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import argparse
from edgetpu.classification.engine import ClassificationEngine
from edgetpu.utils import dataset_utils
from PIL import Image
import multiprocessing
from multiprocessing import Process

def main():
  parser = argparse.ArgumentParser()
  parser.add_argument(

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# Copyright 2019 Google LLC
#
# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

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import threading
import time
import cv2
import cv2 as cv

import tensorflow as tf
from tensorflow.lite.python.interpreter import load_delegate
from edgetpu.basic import edgetpu_utils
from collections import namedtuple
import numpy as np

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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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#  https://www.apache.org/licenses/LICENSE-2.0
#  Copyright 2019 Google LLC
#  Modified by Nam Vu
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r""" Example using tf.compat.v2.lite to classify 

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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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# 1) create this dockerfile
# 2) build: docker build -t "coral-edgetpu" .
# 3) run: docker run -it --privileged -v /dev/bus/usb:/dev/bus/usb coral-edgetpu /bin/bash
# 4) download install requriements: cd ~/tflite/python/examples/classification/ && bash install_requirements.sh
# 5) run example: python3 classify_image.py \
# > --model models/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite \
# > --labels models/inat_bird_labels.txt \
# > --input images/parrot.jpg

FROM tensorflow/tensorflow:1.15.0-py3

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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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# 1) create this dockerfile
# 2) build: docker build -t "coral-edgetpu" .
# 3) run: docker run -it --privileged -v /dev/bus/usb:/dev/bus/usb coral-edgetpu /bin/bash
# 4) download install requriements: cd ~/tflite/python/examples/classification/ && bash install_requirements.sh
# 5) run example: python3 classify_image.py \
# > --model models/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite \
# > --labels models/inat_bird_labels.txt \
# > --input images/parrot.jpg

FROM tensorflow/tensorflow:1.15.0-py3

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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