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PaddleDetection
未验证 提交 59df0f34 编写于 作者: Y YixinKristy 提交者： GitHub
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deploy/pphuman/README.md
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......@@ -5,10 +5,11 @@
PP-Human是基于飞桨深度学习框架的业界首个开源的实时行人分析工具，具有功能丰富，应用广泛和部署高效三大优势。PP-Human
支持图片/单镜头视频/多镜头视频多种输入方式，功能覆盖多目标跟踪、属性识别和行为分析。能够广泛应用于智慧交通、智慧社区、工业巡检等领域。支持服务器端部署及TensorRT加速，T4服务器上可达到实时。
PP-Human赋能社区智能精细化管理, AIStudio快速上手教程[链接](https://aistudio.baidu.com/aistudio/projectdetail/3679564)
## 一、环境准备
环境要求： PaddleDetection版本 >= release/2.4
环境要求： PaddleDetection版本 >= release/2.4 或 develop版本
PaddlePaddle和PaddleDetection安装
......@@ -36,22 +37,24 @@ pip install -r requirements.txt
PP-Human提供了目标检测、属性识别、行为识别、ReID预训练模型，以实现不同使用场景，用户可以直接下载使用
| 任务 | 适用场景 | 精度 | 预测速度（FPS） | 预测部署模型 |
| 任务 | 适用场景 | 精度 | 预测速度（ms） | 预测部署模型 |
| :---------: |:---------: |:--------------- | :-------: | :------: |
| 目标检测 | 图片/视频输入 | - | - | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
| 属性识别 | 图片/视频输入 属性识别 | - | - | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.tar) |
| 关键点检测 | 视频输入 行为识别 | - | - | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip)
| 行为识别 | 视频输入 行为识别 | - | - | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) |
| ReID | 视频输入 跨镜跟踪 | - | - | [下载链接]() |
| 目标检测 | 图片输入 | mAP: 56.3 | 28.0ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
| 目标跟踪 | 视频输入 | MOTA: 72.0 | 33.1ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
| 属性识别 | 图片/视频输入 属性识别 | mA: 94.86 | 单人2ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) |
| 关键点检测 | 视频输入 行为识别 | AP: 87.1 | 单人2.9ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip)
| 行为识别 | 视频输入 行为识别 | 准确率: 96.43 | 单人2.7ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) |
| ReID | 视频输入 跨镜跟踪 | mAP: 99.7 | - | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) |
下载模型后，解压至`./output_inference`文件夹
**注意：**
- 模型精度为融合数据集结果，数据集包含开源数据集和企业数据集
- 预测速度为T4下，开启TensorRT FP16的效果
- ReID模型精度为Market1501数据集测试结果
- 预测速度为T4下，开启TensorRT FP16的效果, 模型预测速度包含数据预处理、模型预测、后处理全流程
### 2. 配置文件准备
### 2. 配置文件说明
PP-Human相关配置位于```deploy/pphuman/config/infer_cfg.yml```中，存放模型路径，完成不同功能需要设置不同的任务类型
......@@ -80,6 +83,10 @@ ATTR:
batch_size: 8
```
**注意：**
- 如果用户仅需要实现不同任务，可以在命令行中加入 `--enable_attr=True` 或 `--enable_action=True`即可，无需修改配置文件
- 如果用户仅需要修改模型文件路径，可以在命令行中加入 `--model_dir det=ppyoloe/` 即可，无需修改配置文件，详细说明参考下方参数说明文档
### 3. 预测部署
......@@ -104,20 +111,21 @@ python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml -
| 参数 | 是否必须|含义 |
|-------|-------|----------|
| --config | Yes | 配置文件路径 |
| --model_dir | Option | PP-Human中各任务模型路径，优先级高于配置文件 |
| --model_dir | Option | PP-Human中各任务模型路径，优先级高于配置文件, 例如`--model_dir det=better_det/ attr=better_attr/`|
| --image_file | Option | 需要预测的图片 |
| --image_dir | Option | 要预测的图片文件夹路径 |
| --video_file | Option | 需要预测的视频 |
| --camera_id | Option | 用来预测的摄像头ID，默认为-1(表示不使用摄像头预测，可设置为：0 - (摄像头数目-1) )，预测过程中在可视化界面按`q`退出输出预测结果到：output/output.mp4|
| --enable_attr| Option | 是否进行属性识别 |
| --enable_action| Option | 是否进行行为识别 |
| --enable_attr| Option | 是否进行属性识别, 默认为False，即不开启属性识别 |
| --enable_action| Option | 是否进行行为识别，默认为False，即不开启行为识别 |
| --device | Option | 运行时的设备，可选择`CPU/GPU/XPU`，默认为`CPU`|
| --output_dir | Option|可视化结果保存的根目录，默认为output/|
| --run_mode | Option |使用GPU时，默认为paddle, 可选（paddle/trt_fp32/trt_fp16/trt_int8）|
| --enable_mkldnn | Option | CPU预测中是否开启MKLDNN加速，默认为False |
| --cpu_threads | Option| 设置cpu线程数，默认为1 |
| --trt_calib_mode | Option| TensorRT是否使用校准功能，默认为False。使用TensorRT的int8功能时，需设置为True，使用PaddleSlim量化后的模型时需要设置为False |
| --do_entrance_counting | Option | 是否统计出入口流量，默认为False |
| --draw_center_traj | Option | 是否绘制跟踪轨迹，默认为False |
## 三、方案介绍
......@@ -130,13 +138,13 @@ PP-Human整体方案如下图所示
### 1. 目标检测
- 采用PP-YOLOE L 作为目标检测模型
- 详细文档参考[PP-YOLOE](../../configs/ppyoloe/)
- 详细文档参考[PP-YOLOE](../../configs/ppyoloe/)和[检测跟踪文档](docs/mot.md)
### 2. 多目标跟踪
- 采用SDE方案完成多目标跟踪
- 检测模型使用PP-YOLOE L
- 跟踪模块采用Bytetrack方案
- 详细文档参考[Bytetrack](configs/mot/bytetrack)
- 详细文档参考[Bytetrack](../../configs/mot/bytetrack)和[检测跟踪文档](docs/mot.md)
### 3. 跨镜跟踪
- 使用PP-YOLOE + Bytetrack得到单镜头多目标跟踪轨迹
......
