from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import smtplib
import argparse
import sys
import time
import mysql.connector
from datetime import datetime

import numpy as np
import tensorflow as tf



def load_graph(model_file):
  graph = tf.Graph()
  graph_def = tf.GraphDef()

  with open(model_file, "rb") as f:
    graph_def.ParseFromString(f.read())
  with graph.as_default():
    tf.import_graph_def(graph_def)

  return graph

def read_tensor_from_image_file(file_name, input_height=299, input_width=299,
				input_mean=0, input_std=255):
  input_name = "file_reader"
  output_name = "normalized"
  file_reader = tf.read_file(file_name, input_name)
  if file_name.endswith(".png"):
    image_reader = tf.image.decode_png(file_reader, channels = 3,
                                       name='png_reader')
  elif file_name.endswith(".gif"):
    image_reader = tf.squeeze(tf.image.decode_gif(file_reader,
                                                  name='gif_reader'))
  elif file_name.endswith(".bmp"):
    image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
  else:
    image_reader = tf.image.decode_jpeg(file_reader, channels = 3,
                                        name='jpeg_reader')
  float_caster = tf.cast(image_reader, tf.float32)
  dims_expander = tf.expand_dims(float_caster, 0);
  resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
  normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
  sess = tf.Session()
  result = sess.run(normalized)

  return result

def load_labels(label_file):
  label = []
  proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
  for l in proto_as_ascii_lines:
    label.append(l.rstrip())
  return label
def convertToBinaryData(filename):
    # Convert digital data to binary format
    with open(filename, 'rb') as file:
        binaryData = file.read()
    return binaryData
    
if __name__ == "__main__":
  file_name = "../download.jpeg"
  model_file = "files/retrained_graph.pb"
  label_file = "files/retrained_labels.txt"
  input_height = 224
  input_width = 224
  input_mean = 128
  input_std = 128
  input_layer = "input"
  output_layer = "final_result"
  date = datetime.today().strftime('%Y-%m-%d')

  parser = argparse.ArgumentParser()
  parser.add_argument("--image", help="image to be processed")
  parser.add_argument("--graph", help="graph/model to be executed")
  parser.add_argument("--labels", help="name of file containing labels")
  parser.add_argument("--input_height", type=int, help="input height")
  parser.add_argument("--input_width", type=int, help="input width")
  parser.add_argument("--input_mean", type=int, help="input mean")
  parser.add_argument("--input_std", type=int, help="input std")
  parser.add_argument("--input_layer", help="name of input layer")
  parser.add_argument("--output_layer", help="name of output layer")
  args = parser.parse_args()

  if args.graph:
    model_file = args.graph
  if args.image:
    file_name = args.image
  if args.labels:
    label_file = args.labels
  if args.input_height:
    input_height = args.input_height
  if args.input_width:
    input_width = args.input_width
  if args.input_mean:
    input_mean = args.input_mean
  if args.input_std:
    input_std = args.input_std
  if args.input_layer:
    input_layer = args.input_layer
  if args.output_layer:
    output_layer = args.output_layer

  graph = load_graph(model_file)
  t = read_tensor_from_image_file(file_name,
                                  input_height=input_height,
                                  input_width=input_width,
                                  input_mean=input_mean,
                                  input_std=input_std)

  input_name = "import/" + input_layer
  output_name = "import/" + output_layer
  input_operation = graph.get_operation_by_name(input_name);
  output_operation = graph.get_operation_by_name(output_name);

  with tf.Session(graph=graph) as sess:
    start = time.time()
    results = sess.run(output_operation.outputs[0],
                      {input_operation.outputs[0]: t})
    end=time.time()
  results = np.squeeze(results)

  top_k = results.argsort()[-5:][::-1]
  labels = load_labels(label_file)
  print(top_k)
  print('\nEvaluation time (1-image): {:.3f}s\n'.format(end-start))
  template = "{} (score={:0.5f})"
  for i in top_k:
        if results[i]> 0.7:
            print(template.format(labels[i], results[i]))
            if labels[i] == "not clean":
                mydb = mysql.connector.connect(
                    host="urhost",
                    user="uruname",
                    passwd="urpass",
                    database="urdbname"
                )
                mycursor = mydb.cursor()
                blob = convertToBinaryData(file_name)
                mySql_insert_query = """Insert into Locations(name,address,image,reward,status,date) values('Lorem Ipsum','VIT Vellore',%s,250,'Available',%s)"""
                recordTuple = (blob,date)
                mycursor.execute(mySql_insert_query, recordTuple)
                mydb.commit()
                print("Record inserted successfully")
               
