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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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import tensorflow as tf
if not str(tf.__version__).startswith('1.15'):
    print('please use tensorflow 1.15')
    exit()
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D

tf.enable_eager_execution()

image_shape = (64,64,3)

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# More infro here on Post Training Quantization here:
# https://www.tensorflow.org/lite/performance/post_training_quantization
# from_frozen_graph api is not n tf2.0 but can still be use with tf.compat.v1.lite, more on this api:
# https://www.tensorflow.org/api_docs/python/tf/compat/v1/lite/TFLiteConverter#from_frozen_graph
# This is an example for converting a frozen graph model to a fully quantized tflite model
# The model used here is http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_192.tgz
# Note that with post training quantization, sometimes it is not guarantee that the model will be fully quantized. 

import sys, os, glob
import tensorflow as tf

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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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# 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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THIS IS DEPRECATED


# docker can be installed on the dev board following these instructions: 
# https://github.com/f0cal/google-coral/issues/32#issuecomment-571629174
# 1) create this dockerfile
# 2) build: docker build -t "coral" .
# 3) run: docker run -it --device /dev/apex_0:/dev/apex_0 coral /bin/bash
# 4) Try the classify_image demo:
# apt-get install edgetpu-examples

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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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# This is an example of starting a systemd object detection service on boot on the Coral Dev Board.

# 1) create a file call detects.service with the following contents:

[Unit]
Description=systemd object detection service
After=weston.target

[Service]
PAMName=login