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GETTING_STARTED.md

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Getting Started

Flags

Checkout the run.md for all flags.

Example Inference

  • Input dir: Run AlphaPose for all images in a folder with:
python scripts/demo_inference.py --cfg ${cfg_file} --checkpoint ${trained_model} --indir ${img_directory} --outdir ${output_directory}
  • Video: Run AlphaPose for a video and save the rendered video with:
python scripts/demo_inference.py --cfg ${cfg_file} --checkpoint ${trained_model} --video ${path to video} --outdir examples/res --save_video
  • Webcam: Run AlphaPose using default webcam and visualize the results with:
python scripts/demo_inference.py --cfg ${cfg_file} --checkpoint ${trained_model} --outdir examples/res --vis --webcam 0
  • Input list: Run AlphaPose for images in a list and save the rendered images with:
python scripts/demo_inference.py --cfg ${cfg_file} --checkpoint ${trained_model} --list examples/list-coco-demo.txt --indir ${img_directory} --outdir examples/res --save_img
  • Only-cpu/Multi-gpus: Run AlphaPose for images in a list by cpu only or multi gpus:
python scripts/demo_inference.py --cfg ${cfg_file} --checkpoint ${trained_model} --list examples/list-coco-demo.txt --indir ${img_directory} --outdir examples/res --gpus ${-1(cpu only)/0,1,2,3(multi-gpus)}
  • Re-ID Track(Experimental): Run AlphaPose for tracking persons in a video by human re-id algorithm:
python scripts/demo_inference.py --cfg ${cfg_file} --checkpoint ${trained_model} --video ${path to video} --outdir examples/res --pose_track --save_video
  • Simple Track(Experimental): Run AlphaPose for tracking persons in a video by MOT tracking algorithm:
python scripts/demo_inference.py --cfg ${cfg_file} --checkpoint ${trained_model} --video ${path to video} --outdir examples/res --detector tracker --save_video
  • Pose Flow(not ready): Run AlphaPose for tracking persons in a video by embedded PoseFlow algorithm:
python scripts/demo_inference.py --cfg ${cfg_file} --checkpoint ${trained_model} --video ${path to video} --outdir examples/res --pose_flow --save_video

Options

  • Note: If you meet OOM(out of memory) problem, decreasing the pose estimation batch until the program can run on your computer:
python scripts/demo_inference.py --cfg ${cfg_file} --checkpoint ${trained_model} --indir ${img_directory} --outdir examples/res --detbatch 1 --posebatch 30
  • Getting more accurate: You can use larger input for pose network to improve performance e.g.:
python scripts/demo_inference.py --cfg ${cfg_file} --checkpoint ${trained_model} --indir ${img_directory} --outdir ${output_directory} --flip
  • Speeding up: Checkout the speed_up.md for more details.

Output format

Checkout the output.md for more details.