English | [简体中文](PPVehicle_QUICK_STARTED.md) # Quick Start for PP-Vehicle ## Content - [Environment Preparation](#Environment-Preparation) - [Model Download](#Model-Download) - [Configuration](#Configuration) - [Inference Deployment](#Inference-Deployment) - [Parameters](#Parameters) - [Solutions](#Solutions) - [Vehicle Detection](#Vehicle-Detection) - [Vehicle Tracking](#Vehicle-Tracking) - [License Plate Recognition](#License-Plate-Recognition) - [Attribute Recognition](#Attribute-Recognition) - [Illegal Parking Detection](#Illegal-Parking-Detection) ## 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 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](../../../../docs/tutorials/INSTALL.md) 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](https://paddleinference.paddlepaddle.org.cn/v2.2/user_guides/download_lib.html#python) or follow the [Instructions]([https://www](https://www). paddlepaddle.org.cn/inference/master/optimize/paddle_trt.html) 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](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | | Vehicle Detection（Lightweight） | 13.2ms | [Multi-Object Tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M | | Vehicle Tracking（high precision） | 40ms | [Multi-Object Tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | | Vehicle Tracking（Lightweight） | 25ms | [Multi-Object Tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M | | License plate recognition | 4.68ms | [License plate recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz)
[License plate character recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) | Vehicle Detection：3.9M
License plate character recognition： 12M | | Vehicle Attribute Recognition | 7.31ms | [Vehicle Attribute](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | 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](... /... /... /... /configs/ppvehicle) - 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 ``` 2. 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 ``` 3. rtsp push/pull stream - 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](https://vlc.onl/) 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](https://github.com/aler9/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](... /... /... /... /configs/ppyoloe/) and [Multiple-Object-Tracking](ppvehicle_mot_en.md) ### 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](... /... /... /... /configs/mot/ocsort) and [Multi-Object Tracking](ppvehicle_mot_en.md) for details ### Attribute Recognition - Use PP-LCNet provided by PaddleClas to recognize vehicle colours and model attributes. - For details, please refer to [Attribute Recognition](ppvehicle_attribute_en.md) ### 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](ppvehicle_plate_en.md) ### 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](ppvehicle_illegal_parking_en.md)