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https://github.com/neild0/first-mlworkshop

This project was developed by Neil Deshmukh to teach attendees how to develop an image recognition Convolutional Neural Network and complimentary app, at the 'Using Machine Learning to Build an Image Classification App' workshop at FIRST Detroit on April 26th at 1:30 - 3:30 pm.
https://github.com/neild0/first-mlworkshop

android convolutional-neural-networks detroit first-robotics-competition image-classification ios machine-learning python workshop

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This project was developed by Neil Deshmukh to teach attendees how to develop an image recognition Convolutional Neural Network and complimentary app, at the 'Using Machine Learning to Build an Image Classification App' workshop at FIRST Detroit on April 26th at 1:30 - 3:30 pm.

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# FIRST-MLWorkshop

This is the workshop github for the 'Using Machine Learning to Build an Image Classification App' presented by Neil Deshmukh at FIRST Detroit on April 26th at 1:30 - 3:30 pm.



In this workshop, we will first train a ML algorithm to classify between any image classes that we give it, and then secondly integrate it into an application. 



The app integration is optional, but it's a pretty cool direct visualization of the Neural Network in action.



All that you need is: 

 * an internet connection (to download the needed materials)

 * (optional) the development environment for your respective phone:

   * Xcode for IOS (only on Mac)

   * Android Studio for Android 

   * Cord to connect the phone and computer



______________________________________________________________________________________________________________________________



## Step #0: Setup 



Download Python (if you don't already have it)



ONLY Mac Users Developing for IOS: 



python2: `sudo pip install --upgrade \

 https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.4.0-py-none-any.whl`



python 3: `sudo pip3 install --upgrade \

 https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.4.0-py3-none-any.whl`

 

Download tensorflow following the instructions at https://www.tensorflow.org/install/ (the CPU version will be sufficient for this workshop)



Download google_images_download, if you would like to train the model on the classes that you want it to detect:



`pip install google_images_download`



______________________________________________________________________________________________________________________________



# Part 1: Developing ML Classification Algorithm:



## 1. Clone this directory onto your computer: 



`git clone https://github.com/QuantumTCode/FIRST-MLWorkshop`



## 2. cd into the directory, where all of the code will be run:



`cd FIRST-MLWorkshop`



## 3. Download the classes of images that you would like the algorithm to detect:



`googleimagesdownload -f jpg -k "[Class 1], [Class 2], [Class 3], [Class 4], [Class 5]" `



So an example would be:



`googleimagesdownload -f jpg -k "train, car, bus, racecar, motorcycle"`



While only 5 classes are downloaded above, you can add more (might decrease accuracy of classification)



Check the images: 



`python scripts/check_images.py`



## 4. Set up variables for training:



`ARCHITECTURE="mobilenet_0.50_224"`



`tensorboard --logdir tf_files/training_summaries &`



If you receive an error stating: `TensorBoard attempted to bind to port 6006, but it was already in use` kill the existing TensorBoard instance using `pkill -f "tensorboard"`



## 5. Time to Initiate Machine Learning!



Start the training of the model using:



```python

python -m scripts.retrain \

  --bottleneck_dir=tf_files/bottlenecks \

  --how_many_training_steps=1000 \

  --model_dir=tf_files/models/ \

  --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \

  --output_graph=tf_files/retrained_graph.pb \

  --output_labels=tf_files/retrained_labels.txt \

  --architecture="${ARCHITECTURE}" \

  --image_dir=downloads

  

  ```

  

If you have time, and want to increase the accuracy of you model, change `--how_many_training_steps=X` to a higher number (4000 is probably the highest you need to go).



## 6. Watch Machine Learning in Action:



To see the real time learning behind the algorithm, go to http://0.0.0.0:6006/ on your computer!



## 7. Test the Accuracy of Your Algorithm:

```python

python -m scripts.label_image \

    --graph=tf_files/retrained_graph.pb  \

    --image=(path_to_image) 

   ```

    

For example:



`python -m scripts.label_image \

    --graph=tf_files/retrained_graph.pb  \

    --image=downloads/car/car.png`

  

______________________________________________________________________________________________________________________________



# Part 2: Implementing ML Algorithm on Phone:



## 1. Setup:



`pip install PILLOW`



If IOS: `sudo gem install cocoapods`



## 2: Optimize model using TensorFlow Lite Optimizing Converter (TOCO):

```python

toco \

  --input_file=tf_files/retrained_graph.pb \

  --output_file=tf_files/optimized_graph.pb \

  --input_format=TENSORFLOW_GRAPHDEF \

  --output_format=TENSORFLOW_GRAPHDEF \

  --input_shape=1,224,224,3 \

  --input_array=input \

  --output_array=final_result \

  --mean_value=128 \

  --std_value=128 \

  --default_range_min=0  \

  --default_ranges_max=6

  ```



## 3: Convert Model to TFLite Format:



```python 

toco \

  --input_file=tf_files/retrained_graph.pb \

  --output_file=tf_files/optimized_graph.lite \

  --input_format=TENSORFLOW_GRAPHDEF \

  --output_format=TFLITE \

  --input_shape=1,224,224,3 \

  --input_array=input \

  --output_array=final_result \

  --inference_type=FLOAT \

  --input_type=FLOAT \

  --mean_value=128 \

  --std_value=128 \

  --default_range_min=0  \

  --default_ranges_max=6

  ```

___________________________

## 4.1: For IOS:

`cd ios/camera`



Update Pods:

`pod update`



Copy the trained models into the app:

`cd ../..`

`cp tf_files/retrained_graph.pb ios/camera/data/retrained_graph.pb`



Copy the labels into the app:

`cp tf_files/retrained_labels.txt ios/camera/data/retrained_labels.txt`

Open the app:

`open ios/camera/tf_camera_example.xcworkspace`



Make sure to go to Xcode>Preferences and sign in to be able to connect your phone and test the application.



  *  Click on the tflite_photos_example file on the left, and name your Bundle Identifier something unique, for example: com.[your name]

  * Click on automatically manage signing

  

Finally, you should be able to run your app!



___________________________

## 4.2: For Android:

Set up Device: https://developer.android.com/studio/debug/dev-options#enable



Copy the labels and models into the app:

`cp tf_files/optimized_graph.lite android/tflite/app/src/main/assets/graph.lite`

`cp tf_files/retrained_labels.txt android/tflite/app/src/main/assets/labels.txt`



Open Android Studio>Open an existing Android Studio project, open FIRST-MLWorkshop/android/tflite, and click 'OK' for Gradle Sync.



Then run the app!