deploy/pphuman/config/infer_cfg.yml
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......@@ -5,7 +5,7 @@ visual: True
warmup_frame: 50
DET:
model_dir: output_inference/mot_ppyolov3/
model_dir: output_inference/mot_ppyoloe_l_36e_pipeline/
batch_size: 1
ATTR:
......@@ -13,7 +13,7 @@ ATTR:
batch_size: 8
MOT:
model_dir: output_inference/mot_ppyolov3/
model_dir: output_inference/mot_ppyoloe_l_36e_pipeline/
tracker_config: deploy/pphuman/config/tracker_config.yml
batch_size: 1
......
deploy/pphuman/datacollector.py
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......@@ -86,9 +86,10 @@ class DataCollector(object):
if attr_res:
self.collector[ids]["attrs"].append(attr_res['output'][idx])
if kpt_res:
self.collector[ids]["kpts"].append(kpt_res['keypoint'][idx])
self.collector[ids]["kpts"].append(
[kpt_res['keypoint'][0][idx], kpt_res['keypoint'][1][idx]])
if action_res:
self.collector[ids]["actions"].append(action_res['output'][idx])
self.collector[ids]["actions"].append(action_res[idx + 1])
else:
# action model generate result per X frames, Not available every frames
self.collector[ids]["actions"].append(None)
......
deploy/pphuman/docs/action.md
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......@@ -21,7 +21,7 @@
1. 检测/跟踪模型精度为MOT17，CrowdHuman,HIEVE和部分业务数据融合训练测试得到。
2. 关键点模型使用COCO，UAVHuman和部分业务数据融合训练, 精度在业务数据测试集上得到。
3. 行为识别模型使用NTU-RGB+D，UR Fall Detection Dataset和部分业务数据融合训练，精度在业务数据测试集上得到。
4. 预测速度为NVIDIA T4 机器上使用TensorRT FP16时的速度。
4. 预测速度为NVIDIA 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。
## 配置说明
[配置文件](../config/infer_cfg.yml)中与行为识别相关的参数如下：
......
deploy/pphuman/docs/attribute.md
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......@@ -5,11 +5,11 @@
| 任务 | 算法 | 精度 | 预测速度(ms) |下载链接 |
|:---------------------|:---------:|:------:|:------:| :---------------------------------------------------------------------------------: |
| 行人检测/跟踪 | PP-YOLOE | mAP: 56.3 <br> MOTA: 72.0 | 检测: 28ms <br> 跟踪：33.1ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
| 行人属性分析 | StrongBaseline | ma: 94.86 | 单人 2ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.tar) |
| 行人属性分析 | StrongBaseline | mA: 94.86 | 单人 2ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) |
1. 检测/跟踪模型精度为MOT17，CrowdHuman，HIEVE和部分业务数据融合训练测试得到
2. 行人属性分析精度为PA100k，RAPv2，PETA和部分业务数据融合训练测试得到
3. 预测速度为T4 机器上使用TensorRT FP16时的速度
3. 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
## 使用方法
......@@ -31,7 +31,7 @@ python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml \
4. 若修改模型路径，有以下两种方式：
- ```./deploy/pphuman/config/infer_cfg.yml```下可以配置不同模型路径，属性识别模型修改ATTR字段下配置
- 命令行中增加`--model_dir`修改模型路径：
- **(推荐)**命令行中增加`--model_dir`修改模型路径：
```python
python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml \
--video_file=test_video.mp4 \
......
deploy/pphuman/docs/images/mot.gif 0 → 100644
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因为 它太大了无法显示 image diff 。你可以改为 查看blob。
deploy/pphuman/docs/mot.md 0 → 100644
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# PP-Human检测跟踪模块
行人检测与跟踪在智慧社区，工业巡检，交通监控等方向都具有广泛应用，PP-Human中集成了检测跟踪模块，是关键点检测、属性行为识别等任务的基础。我们提供了预训练模型，用户可以直接下载使用。
| 任务 | 算法 | 精度 | 预测速度(ms) |下载链接 |
|:---------------------|:---------:|:------:|:------:| :---------------------------------------------------------------------------------: |
| 行人检测/跟踪 | PP-YOLOE | mAP: 56.3 <br> MOTA: 72.0 | 检测: 28ms <br> 跟踪：33.1ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
1. 检测/跟踪模型精度为MOT17，CrowdHuman，HIEVE和部分业务数据融合训练测试得到
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
## 使用方法
1. 从上表链接中下载模型并解压到```./output_inference```路径下
2. 图片输入时，启动命令如下
```python
python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml \
--image_file=test_image.jpg \
--device=gpu
```
3. 视频输入时，启动命令如下
```python
python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml \
--video_file=test_video.mp4 \
--device=gpu
```
4. 若修改模型路径，有以下两种方式：
- ```./deploy/pphuman/config/infer_cfg.yml```下可以配置不同模型路径，检测和跟踪模型分别对应`DET`和`MOT`字段，修改对应字段下的路径为实际期望的路径即可。
- 命令行中增加`--model_dir`修改模型路径：
```python
python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml \
--video_file=test_video.mp4 \
--device=gpu \
--model_dir det=ppyoloe/
--do_entrance_counting \
--draw_center_traj
```
**注意:**
- `--do_entrance_counting`表示是否统计出入口流量，不设置即默认为False
- `--draw_center_traj`表示是否绘制跟踪轨迹，不设置即默认为False。注意绘制跟踪轨迹的测试视频最好是静止摄像头拍摄的。
测试效果如下：
<div width="1000" align="center">
<img src="./images/mot.gif"/>
</div>
数据来源及版权归属：天覆科技，感谢提供并开源实际场景数据，仅限学术研究使用
## 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基于[ByteTrack](https://arxiv.org/pdf/2110.06864.pdf)，采用PP-YOLOE替换原文的YOLOX作为检测器，采用BYTETracker作为跟踪器。
## 参考文献
```
@article{zhang2021bytetrack,
title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box},
