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Signed-off-by: Gines Hidalgo <>
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OpenPose - Output


  1. UI and Visual Output
  2. JSON-UI Mapping
    1. Pose Output Format (BODY_25)
    2. Pose Output Format (COCO)
    3. Face Output Format
    4. Hand Output Format
  3. JSON Output Format
  4. Keypoints in C++/Python
    1. Keypoint Ordering in C++/Python
    2. Keypoint Format in Datum (Advanced)
  5. Reading Saved Results
  6. Advanced
    1. Camera Matrix Output Format
    2. Heatmaps

UI and Visual Output

The visual GUI should show the original image with the poses blended on it, similarly to the pose of this gif:

JSON-UI Mapping

The output of the JSON files consist of a set of keypoints, whose ordering is related with the UI output as follows:

Pose Output Format (BODY_25)

Pose Output Format (COCO)

Face Output Format

Hand Output Format

JSON Output Format

There are 2 alternatives to save the OpenPose output. But both of them follow the keypoint ordering described in the Keypoint Ordering in C++/Python section (which you should read next).

  1. The --write_json flag saves the people pose data onto JSON files. Each file represents a frame, it has a people array of objects, where each object has:
    1. pose_keypoints_2d: Body part locations (x, y) and detection confidence (c) formatted as x0,y0,c0,x1,y1,c1,.... The coordinates x and y can be normalized to the range [0,1], [-1,1], [0, source size], [0, output size], etc. (see the flag --keypoint_scale for more information), while the confidence score (c) in the range [0,1].
    2. face_keypoints_2d, hand_left_keypoints_2d, and hand_right_keypoints_2d are analogous to pose_keypoints_2d but applied to the face and hand parts.
    3. body_keypoints_3d, face_keypoints_3d, hand_left_keypoints_2d, and hand_right_keypoints_2d are analogous but applied to the 3-D parts. They are empty if --3d is not enabled. Their format is x0,y0,z0,c0,x1,y1,z1,c1,..., where c is 1 or 0 depending on whether the 3-D reconstruction was successful or not.
    4. part_candidates (optional and advanced): The body part candidates before being assembled into people. Empty if --part_candidates is not enabled (see that flag for more details).
    // If `--part_candidates` enabled
  1. (Deprecated) --write_keypoint uses the OpenCV cv::FileStorage default formats, i.e., JSON (if OpenCV 3 or higher), XML, and YML. It only prints 2D body information (no 3D or face/hands).

Keypoints in C++/Python

Keypoint Ordering in C++/Python

The body part mapping order of any body model (e.g., BODY_25, COCO, MPI) can be extracted from the C++ and Python APIs.

  • In C++, getPoseBodyPartMapping(const PoseModel poseModel) is available in poseParameters.hpp:
// C++ API call
#include <openpose/pose/poseParameters.hpp>
const auto& poseBodyPartMappingBody25 = getPoseBodyPartMapping(PoseModel::BODY_25);
const auto& poseBodyPartMappingCoco = getPoseBodyPartMapping(PoseModel::COCO_18);
const auto& poseBodyPartMappingMpi = getPoseBodyPartMapping(PoseModel::MPI_15);
const auto& poseBodyPartMappingBody25B = getPoseBodyPartMapping(PoseModel::BODY_25B);
const auto& poseBodyPartMappingBody135 = getPoseBodyPartMapping(PoseModel::BODY_135);

// Result for BODY_25 (25 body parts consisting of COCO + foot)
// const std::map<unsigned int, std::string> POSE_BODY_25_BODY_PARTS {
//     {0,  "Nose"},
//     {1,  "Neck"},
//     {2,  "RShoulder"},
//     {3,  "RElbow"},
//     {4,  "RWrist"},
//     {5,  "LShoulder"},
//     {6,  "LElbow"},
//     {7,  "LWrist"},
//     {8,  "MidHip"},
//     {9,  "RHip"},
//     {10, "RKnee"},
//     {11, "RAnkle"},
//     {12, "LHip"},
//     {13, "LKnee"},
//     {14, "LAnkle"},
//     {15, "REye"},
//     {16, "LEye"},
//     {17, "REar"},
//     {18, "LEar"},
//     {19, "LBigToe"},
//     {20, "LSmallToe"},
//     {21, "LHeel"},
//     {22, "RBigToe"},
//     {23, "RSmallToe"},
//     {24, "RHeel"},
//     {25, "Background"}
// };
  • You can also check them on Python:
poseModel = op.PoseModel.BODY_25

Keypoint Format in Datum (Advanced)

This section is only for advance users that plan to use the C++ API. Not needed for the OpenPose demo and/or Python API.

