Super, merci !
Ca marche pour moi à 3 FPS, Avec une config d'ordinateur portable :
- GeForce GTX 1050
- Intel Core i7-7700HQ 2.8GHz
- 8 Go de RAM
Dans le lien que tu as donné, il y a peut être une piste pour optimiser les paramètres :
Q: Out of memory error - I get an error similar to: Check failed: error == cudaSuccess (2 vs. 0) out of memory.
A: Most probably cuDNN is not installed/enabled, the default Caffe model uses >12 GB of GPU memory, cuDNN reduces it to ~1.5 GB.
Q: Low speed - OpenPose is quite slow, is it normal? How can I speed it up?
A: Check the OpenPose Benchmark to discover the approximate speed of your graphics card. Some speed tips:
1. Use cuDNN 5.1 (cuDNN 6 is ~10% slower).
2. Reduce the `--net_resolution` (e.g. to 320x176) (lower accuracy).
3. For face, reduce the `--face_net_resolution`. The resolution 320x320 usually works pretty decently.
4. Use the `MPI_4_layers` model (lower accuracy and lower number of parts).
5. Change GPU rendering by CPU rendering to get approximately +0.5 FPS (`--render_pose 1`).
Q: Webcam is slow - Using a folder with images matches the speed FPS benchmarks, but the webcam has lower FPS. Note: often on Windows.
A: OpenCV has some issues with some camera drivers (specially on Windows). The first step should be to compile OpenCV by your own and re-compile OpenPose after that (following the Reinstallation section in Ubuntu or cleaning the project on Windows). If the speed is still slower, you can better debug it by running a webcam OpenCV example (e.g. this C++ example). If you are able to get the proper FPS with the OpenCV demo but OpenPose is still low, then let us know!
Q: Video and/or webcam are not working - Using a folder with images does work, but the video and/or the webcam do not. Note: often on Windows.
A: OpenCV has some issues with some camera drivers and video codecs (specially on Windows). Follow the same steps as the Webcam is slow question to test the webcam is working. After re-compiling OpenCV, you can also try this OpenCV example for video.
4. Maximum Accuracy Configuration
This command provides the most accurate results we have been able to achieve for body, hand and face keypoint detection. However, this command will need around 8 GB of GPU memory and runs around 1 FPS on a Titan X.