author={Zhang, Yifu and Sun, Peize and Jiang, Yi and Yu, Dongdong and Yuan, Zehuan and Luo, Ping and Liu, Wenyu and Wang, Xinggang},
journal={arXiv preprint arXiv:2110.06864},
year={2021}
}
```
deploy/pphuman/mtmct.py
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......@@ -111,7 +111,8 @@ def save_mtmct_vis_results(camera_results, captures, output_dir):
for idx, video_file in enumerate(captures):
capture = cv2.VideoCapture(video_file)
cid = camera_ids[idx]
video_out_name = "mtmct_vis_c" + str(cid) + ".mp4"
basename = os.path.basename(video_file)
video_out_name = "vis_" + basename
print("Start visualizing output video: {}".format(video_out_name))
out_path = os.path.join(save_dir, video_out_name)
......@@ -182,7 +183,7 @@ def get_dist_mat(x, y, func_name="euclidean"):
dist_mat = get_cosine(x, y)
elif func_name == "euclidean":
dist_mat = get_euclidean(x, y)
print("Using {func_name} as distance function during evaluation")
print("Using {} as distance function during evaluation".format(func_name))
return dist_mat
......
deploy/pphuman/pipe_utils.py
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......@@ -108,6 +108,21 @@ def argsparser():
default=False,
help="If the model is produced by TRT offline quantitative "
"calibration, trt_calib_mode need to set True.")
parser.add_argument(
"--do_entrance_counting",
action='store_true',
help="Whether counting the numbers of identifiers entering "
"or getting out from the entrance. Note that only support one-class"
"counting, multi-class counting is coming soon.")
parser.add_argument(
"--secs_interval",
type=int,
default=2,
help="The seconds interval to count after tracking")
parser.add_argument(
"--draw_center_traj",
action='store_true',
help="Whether drawing the trajectory of center")
return parser
......
deploy/pphuman/pipeline.py
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......@@ -15,6 +15,7 @@
import os
import yaml
import glob
from collections import defaultdict
import cv2
import numpy as np
......@@ -44,7 +45,8 @@ from python.preprocess import decode_image
from python.visualize import visualize_box_mask, visualize_attr, visualize_pose, visualize_action
from pptracking.python.mot_sde_infer import SDE_Detector
from pptracking.python.mot.visualize import plot_tracking
from pptracking.python.mot.visualize import plot_tracking_dict
from pptracking.python.mot.utils import flow_statistic
class Pipeline(object):
......@@ -72,6 +74,11 @@ class Pipeline(object):
cpu_threads (int): cpu threads, default as 1
enable_mkldnn (bool): whether to open MKLDNN, default as False
output_dir (string): The path of output, default as 'output'
draw_center_traj (bool): Whether drawing the trajectory of center, default as False
secs_interval (int): The seconds interval to count after tracking, default as 10
do_entrance_counting(bool): Whether counting the numbers of identifiers entering
or getting out from the entrance, default as False，only support single class
counting in MOT.
"""
def __init__(self,
......@@ -91,7 +98,10 @@ class Pipeline(object):
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False,
output_dir='output'):
output_dir='output',
draw_center_traj=False,
secs_interval=10,
do_entrance_counting=False):
self.multi_camera = False
self.is_video = False
self.output_dir = output_dir
......@@ -129,10 +139,18 @@ class Pipeline(object):
trt_calib_mode=trt_calib_mode,
cpu_threads=cpu_threads,
enable_mkldnn=enable_mkldnn,
output_dir=output_dir)
output_dir=output_dir,
draw_center_traj=draw_center_traj,
secs_interval=secs_interval,
do_entrance_counting=do_entrance_counting)
if self.is_video:
self.predictor.set_file_name(video_file)
self.output_dir = output_dir
self.draw_center_traj = draw_center_traj
self.secs_interval = secs_interval
self.do_entrance_counting = do_entrance_counting
def _parse_input(self, image_file, image_dir, video_file, video_dir,
camera_id):
......@@ -144,6 +162,7 @@ class Pipeline(object):
self.multi_camera = False
elif video_file is not None:
assert os.path.exists(video_file), "video_file not exists."
self.multi_camera = False
input = video_file
self.is_video = True
......@@ -222,6 +241,11 @@ class PipePredictor(object):
cpu_threads (int): cpu threads, default as 1
enable_mkldnn (bool): whether to open MKLDNN, default as False
output_dir (string): The path of output, default as 'output'
draw_center_traj (bool): Whether drawing the trajectory of center, default as False
secs_interval (int): The seconds interval to count after tracking, default as 10
do_entrance_counting(bool): Whether counting the numbers of identifiers entering
or getting out from the entrance, default as False，only support single class
counting in MOT.