      

b0c959490c135d2217a34acfe2acc585cce032a4
<mask token>


def read_tensor_from_image_file(file_name, input_height=299, input_width=
    299, input_mean=0, input_std=255):
    input_name = 'file_reader'
    output_name = 'normalized'
    file_reader = tf.read_file(file_name, input_name)
    if file_name.endswith('.png'):
        image_reader = tf.image.decode_png(file_reader, channels=3, name=
            'png_reader')
    elif file_name.endswith('.gif'):
        image_reader = tf.squeeze(tf.image.decode_gif(file_reader, name=
            'gif_reader'))
    elif file_name.endswith('.bmp'):
        image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
    else:
        image_reader = tf.image.decode_jpeg(file_reader, channels=3, name=
            'jpeg_reader')
    float_caster = tf.cast(image_reader, tf.float32)
    dims_expander = tf.expand_dims(float_caster, 0)
    resized = tf.image.resize_bilinear(dims_expander, [input_height,
        input_width])
    normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
    sess = tf.Session()
    result = sess.run(normalized)
    return result


<mask token>


def convertToBinaryData(filename):
    with open(filename, 'rb') as file:
        binaryData = file.read()
    return binaryData


<mask token>

<mask token>


def load_graph(model_file):
    graph = tf.Graph()
    graph_def = tf.GraphDef()
    with open(model_file, 'rb') as f:
        graph_def.ParseFromString(f.read())
    with graph.as_default():
        tf.import_graph_def(graph_def)
    return graph


def read_tensor_from_image_file(file_name, input_height=299, input_width=
    299, input_mean=0, input_std=255):
    input_name = 'file_reader'
    output_name = 'normalized'
    file_reader = tf.read_file(file_name, input_name)
    if file_name.endswith('.png'):
        image_reader = tf.image.decode_png(file_reader, channels=3, name=
            'png_reader')
    elif file_name.endswith('.gif'):
        image_reader = tf.squeeze(tf.image.decode_gif(file_reader, name=
            'gif_reader'))
    elif file_name.endswith('.bmp'):
        image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
    else:
        image_reader = tf.image.decode_jpeg(file_reader, channels=3, name=
            'jpeg_reader')
    float_caster = tf.cast(image_reader, tf.float32)
    dims_expander = tf.expand_dims(float_caster, 0)
    resized = tf.image.resize_bilinear(dims_expander, [input_height,
        input_width])
    normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
    sess = tf.Session()
    result = sess.run(normalized)
    return result


def load_labels(label_file):
    label = []
    proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
    for l in proto_as_ascii_lines:
        label.append(l.rstrip())
    return label


def convertToBinaryData(filename):
    with open(filename, 'rb') as file:
        binaryData = file.read()
    return binaryData


<mask token>

<mask token>


def load_graph(model_file):
    graph = tf.Graph()
    graph_def = tf.GraphDef()
    with open(model_file, 'rb') as f:
        graph_def.ParseFromString(f.read())
    with graph.as_default():
        tf.import_graph_def(graph_def)
    return graph


def read_tensor_from_image_file(file_name, input_height=299, input_width=
    299, input_mean=0, input_std=255):
    input_name = 'file_reader'
    output_name = 'normalized'
    file_reader = tf.read_file(file_name, input_name)
    if file_name.endswith('.png'):
        image_reader = tf.image.decode_png(file_reader, channels=3, name=
            'png_reader')
    elif file_name.endswith('.gif'):
        image_reader = tf.squeeze(tf.image.decode_gif(file_reader, name=
            'gif_reader'))
    elif file_name.endswith('.bmp'):
        image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
    else:
        image_reader = tf.image.decode_jpeg(file_reader, channels=3, name=
            'jpeg_reader')
    float_caster = tf.cast(image_reader, tf.float32)
    dims_expander = tf.expand_dims(float_caster, 0)
    resized = tf.image.resize_bilinear(dims_expander, [input_height,
        input_width])
    normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
    sess = tf.Session()
    result = sess.run(normalized)
    return result


