088x608 | 67.2 | 70.4 | 9403 | 124840 | 255007 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml) |
### FairMOT在HT-21 Test Set上结果
| 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 |
| :--------------| :------- | :----: | :----: | :----: | :----: | :----: |:-------: | :----: | :----: |
| DLA-34 | 1088x608 | 58.2 | 61.3 | 13166 | 141872 | 197074 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml) |
### [车辆跟踪 (Vehicle Tracking)](./kitticars/README.md)
### FairMOT在KITTI tracking (2D bounding-boxes) Training Set上Car类别的结果
| 骨干网络 | 输入尺寸 | MOTA | FPS | 下载链接 | 配置文件 |
| :--------------| :------- | :-----: | :-----: | :------: | :----: |
| DLA-34 | 1088x608 | 67.9 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_kitticars.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/kitticars/fairmot_dla34_30e_1088x608_kitticars.yml) |
## 数据集准备
### MOT数据集
......
configs/mot/headtracking21/README.md 0 → 120000
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README_cn.md
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configs/mot/fairmot/headtracking21/README_cn.md → configs/mot/headtracking21/README_cn.md
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简体中文 | [English](README.md)
[English](README.md) | 简体中文
# 特色垂类跟踪模型
# FairMOT (FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking)
## 内容
- [简介](#简介)
- [模型库](#模型库)
- [快速开始](#快速开始)
- [引用](#引用)
## 简介
[FairMOT](https://arxiv.org/abs/2004.01888)以Anchor Free的CenterNet检测器为基础，克服了Anchor-Based的检测框架中anchor和特征不对齐问题，深浅层特征融合使得检测和ReID任务各自获得所需要的特征，并且使用低维度ReID特征，提出了一种由两个同质分支组成的简单baseline来预测像素级目标得分和ReID特征，实现了两个任务之间的公平性，并获得了更高水平的实时多目标跟踪精度。
## 人头跟踪（Head Tracking)
现有行人跟踪器对高人群密度场景表现不佳，人头跟踪更适用于密集场景的跟踪。
[HT-21](https://motchallenge.net/data/Head_Tracking_21)是一个高人群密度拥挤场景的人头跟踪数据集，场景包括不同的光线和环境条件下的拥挤的室内和室外场景，所有序列的帧速率都是25fps。
<div align="center">
<img src="../../../../docs/images/ht_fairmot.gif" width='800'/>
<img src="../../../docs/images/ht_fairmot.gif" width='800'/>
</div>
## 模型库
### FairMOT在HT-21 Training Set上结果
| 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 |
| :--------------| :------- | :----: | :----: | :---: | :----: | :---: | :------: | :----: |:----: |
| DLA-34 | 1088x608 | 67.2 | 70.4 | 9403 | 124840 | 255007 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/fairmot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml) |
| DLA-34 | 1088x608 | 67.2 | 70.4 | 9403 | 124840 | 255007 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml) |
### FairMOT在HT-21 Test Set上结果
| 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FP | FN | FPS | 下载链接 | 配置文件 |
| :--------------| :------- | :----: | :----: | :----: | :----: | :----: |:-------: | :----: | :----: |
| DLA-34 | 1088x608 | 58.2 | 61.3 | 13166 | 141872 | 197074 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/fairmot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml) |
| DLA-34 | 1088x608 | 58.2 | 61.3 | 13166 | 141872 | 197074 | - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml) |
**注意:**
FairMOT使用8个GPU进行训练，每个GPU上batch size为6，训练30个epoch。
......@@ -37,53 +26,42 @@
## 快速开始
### 1. 训练
使用8GPU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_headtracking21/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/mot/fairmot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml
python -m paddle.distributed.launch --log_dir=./fairmot_dla34_30e_1088x608_headtracking21/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.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/fairmot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams
CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams
# 使用训练保存的checkpoint
CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/fairmot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=output/fairmot_dla34_30e_1088x608_headtracking21/model_final.pdparams
CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=output/fairmot_dla34_30e_1088x608_headtracking21/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/fairmot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams --video_file={your video name}.mp4 --save_videos
CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.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/fairmot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams
CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_headtracking21.pdparams
```
### 5. 用导出的模型基于Python去预测
```bash
python deploy/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608_headtracking21 --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。
## 引用
```
@article{zhang2020fair,
......
configs/mot/fairmot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml → configs/mot/headtracking21/fairmot_dla34_30e_1088x608_headtracking21.yml
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_BASE_: [
'../fairmot_dla34_30e_1088x608.yml'
'../fairmot/fairmot_dla34_30e_1088x608.yml'
]
weights: output/fairmot_dla34_30e_1088x608_headtracking21/model_final
# for MOT training
TrainDataset:
......@@ -11,7 +12,7 @@ TrainDataset:
data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
# for MOT evaluation
# If you want to change the MOT evaluation dataset, please modify 'task' and 'data_root'
# If you want to change the MOT evaluation dataset, please modify 'data_root'
EvalMOTDataset:
!MOTImageFolder
dataset_dir: dataset/mot
......@@ -20,6 +21,6 @@ EvalMOTDataset:
# for MOT video inference
TestMOTDataset:
!MOTVideoDataset
!MOTImageFolder
dataset_dir: dataset/mot
keep_ori_im: True # set True if save visualization images or video
configs/mot/kitticars/README.md 0 → 120000
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[English](README.md) | 简体中文
# 特色垂类跟踪模型
## 车辆跟踪 (Vehicle Tracking)
车辆跟踪的主要应用之一是交通监控。
[KITTI-Tracking](http://www.cvlibs.net/datasets/kitti/eval_tracking.php)是一个包含市区、乡村和高速公路等场景采集的数据集，每张图像中最多达15辆车和30个行人，还有各种程度的遮挡与截断。其中用于目标跟踪的数据集一共有50个视频序列，21个为训练集，29个为测试集，目标是估计类别“Car”和”Pedestrian“的目标轨迹，此处只使用类别“Car”。
<div align="center">
<img src="../../../docs/images/kitticars_fairmot.gif" width='800'/>
</div>
## 模型库
### FairMOT在KITTI tracking (2D bounding-boxes) Training Set上Car类别的结果
| 骨干网络 | 输入尺寸 | MOTA | FPS | 下载链接 | 配置文件 |
| :--------------| :------- | :-----: | :-----: | :------: | :----: |
| DLA-34 | 1088x608 | 67.9 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_kitticars.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/kitticars/fairmot_dla34_30e_1088x608_kitticars.yml) |
**注意:**
FairMOT使用8个GPU进行训练，每个GPU上batch size为6，训练30个epoch。
## 快速开始
### 1. 训练
使用8GPU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_kitticars/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/mot/kitticars/fairmot_dla34_30e_1088x608_kitticars.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/kitticars/fairmot_dla34_30e_1088x608_kitticars.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_kitticars.pdparams
# 使用训练保存的checkpoint
CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/kitticars/fairmot_dla34_30e_1088x608_kitticars.yml -o weights=output/fairmot_dla34_30e_1088x608_kitticars/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/kitticars/fairmot_dla34_30e_1088x608_kitticars.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_kitticars.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/kitticars/fairmot_dla34_30e_1088x608_kitticars.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_kitticars.pdparams
```
### 5. 用导出的模型基于Python去预测
```bash
python deploy/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608_kitticars --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。
## 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{Geiger2012CVPR,
author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2012}
}
```
configs/mot/kitticars/fairmot_dla34_30e_1088x608_kitticars.yml 0 → 100755
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_BASE_: [
'../fairmot/fairmot_dla34_30e_1088x608.yml'
]
metric: KITTI
weights: output/fairmot_dla34_30e_1088x608_kitticars/model_final
# for MOT training
TrainDataset:
!MOTDataSet
dataset_dir: dataset/mot
image_lists: ['kitticars.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: kitticars/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
deploy/python/mot_jde_infer.py
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......@@ -179,6 +179,7 @@ def write_mot_results(filename, results, data_type='mot'):
def predict_image(detector, image_list):
results = []
image_list.sort()
for i, img_file in enumerate(image_list):
frame = cv2.imread(img_file)
if FLAGS.run_benchmark:
......
