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PPVehicle_QUICK_STARTED_en.md

PPVehicle_QUICK_STARTED_en.md 19 KB
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Quick Start for PP-Vehicle

Content

Environment Preparation

Environment Preparation: PaddleDetection version >= release/2.5 or develop

Installation of PaddlePaddle and PaddleDetection

# PaddlePaddle CUDA10.1
python -m pip install paddlepaddle-gpu==2.2.2.post101 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html

# PaddlePaddle CPU
python -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple

# Clone PaddleDetectionrepositories
cd <path/to/clone/PaddleDetection>
git clone https://github.com/PaddlePaddle/PaddleDetection.git

# Installing dependencies
cd PaddleDetection
pip install -r requirements.txt
  1. For installation details, please refer to Installation Tutorials
  2. If you need TensorRT inference acceleration (speed measurement), please install PaddlePaddle with TensorRT version. You can download and install it from the PaddlePaddle Installation Package or follow the Instructions or use docker, or self-compiling to prepare the environment.

Model Download

PP-Vehicle provides object detection, attribute recognition, behaviour recognition and ReID pre-trained models for different applications. Developers can download them directly.

Task End-to（ms） Model Solution Model Size
Vehicle Detection（high precision） 25.7ms Multi-Object Tracking 182M
Vehicle Detection（Lightweight） 13.2ms Multi-Object Tracking 27M
Vehicle Tracking（high precision） 40ms Multi-Object Tracking 182M
Vehicle Tracking（Lightweight） 25ms Multi-Object Tracking 27M
License plate recognition 4.68ms License plate recognition
License plate character recognition		 Vehicle Detection：3.9M
License plate character recognition： 12M
Vehicle Attribute Recognition 7.31ms Vehicle Attribute 7.2M

Download the model and unzip it into the . /output_inference folder.

In the configuration file, the model path defaults to the download path of the model. If the user does not change it, the corresponding model will be downloaded automatically upon inference.

Notes：

  • The accuracy of detection tracking model is obtained from the joint dataset PPVehicle (integration of the public dataset BDD100K-MOT and UA-DETRAC). For more details, please refer to PP-Vehicle
  • Inference speed is obtained at T4 with TensorRT FP16 enabled, which includes data pre-processing, model inference and post-processing.

Configuration

PP-Vehicle related configuration locates in deploy/pipeline/config/infer_cfg_ppvehicle.yml. Developers need to set specific task types to use different features.

The features and corresponding task types are as follows.

Input Feature Task Config
Image Attribute Recognition Object Detection Attribute Recognition DET ATTR
Single-camera video Attribute Recognition Multi-Object Tracking Attribute Recognition MOT ATTR
Single-camera video License-plate Recognition Multi-Object Tracking License-plate Recognition MOT VEHICLEPLATE

Take attribute recognition based on video input as an example: Its task type includes multi-object tracking and attributes recognition. The specific configuration is as follows.

crop_thresh: 0.5
visual: True
warmup_frame: 50

MOT:
  model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip
  tracker_config: deploy/pipeline/config/tracker_config.yml
  batch_size: 1
  enable: True

VEHICLE_ATTR:
  model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip
  batch_size: 8
  color_threshold: 0.5
  type_threshold: 0.5
  enable: True

Notes：

  • If the developer needs to carry out different tasks, set the corresponding enables option to be True in the configuration file.
  • If the developer only needs to modify the model file path, run the command line with -o MOT.model_dir=ppyoloe/ after --config, or manually modify the corresponding model path in the configuration file. For more details, please refer to the following parameter descriptions

Inference Deployment

  1. Use the default configuration directly or the configuration file in examples, or modify the configuration in infer_cfg_ppvehicle.yml
# Example：In vehicle detection，specify configuration file path and test image
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml --image_file=test_image.jpg --device=gpu

# Example：In license plate recognition，directly configure the examples
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_vehicle_plate.yml --video_file=test_video.mp4 --device=gpu
  1. Use the command line to enable functions or change the model path.
   #  Example：In vehicle tracking，specify configuration file path and test video, Turn on the MOT model and modify the model path on the command line, the model path specified on the command line has higher priority than the configuration file
   python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml -o MOT.enable=True MOT.model_dir=ppyoloe_infer/ --video_file=test_video.mp4 --device=gpu

   # Example：In vehicle illegal action analysis，specify configuration file path and test video, 命令行中指定违停区域设置、违停时间判断。
   python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_illegal_parking.yml \
                                                      --video_file=../car_test.mov \
                                                      --device=gpu \
                                                      --draw_center_traj \
                                                      --illegal_parking_time=3 \
                                                      --region_type=custom \
                                                      --region_polygon 600 300 1300 300 1300 800 600 800
  1. rtsp push/pull stream
  2. rtsp pull stream

For rtsp pull stream, use --rtsp RTSP [RTSP ...] parameter to specify one or more rtsp streams. Separate the multiple addresses with a space, or replace the video address directly after the video_file with the rtsp stream address), examples as follows