def load_labels(label_file):
    label = []
    proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
    for l in proto_as_ascii_lines:
        label.append(l.rstrip())
    return label


def convertToBinaryData(filename):
    with open(filename, 'rb') as file:
        binaryData = file.read()
    return binaryData


if __name__ == '__main__':
    file_name = '../download.jpeg'
    model_file = 'files/retrained_graph.pb'
    label_file = 'files/retrained_labels.txt'
    input_height = 224
    input_width = 224
    input_mean = 128
    input_std = 128
    input_layer = 'input'
    output_layer = 'final_result'
    date = datetime.today().strftime('%Y-%m-%d')
    parser = argparse.ArgumentParser()
    parser.add_argument('--image', help='image to be processed')
    parser.add_argument('--graph', help='graph/model to be executed')
    parser.add_argument('--labels', help='name of file containing labels')
    parser.add_argument('--input_height', type=int, help='input height')
    parser.add_argument('--input_width', type=int, help='input width')
    parser.add_argument('--input_mean', type=int, help='input mean')
    parser.add_argument('--input_std', type=int, help='input std')
    parser.add_argument('--input_layer', help='name of input layer')
    parser.add_argument('--output_layer', help='name of output layer')
    args = parser.parse_args()
    if args.graph:
        model_file = args.graph
    if args.image:
        file_name = args.image
    if args.labels:
        label_file = args.labels
    if args.input_height:
        input_height = args.input_height
    if args.input_width:
        input_width = args.input_width
    if args.input_mean:
        input_mean = args.input_mean
    if args.input_std:
        input_std = args.input_std
    if args.input_layer:
        input_layer = args.input_layer
    if args.output_layer:
        output_layer = args.output_layer
    graph = load_graph(model_file)
    t = read_tensor_from_image_file(file_name, input_height=input_height,
        input_width=input_width, input_mean=input_mean, input_std=input_std)
    input_name = 'import/' + input_layer
    output_name = 'import/' + output_layer
    input_operation = graph.get_operation_by_name(input_name)
    output_operation = graph.get_operation_by_name(output_name)
    with tf.Session(graph=graph) as sess:
        start = time.time()
        results = sess.run(output_operation.outputs[0], {input_operation.
            outputs[0]: t})
        end = time.time()
    results = np.squeeze(results)
    top_k = results.argsort()[-5:][::-1]
    labels = load_labels(label_file)
    print(top_k)
    print('\nEvaluation time (1-image): {:.3f}s\n'.format(end - start))
    template = '{} (score={:0.5f})'
    for i in top_k:
        if results[i] > 0.7:
            print(template.format(labels[i], results[i]))
            if labels[i] == 'not clean':
                mydb = mysql.connector.connect(host='urhost', user=
                    'uruname', passwd='urpass', database='urdbname')
                mycursor = mydb.cursor()
                blob = convertToBinaryData(file_name)
                mySql_insert_query = (
                    "Insert into Locations(name,address,image,reward,status,date) values('Lorem Ipsum','VIT Vellore',%s,250,'Available',%s)"
                    )
                recordTuple = blob, date
                mycursor.execute(mySql_insert_query, recordTuple)
                mydb.commit()
                print('Record inserted successfully')

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import smtplib
import argparse
import sys
import time
import mysql.connector
from datetime import datetime
import numpy as np
import tensorflow as tf


def load_graph(model_file):
    graph = tf.Graph()
    graph_def = tf.GraphDef()
    with open(model_file, 'rb') as f:
        graph_def.ParseFromString(f.read())
    with graph.as_default():
        tf.import_graph_def(graph_def)
    return graph


