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# Copyright 2019 Google LLC
#
# Changes made by Nam Vu 01/20/2020
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#

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# Copyright 2019 Google LLC
#
# Changes made by Nam Vu 01/20/2020
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#

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# Copyright 2019 Google LLC
#
# Changes made by Nam Vu 02/25/2020
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#

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import os
import argparse
import cv2
import numpy as np
import sys
import time
import importlib.util
from tflite_runtime.interpreter import Interpreter
from tflite_runtime.interpreter import load_delegate

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import os, argparse, cv2, sys, time, numpy
from tflite_runtime.interpreter import Interpreter
from tflite_runtime.interpreter import load_delegate

'''
Requirements: 
1) Install the tflite_runtime package from here:
https://www.tensorflow.org/lite/guide/python
2) Camera to take inputs
3) Install libedgetpu

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I got librosa working on the Dev Board running Mendel Day (4.0). 
Some steps may not be necessary at later OS release.

1) Preparations:
The Dev Board may not have enough storage, so I suggest first to add an sdcard (I have a 16GB card), than mount it:

mkdir /home/mendel/sdcard
sudo mount /dev/mmcblk1p1 /home/mendel/sdcard

With extra storage now, we can add some swap which may be necessary for the build:

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# update and install needed packages
sudo su
apt-get update
apt-get -y install unclutter xorg chromium openbox lightdm locales

# add kiosk group and user
groupadd kiosk
useradd -m kiosk -g kiosk -s /bin/bash
mkdir -p /home/kiosk/.config/openbox
chown -R kiosk:kiosk

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import tempfile
import os

import tensorflow as tf
import numpy as np

from tensorflow import keras

# Load MNIST dataset
mnist = keras.datasets.mnist

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import numpy as np 
import sys
from tflite_runtime.interpreter import Interpreter
from tflite_runtime.interpreter import load_delegate
import time

if len(sys.argv) < 2:
    print('Usage:', sys.argv[0], 'model_path')
    exit()

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import tempfile
import os
import tensorflow as tf
from tensorflow import keras

# Load MNIST dataset
mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Normalize the input image so that each pixel value is between 0 to 1.