@@ -170,7 +170,7 @@ Each flag is divided into flag name, default value, and description.
4. OpenPose Body Pose
- DEFINE_int32(body, 1, "Select 0 to disable body keypoint detection (e.g., for faster but less accurate face keypoint detection, custom hand detector, etc.), 1 (default) for body keypoint estimation, and 2 to disable its internal body pose estimation network but still still run the greedy association parsing algorithm");
- DEFINE_string(model_pose, "BODY_25", "Model to be used. E.g., `COCO` (18 keypoints), `MPI` (15 keypoints, ~10% faster), `MPI_4_layers` (15 keypoints, even faster but less accurate).");
- DEFINE_string(model_pose, "BODY_25", "Model to be used. E.g., `BODY_25` (fastest for CUDA version, most accurate, and includes foot keypoints), `COCO` (18 keypoints), `MPI` (15 keypoints, least accurate model but fastest on CPU), `MPI_4_layers` (15 keypoints, even faster but less accurate).");
- DEFINE_string(net_resolution, "-1x368", "Multiples of 16. If it is increased, the accuracy potentially increases. If it is decreased, the speed increases. For maximum speed-accuracy balance, it should keep the closest aspect ratio possible to the images or videos to be processed. Using `-1` in any of the dimensions, OP will choose the optimal aspect ratio depending on the user's input value. E.g., the default `-1x368` is equivalent to `656x368` in 16:9 resolutions, e.g., full HD (1980x1080) and HD (1280x720) resolutions.");
- DEFINE_int32(scale_number, 1, "Number of scales to average.");
- DEFINE_double(scale_gap, 0.25, "Scale gap between scales. No effect unless scale_number > 1. Initial scale is always 1. If you want to change the initial scale, you actually want to multiply the `net_resolution` by your desired initial scale.");
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@@ -206,7 +206,7 @@ Each flag is divided into flag name, default value, and description.
- DEFINE_int32(ik_threads, 0, "Experimental, not available yet. Whether to enable inverse kinematics (IK) from 3-D keypoints to obtain 3-D joint angles. By default (0 threads), it is disabled. Increasing the number of threads will increase the speed but also the global system latency.");
10. OpenPose Rendering
- DEFINE_int32(part_to_show, 0, "Prediction channel to visualize (default: 0). 0 for 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, 22-40 for each body part pair PAF.");
- DEFINE_int32(part_to_show, 0, "Prediction channel to visualize: 0 (default) for all the body parts, 1 for the background heat map, 2 for the superposition of heatmaps, 3 for the superposition of PAFs, 4-(4+#keypoints) for each body part heat map, the following ones for each body part pair PAF.");
- DEFINE_bool(disable_blending, false, "If enabled, it will render the results (keypoint skeletons or heatmaps) on a black background, instead of being rendered into the original image. Related: `part_to_show`, `alpha_pose`, and `alpha_pose`.");
Check that the library is working properly by running any of the following commands on any command-line interface program. In Ubuntu, Mac, and other Unix systems, use any command-line interface, such as `Terminal` or `Terminator`. In Windows, open the `PowerShell` (recommended) or Windows Command Prompt (CMD). They can be open by pressing the Windows button + X, and then A. Feel free to watch any Youtube video tutorial if you are not familiar with these non-GUI tools. Make sure that you are in the **root directory of the project** (i.e., in the OpenPose folder, not inside `build/` nor `windows/` nor `bin/`). In addition, `examples/media/video.avi` and `examples/media` do exist, no need to change the paths.
### BODY_25 vs. COCO vs. MPI Models
The BODY_25 model (`--model_pose BODY_25`) includes both body and foot keypoints and it is based in [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://arxiv.org/abs/1812.08008). COCO and MPI models are slower, less accurate, and do not contain foot keypoints. They are based in our older paper [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://arxiv.org/abs/1611.08050). We highly recommend only using the BODY_25 model.
There is an exception, for CPU version, the COCO and MPI models seems to be faster. Accuracy is still better for the BODY_25 model.