"""
def __init__(self,
......@@ -238,7 +262,10 @@ class PipePredictor(object):
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False,
output_dir='output'):
output_dir='output',
draw_center_traj=False,
secs_interval=10,
do_entrance_counting=False):
if enable_attr and not cfg.get('ATTR', False):
ValueError(
......@@ -268,6 +295,9 @@ class PipePredictor(object):
self.multi_camera = multi_camera
self.cfg = cfg
self.output_dir = output_dir
self.draw_center_traj = draw_center_traj
self.secs_interval = secs_interval
self.do_entrance_counting = do_entrance_counting
self.warmup_frame = self.cfg['warmup_frame']
self.pipeline_res = Result()
......@@ -298,9 +328,20 @@ class PipePredictor(object):
tracker_config = mot_cfg['tracker_config']
batch_size = mot_cfg['batch_size']
self.mot_predictor = SDE_Detector(
model_dir, tracker_config, device, run_mode, batch_size,
trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode,
cpu_threads, enable_mkldnn)
model_dir,
tracker_config,
device,
run_mode,
batch_size,
trt_min_shape,
trt_max_shape,
trt_opt_shape,
trt_calib_mode,
cpu_threads,
enable_mkldnn,
draw_center_traj=draw_center_traj,
secs_interval=secs_interval,
do_entrance_counting=do_entrance_counting)
if self.with_attr:
attr_cfg = self.cfg['ATTR']
model_dir = attr_cfg['model_dir']
......@@ -431,6 +472,7 @@ class PipePredictor(object):
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(capture.get(cv2.CAP_PROP_FPS))
frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
print("video fps: %d, frame_count: %d" % (fps, frame_count))
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
......@@ -438,6 +480,19 @@ class PipePredictor(object):
fourcc = cv2.VideoWriter_fourcc(* 'mp4v')
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
frame_id = 0
entrance, records, center_traj = None, None, None
if self.draw_center_traj:
center_traj = [{}]
id_set = set()
interval_id_set = set()
in_id_list = list()
out_id_list = list()
prev_center = dict()
records = list()
entrance = [0, height / 2., width, height / 2.]
video_fps = fps
while (1):
if frame_id % 10 == 0:
print('frame id: ', frame_id)
......@@ -457,6 +512,16 @@ class PipePredictor(object):
# mot output format: id, class, score, xmin, ymin, xmax, ymax
mot_res = parse_mot_res(res)
# flow_statistic only support single class MOT
boxes, scores, ids = res[0] # batch size = 1 in MOT
mot_result = (frame_id + 1, boxes[0], scores[0],
ids[0]) # single class
statistic = flow_statistic(
mot_result, self.secs_interval, self.do_entrance_counting,
video_fps, entrance, id_set, interval_id_set, in_id_list,
out_id_list, prev_center, records)
records = statistic['records']
# nothing detected
if len(mot_res['boxes']) == 0:
frame_id += 1
......@@ -549,13 +614,21 @@ class PipePredictor(object):
if self.cfg['visual']:
_, _, fps = self.pipe_timer.get_total_time()
im = self.visualize_video(frame, self.pipeline_res, frame_id,
fps) # visualize
fps, entrance, records,
center_traj) # visualize
writer.write(im)
writer.release()
print('save result to {}'.format(out_path))
def visualize_video(self, image, result, frame_id, fps):
def visualize_video(self,
image,
result,
frame_id,
fps,
entrance=None,
records=None,
center_traj=None):
mot_res = copy.deepcopy(result.get('mot'))
if mot_res is not None:
ids = mot_res['boxes'][:, 0]
......@@ -567,8 +640,28 @@ class PipePredictor(object):
boxes = np.zeros([0, 4])
ids = np.zeros([0])
scores = np.zeros([0])
image = plot_tracking(
image, boxes, ids, scores, frame_id=frame_id, fps=fps)
# single class, still need to be defaultdict type for ploting
num_classes = 1
online_tlwhs = defaultdict(list)
online_scores = defaultdict(list)
online_ids = defaultdict(list)
online_tlwhs[0] = boxes
online_scores[0] = scores
online_ids[0] = ids
image = plot_tracking_dict(
image,
num_classes,
online_tlwhs,
online_ids,
online_scores,
frame_id=frame_id,
fps=fps,
do_entrance_counting=self.do_entrance_counting,
entrance=entrance,
records=records,
center_traj=center_traj)
attr_res = result.get('attr')
if attr_res is not None:
......@@ -630,7 +723,8 @@ def main():
FLAGS.video_dir, FLAGS.camera_id, FLAGS.enable_attr,
FLAGS.enable_action, FLAGS.device, FLAGS.run_mode, FLAGS.trt_min_shape,
FLAGS.trt_max_shape, FLAGS.trt_opt_shape, FLAGS.trt_calib_mode,
FLAGS.cpu_threads, FLAGS.enable_mkldnn, FLAGS.output_dir)
FLAGS.cpu_threads, FLAGS.enable_mkldnn, FLAGS.output_dir,
FLAGS.draw_center_traj, FLAGS.secs_interval, FLAGS.do_entrance_counting)
pipeline.run()
......
deploy/pptracking/python/README.md
浏览文件 @ 59df0f34
......@@ -35,10 +35,21 @@ wget https://bj.bcebos.com/v1/paddledet/data/mot/demo/mot17_demo.mp4
# Python预测视频
python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video_file=mot17_demo.mp4 --device=GPU --threshold=0.5 --save_mot_txts --save_images
```
### 1.3 用导出的模型基于Python去预测，以及进行流量计数、出入口统计和绘制跟踪轨迹等
```bash
# 下载出入口统计demo视频：
wget https://bj.bcebos.com/v1/paddledet/data/mot/demo/entrance_count_demo.mp4
# Python预测视频
python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video_file=entrance_count_demo.mp4 --device=GPU --do_entrance_counting --draw_center_traj
```
**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`。
- `--threshold`表示结果可视化的置信度阈值，默认为0.5，低于该阈值的结果会被过滤掉，为了可视化效果更佳，可根据实际情况自行修改。
- `--do_entrance_counting`表示是否统计出入口流量，默认为False，`--draw_center_traj`表示是否绘制跟踪轨迹，默认为False。注意绘制跟踪轨迹的测试视频最好是静止摄像头拍摄的。
- 对于多类别或车辆的FairMOT模型的导出和Python预测只需更改相应的config和模型权重即可。如：
```bash
job_name=mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone
......
