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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. 

Continue reading “A Taste of TensorFlow on My Android Phone (III)”

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

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

code128_1

Continue reading “QR Code Detector with Webcam (Python / OpenCV / Pyzbar)”

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Sharing My Data Science Notebook （Python & TensorFlow）

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.

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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. 

Continue reading “Install PyTorch on Window 10”

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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) (May-2018).

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 😛

Continue reading “A Taste of TensorFlow on My Android Phone (II)”

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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:

Continue reading “A Taste of TensorFlow on My Android Phone”

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Fast Neural Style Transfer by PyTorch (Mac OS)

Continue my last post Image Style Transfer Using ConvNets by TensorFlow (Windows), this article will introduce the Fast Neural Style Transfer by PyTorch on MacOS.

The original program is written in Python, and uses [PyTorch], [SciPy]. A GPU is not necessary but can provide a significant speedup especially for training a new model. Regular sized images can be styled on a laptop or desktop using saved models.

More details about the algorithm could be found in the following papers:

  1. Perceptual Losses for Real-Time Style Transfer and Super-Resolution ;
  2. Instance Normalization: The Missing Ingredient for Fast Stylization.

If you could not download the papers, here are the Papers.

Continue reading “Fast Neural Style Transfer by PyTorch (Mac OS)”

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Image Style Transfer Using ConvNets by TensorFlow (Windows)

This post is talking about how to setup a basic developing environment of Google’s TensorFlow on Windows 10 and apply the awesome application called “Image style transfer”, which is using the convolutional neural networks to create artistic images based on the content image and style image provided by the users.

The early research paper is “A Neural Algorithm of Artistic Style” by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge on arXiv. You could also try their website application on DeepArt where there are a lot of amazing images uploaded by the people all over the world. DeepArt has apps on Google Play and App Store, but I suggest you use a much faster app Prisma, which is as awesome as DeepArt! [More to read: Why-does-the-Prisma-app-run-so-fast].

Moreover, I strongly recommend the formal paper Image Style Transfer Using Convolutional Neural Networks by the same authors published on CVPR-2016. This paper gives more details about how this method works. In short, the following two figures cover the idea behind the magic.

Continue reading “Image Style Transfer Using ConvNets by TensorFlow (Windows)”

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

Here are some screen captures:

 

Screen Shot 2018-05-25 at 16.35.37
Build completed Successfully 🙂  I did this on my MacBook Air 11, cost me about 4hrs.

 

Screen Shot 2018-05-25 at 13.31.04
Be careful about the configuration!!! If you want to do this on your own machine.

 

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