fix pphuman det mot doc (#6458)

* fix pphuman det mot doc, test=document_fix * fix doc, test=document_fix * fix doc, test=document_fix

fix pphuman det mot doc (#6458)
* fix pphuman det mot doc, test=document_fix * fix doc, test=document_fix * fix doc, test=document_fix
7e162e6a · Feng Ni · GitHub · ce328c90 · 7e162e6a · 7e162e6a
4 changed file
--- a/configs/pphuman/README.md
+++ b/configs/pphuman/README.md
@@ -17,12 +17,11 @@ PaddleDetection团队提供了针对行人的基于PP-YOLOE的检测模型，用


 # PP-YOLOE 香烟检测模型
-基于PP-YOLOE模型的香烟检测模型，是实现PP-Human中的基于检测的行为识别方案的一环，如何在PP-Human中使用该模型进行吸烟行为识别，可参考[PP-Human行为识别模块](../../deploy/pipeline/docs/tutorials/action.md)。该模型检测类别仅包含香烟一类。由于数据来源限制，目前暂无法直接公开训练数据。该模型使用了小目标数据集Visdrone上的权重作为预训练模型，以提升检测效果。
+基于PP-YOLOE模型的香烟检测模型，是实现PP-Human中的基于检测的行为识别方案的一环，如何在PP-Human中使用该模型进行吸烟行为识别，可参考[PP-Human行为识别模块](../../deploy/pipeline/docs/tutorials/action.md)。该模型检测类别仅包含香烟一类。由于数据来源限制，目前暂无法直接公开训练数据。该模型使用了小目标数据集VisDrone上的权重(参照[visdrone](../visdrone))作为预训练模型，以提升检测效果。

 |    模型   |  数据集  | mAP<sup>val<br>0.5:0.95 |  mAP<sup>val<br>0.5 | 下载  | 配置文件 |
 |:---------|:-------:|:------:|:------:| :----: | :------:|
-
-| PP-YOLOE-s | 业务数据集 |  39.7 | 79.5 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppyoloe_crn_s_80e_smoking_visdrone.pdparams) | [配置文件](./ppyoloe_crn_s_80e_smoking_visdrone.yml) |
+| PP-YOLOE-s | 香烟业务数据集 |  39.7 | 79.5 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppyoloe_crn_s_80e_smoking_visdrone.pdparams) | [配置文件](./ppyoloe_crn_s_80e_smoking_visdrone.yml) |


 ## 引用

--- a/configs/ppvehicle/README.md
+++ b/configs/ppvehicle/README.md
@@ -23,7 +23,17 @@ PaddleDetection团队提供了针对自动驾驶场景的基于PP-YOLOE的检测
 **注意:**
 - PP-YOLOE模型训练过程中使用8 GPUs进行混合精度训练，如果**GPU卡数**或者**batch size**发生了改变，你需要按照公式 **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)** 调整学习率。
 - 具体使用教程请参考[ppyoloe](../ppyoloe#getting-start)。
-
+- 如需预测出对应类别，可自行修改和添加对应的label_list.txt文件(一行记录一个对应种类)，TestDataset中的anno_path为绝对路径，如：
+```
+TestDataset:
+  !ImageFolder
+    anno_path: label_list.txt # 如不使用dataset_dir，则anno_path即为相对于PaddleDetection主目录的相对路径
+    # dataset_dir: dataset/ppvehicle # 如使用dataset_dir，则dataset_dir/anno_path作为新的anno_path
+```
+label_list.txt里的一行记录一个对应种类，如下所示：
+```
+vehicle
+```