deploy/python/mot_keypoint_unite_infer.py
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......@@ -56,6 +56,7 @@ def mot_keypoint_unite_predict_image(mot_model,
keypoint_model,
image_list,
keypoint_batch_size=1):
image_list.sort()
for i, img_file in enumerate(image_list):
frame = cv2.imread(img_file)
......
deploy/python/mot_sde_infer.py
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......@@ -297,6 +297,7 @@ class SDE_ReID(object):
def predict_image(detector, reid_model, image_list):
results = []
image_list.sort()
for i, img_file in enumerate(image_list):
frame = cv2.imread(img_file)
if FLAGS.run_benchmark:
......
ppdet/engine/tracker.py
浏览文件 @ 3c95b81d
......@@ -28,7 +28,7 @@ from ppdet.modeling.mot.utils import Detection, get_crops, scale_coords, clip_bo
from ppdet.modeling.mot.utils import Timer, load_det_results
from ppdet.modeling.mot import visualization as mot_vis
from ppdet.metrics import Metric, MOTMetric
from ppdet.metrics import Metric, MOTMetric, KITTIMOTMetric
import ppdet.utils.stats as stats
from .callbacks import Callback, ComposeCallback
......@@ -74,6 +74,8 @@ class Tracker(object):
if self.cfg.metric == 'MOT':
self._metrics = [MOTMetric(), ]
elif self.cfg.metric == 'KITTI':
self._metrics = [KITTIMOTMetric(), ]
else:
logger.warning("Metric not support for metric type {}".format(
self.cfg.metric))
......@@ -329,7 +331,7 @@ class Tracker(object):
if save_videos:
output_video_path = os.path.join(save_dir, '..',
'{}_vis.mp4'.format(seq))
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg {}'.format(
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -vf "scale=trunc(iw/2)*2:trunc(ih/2)*2" {}'.format(
save_dir, output_video_path)
os.system(cmd_str)
logger.info('Save video in {}.'.format(output_video_path))
......@@ -445,7 +447,7 @@ class Tracker(object):
if save_videos:
output_video_path = os.path.join(save_dir, '..',
'{}_vis.mp4'.format(seq))
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg {}'.format(
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -vf "scale=trunc(iw/2)*2:trunc(ih/2)*2" {}'.format(
save_dir, output_video_path)
os.system(cmd_str)
logger.info('Save video in {}'.format(output_video_path))
......@@ -454,7 +456,7 @@ class Tracker(object):
if data_type in ['mot', 'mcmot', 'lab']:
save_format = '{frame},{id},{x1},{y1},{w},{h},{score},-1,-1,-1\n'
elif data_type == 'kitti':
save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n'
save_format = '{frame} {id} car 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n'
else:
raise ValueError(data_type)
......
ppdet/metrics/mot_metrics.py
浏览文件 @ 3c95b81d
......@@ -11,23 +11,28 @@
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import copy
import sys
import math
from collections import defaultdict
import numpy as np
import paddle
import paddle.nn.functional as F
from ppdet.modeling.bbox_utils import bbox_iou_np_expand
from .map_utils import ap_per_class
from .metrics import Metric
from .munkres import Munkres
from ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)
__all__ = ['MOTEvaluator', 'MOTMetric', 'JDEDetMetric']
__all__ = ['MOTEvaluator', 'MOTMetric', 'JDEDetMetric', 'KITTIMOTMetric']
def read_mot_results(filename, is_gt=False, is_ignore=False):
......@@ -74,6 +79,7 @@ def read_mot_results(filename, is_gt=False, is_ignore=False):
"""
MOT dataset label list, see in https://motchallenge.net
labels={'ped', ... % 1
'person_on_vhcl', ... % 2
'car', ... % 3
......@@ -302,3 +308,926 @@ class JDEDetMetric(Metric):
def get_results(self):
return self.map_stat
"""
Following code is borrow from https://github.com/xingyizhou/CenterTrack/blob/master/src/tools/eval_kitti_track/evaluate_tracking.py
"""
class tData:
"""
Utility class to load data.
"""
def __init__(self,frame=-1,obj_type="unset",truncation=-1,occlusion=-1,\
obs_angle=-10,x1=-1,y1=-1,x2=-1,y2=-1,w=-1,h=-1,l=-1,\
X=-1000,Y=-1000,Z=-1000,yaw=-10,score=-1000,track_id=-1):
"""
Constructor, initializes the object given the parameters.