deploy/pptracking/python/mot/mtmct/postprocess.py
浏览文件 @ 59df0f34
......@@ -27,7 +27,7 @@ from .utils import parse_pt_gt, parse_pt, compare_dataframes_mtmc
from .utils import get_labels, getData, gen_new_mot
from .camera_utils import get_labels_with_camera
from .zone import Zone
from ..utils import plot_tracking
from ..visualize import plot_tracking
__all__ = [
'trajectory_fusion',
......@@ -68,8 +68,8 @@ def trajectory_fusion(mot_feature, cid, cid_bias, use_zone=False, zone_path=''):
zone_list = [tracklet[f]['zone'] for f in frame_list]
feature_list = [
tracklet[f]['feat'] for f in frame_list
if (tracklet[f]['bbox'][3] - tracklet[f]['bbox'][1]
) * (tracklet[f]['bbox'][2] - tracklet[f]['bbox'][0]) > 2000
if (tracklet[f]['bbox'][3] - tracklet[f]['bbox'][1]) *
(tracklet[f]['bbox'][2] - tracklet[f]['bbox'][0]) > 2000
]
if len(feature_list) < 2:
feature_list = [tracklet[f]['feat'] for f in frame_list]
......@@ -293,9 +293,9 @@ def save_mtmct_crops(cid_tid_fid_res,
for f_id in cid_tid_fid_res[c_id][t_id].keys():
frame_idx = f_id - 1 if f_id > 0 else 0
im_path = os.path.join(infer_dir, all_images[frame_idx])
im = cv2.imread(im_path) # (H, W, 3)
# only select one track
track = cid_tid_fid_res[c_id][t_id][f_id][0]
......
deploy/pptracking/python/mot/utils.py
浏览文件 @ 59df0f34
......@@ -20,8 +20,7 @@ import collections
__all__ = [
'MOTTimer', 'Detection', 'write_mot_results', 'load_det_results',
'preprocess_reid', 'get_crops', 'clip_box', 'scale_coords', 'flow_statistic',
'plot_tracking'
'preprocess_reid', 'get_crops', 'clip_box', 'scale_coords', 'flow_statistic'
]
......@@ -182,7 +181,7 @@ def clip_box(xyxy, ori_image_shape):
def get_crops(xyxy, ori_img, w, h):
crops = []
xyxy = xyxy.astype(np.int64)
ori_img = ori_img.transpose(1, 0, 2) # [h,w,3]->[w,h,3]
ori_img = ori_img.transpose(1, 0, 2) # [h,w,3]->[w,h,3]
for i, bbox in enumerate(xyxy):
crop = ori_img[bbox[0]:bbox[2], bbox[1]:bbox[3], :]
crops.append(crop)
......@@ -197,10 +196,7 @@ def preprocess_reid(imgs,
std=[0.229, 0.224, 0.225]):
im_batch = []
for img in imgs:
try:
img = cv2.resize(img, (w, h))
except:
embed()
img = cv2.resize(img, (w, h))
img = img[:, :, ::-1].astype('float32').transpose((2, 0, 1)) / 255
img_mean = np.array(mean).reshape((3, 1, 1))
img_std = np.array(std).reshape((3, 1, 1))
......@@ -288,77 +284,3 @@ def flow_statistic(result,
"prev_center": prev_center,
"records": records
}
def get_color(idx):
idx = idx * 3
color = ((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255)
return color
def plot_tracking(image,
tlwhs,
obj_ids,
scores=None,
frame_id=0,
fps=0.,
ids2names=[],
do_entrance_counting=False,
entrance=None):
im = np.ascontiguousarray(np.copy(image))
im_h, im_w = im.shape[:2]
text_scale = max(1, image.shape[1] / 1600.)
text_thickness = 2
line_thickness = max(1, int(image.shape[1] / 500.))
if fps > 0:
_line = 'frame: %d fps: %.2f num: %d' % (frame_id, fps, len(tlwhs))
else:
_line = 'frame: %d num: %d' % (frame_id, len(tlwhs))
cv2.putText(
im,
_line,
(0, int(15 * text_scale)),
cv2.FONT_HERSHEY_PLAIN,
text_scale, (0, 0, 255),
thickness=2)
for i, tlwh in enumerate(tlwhs):
x1, y1, w, h = tlwh
intbox = tuple(map(int, (x1, y1, x1 + w, y1 + h)))
obj_id = int(obj_ids[i])
id_text = '{}'.format(int(obj_id))
if ids2names != []:
assert len(
ids2names) == 1, "plot_tracking only supports single classes."