There are 3 different keypoint Array<float> elements in the Datum class:

  1. Array poseKeypoints: In order to access person person and body part part (where the index matches POSE_COCO_BODY_PARTS or POSE_MPI_BODY_PARTS), you can simply output:
    // Common parameters needed
    const auto numberPeopleDetected = poseKeypoints.getSize(0);
    const auto numberBodyParts = poseKeypoints.getSize(1);
    // Easy version
    const auto x = poseKeypoints[{person, part, 0}];
    const auto y = poseKeypoints[{person, part, 1}];
    const auto score = poseKeypoints[{person, part, 2}];
    // Slightly more efficient version
    // If you want to access these elements on a huge loop, you can get the index
    // by your own, but it is usually not faster enough to be worthy
    const auto baseIndex = poseKeypoints.getSize(2)*(person*numberBodyParts + part);
    const auto x = poseKeypoints[baseIndex];
    const auto y = poseKeypoints[baseIndex + 1];
    const auto score = poseKeypoints[baseIndex + 2];
  1. Array faceKeypoints: It is completely analogous to poseKeypoints.
    // Common parameters needed
    const auto numberPeopleDetected = faceKeypoints.getSize(0);
    const auto numberFaceParts = faceKeypoints.getSize(1);
    // Easy version
    const auto x = faceKeypoints[{person, part, 0}];
    const auto y = faceKeypoints[{person, part, 1}];
    const auto score = faceKeypoints[{person, part, 2}];
    // Slightly more efficient version
    const auto baseIndex = faceKeypoints.getSize(2)*(person*numberFaceParts + part);
    const auto x = faceKeypoints[baseIndex];
    const auto y = faceKeypoints[baseIndex + 1];
    const auto score = faceKeypoints[baseIndex + 2];
  1. std::array<Array, 2> handKeypoints, where handKeypoints[0] corresponds to the left hand and handKeypoints[1] to the right one. Each handKeypoints[i] is analogous to poseKeypoints and faceKeypoints:
    // Common parameters needed
    const auto numberPeopleDetected = handKeypoints[0].getSize(0); // = handKeypoints[1].getSize(0)
    const auto numberHandParts = handKeypoints[0].getSize(1); // = handKeypoints[1].getSize(1)

    // Easy version
    // Left Hand
    const auto xL = handKeypoints[0][{person, part, 0}];
    const auto yL = handKeypoints[0][{person, part, 1}];
    const auto scoreL = handKeypoints[0][{person, part, 2}];
    // Right Hand
    const auto xR = handKeypoints[1][{person, part, 0}];
    const auto yR = handKeypoints[1][{person, part, 1}];
    const auto scoreR = handKeypoints[1][{person, part, 2}];

    // Slightly more efficient version
    const auto baseIndex = handKeypoints[0].getSize(2)*(person*numberHandParts + part);
    // Left Hand
    const auto xL = handKeypoints[0][baseIndex];
    const auto yL = handKeypoints[0][baseIndex + 1];
    const auto scoreL = handKeypoints[0][baseIndex + 2];
    // Right Hand
    const auto xR = handKeypoints[1][baseIndex];
    const auto yR = handKeypoints[1][baseIndex + 1];
    const auto scoreR = handKeypoints[1][baseIndex + 2];

Reading Saved Results

We use the standard formats (JSON, PNG, JPG, ...) to save our results, so there are many open-source libraries to read them in most programming languages (especially Python). For C++, you might want to check include/openpose/filestream/fileStream.hpp. In particular, loadData (for JSON, XML and YML files) and loadImage (for image formats such as PNG or JPG) to load the data into cv::Mat format.


Camera Matrix Output Format

If you need to use the camera calibration or 3D modules, the camera matrix output format is detailed in doc/advanced/


If you need to use heatmaps, check doc/

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