"""
self.frame = frame
self.track_id = track_id
self.obj_type = obj_type
self.truncation = truncation
self.occlusion = occlusion
self.obs_angle = obs_angle
self.x1 = x1
self.y1 = y1
self.x2 = x2
self.y2 = y2
self.w = w
self.h = h
self.l = l
self.X = X
self.Y = Y
self.Z = Z
self.yaw = yaw
self.score = score
self.ignored = False
self.valid = False
self.tracker = -1
def __str__(self):
attrs = vars(self)
return '\n'.join("%s: %s" % item for item in attrs.items())
class KITTIEvaluation(object):
""" KITTI tracking statistics (CLEAR MOT, id-switches, fragments, ML/PT/MT, precision/recall)
MOTA - Multi-object tracking accuracy in [0,100]
MOTP - Multi-object tracking precision in [0,100] (3D) / [td,100] (2D)
MOTAL - Multi-object tracking accuracy in [0,100] with log10(id-switches)
id-switches - number of id switches
fragments - number of fragmentations
MT, PT, ML - number of mostly tracked, partially tracked and mostly lost trajectories
recall - recall = percentage of detected targets
precision - precision = percentage of correctly detected targets
FAR - number of false alarms per frame
falsepositives - number of false positives (FP)
missed - number of missed targets (FN)
"""
def __init__(self, result_path, gt_path, min_overlap=0.5, max_truncation = 0,\
min_height = 25, max_occlusion = 2, cls="car",\
n_frames=[], seqs=[], n_sequences=0):
# get number of sequences and
# get number of frames per sequence from test mapping
# (created while extracting the benchmark)
self.gt_path = os.path.join(gt_path, "label_02")
self.n_frames = n_frames
self.sequence_name = seqs
self.n_sequences = n_sequences
self.cls = cls # class to evaluate, i.e. pedestrian or car
self.result_path = result_path
# statistics and numbers for evaluation
self.n_gt = 0 # number of ground truth detections minus ignored false negatives and true positives
self.n_igt = 0 # number of ignored ground truth detections
self.n_gts = [
] # number of ground truth detections minus ignored false negatives and true positives PER SEQUENCE
self.n_igts = [
] # number of ground ignored truth detections PER SEQUENCE
self.n_gt_trajectories = 0
self.n_gt_seq = []
self.n_tr = 0 # number of tracker detections minus ignored tracker detections
self.n_trs = [
] # number of tracker detections minus ignored tracker detections PER SEQUENCE
self.n_itr = 0 # number of ignored tracker detections
self.n_itrs = [] # number of ignored tracker detections PER SEQUENCE
self.n_igttr = 0 # number of ignored ground truth detections where the corresponding associated tracker detection is also ignored
self.n_tr_trajectories = 0
self.n_tr_seq = []
self.MOTA = 0
self.MOTP = 0
self.MOTAL = 0
self.MODA = 0
self.MODP = 0
self.MODP_t = []
self.recall = 0
self.precision = 0
self.F1 = 0
self.FAR = 0
self.total_cost = 0
self.itp = 0 # number of ignored true positives
self.itps = [] # number of ignored true positives PER SEQUENCE
self.tp = 0 # number of true positives including ignored true positives!
self.tps = [
] # number of true positives including ignored true positives PER SEQUENCE
self.fn = 0 # number of false negatives WITHOUT ignored false negatives
self.fns = [
] # number of false negatives WITHOUT ignored false negatives PER SEQUENCE
self.ifn = 0 # number of ignored false negatives
self.ifns = [] # number of ignored false negatives PER SEQUENCE
self.fp = 0 # number of false positives
# a bit tricky, the number of ignored false negatives and ignored true positives
# is subtracted, but if both tracker detection and ground truth detection
# are ignored this number is added again to avoid double counting
self.fps = [] # above PER SEQUENCE
self.mme = 0
self.fragments = 0
self.id_switches = 0
self.MT = 0
self.PT = 0
self.ML = 0
self.min_overlap = min_overlap # minimum bounding box overlap for 3rd party metrics
self.max_truncation = max_truncation # maximum truncation of an object for evaluation
self.max_occlusion = max_occlusion # maximum occlusion of an object for evaluation
self.min_height = min_height # minimum height of an object for evaluation
self.n_sample_points = 500
# this should be enough to hold all groundtruth trajectories
# is expanded if necessary and reduced in any case
self.gt_trajectories = [[] for x in range(self.n_sequences)]
self.ign_trajectories = [[] for x in range(self.n_sequences)]
def loadGroundtruth(self):
try:
self._loadData(self.gt_path, cls=self.cls, loading_groundtruth=True)
except IOError:
return False
return True
def loadTracker(self):
try:
if not self._loadData(
self.result_path, cls=self.cls, loading_groundtruth=False):
return False
except IOError:
return False
return True
def _loadData(self,
root_dir,
cls,
min_score=-1000,
loading_groundtruth=False):
"""
Generic loader for ground truth and tracking data.
Use loadGroundtruth() or loadTracker() to load this data.
Loads detections in KITTI format from textfiles.
"""
# construct objectDetections object to hold detection data
t_data = tData()
data = []
eval_2d = True
eval_3d = True
seq_data = []
n_trajectories = 0
n_trajectories_seq = []
for seq, s_name in enumerate(self.sequence_name):
i = 0
filename = os.path.join(root_dir, "%s.txt" % s_name)
f = open(filename, "r")
f_data = [
[] for x in range(self.n_frames[seq])
] # current set has only 1059 entries, sufficient length is checked anyway
ids = []
n_in_seq = 0
id_frame_cache = []
for line in f:
# KITTI tracking benchmark data format:
# (frame,tracklet_id,objectType,truncation,occlusion,alpha,x1,y1,x2,y2,h,w,l,X,Y,Z,ry)
line = line.strip()
fields = line.split(" ")
# classes that should be loaded (ignored neighboring classes)
if "car" in cls.lower():
classes = ["car", "van"]
elif "pedestrian" in cls.lower():
classes = ["pedestrian", "person_sitting"]
else:
classes = [cls.lower()]
classes += ["dontcare"]
if not any([s for s in classes if s in fields[2].lower()]):
continue
# get fields from table
t_data.frame = int(float(fields[0])) # frame
t_data.track_id = int(float(fields[1])) # id
t_data.obj_type = fields[
2].lower() # object type [car, pedestrian, cyclist, ...]
t_data.truncation = int(
float(fields[3])) # truncation [-1,0,1,2]
t_data.occlusion = int(
float(fields[4])) # occlusion [-1,0,1,2]
t_data.obs_angle = float(fields[5]) # observation angle [rad]
t_data.x1 = float(fields[6]) # left [px]
t_data.y1 = float(fields[7]) # top [px]
t_data.x2 = float(fields[8]) # right [px]
t_data.y2 = float(fields[9]) # bottom [px]
t_data.h = float(fields[10]) # height [m]
t_data.w = float(fields[11]) # width [m]
t_data.l = float(fields[12]) # length [m]
t_data.X = float(fields[13]) # X [m]
t_data.Y = float(fields[14]) # Y [m]
t_data.Z = float(fields[15]) # Z [m]
t_data.yaw = float(fields[16]) # yaw angle [rad]
if not loading_groundtruth:
if len(fields) == 17:
t_data.score = -1
elif len(fields) == 18:
t_data.score = float(fields[17]) # detection score
else:
logger.info("file is not in KITTI format")
return
# do not consider objects marked as invalid
if t_data.track_id is -1 and t_data.obj_type != "dontcare":
continue
idx = t_data.frame
# check if length for frame data is sufficient
if idx >= len(f_data):
print("extend f_data", idx, len(f_data))
f_data += [[] for x in range(max(500, idx - len(f_data)))]
try:
id_frame = (t_data.frame, t_data.track_id)
if id_frame in id_frame_cache and not loading_groundtruth:
logger.info(
"track ids are not unique for sequence %d: frame %d"
% (seq, t_data.frame))
logger.info(
"track id %d occured at least twice for this frame"
% t_data.track_id)
logger.info("Exiting...")