id_text = '{}_'.format(ids2names[0]) + id_text
_line_thickness = 1 if obj_id <= 0 else line_thickness
color = get_color(abs(obj_id))
cv2.rectangle(
im, intbox[0:2], intbox[2:4], color=color, thickness=line_thickness)
cv2.putText(
im,
id_text, (intbox[0], intbox[1] - 10),
cv2.FONT_HERSHEY_PLAIN,
text_scale, (0, 0, 255),
thickness=text_thickness)
if scores is not None:
text = '{:.2f}'.format(float(scores[i]))
cv2.putText(
im,
text, (intbox[0], intbox[1] + 10),
cv2.FONT_HERSHEY_PLAIN,
text_scale, (0, 255, 255),
thickness=text_thickness)
if do_entrance_counting:
entrance_line = tuple(map(int, entrance))
cv2.rectangle(
im,
entrance_line[0:2],
entrance_line[2:4],
color=(0, 255, 255),
thickness=line_thickness)
return im
deploy/pptracking/python/mot/visualize.py
浏览文件 @ 59df0f34
......@@ -209,7 +209,7 @@ def plot_tracking_dict(image,
end = records[-1].find('In')
cv2.putText(
im,
records[-1][start:end], (0, int(40 * text_scale)),
records[-1][start:end], (0, int(40 * text_scale) + 10),
cv2.FONT_ITALIC,
text_scale, (0, 0, 255),
thickness=text_thickness)
......@@ -226,7 +226,7 @@ def plot_tracking_dict(image,
start = records[-1].find('In')
cv2.putText(
im,
records[-1][start:-1], (0, int(60 * text_scale)),
records[-1][start:-1], (0, int(60 * text_scale) + 10),
cv2.FONT_ITALIC,
text_scale, (0, 0, 255),
thickness=text_thickness)
......
deploy/pptracking/python/mot_jde_infer.py
浏览文件 @ 59df0f34
......@@ -31,7 +31,7 @@ parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
sys.path.insert(0, parent_path)
from mot import JDETracker
from mot.utils import MOTTimer, write_mot_results
from mot.utils import MOTTimer, write_mot_results, flow_statistic
from mot.visualize import plot_tracking, plot_tracking_dict
# Global dictionary
......@@ -54,23 +54,38 @@ class JDE_Detector(Detector):
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
calibration, trt_calib_mode need to set True
cpu_threads (int): cpu threads
enable_mkldnn (bool): whether to open MKLDNN
enable_mkldnn (bool): whether to open MKLDNN
output_dir (string): The path of output, default as 'output'
threshold (float): Score threshold of the detected bbox, default as 0.5
save_images (bool): Whether to save visualization image results, default as False
save_mot_txts (bool): Whether to save tracking results (txt), default as False
draw_center_traj (bool): Whether drawing the trajectory of center, default as False
secs_interval (int): The seconds interval to count after tracking, default as 10
do_entrance_counting(bool): Whether counting the numbers of identifiers entering
or getting out from the entrance, default as False，only support single class
counting in MOT.
"""
def __init__(self,
model_dir,
tracker_config=None,
device='CPU',
run_mode='paddle',
batch_size=1,
trt_min_shape=1,
trt_max_shape=1088,
trt_opt_shape=608,
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False,
output_dir='output',
threshold=0.5):
def __init__(
self,
model_dir,
tracker_config=None,
device='CPU',
run_mode='paddle',
batch_size=1,
trt_min_shape=1,
trt_max_shape=1088,
trt_opt_shape=608,
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False,
output_dir='output',
threshold=0.5,
save_images=False,
save_mot_txts=False,
draw_center_traj=False,
secs_interval=10,
do_entrance_counting=False, ):
super(JDE_Detector, self).__init__(
model_dir=model_dir,
device=device,
......@@ -84,6 +99,12 @@ class JDE_Detector(Detector):
enable_mkldnn=enable_mkldnn,
output_dir=output_dir,
threshold=threshold, )
self.save_images = save_images
self.save_mot_txts = save_mot_txts
self.draw_center_traj = draw_center_traj
self.secs_interval = secs_interval
self.do_entrance_counting = do_entrance_counting
assert batch_size == 1, "MOT model only supports batch_size=1."
self.det_times = Timer(with_tracker=True)
self.num_classes = len(self.pred_config.labels)
......@@ -115,7 +136,7 @@ class JDE_Detector(Detector):
return result
def tracking(self, det_results):
pred_dets = det_results['pred_dets']
pred_dets = det_results['pred_dets'] # cls_id, score, x0, y0, x1, y1
pred_embs = det_results['pred_embs']
online_targets_dict = self.tracker.update(pred_dets, pred_embs)
......@@ -164,7 +185,8 @@ class JDE_Detector(Detector):
image_list,
run_benchmark=False,
repeats=1,
visual=True):
visual=True,
seq_name=None):
mot_results = []
num_classes = self.num_classes
image_list.sort()
......@@ -225,7 +247,7 @@ class JDE_Detector(Detector):
self.det_times.img_num += 1
if visual:
if frame_id % 10 == 0:
if len(image_list) > 1 and frame_id % 10 == 0:
print('Tracking frame {}'.format(frame_id))
frame, _ = decode_image(img_file, {})
......@@ -237,7 +259,8 @@ class JDE_Detector(Detector):
online_scores,
frame_id=frame_id,
ids2names=ids2names)
seq_name = image_list[0].split('/')[-2]
if seq_name is None:
seq_name = image_list[0].split('/')[-2]
save_dir = os.path.join(self.output_dir, seq_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
......@@ -264,7 +287,8 @@ class JDE_Detector(Detector):
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
out_path = os.path.join(self.output_dir, video_out_name)
fourcc = cv2.VideoWriter_fourcc(* 'mp4v')
video_format = 'mp4v'
fourcc = cv2.VideoWriter_fourcc(*video_format)
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
frame_id = 1
......@@ -273,6 +297,23 @@ class JDE_Detector(Detector):
num_classes = self.num_classes
data_type = 'mcmot' if num_classes > 1 else 'mot'
ids2names = self.pred_config.labels
center_traj = None
entrance = None
records = None
if self.draw_center_traj:
center_traj = [{} for i in range(num_classes)]
if num_classes == 1:
id_set = set()
interval_id_set = set()
in_id_list = list()
out_id_list = list()
prev_center = dict()
records = list()
entrance = [0, height / 2., width, height / 2.]
