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Galliot Adaptive Pose Estimation

This is the Galliot's Adaptive Pose Estimation repository. With this module you can run specialized pose estimation models with the help of Galliot's Adaptive Learning API.

Supported Devices

• X86 devices with Nvidia GPUs
• Nvidia Jetson family are supported. (JetPack 4.4)
  For running pose estimator you should have Docker and Nvidia Docker Toolkit on your system

Getting Started

1. Start an Adaptive Learning Job in API's website for your specific video and download the specialized model.

Note that your models categories must contains person class.

2. Clone this repository:

git clone --recurse-submodules https://github.com/galliot-us/adaptive-pose-estimation.git
cd adaptive-pose-estimation

3-1. Build and run docker container on X86 with GPU:

docker build -f x86-gpu.Dockerfile -t "galliot/pose-estimation:latest-x86_64_gpu" .
docker run -it --gpus all -v "$PWD":/repo galliot/pose-estimation:latest-x86_64_gpu

3-2. Build and run docker container on Jetson devices:

docker build -f jetson.Dockerfile -t "galliot/pose-estimation:latest-jetson" .
docker run --runtime nvidia --entrypoint bash --privileged -it -v $PWD/:/repo galliot/pose-estimation:latest-jetson

• Make sure you run the following bash script inside the Jetson container to export TRT model.

  # bash generate_pose_tensorrt.bash [ONNX FILE URL] [Stored on FLOAT16(fp16)/ FLOAT32(fp32)] [BATCH_SIZE]
  bash generate_pose_tensorrt.bash https://media.githubusercontent.com/media/galliot-us/models/master/ONNX/fastpose/fastpose_resnet50_256_192_tf.onnx fp16 8
  

NOTE: For Jetson TX2 batch size 8 is recommended. For other devices you can test multiple batch sizes to find the optimum one.

4. Start Inference:

python3 inference/inference.py --device DEVICE --input_video INPUT_VIDEO                                
                    --out_dir OUT_DIR
                    --detector_model_path DETECTOR_MODEL_PATH                                                                                                      
                    --label_map LABEL_MAP                                                                                                                                  
                    --detector_threshold DETECTOR_THRESHOLD                                                                                                             
                    --detector_input_width DETECTOR_INPUT_WIDTH                                                                                                           
                    --detector_input_height DETECTOR_INPUT_HEIGHT                                                                                                          
                    --pose_input_width POSE_INPUT_WIDTH
                    --pose_input_height POSE_INPUT_HEIGHT
                    --heatmap_width HEATMAP_WIDTH
                    --heatmap_height HEATMAP_HEIGHT
                    --out_width OUT_WIDTH
                    --out_height OUT_HEIGHT
                    --batch_size BATCH_SIZE
                    --trt_model_path TRT_MODEL_PATH

optional arguments:
  -h, --help            show this help message and exit
  --device DEVICE       supports x86 and jetson
  --input_video INPUT_VIDEO
                        input video path
  --out_dir OUT_DIR     directory to store output video
  --detector_model_path DETECTOR_MODEL_PATH
                        path to the detector model files, if not provided the
                        default COCO models will be used
  --label_map LABEL_MAP
                        path to the label map file
  --detector_threshold DETECTOR_THRESHOLD
                        detection's score threshold
  --detector_input_width DETECTOR_INPUT_WIDTH
                        width of the detector's input
  --detector_input_height DETECTOR_INPUT_HEIGHT
                        height of the detector's input
  --pose_input_width POSE_INPUT_WIDTH
                        width of the pose estimator's input
  --pose_input_height POSE_INPUT_HEIGHT
                        height of the pose estomator's input
  --heatmap_width HEATMAP_WIDTH
                        width of the pose haetmap
  --heatmap_height HEATMAP_HEIGHT
                        height of the pose heatmap
  --out_width OUT_WIDTH
                        width of the output video
  --out_height OUT_HEIGHT
                        height of the output video
  --batch_size BATCH_SIZE
                        the trt model batch size (works only for Jetson devices)
  --trt_model_path TRT_MODEL_PATH
                        the path of trt model (works only for Jetson devices)

Note: pass your specialized model to the detecor_model_path argument and its label_map.pbtxt file to the label_map argument. Otherwise the default COCO model will be used.