#continue # this allows to evaluate non-unique result files
return False
id_frame_cache.append(id_frame)
f_data[t_data.frame].append(copy.copy(t_data))
except:
print(len(f_data), idx)
raise
if t_data.track_id not in ids and t_data.obj_type != "dontcare":
ids.append(t_data.track_id)
n_trajectories += 1
n_in_seq += 1
# check if uploaded data provides information for 2D and 3D evaluation
if not loading_groundtruth and eval_2d is True and (
t_data.x1 == -1 or t_data.x2 == -1 or t_data.y1 == -1 or
t_data.y2 == -1):
eval_2d = False
if not loading_groundtruth and eval_3d is True and (
t_data.X == -1000 or t_data.Y == -1000 or
t_data.Z == -1000):
eval_3d = False
# only add existing frames
n_trajectories_seq.append(n_in_seq)
seq_data.append(f_data)
f.close()
if not loading_groundtruth:
self.tracker = seq_data
self.n_tr_trajectories = n_trajectories
self.eval_2d = eval_2d
self.eval_3d = eval_3d
self.n_tr_seq = n_trajectories_seq
if self.n_tr_trajectories == 0:
return False
else:
# split ground truth and DontCare areas
self.dcareas = []
self.groundtruth = []
for seq_idx in range(len(seq_data)):
seq_gt = seq_data[seq_idx]
s_g, s_dc = [], []
for f in range(len(seq_gt)):
all_gt = seq_gt[f]
g, dc = [], []
for gg in all_gt:
if gg.obj_type == "dontcare":
dc.append(gg)
else:
g.append(gg)
s_g.append(g)
s_dc.append(dc)
self.dcareas.append(s_dc)
self.groundtruth.append(s_g)
self.n_gt_seq = n_trajectories_seq
self.n_gt_trajectories = n_trajectories
return True
def boxoverlap(self, a, b, criterion="union"):
"""
boxoverlap computes intersection over union for bbox a and b in KITTI format.
If the criterion is 'union', overlap = (a inter b) / a union b).
If the criterion is 'a', overlap = (a inter b) / a, where b should be a dontcare area.
"""
x1 = max(a.x1, b.x1)
y1 = max(a.y1, b.y1)
x2 = min(a.x2, b.x2)
y2 = min(a.y2, b.y2)
w = x2 - x1
h = y2 - y1
if w <= 0. or h <= 0.:
return 0.
inter = w * h
aarea = (a.x2 - a.x1) * (a.y2 - a.y1)
barea = (b.x2 - b.x1) * (b.y2 - b.y1)
# intersection over union overlap
if criterion.lower() == "union":
o = inter / float(aarea + barea - inter)
elif criterion.lower() == "a":
o = float(inter) / float(aarea)
else:
raise TypeError("Unkown type for criterion")
return o
def compute3rdPartyMetrics(self):
"""
Computes the metrics defined in
- Stiefelhagen 2008: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
MOTA, MOTAL, MOTP
- Nevatia 2008: Global Data Association for Multi-Object Tracking Using Network Flows
MT/PT/ML
"""
# construct Munkres object for Hungarian Method association
hm = Munkres()
max_cost = 1e9
# go through all frames and associate ground truth and tracker results
# groundtruth and tracker contain lists for every single frame containing lists of KITTI format detections
fr, ids = 0, 0
for seq_idx in range(len(self.groundtruth)):
seq_gt = self.groundtruth[seq_idx]
seq_dc = self.dcareas[seq_idx] # don't care areas
seq_tracker = self.tracker[seq_idx]
seq_trajectories = defaultdict(list)
seq_ignored = defaultdict(list)
# statistics over the current sequence, check the corresponding
# variable comments in __init__ to get their meaning
seqtp = 0
seqitp = 0
seqfn = 0
seqifn = 0
seqfp = 0
seqigt = 0
seqitr = 0
last_ids = [[], []]
n_gts = 0
n_trs = 0
for f in range(len(seq_gt)):
g = seq_gt[f]
dc = seq_dc[f]
t = seq_tracker[f]
# counting total number of ground truth and tracker objects
self.n_gt += len(g)
self.n_tr += len(t)
n_gts += len(g)
n_trs += len(t)
# use hungarian method to associate, using boxoverlap 0..1 as cost
# build cost matrix
cost_matrix = []
this_ids = [[], []]
for gg in g:
# save current ids
this_ids[0].append(gg.track_id)
this_ids[1].append(-1)
gg.tracker = -1
gg.id_switch = 0
gg.fragmentation = 0
cost_row = []
for tt in t:
# overlap == 1 is cost ==0
c = 1 - self.boxoverlap(gg, tt)
# gating for boxoverlap
if c <= self.min_overlap:
cost_row.append(c)
else:
cost_row.append(max_cost) # = 1e9
cost_matrix.append(cost_row)
# all ground truth trajectories are initially not associated
# extend groundtruth trajectories lists (merge lists)
seq_trajectories[gg.track_id].append(-1)
seq_ignored[gg.track_id].append(False)
if len(g) is 0:
cost_matrix = [[]]
# associate
association_matrix = hm.compute(cost_matrix)
# tmp variables for sanity checks and MODP computation
tmptp = 0
tmpfp = 0
tmpfn = 0
tmpc = 0 # this will sum up the overlaps for all true positives
tmpcs = [0] * len(
g) # this will save the overlaps for all true positives
# the reason is that some true positives might be ignored
# later such that the corrsponding overlaps can
# be subtracted from tmpc for MODP computation
# mapping for tracker ids and ground truth ids
for row, col in association_matrix:
# apply gating on boxoverlap
c = cost_matrix[row][col]
if c < max_cost:
g[row].tracker = t[col].track_id
this_ids[1][row] = t[col].track_id
t[col].valid = True
g[row].distance = c
self.total_cost += 1 - c
tmpc += 1 - c
tmpcs[row] = 1 - c
seq_trajectories[g[row].track_id][-1] = t[col].track_id
# true positives are only valid associations
self.tp += 1
tmptp += 1
else:
g[row].tracker = -1
self.fn += 1
tmpfn += 1
# associate tracker and DontCare areas
# ignore tracker in neighboring classes
nignoredtracker = 0 # number of ignored tracker detections
ignoredtrackers = dict() # will associate the track_id with -1
# if it is not ignored and 1 if it is
# ignored;
# this is used to avoid double counting ignored
# cases, see the next loop
for tt in t:
ignoredtrackers[tt.track_id] = -1
# ignore detection if it belongs to a neighboring class or is
# smaller or equal to the minimum height
tt_height = abs(tt.y1 - tt.y2)
if ((self.cls == "car" and tt.obj_type == "van") or
(self.cls == "pedestrian" and
tt.obj_type == "person_sitting") or
tt_height <= self.min_height) and not tt.valid:
nignoredtracker += 1
tt.ignored = True
ignoredtrackers[tt.track_id] = 1
continue
for d in dc:
overlap = self.boxoverlap(tt, d, "a")
if overlap > 0.5 and not tt.valid:
tt.ignored = True
nignoredtracker += 1
ignoredtrackers[tt.track_id] = 1
break
# check for ignored FN/TP (truncation or neighboring object class)
ignoredfn = 0 # the number of ignored false negatives
nignoredtp = 0 # the number of ignored true positives
nignoredpairs = 0 # the number of ignored pairs, i.e. a true positive
# which is ignored but where the associated tracker
# detection has already been ignored
gi = 0
for gg in g:
if gg.tracker < 0:
if gg.occlusion>self.max_occlusion or gg.truncation>self.max_truncation\
