Roads from Above: Augmenting Civil Engineering & Geospatial Workflows with Machine Learning

Road from Above is partly based on my Australia Postgraduate Intern Projects (Computer Vision and Machine Learning for Feature Extraction) within Aureon Group in Melbourne.

Aurecon’s experts, across Cape Town, Melbourne and Auckland offices, have been teamed up to develop and test approaches that capture and validate new and existing measurements of the metropolitan road network. Due to the confidentiality, we reduced the resolutions of the aerial images and only opened limited results on the public domain at https://roadsfromabove.netlify.com/. Thanks to Greg More, the design of this website got the best feedback from the workshop (Visualization for AI Explainability) of the IEEE VIS 2018 conference in Berlin, Germany

Visualization for AI Explainability: Projections and Dimensionality Reduction. The goal of this workshop is to initiate a call for “explainable” that explain how AI techniques work using visualizations. We believe the VIS community can leverage their expertise in creating visual narratives to bring new insight into the often obfuscated complexity of AI systems.

Road from Above

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MacOS X: Installing TensorFlow from Sources [TF Binary Attached]

When I am using TensorFlow on my MacBook Air, I always get annoyed by the warnings comes from nowhere, so I followed the documentation below to build TensorFlow sources into a TensorFlow binary and installed it successfully.  In theory, this will make the TF running faster on my machine.

Here is the document:

If you are a Mac user, you could download the TF binary from here:

Then, you could use conda to initialize an environment with Python=3.6 and install TF by typing:

sudo pip install tensorflow-1.8.0-py2-none-any.whl

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A Taste of TensorFlow on My Android Phone (III)

This is the 3rd post about my implementation of TensorFlow Apps on my Android Phone.

This time I fixed one small bug in the app of  “TF Detect” so the object tracking function could work. The project is compiled by cmake with NDK Archives in this version. You can download the new “apk files here:  Tensorflow_Demo_Debug.apk.

“Once the app is installed it can be started via the “TF Classify”, “TF Detect”, “TF Stylize”, and “TF Speech” icons, which have the orange TensorFlow logo as their icon. 

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QR Code Detector with Webcam (Python / OpenCV / Pyzbar)

This project is forked from zbar library, I added some modifications, so the webcam can be used as an image reader to detect QR and Barcodes.

code128_1

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Sharing My Data Science Notebook (Python & TensorFlow) on GitHub

Python is a great general-purpose programming language on its own, but with the help of a few popular libraries (Numpy, SciPy, Matplotlib, TensorFlow) it becomes a powerful environment for scientific computing and data analysis.

I start sharing my notebooks on learning Python and TensorFlow, here is my GitHub repository:  Data_Science_Python.

Logo

 

 

 

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Install PyTorch on Window 10

Great news! PyTorch now is supporting Windows!

If you have a PC with suitable Nvidia graphics card and installed CUDA 9.0 and Anaconda, type the following commands;

conda install pytorch cuda90 -c pytorch
pip3 install torchvision

It is about 500 MB, so be patient!

Screen Shot 2018-04-29 at 21.44.50

Underline is the old post.

PyTorch is a deep learning framework that puts Python first. Currently, it only supports MacOS or Linux.Capture1

But, can we use it on WIN10 without changing the system/computer?

Yes,  we can. 

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A Taste of TensorFlow on My Android Phone (II)

This post is a follow-up to my last post: A Taste of TensorFlow on My Android Phone.

You could download my compiled Apk file here and install on your android device (>Android 7.0) (22-May-2018).

Update: 25-May-2018, fixed the bug of object tracking function in TF Detect. PS: While running the activities, pressing the volume keys on your device will
toggle debug visualizations on/off, rendering additional info to the screen that
may be useful for development purposes.

 

Feel free to let me know if there are any bugs 🙂

TensorFlow Mobile

In the last post, I just tried the TensorFlow for object classification (TF Classify). This time I installed all four demos of the TensorFlow Mobile for Android according to this tutorial: TensorFlow Lite Demo for Android. They are awesome 😛

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A Taste of TensorFlow on My Android Phone

If you like Google’s open-source machine learning framework, TensorFlow, do not miss this “TensorFlow For Poets“.  I went through the tutorial this afternoon and found it is super Awesome. See the photos below, I first tested it on the coffee mug from my Intern company, Aurecon Group. I used the virtual device, Nexus 5X, from Android Studio 3.0.1 on MacBook Air 11′  (Do not do this unless you have enough SSD 😛 ).

 

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Then, I successfully installed the compiled app (TF_Classify) on my XIAO MI – 4C (MIUI 9.0 – Android 7.0) and tested it on my coffee mug at home.
You can download and install it on your own Android devices from the following link:

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Starting My First Intern at Melbourne Australia Tomorrow

Dear All,

I am using the song above to thank you all for your help and support in the past. You know that I have spent the last three years (2014-2017) in pursuing my Ph.D. degree in Computer Science and got a plan to be graduated in 2018.

Continue reading “Starting My First Intern at Melbourne Australia Tomorrow”