def read_tensor_from_image_file(file_name, input_height=299, input_width=
    299, input_mean=0, input_std=255):
    input_name = 'file_reader'
    output_name = 'normalized'
    file_reader = tf.read_file(file_name, input_name)
    if file_name.endswith('.png'):
        image_reader = tf.image.decode_png(file_reader, channels=3, name=
            'png_reader')
    elif file_name.endswith('.gif'):
        image_reader = tf.squeeze(tf.image.decode_gif(file_reader, name=
            'gif_reader'))
    elif file_name.endswith('.bmp'):
        image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
    else:
        image_reader = tf.image.decode_jpeg(file_reader, channels=3, name=
            'jpeg_reader')
    float_caster = tf.cast(image_reader, tf.float32)
    dims_expander = tf.expand_dims(float_caster, 0)
    resized = tf.image.resize_bilinear(dims_expander, [input_height,
        input_width])
    normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
    sess = tf.Session()
    result = sess.run(normalized)
    return result


def load_labels(label_file):
    label = []
    proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
    for l in proto_as_ascii_lines:
        label.append(l.rstrip())
    return label


def convertToBinaryData(filename):
    with open(filename, 'rb') as file:
        binaryData = file.read()
    return binaryData


if __name__ == '__main__':
    file_name = '../download.jpeg'
    model_file = 'files/retrained_graph.pb'
    label_file = 'files/retrained_labels.txt'
    input_height = 224
    input_width = 224
    input_mean = 128
    input_std = 128
    input_layer = 'input'
    output_layer = 'final_result'
    date = datetime.today().strftime('%Y-%m-%d')
    parser = argparse.ArgumentParser()
    parser.add_argument('--image', help='image to be processed')
    parser.add_argument('--graph', help='graph/model to be executed')
    parser.add_argument('--labels', help='name of file containing labels')
    parser.add_argument('--input_height', type=int, help='input height')
    parser.add_argument('--input_width', type=int, help='input width')
    parser.add_argument('--input_mean', type=int, help='input mean')
    parser.add_argument('--input_std', type=int, help='input std')
    parser.add_argument('--input_layer', help='name of input layer')
    parser.add_argument('--output_layer', help='name of output layer')
    args = parser.parse_args()
    if args.graph:
        model_file = args.graph
    if args.image:
        file_name = args.image
    if args.labels:
        label_file = args.labels
    if args.input_height:
        input_height = args.input_height
    if args.input_width:
        input_width = args.input_width
    if args.input_mean:
        input_mean = args.input_mean
    if args.input_std:
        input_std = args.input_std
    if args.input_layer:
        input_layer = args.input_layer
    if args.output_layer:
        output_layer = args.output_layer
    graph = load_graph(model_file)
    t = read_tensor_from_image_file(file_name, input_height=input_height,
        input_width=input_width, input_mean=input_mean, input_std=input_std)
    input_name = 'import/' + input_layer
    output_name = 'import/' + output_layer
    input_operation = graph.get_operation_by_name(input_name)
    output_operation = graph.get_operation_by_name(output_name)
    with tf.Session(graph=graph) as sess:
        start = time.time()
        results = sess.run(output_operation.outputs[0], {input_operation.
            outputs[0]: t})
        end = time.time()
    results = np.squeeze(results)
    top_k = results.argsort()[-5:][::-1]
    labels = load_labels(label_file)
    print(top_k)
    print('\nEvaluation time (1-image): {:.3f}s\n'.format(end - start))
    template = '{} (score={:0.5f})'
    for i in top_k:
        if results[i] > 0.7:
            print(template.format(labels[i], results[i]))
            if labels[i] == 'not clean':
                mydb = mysql.connector.connect(host='urhost', user=
                    'uruname', passwd='urpass', database='urdbname')
                mycursor = mydb.cursor()
                blob = convertToBinaryData(file_name)
                mySql_insert_query = (
                    "Insert into Locations(name,address,image,reward,status,date) values('Lorem Ipsum','VIT Vellore',%s,250,'Available',%s)"
                    )
                recordTuple = blob, date
                mycursor.execute(mySql_insert_query, recordTuple)
                mydb.commit()
                print('Record inserted successfully')

