cnn.py

# -*- coding: utf-8 -*-
"""
Created on Sat Jan 18 10:02:09 2020

@author: Nishant
"""
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2

DATADIR = "C:/Graph Image/"
CATEGORIES = ["frontup", "sideup"]

for category in CATEGORIES:
    path = os.path.join(DATADIR, category)
    for img in os.listdir(path):
        img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
        plt.imshow(img_array, cmap="gray")
        plt.show()
        break
    break
    


print(img_array)
print(img_array.shape) 
IMG_SIZE = 100

new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
plt.imshow(new_array, cmap='gray')
plt.show()



training_data = []

def create_training_data():
    for category in CATEGORIES:
        path = os.path.join(DATADIR, category)
        class_num = CATEGORIES.index(category)
        for img in os.listdir(path):
            try:
                img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
                training_data.append([new_array, class_num])
            except Exception as e:
                pass
create_training_data()
      


print(len(training_data))



import random

random.shuffle(training_data)



X=  [] #feature set
y = [] #label set


for features, label in training_data:
    X.append(features)
    y.append(label)
X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE,1)



import pickle

pickle_out = open("X.pickle", "wb")
pickle.dump(X, pickle_out)
pickle_out.close()

pickle_out = open("y.pickle", "wb")
pickle.dump(y, pickle_out)
pickle_out.close()




pickle_in = open("X.pickle", "rb")
X = pickle.load(pickle_in)




import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import TensorBoard
import pickle

X = pickle.load(open("X.pickle","rb"))
y = pickle.load(open("y.pickle","rb"))

X = X/255.0

model=Sequential()
model.add(Conv2D(64, (3,3), input_shape = X.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dense(64))

model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss="binary_crossentropy",
              optimizer="adam",
              metrics=['accuracy'])

model.fit(X, y, batch_size=3, validation_split=0.2, verbose=1, epochs=5)