 ## 引用
 ```

--- a/deploy/pipeline/docs/tutorials/mot.md
+++ b/deploy/pipeline/docs/tutorials/mot.md
@@ -6,8 +6,8 @@

 | 任务                 | 算法 | 精度 | 预测速度(ms) |下载链接                                                                               |
 |:---------------------|:---------:|:------:|:------:| :---------------------------------------------------------------------------------: |
-| 行人检测/跟踪    |  PP-YOLOE-l | mAP: 56.6 <br> MOTA: 79.5 | 检测: 25.1ms <br> 跟踪：31.8ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
-| 行人检测/跟踪    |  PP-YOLOE-s | mAP: 53.2 <br> MOTA: 69.1 | 检测: 16.2ms <br> 跟踪：21.0ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) |
+| 行人检测/跟踪    |  PP-YOLOE-l | mAP: 57.8 <br> MOTA: 82.2 | 检测: 25.1ms <br> 跟踪：31.8ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| 行人检测/跟踪    |  PP-YOLOE-s | mAP: 53.2 <br> MOTA: 73.9 | 检测: 16.2ms <br> 跟踪：21.0ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) |

 1. 检测/跟踪模型精度为[COCO-Person](http://cocodataset.org/), [CrowdHuman](http://www.crowdhuman.org/), [HIEVE](http://humaninevents.org/) 和部分业务数据融合训练测试得到，验证集为业务数据
 2. 预测速度为T4 机器上使用TensorRT FP16时的速度, 速度包含数据预处理、模型预测、后处理全流程
@@ -69,12 +69,11 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 **注意:**
 - `--do_break_in_counting`表示是否进行区域出入后计数，不设置即默认为False。
 - `--region_type`表示流量计数的区域，当设置`--do_break_in_counting`时仅可选择`custom`，默认是`custom`，表示以用户自定义区域为出入口，同一物体框的下边界中点坐标在相邻两秒内从区域外到区域内，即完成计数加一。
- - `--region_polygon`表示用户自定义区域的多边形的点坐标序列，每两个为一对点坐标(x,y坐标),按顺时针顺序连成一个封闭区域，至少需要3对点也即6个整数，默认值是`[]`，需要用户自行设置点坐标。用户可以运行[此段代码](../../tools/get_video_info.py)获取所测视频的分辨率帧数，以及可以自定义画出自己想要的多边形区域的可视化并自己调整。运行方式如下：``` ```
+ - `--region_polygon`表示用户自定义区域的多边形的点坐标序列，每两个为一对点坐标(x,y),按顺时针顺序连成一个封闭区域，至少需要3对点也即6个整数，默认值是`[]`，需要用户自行设置点坐标。用户可以运行[此段代码](../../tools/get_video_info.py)获取所测视频的分辨率帧数，以及可以自定义画出自己想要的多边形区域的可视化并自己调整。
 自定义多边形区域的可视化代码运行如下：
-
-```python
-  python3.7 get_video_info.py --video_file=demo.mp4 --region_polygon 200 200 400 200 300 400 100 400
-```
+ ```python
+ python get_video_info.py --video_file=demo.mp4 --region_polygon 200 200 400 200 300 400 100 400
+ ```

 测试效果如下：

@@ -84,8 +83,8 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph

 ## 方案说明

-1. 目标检测/多目标跟踪获取图片/视频输入中的行人检测框，模型方案为PP-YOLOE，详细文档参考[PP-YOLOE](../../../../configs/ppyoloe/)
-2. 多目标跟踪模型方案采用[ByteTrack](https://arxiv.org/pdf/2110.06864.pdf)和[OC-SORT](https://arxiv.org/pdf/2203.14360.pdf)，采用PP-YOLOE替换原文的YOLOX作为检测器，采用BYTETracker和OCSORTTracker作为跟踪器，详细文档参考[ByteTrack](../../../../configs/mot/bytetrack)和[OC-SORT](../../../../configs/mot/ocsort)
+1. 使用目标检测/多目标跟踪技术来获取图片/视频输入中的行人检测框，检测模型方案为PP-YOLOE，详细文档参考[PP-YOLOE](../../../../configs/ppyoloe)。
+2. 多目标跟踪模型方案采用[ByteTrack](https://arxiv.org/pdf/2110.06864.pdf)和[OC-SORT](https://arxiv.org/pdf/2203.14360.pdf)，采用PP-YOLOE替换原文的YOLOX作为检测器，采用BYTETracker和OCSORTTracker作为跟踪器，详细文档参考[ByteTrack](../../../../configs/mot/bytetrack)和[OC-SORT](../../../../configs/mot/ocsort)。

 ## 参考文献
 ```

--- a/deploy/pipeline/docs/tutorials/mot_en.md
+++ b/deploy/pipeline/docs/tutorials/mot_en.md
@@ -6,8 +6,8 @@ Pedestrian detection and tracking is widely used in the intelligent community, i

 | Task                 | Algorithm | Precision | Inference Speed(ms) | Download Link                                                                               |
 |:---------------------|:---------:|:------:|:------:| :---------------------------------------------------------------------------------: |
-| Pedestrian Detection/ Tracking    |  PP-YOLOE-l | mAP: 56.6 <br> MOTA: 79.5 | Detection: 28.0ms <br> Tracking：33.1ms | [Download Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
-| Pedestrian Detection/ Tracking    |  PP-YOLOE-s | mAP: 53.2 <br> MOTA: 69.2 | Detection: 22.1ms <br> Tracking：27.2ms | [Download Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) |
+| Pedestrian Detection/ Tracking    |  PP-YOLOE-l | mAP: 57.8 <br> MOTA: 82.2 | Detection: 25.1ms <br> Tracking：31.8ms | [Download](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| Pedestrian Detection/ Tracking    |  PP-YOLOE-s | mAP: 53.2 <br> MOTA: 73.9 | Detection: 16.2ms <br> Tracking：21.0ms | [Download](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) |

