PallawiSinghal

from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img

datagen = ImageDataGenerator(
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        fill_mode='nearest')

PallawiSinghal

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
no_train_img,no_validation_img = 1001,800
img_width, img_height = 150, 150
batch_size = 16
if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)#theano

PallawiSinghal

#import all dependencies
import PIL
import keras
import numpy as np
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img


print ("\n")
print("Your backend is ----------------------------------------------------------------->",keras.backend.backend())

PallawiSinghal

#import all dependencies
import PIL 
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import numpy as np


datagen = ImageDataGenerator(
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,

PallawiSinghal

import cv2
import numpy as np

filename = '/dog_clip.png'
img = cv2.imread(filename)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

gray = np.float32(gray)
dst = cv2.cornerHarris(gray,2,3,0.04)

PallawiSinghal

# Import necessary components to build LeNet
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2

# Initialize model
alexnet = Sequential()

PallawiSinghal

# set the matplotlib backend so figures can be saved in the background
import matplotlib
matplotlib.use("Agg")

# import the necessary packages
from sklearn.preprocessing import LabelBinarizer
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from keras.models import Sequential

PallawiSinghal

# set the matplotlib backend so figures can be saved in the background
import matplotlib
matplotlib.use("Agg")

# import the necessary packages
from sklearn.preprocessing import LabelBinarizer
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from keras.models import Sequential

PallawiSinghal

# import the necessary packages
from keras.models import load_model
import argparse
import pickle
import cv2
import os

test_image_path = "/test_image/cats.jpg"

PallawiSinghal

# import the necessary packages
from keras.models import load_model
import argparse
import pickle
import cv2
import os
import imutils

test_image_path = "/panda_00125.jpg"
model_path = "/simple_binary_classifcation_model.model"