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import smtplib
import argparse
import sys
import time
import mysql.connector
from datetime import datetime

import numpy as np
import tensorflow as tf



def load_graph(model_file):
  graph = tf.Graph()
  graph_def = tf.GraphDef()

  with open(model_file, "rb") as f:
    graph_def.ParseFromString(f.read())
  with graph.as_default():
    tf.import_graph_def(graph_def)

  return graph

def read_tensor_from_image_file(file_name, input_height=299, input_width=299,
				input_mean=0, input_std=255):
  input_name = "file_reader"
  output_name = "normalized"
  file_reader = tf.read_file(file_name, input_name)
  if file_name.endswith(".png"):
    image_reader = tf.image.decode_png(file_reader, channels = 3,
                                       name='png_reader')
  elif file_name.endswith(".gif"):
    image_reader = tf.squeeze(tf.image.decode_gif(file_reader,
                                                  name='gif_reader'))
  elif file_name.endswith(".bmp"):
    image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
  else:
    image_reader = tf.image.decode_jpeg(file_reader, channels = 3,
                                        name='jpeg_reader')
  float_caster = tf.cast(image_reader, tf.float32)
  dims_expander = tf.expand_dims(float_caster, 0);
  resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
  normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
  sess = tf.Session()
  result = sess.run(normalized)

  return result

def load_labels(label_file):
  label = []
  proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
  for l in proto_as_ascii_lines:
    label.append(l.rstrip())
  return label
def convertToBinaryData(filename):
    # Convert digital data to binary format
    with open(filename, 'rb') as file:
        binaryData = file.read()
    return binaryData
    
if __name__ == "__main__":
  file_name = "../download.jpeg"
  model_file = "files/retrained_graph.pb"
  label_file = "files/retrained_labels.txt"
  input_height = 224
  input_width = 224
  input_mean = 128
  input_std = 128
  input_layer = "input"
  output_layer = "final_result"
  date = datetime.today().strftime('%Y-%m-%d')

  parser = argparse.ArgumentParser()
  parser.add_argument("--image", help="image to be processed")
  parser.add_argument("--graph", help="graph/model to be executed")
  parser.add_argument("--labels", help="name of file containing labels")
  parser.add_argument("--input_height", type=int, help="input height")
  parser.add_argument("--input_width", type=int, help="input width")
  parser.add_argument("--input_mean", type=int, help="input mean")
  parser.add_argument("--input_std", type=int, help="input std")
  parser.add_argument("--input_layer", help="name of input layer")
  parser.add_argument("--output_layer", help="name of output layer")
  args = parser.parse_args()

  if args.graph:
    model_file = args.graph
  if args.image:
    file_name = args.image
  if args.labels:
    label_file = args.labels
  if args.input_height:
    input_height = args.input_height
  if args.input_width:
    input_width = args.input_width
  if args.input_mean:
    input_mean = args.input_mean
  if args.input_std:
    input_std = args.input_std
  if args.input_layer:
    input_layer = args.input_layer
  if args.output_layer:
    output_layer = args.output_layer

  graph = load_graph(model_file)
  t = read_tensor_from_image_file(file_name,
                                  input_height=input_height,
                                  input_width=input_width,
                                  input_mean=input_mean,
                                  input_std=input_std)

  input_name = "import/" + input_layer
  output_name = "import/" + output_layer
  input_operation = graph.get_operation_by_name(input_name);
  output_operation = graph.get_operation_by_name(output_name);

  with tf.Session(graph=graph) as sess:
    start = time.time()
    results = sess.run(output_operation.outputs[0],
                      {input_operation.outputs[0]: t})
    end=time.time()
  results = np.squeeze(results)

  top_k = results.argsort()[-5:][::-1]
  labels = load_labels(label_file)
  print(top_k)
  print('\nEvaluation time (1-image): {:.3f}s\n'.format(end-start))
  template = "{} (score={:0.5f})"
  for i in top_k:
        if results[i]> 0.7:
            print(template.format(labels[i], results[i]))
            if labels[i] == "not clean":
                mydb = mysql.connector.connect(
                    host="urhost",
                    user="uruname",
                    passwd="urpass",
                    database="urdbname"
                )
                mycursor = mydb.cursor()
                blob = convertToBinaryData(file_name)
                mySql_insert_query = """Insert into Locations(name,address,image,reward,status,date) values('Lorem Ipsum','VIT Vellore',%s,250,'Available',%s)"""
                recordTuple = (blob,date)
                mycursor.execute(mySql_insert_query, recordTuple)
                mydb.commit()
                print("Record inserted successfully")
               
      