 1. The precision of the pedestrian detection/ tracking model is obtained by trainning and testing on [COCO-Person](http://cocodataset.org/), [CrowdHuman](http://www.crowdhuman.org/), [HIEVE](http://humaninevents.org/) and some business data.
 2. The inference speed is the speed of using TensorRT FP16 on T4, the total number of data pre-training, model inference, and post-processing.
@@ -36,15 +36,18 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
                                                   --video_file=test_video.mp4 \
                                                   --device=gpu \
-                                                   --model_dir det=ppyoloe/
+                                                   --region_type=horizontal \
                                                   --do_entrance_counting \
-                                                   --draw_center_traj
+                                                   --draw_center_traj \
+                                                   --model_dir det=ppyoloe/

 ```
 **Note:**

 - `--do_entrance_counting` is whether to calculate flow at the gateway, and the default setting is False.
 - `--draw_center_traj` means whether to draw the track, and the default setting is False. It's worth noting that the test video of track drawing should be filmed by the still camera.
+ - `--region_type` means the region type of flow counting. When set `--do_entrance_counting`, you can select from `horizontal` or `vertical`, the default setting is `horizontal`, means that the central horizontal line of the video picture is used as the entrance and exit, and when the central point of the same object box is on both sides of the central horizontal line of the area in two adjacent seconds, the counting plus one is completed.
+
 The test result is：

 <div width="1000" align="center">
@@ -53,12 +56,40 @@ The test result is：

 Data source and copyright owner：Skyinfor Technology. Thanks for the provision of actual scenario data, which are only used for academic research here.

+5. Break in and counting
+
+Please set the "enable: True" in MOT of infer_cfg_pphuman.yml at first, and then the start command is as follows:
+```python
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
+                                                   --video_file=test_video.mp4 \
+                                                   --device=gpu \
+                                                   --draw_center_traj \
+                                                   --do_break_in_counting \
+                                                   --region_type=custom \
+                                                   --region_polygon 200 200 400 200 300 400 100 400
+```
+
+**Note:**
+ - `--do_break_in_counting` is whether to calculate flow when break in the user-defined region, and the default setting is False.
+ - `--region_type` means the region type of flow counting. When set `--do_break_in_counting`, only `custom` can be selected, and the default is `custom`, which means that the user-defined region is used as the entrance and exit, and when the midpoint coords of the bottom boundary of the same object moves from outside to inside the region within two adjacent seconds, the counting plus one is completed.
+ - `--region_polygon` means the point coords sequence of the polygon in the user-defined region. Every two integers are a pair of point coords (x,y), which are connected into a closed area in clockwise order. At least 3 pairs of points, that is, 6 integers, are required. The default value is `[]`, and the user needs to set the point coords by himself. Users can run this [code](../../tools/get_video_info.py) to obtain the resolution and frame number of the measured video, and can customize the visualization of drawing the polygon area they want and adjust it by themselves.
+ The visualization code of the custom polygon region runs as follows:
+ ```python
+ python get_video_info.py --video_file=demo.mp4 --region_polygon 200 200 400 200 300 400 100 400
+ ```
+
+The test result is：
+
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/22989727/178769370-03ab1965-cfd1-401b-9902-82620a06e43c.gif" width='1000'/>
+</div>
+

 ## Introduction to the Solution

-1. Get the pedestrian detection box of the image/ video input through object detection and multi-object tracking. The adopted model is PP-YOLOE, and for details, please refer to [PP-YOLOE](../../../../configs/ppyoloe).
+1. Get the pedestrian detection box of the image/ video input through object detection and multi-object tracking. The detection model is PP-YOLOE, please refer to [PP-YOLOE](../../../../configs/ppyoloe) for details.

-2. The multi-object tracking model solution is based on [ByteTrack](https://arxiv.org/pdf/2110.06864.pdf), and replace the original YOLOX with P-YOLOE as the detector，and BYTETracker as the tracker, please refer to [ByteTrack](../../../../configs/mot/bytetrack).
+2. The multi-object tracking solution is based on [ByteTrack](https://arxiv.org/pdf/2110.06864.pdf) and [OC-SORT](https://arxiv.org/pdf/2203.14360.pdf), and replace the original YOLOX with PP-YOLOE as the detector，and BYTETracker or OC-SORT Tracker as the tracker, please refer to [ByteTrack](../../../../configs/mot/bytetrack) and [OC-SORT](../../../../configs/mot/ocsort).

 ## Reference
 ```