or (self.cls=="car" and gg.obj_type=="van") or (self.cls=="pedestrian" and gg.obj_type=="person_sitting"):
seq_ignored[gg.track_id][-1] = True
gg.ignored = True
ignoredfn += 1
elif gg.tracker >= 0:
if gg.occlusion>self.max_occlusion or gg.truncation>self.max_truncation\
or (self.cls=="car" and gg.obj_type=="van") or (self.cls=="pedestrian" and gg.obj_type=="person_sitting"):
seq_ignored[gg.track_id][-1] = True
gg.ignored = True
nignoredtp += 1
# if the associated tracker detection is already ignored,
# we want to avoid double counting ignored detections
if ignoredtrackers[gg.tracker] > 0:
nignoredpairs += 1
# for computing MODP, the overlaps from ignored detections
# are subtracted
tmpc -= tmpcs[gi]
gi += 1
# the below might be confusion, check the comments in __init__
# to see what the individual statistics represent
# correct TP by number of ignored TP due to truncation
# ignored TP are shown as tracked in visualization
tmptp -= nignoredtp
# count the number of ignored true positives
self.itp += nignoredtp
# adjust the number of ground truth objects considered
self.n_gt -= (ignoredfn + nignoredtp)
# count the number of ignored ground truth objects
self.n_igt += ignoredfn + nignoredtp
# count the number of ignored tracker objects
self.n_itr += nignoredtracker
# count the number of ignored pairs, i.e. associated tracker and
# ground truth objects that are both ignored
self.n_igttr += nignoredpairs
# false negatives = associated gt bboxes exceding association threshold + non-associated gt bboxes
tmpfn += len(g) - len(association_matrix) - ignoredfn
self.fn += len(g) - len(association_matrix) - ignoredfn
self.ifn += ignoredfn
# false positives = tracker bboxes - associated tracker bboxes
# mismatches (mme_t)
tmpfp += len(
t) - tmptp - nignoredtracker - nignoredtp + nignoredpairs
self.fp += len(
t) - tmptp - nignoredtracker - nignoredtp + nignoredpairs
# update sequence data
seqtp += tmptp
seqitp += nignoredtp
seqfp += tmpfp
seqfn += tmpfn
seqifn += ignoredfn
seqigt += ignoredfn + nignoredtp
seqitr += nignoredtracker
# sanity checks
# - the number of true positives minues ignored true positives
# should be greater or equal to 0
# - the number of false negatives should be greater or equal to 0
# - the number of false positives needs to be greater or equal to 0
# otherwise ignored detections might be counted double
# - the number of counted true positives (plus ignored ones)
# and the number of counted false negatives (plus ignored ones)
# should match the total number of ground truth objects
# - the number of counted true positives (plus ignored ones)
# and the number of counted false positives
# plus the number of ignored tracker detections should
# match the total number of tracker detections; note that
# nignoredpairs is subtracted here to avoid double counting
# of ignored detection sin nignoredtp and nignoredtracker
if tmptp < 0:
print(tmptp, nignoredtp)
raise NameError("Something went wrong! TP is negative")
if tmpfn < 0:
print(tmpfn,
len(g),
len(association_matrix), ignoredfn, nignoredpairs)
raise NameError("Something went wrong! FN is negative")
if tmpfp < 0:
print(tmpfp,
len(t), tmptp, nignoredtracker, nignoredtp,
nignoredpairs)
raise NameError("Something went wrong! FP is negative")
if tmptp + tmpfn is not len(g) - ignoredfn - nignoredtp:
print("seqidx", seq_idx)
print("frame ", f)
print("TP ", tmptp)
print("FN ", tmpfn)
print("FP ", tmpfp)
print("nGT ", len(g))
print("nAss ", len(association_matrix))
print("ign GT", ignoredfn)
print("ign TP", nignoredtp)
raise NameError(
"Something went wrong! nGroundtruth is not TP+FN")
if tmptp + tmpfp + nignoredtp + nignoredtracker - nignoredpairs is not len(
t):
print(seq_idx, f, len(t), tmptp, tmpfp)
print(len(association_matrix), association_matrix)
raise NameError(
"Something went wrong! nTracker is not TP+FP")
# check for id switches or fragmentations
for i, tt in enumerate(this_ids[0]):
if tt in last_ids[0]:
idx = last_ids[0].index(tt)
tid = this_ids[1][i]
lid = last_ids[1][idx]
if tid != lid and lid != -1 and tid != -1:
if g[i].truncation < self.max_truncation:
g[i].id_switch = 1
ids += 1
if tid != lid and lid != -1:
if g[i].truncation < self.max_truncation:
g[i].fragmentation = 1
fr += 1
# save current index
last_ids = this_ids
# compute MOTP_t
MODP_t = 1
if tmptp != 0:
MODP_t = tmpc / float(tmptp)
self.MODP_t.append(MODP_t)
# remove empty lists for current gt trajectories
self.gt_trajectories[seq_idx] = seq_trajectories
self.ign_trajectories[seq_idx] = seq_ignored
# gather statistics for "per sequence" statistics.
self.n_gts.append(n_gts)
self.n_trs.append(n_trs)
self.tps.append(seqtp)
self.itps.append(seqitp)
self.fps.append(seqfp)
self.fns.append(seqfn)
self.ifns.append(seqifn)
self.n_igts.append(seqigt)
self.n_itrs.append(seqitr)
# compute MT/PT/ML, fragments, idswitches for all groundtruth trajectories
n_ignored_tr_total = 0
for seq_idx, (
seq_trajectories, seq_ignored
) in enumerate(zip(self.gt_trajectories, self.ign_trajectories)):
if len(seq_trajectories) == 0:
continue
tmpMT, tmpML, tmpPT, tmpId_switches, tmpFragments = [0] * 5
n_ignored_tr = 0
for g, ign_g in zip(seq_trajectories.values(),
seq_ignored.values()):
# all frames of this gt trajectory are ignored
if all(ign_g):
n_ignored_tr += 1
n_ignored_tr_total += 1
continue
# all frames of this gt trajectory are not assigned to any detections
if all([this == -1 for this in g]):
tmpML += 1
self.ML += 1
continue
# compute tracked frames in trajectory
last_id = g[0]
# first detection (necessary to be in gt_trajectories) is always tracked
tracked = 1 if g[0] >= 0 else 0
lgt = 0 if ign_g[0] else 1
for f in range(1, len(g)):
if ign_g[f]:
last_id = -1
continue
lgt += 1
if last_id != g[f] and last_id != -1 and g[f] != -1 and g[
f - 1] != -1:
tmpId_switches += 1
self.id_switches += 1
if f < len(g) - 1 and g[f - 1] != g[
f] and last_id != -1 and g[f] != -1 and g[f +
1] != -1:
tmpFragments += 1
self.fragments += 1
if g[f] != -1:
tracked += 1
last_id = g[f]
# handle last frame; tracked state is handled in for loop (g[f]!=-1)
if len(g) > 1 and g[f - 1] != g[f] and last_id != -1 and g[
f] != -1 and not ign_g[f]:
tmpFragments += 1
self.fragments += 1
# compute MT/PT/ML
tracking_ratio = tracked / float(len(g) - sum(ign_g))
if tracking_ratio > 0.8:
tmpMT += 1
self.MT += 1
elif tracking_ratio < 0.2:
tmpML += 1
self.ML += 1
else: # 0.2 <= tracking_ratio <= 0.8
tmpPT += 1
self.PT += 1
if (self.n_gt_trajectories - n_ignored_tr_total) == 0:
self.MT = 0.