video_fps = fps
while (1):
ret, frame = capture.read()
if not ret:
......@@ -282,7 +323,9 @@ class JDE_Detector(Detector):
frame_id += 1
timer.tic()
mot_results = self.predict_image([frame], visual=False)
seq_name = video_out_name.split('.')[0]
mot_results = self.predict_image(
[frame], visual=False, seq_name=seq_name)
timer.toc()
online_tlwhs, online_scores, online_ids = mot_results[0]
......@@ -291,6 +334,16 @@ class JDE_Detector(Detector):
(frame_id + 1, online_tlwhs[cls_id], online_scores[cls_id],
online_ids[cls_id]))
# NOTE: just implement flow statistic for single class
if num_classes == 1:
result = (frame_id + 1, online_tlwhs[0], online_scores[0],
online_ids[0])
statistic = flow_statistic(
result, self.secs_interval, self.do_entrance_counting,
video_fps, entrance, id_set, interval_id_set, in_id_list,
out_id_list, prev_center, records, data_type, num_classes)
records = statistic['records']
fps = 1. / timer.duration
im = plot_tracking_dict(
frame,
......@@ -300,27 +353,57 @@ class JDE_Detector(Detector):
online_scores,
frame_id=frame_id,
fps=fps,
ids2names=ids2names)
ids2names=ids2names,
do_entrance_counting=self.do_entrance_counting,
entrance=entrance,
records=records,
center_traj=center_traj)
writer.write(im)
if camera_id != -1:
cv2.imshow('Mask Detection', im)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if self.save_mot_txts:
result_filename = os.path.join(
self.output_dir, video_out_name.split('.')[-2] + '.txt')
write_mot_results(result_filename, results, data_type, num_classes)
if num_classes == 1:
result_filename = os.path.join(
self.output_dir,
video_out_name.split('.')[-2] + '_flow_statistic.txt')
f = open(result_filename, 'w')
for line in records:
f.write(line)
print('Flow statistic save in {}'.format(result_filename))
f.close()
writer.release()
def main():
detector = JDE_Detector(
FLAGS.model_dir,
tracker_config=None,
device=FLAGS.device,
run_mode=FLAGS.run_mode,
batch_size=1,
trt_min_shape=FLAGS.trt_min_shape,
trt_max_shape=FLAGS.trt_max_shape,
trt_opt_shape=FLAGS.trt_opt_shape,
trt_calib_mode=FLAGS.trt_calib_mode,
cpu_threads=FLAGS.cpu_threads,
enable_mkldnn=FLAGS.enable_mkldnn)
enable_mkldnn=FLAGS.enable_mkldnn,
output_dir=FLAGS.output_dir,
threshold=FLAGS.threshold,
save_images=FLAGS.save_images,
save_mot_txts=FLAGS.save_mot_txts,
draw_center_traj=FLAGS.draw_center_traj,
secs_interval=FLAGS.secs_interval,
do_entrance_counting=FLAGS.do_entrance_counting, )
# predict from video file or camera video stream
if FLAGS.video_file is not None or FLAGS.camera_id != -1:
......
deploy/pptracking/python/mot_sde_infer.py
浏览文件 @ 59df0f34
......@@ -33,7 +33,7 @@ sys.path.insert(0, parent_path)
from det_infer import Detector, get_test_images, print_arguments, bench_log, PredictConfig, load_predictor
from mot_utils import argsparser, Timer, get_current_memory_mb, video2frames, _is_valid_video
from mot.tracker import JDETracker, DeepSORTTracker
from mot.utils import MOTTimer, write_mot_results, flow_statistic, get_crops, clip_box
from mot.utils import MOTTimer, write_mot_results, get_crops, clip_box, flow_statistic
from mot.visualize import plot_tracking, plot_tracking_dict
from mot.mtmct.utils import parse_bias
......@@ -56,6 +56,15 @@ class SDE_Detector(Detector):
calibration, trt_calib_mode need to set True
cpu_threads (int): cpu threads
enable_mkldnn (bool): whether to open MKLDNN
output_dir (string): The path of output, default as 'output'
threshold (float): Score threshold of the detected bbox, default as 0.5
save_images (bool): Whether to save visualization image results, default as False
save_mot_txts (bool): Whether to save tracking results (txt), default as False
draw_center_traj (bool): Whether drawing the trajectory of center, default as False
secs_interval (int): The seconds interval to count after tracking, default as 10
do_entrance_counting(bool): Whether counting the numbers of identifiers entering
or getting out from the entrance, default as False，only support single class
counting in MOT.
reid_model_dir (str): reid model dir, default None for ByteTrack, but set for DeepSORT
mtmct_dir (str): MTMCT dir, default None, set for doing MTMCT
"""
......@@ -74,6 +83,11 @@ class SDE_Detector(Detector):
enable_mkldnn=False,
output_dir='output',
threshold=0.5,
save_images=False,
save_mot_txts=False,
draw_center_traj=False,
secs_interval=10,
do_entrance_counting=False,
reid_model_dir=None,
mtmct_dir=None):
super(SDE_Detector, self).__init__(
......@@ -89,6 +103,12 @@ class SDE_Detector(Detector):
enable_mkldnn=enable_mkldnn,
output_dir=output_dir,
threshold=threshold, )
self.save_images = save_images
self.save_mot_txts = save_mot_txts
self.draw_center_traj = draw_center_traj
self.secs_interval = secs_interval
self.do_entrance_counting = do_entrance_counting
assert batch_size == 1, "MOT model only supports batch_size=1."