   # Example: Single video stream for pedestrian attribute recognition
   python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_human_attr.yml -o visual=False --rtsp rtsp://[YOUR_RTSP_SITE]  --device=gpu
   # Example: Multiple-video stream for pedestrian attribute recognition
   python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_human_attr.yml -o visual=False --rtsp rtsp://[YOUR_RTSP_SITE1] rtsp://[YOUR_RTSP_SITE2] --device=gpu                                                                      |
  • rtsp push stream

For rtsp push stream, use --pushurl rtsp:[IP] parameter to push stream to a IP set, and you can visualize the output video by VLC Player with the open network funciton. the whole url path is rtsp:[IP]/videoname, the videoname here is the basename of the video file to infer, and the default of videoname is output when the video come from local camera and there is no video name.

# Example：license plate recognition，in this example the whole url path is: rtsp://[YOUR_SERVER_IP]:8554/test_video
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_vehicle_plate.yml --video_file=test_video.mp4  --device=gpu --pushurl rtsp://[YOUR_SERVER_IP]:8554

Note:

  1. rtsp push stream is based on rtsp-simple-server, please enable this serving first.
  2. the output visualize will be frozen frequently if the model cost too much time, we suggest to use faster model like ppyoloe_s in tracking, this is simply replace mot_ppyoloe_l_36e_pipeline.zip with mot_ppyoloe_s_36e_pipeline.zip in model config yaml file.

Jetson Deployment

Due to the large gap in computing power of the Jetson platform compared to the server, we suggest:

  1. choose a lightweight model, especially for tracking model, ppyoloe_s: https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip is recommended
  2. For frame skipping of tracking; we recommend 2 or 3: skip_frame_num: 3

With this recommended configuration, it is possible to achieve higher speeds on the TX2 platform. It has been tested with attribute case, with speeds up to 20fps. The configuration file can be modified directly (recommended) or from the command line (not recommended due to its long fields).

Parameters

#

Parameters Necessity Implications
--config Yes Path to configuration file
-o Option Overwrite the corresponding configuration in the configuration file
--image_file Option Images to be predicted
--image_dir Option Path to the images folder to be predicted
--video_file Option Video to be predicted, or rtsp stream address (rtsp parameter recommended)
--rtsp Option rtsp video stream address, supports one or more simultaneous streams input
--camera_id Option The camera ID for prediction, default is -1 ( for no camera prediction, can be set to 0 - (number of cameras - 1) ), press q in the visualization interface during the prediction process to output the prediction result to: output/output.mp4
--device Option Running device, options include CPU/GPU/XPU, and the default is CPU.
--pushurl Option push the output video to rtsp stream, normaly start with rtsp://; this has higher priority than local video save, while this is set, pipeline will not save local visualize video, the default is "", means this will not work now.
--output_dir Option The root directory for the visualization results, and the default is output/
--run_mode Option For GPU, the default is paddle, with (paddle/trt_fp32/trt_fp16/trt_int8) as optional
--enable_mkldnn Option Whether to enable MKLDNN acceleration in CPU prediction, the default is False
--cpu_threads Option Set the number of cpu threads, and the default is 1
--trt_calib_mode Option Whether TensorRT uses the calibration function, and the default is False; set to True when using TensorRT's int8 function and False when using the PaddleSlim quantized model
--do_entrance_counting Option Whether to count entrance/exit traffic flows, the default is False
--draw_center_traj Option Whether to draw center trajectory, the default is False
--region_type Option 'horizontal' (default), 'vertical': traffic count direction; 'custom': set illegal parking area
--region_polygon Option Set the coordinates of the polygon multipoint in the illegal parking area. No default.
--illegal_parking_time Option Set the time threshold for illegal parking in seconds (s), -1 (default) indicates no check

Solutions

The overall solution for PP-Vehicle v2 is shown in the graph below:

###

Vehicle detection

  • Take PP-YOLOE L as the object detection model
  • For detailed documentation, please refer to PP-YOLOE and Multiple-Object-Tracking

Vehicle tracking

  • Vehicle tracking by SDE solution
  • Adopt PP-YOLOE L (high precision) and S (lightweight) for detection models
  • Adopt the OC-SORT solution for racking module
  • Refer to OC-SORT and Multi-Object Tracking for details

Attribute Recognition

  • Use PP-LCNet provided by PaddleClas to recognize vehicle colours and model attributes.
  • For details, please refer to Attribute Recognition

License plate recognition

  • Use ch_PP-OCRv3_det+ch_PP-OCRv3_rec model to recognize license plate number
  • For details, please refer to Plate Recognition

Illegal Parking Detection

  • Use vehicle tracking model (high precision) PP-YOLOE L to determine whether the parking is illegal based on the vehicle's trajectory and the designated illegal parking area. If it is illegal parking, display the illegal parking plate number.

  • For details, please refer to Illegal Parking Detection