self.PT = 0.
self.ML = 0.
else:
self.MT /= float(self.n_gt_trajectories - n_ignored_tr_total)
self.PT /= float(self.n_gt_trajectories - n_ignored_tr_total)
self.ML /= float(self.n_gt_trajectories - n_ignored_tr_total)
# precision/recall etc.
if (self.fp + self.tp) == 0 or (self.tp + self.fn) == 0:
self.recall = 0.
self.precision = 0.
else:
self.recall = self.tp / float(self.tp + self.fn)
self.precision = self.tp / float(self.fp + self.tp)
if (self.recall + self.precision) == 0:
self.F1 = 0.
else:
self.F1 = 2. * (self.precision * self.recall) / (
self.precision + self.recall)
if sum(self.n_frames) == 0:
self.FAR = "n/a"
else:
self.FAR = self.fp / float(sum(self.n_frames))
# compute CLEARMOT
if self.n_gt == 0:
self.MOTA = -float("inf")
self.MODA = -float("inf")
else:
self.MOTA = 1 - (self.fn + self.fp + self.id_switches
) / float(self.n_gt)
self.MODA = 1 - (self.fn + self.fp) / float(self.n_gt)
if self.tp == 0:
self.MOTP = float("inf")
else:
self.MOTP = self.total_cost / float(self.tp)
if self.n_gt != 0:
if self.id_switches == 0:
self.MOTAL = 1 - (self.fn + self.fp + self.id_switches
) / float(self.n_gt)
else:
self.MOTAL = 1 - (self.fn + self.fp +
math.log10(self.id_switches)
) / float(self.n_gt)
else:
self.MOTAL = -float("inf")
if sum(self.n_frames) == 0:
self.MODP = "n/a"
else:
self.MODP = sum(self.MODP_t) / float(sum(self.n_frames))
return True
def createSummary(self):
summary = ""
summary += "tracking evaluation summary".center(80, "=") + "\n"
summary += self.printEntry("Multiple Object Tracking Accuracy (MOTA)",
self.MOTA) + "\n"
summary += self.printEntry("Multiple Object Tracking Precision (MOTP)",
self.MOTP) + "\n"
summary += self.printEntry("Multiple Object Tracking Accuracy (MOTAL)",
self.MOTAL) + "\n"
summary += self.printEntry("Multiple Object Detection Accuracy (MODA)",
self.MODA) + "\n"
summary += self.printEntry("Multiple Object Detection Precision (MODP)",
self.MODP) + "\n"
summary += "\n"
summary += self.printEntry("Recall", self.recall) + "\n"
summary += self.printEntry("Precision", self.precision) + "\n"
summary += self.printEntry("F1", self.F1) + "\n"
summary += self.printEntry("False Alarm Rate", self.FAR) + "\n"
summary += "\n"
summary += self.printEntry("Mostly Tracked", self.MT) + "\n"
summary += self.printEntry("Partly Tracked", self.PT) + "\n"
summary += self.printEntry("Mostly Lost", self.ML) + "\n"
summary += "\n"
summary += self.printEntry("True Positives", self.tp) + "\n"
#summary += self.printEntry("True Positives per Sequence", self.tps) + "\n"
summary += self.printEntry("Ignored True Positives", self.itp) + "\n"
#summary += self.printEntry("Ignored True Positives per Sequence", self.itps) + "\n"
summary += self.printEntry("False Positives", self.fp) + "\n"
#summary += self.printEntry("False Positives per Sequence", self.fps) + "\n"
summary += self.printEntry("False Negatives", self.fn) + "\n"
#summary += self.printEntry("False Negatives per Sequence", self.fns) + "\n"
summary += self.printEntry("ID-switches", self.id_switches) + "\n"
self.fp = self.fp / self.n_gt
self.fn = self.fn / self.n_gt
self.id_switches = self.id_switches / self.n_gt
summary += self.printEntry("False Positives Ratio", self.fp) + "\n"
#summary += self.printEntry("False Positives per Sequence", self.fps) + "\n"
summary += self.printEntry("False Negatives Ratio", self.fn) + "\n"
#summary += self.printEntry("False Negatives per Sequence", self.fns) + "\n"
summary += self.printEntry("Ignored False Negatives Ratio",
self.ifn) + "\n"
#summary += self.printEntry("Ignored False Negatives per Sequence", self.ifns) + "\n"
summary += self.printEntry("Missed Targets", self.fn) + "\n"
summary += self.printEntry("ID-switches", self.id_switches) + "\n"
summary += self.printEntry("Fragmentations", self.fragments) + "\n"
summary += "\n"
summary += self.printEntry("Ground Truth Objects (Total)", self.n_gt +
self.n_igt) + "\n"
#summary += self.printEntry("Ground Truth Objects (Total) per Sequence", self.n_gts) + "\n"
summary += self.printEntry("Ignored Ground Truth Objects",
self.n_igt) + "\n"
#summary += self.printEntry("Ignored Ground Truth Objects per Sequence", self.n_igts) + "\n"
summary += self.printEntry("Ground Truth Trajectories",
self.n_gt_trajectories) + "\n"
summary += "\n"
summary += self.printEntry("Tracker Objects (Total)", self.n_tr) + "\n"
#summary += self.printEntry("Tracker Objects (Total) per Sequence", self.n_trs) + "\n"
summary += self.printEntry("Ignored Tracker Objects", self.n_itr) + "\n"
#summary += self.printEntry("Ignored Tracker Objects per Sequence", self.n_itrs) + "\n"
summary += self.printEntry("Tracker Trajectories",
self.n_tr_trajectories) + "\n"
#summary += "\n"
#summary += self.printEntry("Ignored Tracker Objects with Associated Ignored Ground Truth Objects", self.n_igttr) + "\n"
summary += "=" * 80
return summary
def printEntry(self, key, val, width=(70, 10)):
"""
Pretty print an entry in a table fashion.
