Forget about the OpenPose library code, just compile the library and use the demo ./build/examples/openpose/openpose.bin.
In order to learn how to use it, run ./build/examples/openpose/openpose.bin --help in your bash and read all the available flags (check only the flags for examples/openpose/openpose.cpp itself, i.e. the section Flags from examples/openpose/openpose.cpp:). We detail some of them in the following sections.
Check that the library is working properly by using any of the following commands. Note that examples/media/video.avi and examples/media exist, so you do not need to change the paths.
- Running on Video
./build/examples/openpose/openpose.bin --video examples/media/video.avi
- Running on Webcam
./build/examples/openpose/openpose.bin
- Running on Images
./build/examples/openpose/openpose.bin --image_dir examples/media/
The visual GUI should show the original image with the poses blended on it, similarly to the pose of this gif:
If you choose to visualize a body part or a PAF (Part Affinity Field) heat map with the command option --part_to_show, the result should be similar to one of the following images:
Please, in order to check all the real time pose demo options and their details, run ./build/examples/openpose/openpose.bin --help. We describe here some of the most important ones.
--video input.mp4: Input video. If omitted, it will use the webcam.
--camera 3: Choose webcam number (default: 0). If --camera, --image_dir and --write_video are omitted, it is equivalent to use --camera 0.
--image_dir path_to_images/: Run on all images (jpg, png, bmp, etc.) in path_to_images/. You can test the program with the image directory examples/media/.
--write_video path.avi: Render images with this prefix: path.avi. You can test the program with the example video examples/media/video.avi.
--write_pose path/: Output JSON, XML or YML files with the people pose data on the path/ folder.
--process_real_time: It might skip frames in order to keep the final output displaying frames on real time.
--disable_blending: If selected, it will only render the pose skeleton or desired heat maps, while blocking the original background. Also related: part_to_show, alpha_pose, and alpha_pose.
--part_to_show: Select the prediction channel to visualize (default: 0). 0 to visualize all the body parts, 1-18 for each body part heat map, 19 for the background heat map, 20 for all the body part heat maps together, 21 for all the PAFs, 22-69 for each body part pair PAF.
--no_display: Display window not opened. Useful if there is no X server and/or to slightly speed up the processing if visual output is not required.
--num_gpu 2 --num_gpu_start 0: Parallelize over this number of GPUs starting by the desired device id. Default is 1 and 0, respectively.
--num_scales 3 --scale_gap 0.15: Use 3 scales, 1, (1-0.15), (1-0.15*2). Default is one scale. If you want to change the initial scale, you actually want to multiply your desired initial scale by the net_resolution.
--net_resolution 656x368 --resolution 1280x720: For HD images and video (default values).
--net_resolution 496x368 --resolution 640x480: For VGA images and video.
--model_pose MPI: It will use MPI (15 body keypoints). Default: COCO (18 body keypoints). MPI is slightly faster. The variation MPI_4_layers sacrifies accuracy in order to further increase speed.
--logging_level 3: Logging messages threshold, range [0,255]: 0 will output any message & 255 will output none. Current messages in the range [1-4], 1 for low priority messages and 4 for important ones.
Running at multiple scales might drastically slow down the speed, but it will increase the accuracy. Given the CNN input size (set with net_resolution), num_scales and scale_gap configure the number of scales to use and the gap between them, respectively. For instance, --num_scales 3 --scale_gap 0.15 means using 3 scales at resolution: (1), (1-0.15) and (1-2*0.15) times the net_resolution.
The following command will save all the body part heat maps, background heat map and Part Affinity Fields (PAFs) in the folder output_heatmaps_folder. It will save them on PNG format. Instead of individually saving each of the 67 heatmaps (18 body parts + background + 2 x 19 PAFs) individually, the library concatenate them vertically into a huge (width x #heatmaps) x (height) matrix. The PAFs channels are multiplied by 2 because there is one heatmpa for the x-coordinates and one for the y-coordinates. The order is body parts + bkg + PAFs. It will follow the sequence on POSE_BODY_PART_MAPPING in include/openpose/pose/poseParameters.hpp.
./build/examples/openpose/openpose.bin --video examples/media/video.avi --heatmaps_add_parts --heatmaps_add_bkg --heatmaps_add_PAFs --write_heatmaps output_heatmaps_folder/
The following example runs the video vid.mp4, renders image frames on output/result.avi, and outputs JSON files as output/%12d.json, parallelizing over 2 GPUs:
./build/examples/openpose/openpose.bin --video examples/media/video.avi --num_gpu 2 --write_video output/result.avi --write_json output/