Pose recognition with Intel's Realsense and OpenVino

Just a short post today on Intel's Openvino toolkit. It is really a great collection of machine learning models that can speed up development. I played around with their pose estimation model and the results are quite good.

Final result of the pose detection system

This system combined with the Intel Realsense depth cameras offer a promising real world solution to the pose estimation problem. As shown in the video, it works reasonably well on my laptop with an NVIDIA GPU (1060) Graphics card. The white spheres indicate the feet limbs and thanks to the Realsense depth camera we can estimate the positions of the limbs in 3D space.

Although I haven't tried it yet but the models can be converted to work on edge platforms such as the Movidius device.

Obviously this opens up a whole array of applications which I hope to share again in a future post.

Comments

Object detection with Google Colab and Tensorflow

This is just a memo of the challenges I faced when running a model training on Google Colab, while following a great tutorial here . Mind the versions Tensorflow is currently at version 2.2.0 but most tutorials are still using the contrib package, and there is no known easy way to update the code to remove dependency on contrib. So my best bet is to downgrade the tensorflow version to 1.x. Since Google Colab only gives the options of either 1.x or 2.x and we cannot specify the exact version, I ended up with version 1.15.2. Even with the command : %tensorflow_version 1.15.0 I ended up with : 1.15.2 Another pitfall was the version of numpy. Installing numpy gives us the version 1.18.3 but for some reason this generates the error : TypeError: 'numpy.float64' object cannot be interpreted as an integer Downgrading numpy to version 1.17.4 solved this for me. It seems we don't need ngrok for tensorboard With the command : %load_ext tensorboard W...

Installing a custom ROM on Android (on the GT-N8013)

It's been a while since my last entry and since it is a new start in 2019, I thought I'd write something about "gone with the old and in with the new". I've had my Samsung Galaxy Note 10.1 (pnotewifi) since 2014, and it's one of the early Galaxy Note tablet series. It has served me well all this years but now it just sits there collecting dust. My old Samsung GT-N8013 I've known a long time about custom Android ROMs like CyanogenMod but has never had the motivation to try them out, until now ! Overview of the process For beginners like me, I didn't have an understanding of the installation process and so it looked complicated and it was one of the reasons I was put off in trying the custom ROM. I just want to say, it's not complicated at all! Basically you will need to Prepare an SD card and install Android SDK (you need adb ). Install a custom boot loader ( TWRP is the de facto tool at the moment). Use adb to copy custom...

Using FCM with the new HTTP v1 API and NodeJS

When trying to send FCM notifications I found out that Google has changed their API specifications. The legacy API still works but if you want to use the latest v1 API you need to make several changes. The list of changes is listed on their site so I won't be repeating them again but I'll just mention some of the things that caused some trial and error on my project. The official guide from Google is here : Official Migration Guide to v1 . The request must have a Body with the JSON containing the message data. Most importantly it needs to have "message" field which must contain the target of the notification. Usually this is a Topic, or Device IDs. Since my previous project was using GAS, my request had a field called "payload" instead of "body". Using the request from my previous project, my request in Node JS was as follows: request ({ url: 'https://fcm.googleapis.com/v1/projects/safe-door-278108/messages:send' , method: ...

Time is a limited resource

Search This Blog