"""
s_out = key.ljust(width[0])
if type(val) == int:
s = "%%%dd" % width[1]
s_out += s % val
elif type(val) == float:
s = "%%%df" % (width[1])
s_out += s % val
else:
s_out += ("%s" % val).rjust(width[1])
return s_out
def saveToStats(self, save_summary):
"""
Save the statistics in a whitespace separate file.
"""
summary = self.createSummary()
if save_summary:
filename = os.path.join(self.result_path,
"summary_%s.txt" % self.cls)
dump = open(filename, "w+")
dump.write(summary)
dump.close()
return summary
class KITTIMOTMetric(Metric):
def __init__(self, save_summary=True):
self.save_summary = save_summary
self.MOTEvaluator = KITTIEvaluation
self.result_root = None
self.reset()
def reset(self):
self.seqs = []
self.n_sequences = 0
self.n_frames = []
self.strsummary = ''
def update(self, data_root, seq, data_type, result_root, result_filename):
assert data_type == 'kitti', "data_type should 'kitti'"
self.result_root = result_root
self.gt_path = data_root
gt_path = '{}/label_02/{}.txt'.format(data_root, seq)
gt = open(gt_path, "r")
max_frame = 0
for line in gt:
line = line.strip()
line_list = line.split(" ")
if int(line_list[0]) > max_frame:
max_frame = int(line_list[0])
rs = open(result_filename, "r")
for line in rs:
line = line.strip()
line_list = line.split(" ")
if int(line_list[0]) > max_frame:
max_frame = int(line_list[0])
gt.close()
rs.close()
self.n_frames.append(max_frame + 1)
self.seqs.append(seq)
self.n_sequences += 1
def accumulate(self):
logger.info("Processing Result for KITTI Tracking Benchmark")
e = self.MOTEvaluator(result_path=self.result_root, gt_path=self.gt_path,\
n_frames=self.n_frames, seqs=self.seqs, n_sequences=self.n_sequences)
try:
if not e.loadTracker():
return
logger.info("Loading Results - Success")
logger.info("Evaluate Object Class: %s" % c.upper())
except:
logger.info("Caught exception while loading result data.")
if not e.loadGroundtruth():
raise ValueError("Ground truth not found.")
logger.info("Loading Groundtruth - Success")
# sanity checks
if len(e.groundtruth) is not len(e.tracker):
logger.info(
"The uploaded data does not provide results for every sequence.")
return False
logger.info("Loaded %d Sequences." % len(e.groundtruth))
logger.info("Start Evaluation...")
if e.compute3rdPartyMetrics():
self.strsummary = e.saveToStats(self.save_summary)
else:
logger.info(
"There seem to be no true positives or false positives at all in the submitted data."
)
def log(self):
print(self.strsummary)
def get_results(self):
return self.strsummary
ppdet/metrics/munkres.py 0 → 100644
浏览文件 @ 3c95b81d
# 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.
"""
This code is borrow from https://github.com/xingyizhou/CenterTrack/blob/master/src/tools/eval_kitti_track/munkres.py
"""
import sys
__all__ = ['Munkres', 'make_cost_matrix']
class Munkres:
"""
Calculate the Munkres solution to the classical assignment problem.
See the module documentation for usage.
"""
def __init__(self):
"""Create a new instance"""
self.C = None
self.row_covered = []
self.col_covered = []
self.n = 0
self.Z0_r = 0
self.Z0_c = 0
self.marked = None
self.path = None
def make_cost_matrix(profit_matrix, inversion_function):
"""
**DEPRECATED**
Please use the module function ``make_cost_matrix()``.
"""
import munkres
return munkres.make_cost_matrix(profit_matrix, inversion_function)
make_cost_matrix = staticmethod(make_cost_matrix)
def pad_matrix(self, matrix, pad_value=0):
"""
Pad a possibly non-square matrix to make it square.
:Parameters:
matrix : list of lists
matrix to pad
pad_value : int
value to use to pad the matrix
:rtype: list of lists
:return: a new, possibly padded, matrix
"""
max_columns = 0
total_rows = len(matrix)
for row in matrix:
max_columns = max(max_columns, len(row))
total_rows = max(max_columns, total_rows)
new_matrix = []
for row in matrix:
row_len = len(row)
new_row = row[:]
if total_rows > row_len:
# Row too short. Pad it.
new_row += [0] * (total_rows - row_len)
new_matrix += [new_row]
while len(new_matrix) < total_rows:
new_matrix += [[0] * total_rows]
return new_matrix
def compute(self, cost_matrix):
"""
Compute the indexes for the lowest-cost pairings between rows and
columns in the database. Returns a list of (row, column) tuples
that can be used to traverse the matrix.
:Parameters:
cost_matrix : list of lists
The cost matrix. If this cost matrix is not square, it
will be padded with zeros, via a call to ``pad_matrix()``.
(This method does *not* modify the caller's matrix. It
operates on a copy of the matrix.)
**WARNING**: This code handles square and rectangular
matrices. It does *not* handle irregular matrices.
:rtype: list
:return: A list of ``(row, column)`` tuples that describe the lowest
cost path through the matrix
"""
self.C = self.pad_matrix(cost_matrix)
self.n = len(self.C)
self.original_length = len(cost_matrix)
self.original_width = len(cost_matrix[0])
self.row_covered = [False for i in range(self.n)]
self.col_covered = [False for i in range(self.n)]
self.Z0_r = 0
self.Z0_c = 0
self.path = self.__make_matrix(self.n * 2, 0)
self.marked = self.__make_matrix(self.n, 0)
done = False
step = 1
steps = {
1: self.__step1,
2: self.__step2,
3: self.__step3,
4: self.__step4,
5: self.__step5,
6: self.__step6
}
while not done:
try:
func = steps[step]
step = func()
except KeyError:
done = True
# Look for the starred columns
results = []
for i in range(self.original_length):
for j in range(self.original_width):
if self.marked[i][j] == 1:
results += [(i, j)]
return results
def __copy_matrix(self, matrix):
"""Return an exact copy of the supplied matrix"""
return copy.deepcopy(matrix)
def __make_matrix(self, n, val):
"""Create an *n*x*n* matrix, populating it with the specific value."""
matrix = []
for i in range(n):
matrix += [[val for j in range(n)]]
return matrix
def __step1(self):
"""
For each row of the matrix, find the smallest element and
subtract it from every element in its row. Go to Step 2.
"""
C = self.C
n = self.n
for i in range(n):
minval = min(self.C[i])
# Find the minimum value for this row and subtract that minimum
# from every element in the row.
for j in range(n):
self.C[i][j] -= minval
return 2
def __step2(self):
"""
Find a zero (Z) in the resulting matrix. If there is no starred
zero in its row or column, star Z. Repeat for each element in the
matrix. Go to Step 3.
"""
n = self.n
for i in range(n):
for j in range(n):
if (self.C[i][j] == 0) and \
(not self.col_covered[j]) and \
(not self.row_covered[i]):
self.marked[i][j] = 1
self.col_covered[j] = True
self.row_covered[i] = True
self.__clear_covers()
return 3
def __step3(self):
"""
Cover each column containing a starred zero. If K columns are
covered, the starred zeros describe a complete set of unique
assignments. In this case, Go to DONE, otherwise, Go to Step 4.
