check.py

#!/usr/bin/env python
# coding: utf-8




import numpy as np 
import pandas as pd 

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from PIL import Image
from keras.utils.vis_utils import plot_model
from keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt
from IPython.display import Image 
import os


# In[3]:



classifier = Sequential()


classifier.add(Conv2D(filters = 32, kernel_size = (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
classifier.add(Conv2D(filters = 32, kernel_size = (3,3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2D(64, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2D(128, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))


classifier.add(Conv2D(128, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Flatten())


classifier.add(Dense(units = 64, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))

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


# In[4]:


from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory(r'C:\Users\Yagna\Desktop\yagnaaa\SSIP Yagna\SSIP\training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')


test_set = test_datagen.flow_from_directory(r'C:\Users\Yagna\Desktop\yagnaaa\SSIP Yagna\SSIP\test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')


# In[5]:


from PIL import features
print (features.check_module('webp'))


# In[ ]:


filepath = "best_model.hdf5"
metric = 'val_accuracy'
checkpoint = ModelCheckpoint(filepath, monitor=metric, verbose=1, save_best_only=True, mode='max')
history = classifier.fit_generator(test_set,
                         steps_per_epoch = 10,
                         epochs = 15,
                         validation_data = test_set,
                         validation_steps = 10,
                         callbacks = [checkpoint])
print(history.history.keys())


# In[ ]:


plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()


plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epochs')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()


# In[ ]:


import numpy as np
from keras.preprocessing import image
test_image = image.load_img(r'C:\Users\Yagna\Desktop\yagnaaa\SSIP Yagna\SSIP\training_set\potholes\16.jpg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
print(result)
print(training_set.class_indices)
if result[0][0] == 1:
    prediction = 'normal'
else:
    prediction = 'pothole'
print(prediction)