self.det_times = Timer(with_tracker=True)
self.num_classes = len(self.pred_config.labels)
......@@ -309,6 +329,7 @@ class SDE_Detector(Detector):
feat_data['feat'] = _feat
tracking_outs['feat_data'].update({_imgname: feat_data})
return tracking_outs
else:
tracking_outs = {
'online_tlwhs': online_tlwhs,
......@@ -409,7 +430,7 @@ class SDE_Detector(Detector):
mot_results.append([online_tlwhs, online_scores, online_ids])
if visual:
if frame_id % 10 == 0:
if len(image_list) > 1 and frame_id % 10 == 0:
print('Tracking frame {}'.format(frame_id))
frame, _ = decode_image(img_file, {})
if isinstance(online_tlwhs, defaultdict):
......@@ -456,13 +477,32 @@ class SDE_Detector(Detector):
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
out_path = os.path.join(self.output_dir, video_out_name)
fourcc = cv2.VideoWriter_fourcc(* 'mp4v')
video_format = 'mp4v'
fourcc = cv2.VideoWriter_fourcc(*video_format)
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
frame_id = 1
timer = MOTTimer()
results = defaultdict(list) # support single class and multi classes
results = defaultdict(list)
num_classes = self.num_classes
data_type = 'mcmot' if num_classes > 1 else 'mot'
ids2names = self.pred_config.labels
center_traj = None
entrance = None
records = None
if self.draw_center_traj:
center_traj = [{} for i in range(num_classes)]
if num_classes == 1:
id_set = set()
interval_id_set = set()
in_id_list = list()
out_id_list = list()
prev_center = dict()
records = list()
entrance = [0, height / 2., width, height / 2.]
video_fps = fps
while (1):
ret, frame = capture.read()
if not ret:
......@@ -477,10 +517,21 @@ class SDE_Detector(Detector):
[frame], visual=False, seq_name=seq_name)
timer.toc()
online_tlwhs, online_scores, online_ids = mot_results[
0] # bs=1 in MOT model
# bs=1 in MOT model
online_tlwhs, online_scores, online_ids = mot_results[0]
# NOTE: just implement flow statistic for one class
if num_classes == 1:
result = (frame_id + 1, online_tlwhs[0], online_scores[0],
online_ids[0])
statistic = flow_statistic(
result, self.secs_interval, self.do_entrance_counting,
video_fps, entrance, id_set, interval_id_set, in_id_list,
out_id_list, prev_center, records, data_type, num_classes)
records = statistic['records']
fps = 1. / timer.duration
if num_classes == 1 and self.use_reid:
if self.use_deepsort_tracker:
# use DeepSORTTracker, only support singe class
results[0].append(
(frame_id + 1, online_tlwhs, online_scores, online_ids))
......@@ -490,7 +541,9 @@ class SDE_Detector(Detector):
online_ids,
online_scores,
frame_id=frame_id,
fps=fps)
fps=fps,
do_entrance_counting=self.do_entrance_counting,
entrance=entrance)
else:
# use ByteTracker, support multiple class
for cls_id in range(num_classes):
......@@ -505,13 +558,32 @@ class SDE_Detector(Detector):
online_scores,
frame_id=frame_id,
fps=fps,
ids2names=[])
ids2names=ids2names,
do_entrance_counting=self.do_entrance_counting,
entrance=entrance,
records=records,
center_traj=center_traj)
writer.write(im)
if camera_id != -1:
cv2.imshow('Mask Detection', im)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if self.save_mot_txts:
result_filename = os.path.join(
self.output_dir, video_out_name.split('.')[-2] + '.txt')
write_mot_results(result_filename, results)
result_filename = os.path.join(
self.output_dir,
video_out_name.split('.')[-2] + '_flow_statistic.txt')
f = open(result_filename, 'w')
for line in records:
f.write(line)
print('Flow statistic save in {}'.format(result_filename))
f.close()
writer.release()
def predict_mtmct(self, mtmct_dir, mtmct_cfg):
......@@ -623,18 +695,23 @@ def main():
arch = yml_conf['arch']
detector = SDE_Detector(
FLAGS.model_dir,
FLAGS.tracker_config,
tracker_config=FLAGS.tracker_config,
device=FLAGS.device,
run_mode=FLAGS.run_mode,
batch_size=FLAGS.batch_size,
batch_size=1,
trt_min_shape=FLAGS.trt_min_shape,
trt_max_shape=FLAGS.trt_max_shape,
trt_opt_shape=FLAGS.trt_opt_shape,
trt_calib_mode=FLAGS.trt_calib_mode,
cpu_threads=FLAGS.cpu_threads,
enable_mkldnn=FLAGS.enable_mkldnn,
threshold=FLAGS.threshold,
output_dir=FLAGS.output_dir,
threshold=FLAGS.threshold,
save_images=FLAGS.save_images,
save_mot_txts=FLAGS.save_mot_txts,
draw_center_traj=FLAGS.draw_center_traj,
secs_interval=FLAGS.secs_interval,
do_entrance_counting=FLAGS.do_entrance_counting,
reid_model_dir=FLAGS.reid_model_dir,
mtmct_dir=FLAGS.mtmct_dir, )
......
deploy/pptracking/python/mot_utils.py
浏览文件 @ 59df0f34
......@@ -137,6 +137,21 @@ def argsparser():
type=ast.literal_eval,
default=True,
help='whether to use darkpose to get better keypoint position predict ')
parser.add_argument(
"--do_entrance_counting",
action='store_true',
help="Whether counting the numbers of identifiers entering "
"or getting out from the entrance. Note that only support one-class"
"counting, multi-class counting is coming soon.")
parser.add_argument(
"--secs_interval",
type=int,
default=2,
help="The seconds interval to count after tracking")
parser.add_argument(
"--draw_center_traj",
action='store_true',
help="Whether drawing the trajectory of center")
parser.add_argument(
"--mtmct_dir",
type=str,
......
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