"""
n = self.n
count = 0
for i in range(n):
for j in range(n):
if self.marked[i][j] == 1:
self.col_covered[j] = True
count += 1
if count >= n:
step = 7 # done
else:
step = 4
return step
def __step4(self):
"""
Find a noncovered zero and prime it. If there is no starred zero
in the row containing this primed zero, Go to Step 5. Otherwise,
cover this row and uncover the column containing the starred
zero. Continue in this manner until there are no uncovered zeros
left. Save the smallest uncovered value and Go to Step 6.
"""
step = 0
done = False
row = -1
col = -1
star_col = -1
while not done:
(row, col) = self.__find_a_zero()
if row < 0:
done = True
step = 6
else:
self.marked[row][col] = 2
star_col = self.__find_star_in_row(row)
if star_col >= 0:
col = star_col
self.row_covered[row] = True
self.col_covered[col] = False
else:
done = True
self.Z0_r = row
self.Z0_c = col
step = 5
return step
def __step5(self):
"""
Construct a series of alternating primed and starred zeros as
follows. Let Z0 represent the uncovered primed zero found in Step 4.
Let Z1 denote the starred zero in the column of Z0 (if any).
Let Z2 denote the primed zero in the row of Z1 (there will always
be one). Continue until the series terminates at a primed zero
that has no starred zero in its column. Unstar each starred zero
of the series, star each primed zero of the series, erase all
primes and uncover every line in the matrix. Return to Step 3
"""
count = 0
path = self.path
path[count][0] = self.Z0_r
path[count][1] = self.Z0_c
done = False
while not done:
row = self.__find_star_in_col(path[count][1])
if row >= 0:
count += 1
path[count][0] = row
path[count][1] = path[count - 1][1]
else:
done = True
if not done:
col = self.__find_prime_in_row(path[count][0])
count += 1
path[count][0] = path[count - 1][0]
path[count][1] = col
self.__convert_path(path, count)
self.__clear_covers()
self.__erase_primes()
return 3
def __step6(self):
"""
Add the value found in Step 4 to every element of each covered
row, and subtract it from every element of each uncovered column.
Return to Step 4 without altering any stars, primes, or covered
lines.
"""
minval = self.__find_smallest()
for i in range(self.n):
for j in range(self.n):
if self.row_covered[i]:
self.C[i][j] += minval
if not self.col_covered[j]:
self.C[i][j] -= minval
return 4
def __find_smallest(self):
"""Find the smallest uncovered value in the matrix."""
minval = 2e9 # sys.maxint
for i in range(self.n):
for j in range(self.n):
if (not self.row_covered[i]) and (not self.col_covered[j]):
if minval > self.C[i][j]:
minval = self.C[i][j]
return minval
def __find_a_zero(self):
"""Find the first uncovered element with value 0"""
row = -1
col = -1
i = 0
n = self.n
done = False
while not done:
j = 0
while True:
if (self.C[i][j] == 0) and \
(not self.row_covered[i]) and \
(not self.col_covered[j]):
row = i
col = j
done = True
j += 1
if j >= n:
break
i += 1
if i >= n:
done = True
return (row, col)
def __find_star_in_row(self, row):
"""
Find the first starred element in the specified row. Returns
the column index, or -1 if no starred element was found.
"""
col = -1
for j in range(self.n):
if self.marked[row][j] == 1:
col = j
break
return col
def __find_star_in_col(self, col):
"""
Find the first starred element in the specified row. Returns
the row index, or -1 if no starred element was found.
"""
row = -1
for i in range(self.n):
if self.marked[i][col] == 1:
row = i
break
return row
def __find_prime_in_row(self, row):
"""
Find the first prime element in the specified row. Returns
the column index, or -1 if no starred element was found.
"""
col = -1
for j in range(self.n):
if self.marked[row][j] == 2:
col = j
break
return col
def __convert_path(self, path, count):
for i in range(count + 1):
if self.marked[path[i][0]][path[i][1]] == 1:
self.marked[path[i][0]][path[i][1]] = 0
else:
self.marked[path[i][0]][path[i][1]] = 1
def __clear_covers(self):
"""Clear all covered matrix cells"""
for i in range(self.n):
self.row_covered[i] = False
self.col_covered[i] = False
def __erase_primes(self):
"""Erase all prime markings"""
for i in range(self.n):
for j in range(self.n):
if self.marked[i][j] == 2:
self.marked[i][j] = 0
def make_cost_matrix(profit_matrix, inversion_function):
"""
Create a cost matrix from a profit matrix by calling
'inversion_function' to invert each value. The inversion
function must take one numeric argument (of any type) and return
another numeric argument which is presumed to be the cost inverse
of the original profit.
This is a static method. Call it like this:
.. python::
cost_matrix = Munkres.make_cost_matrix(matrix, inversion_func)
For example:
.. python::
cost_matrix = Munkres.make_cost_matrix(matrix, lambda x : sys.maxint - x)
:Parameters:
profit_matrix : list of lists
The matrix to convert from a profit to a cost matrix
inversion_function : function
The function to use to invert each entry in the profit matrix
:rtype: list of lists
:return: The converted matrix
"""
cost_matrix = []
for row in profit_matrix:
cost_matrix.append([inversion_function(value) for value in row])
return cost_matrix
tools/eval_mot.py
浏览文件 @ 3c95b81d
......@@ -41,11 +41,6 @@ logger = setup_logger('eval')
def parse_args():
parser = ArgsParser()
parser.add_argument(
"--data_type",
type=str,
default='mot',
help='Data type of tracking dataset, should be in ["mot", "kitti"]')
parser.add_argument(
"--det_results_dir",
type=str,
......@@ -95,7 +90,7 @@ def run(FLAGS, cfg):
tracker.mot_evaluate(
data_root=data_root,
seqs=seqs,
data_type=FLAGS.data_type,
data_type=cfg.metric.lower(),
model_type=cfg.architecture,
output_dir=FLAGS.output_dir,
save_images=FLAGS.save_images,
......
tools/infer_mot.py
浏览文件 @ 3c95b81d
......@@ -48,11 +48,6 @@ def parse_args():
type=str,
default=None,
help="Directory for images to perform inference on.")
parser.add_argument(
"--data_type",
type=str,
default='mot',
help='Data type of tracking dataset, should be in ["mot", "kitti"]')
parser.add_argument(
"--det_results_dir",
type=str,
......@@ -101,7 +96,7 @@ def run(FLAGS, cfg):
tracker.mot_predict(
video_file=FLAGS.video_file,
image_dir=FLAGS.image_dir,
data_type=FLAGS.data_type,
data_type=cfg.metric.lower(),
model_type=cfg.architecture,
output_dir=FLAGS.output_dir,
save_images=FLAGS.save_images,
......
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