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live-image-classifier

Perform image classification on a LIVE camera feed using deep neural networks (DNNs) on Intel® Movidius™ Neural Compute Stick (NCS). This project was used to build a battery powered, RPi based, portable inference device. You can read more about this project at the NCS developer blog.

This sample code doesn't necessarily need a RPi board; it can be run on any computer with a camera.

Prerequisites

This code example requires that the following components are available:

  1. Movidius Neural Compute Stick
  2. Movidius Neural Compute SDK
  3. A computer with web camera

Running this example

mkdir -p ~/workspace
cd ~/workspace
git clone https://github.com/movidius/ncappzoo
cd ~/workspace/ncappzoo/apps/live-image-classifier/
make run

When the application runs normally and is able to connect to the NCS device, you will see a live feed from your camera with a virtual box drawn over the feed. Place an item/object within this box to see inference results. You can hit 'Q' or 'q' at any time to quit the app.

Configuring this example

This example runs GoogLeNet by default, but you can configure it run other pre-trained deep neural networks. Below are some example commands:

AlexNet (Caffe)

python3 live-image-classifier.py --graph ../../caffe/AlexNet/graph --dim 227 227

SqueezeNet (Caffe)

python3 live-image-classifier.py --graph ../../caffe/SqueezeNet/graph --dim 227 227

Mobilenet (Tensorflow)

python3 live-image-classifier.py --graph ../../tensorflow/mobilenets/graph --labels ../../tensorflow/mobilenets/model/labels.txt --mean 127.5 --scale 0.00789 --dim 224 224 --colormode="RGB"

Inception (Tensorflow)

python3 live-image-classifier.py --graph ../../tensorflow/inception/model/v3/graph --labels ../../tensorflow/inception/model/v3/label.txt --mean 127.5 --scale 0.00789 --dim 299 299 --colormode="RGB"

Customizing this example

You can use this project as a template for your custom image classifier app. Below are some tips to help customize the example.

  1. Before attemping to customize, check if the built-in options would suffice. Run python3 live-image-classifier.py -h to list all available options.
  2. Steps 1, 2 and 5 are common across all Neural Compute Stick apps, so you can re-use those fuctions without modifications.
  3. Step 3, 'Pre-process the images' is probably the most customizable function. As the name suggests, you can include all image pre-processing tasks in this function. Ex. if you don't want to warp the input image, just crop it before calling skimage.transform.resize.
  4. Step 4 should be modified only if there is a need to change the way inference results are read and printed.