Basic CNN for rooftop detection

Computer Vision Models/rooftop_basic_cnn.py

@@ -0,0 +1,120 @@
+import glob
+import os
+from os.path import basename
+import numpy as np
+import pandas as pd
+from PIL import Image
+import keras
+from keras import optimizers
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Conv2D, MaxPooling2D,ZeroPadding2D
+from keras.callbacks import ModelCheckpoint
+from sklearn.cross_validation import train_test_split
+
+path="C://rooftop-detection//"
+#load labels
+labels= pd.read_csv(path +"labels.csv", delimiter=",",header = None) 
+
+#load rooftop image and resize
+L=[]
+train=np.array([])
+images=glob.glob(path +"images"+"/*.*") #importation de images
+
+for i in range(0,len(images)):
+ im = Image.open(path +"images//"+labels.iloc[i][0]+".jpg")
+ im_rz=im.resize((64,64), Image.ANTIALIAS)
+ #im_rz.save(path+"//resized//"+basename(images[i]), 'JPEG',quality=100,optimize=True) #optionally if you want to save rezized images
+ L.append(np.array(im_rz)) 
+data=np.array(L)
+
+#transformation of labels into categorial variables to be interpreted by keras
+y=pd.get_dummies(labels.iloc[:,1])
+
+#seperate data into training set and test set
+x_train, x_test, y_train, y_test = train_test_split(data,y, train_size=0.8)
+
+batch_size = 128
+nb_classes = 4
+epochs = 150
+
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+y_train=np.array(y_train).astype('float32')
+y_test=np.array(y_test).astype('float32')
+x_train /= 255
+x_test /= 255
+
+x_train = x_train.astype('float32')
+ x_test = x_test.astype('float32')
+ y_train=np.array(y_train).astype('float32')
+ y_test=np.array(y_test).astype('float32')
+ x_train /= 255
+ x_test /= 255
+
+
+
+# start of architecture Convolutional neural network
+model = Sequential()
+
+model.add(Conv2D(64, (3, 3), padding='same',
+input_shape=x_train.shape[1:]))
+model.add(Activation('relu'))
+model.add(Conv2D(110, (3, 3)))
+ model.add(Activation('relu'))
+ model.add(MaxPooling2D(pool_size=(2, 2)))
+ model.add(Dropout(0.15))
+
+model.add(Conv2D(84, (3, 3), padding='same'))
+ model.add(Activation('relu'))
+ model.add(Conv2D(84, (3, 3)))
+ model.add(Activation('relu'))
+ model.add(MaxPooling2D(pool_size=(2, 2)))
+ model.add(Dropout(0.20))
+
+model.add(Conv2D(64, (3, 3), padding='same'))
+ model.add(Activation('relu'))
+ model.add(Conv2D(64, (3, 3)))
+ model.add(Activation('relu'))
+ model.add(MaxPooling2D(pool_size=(2, 2)))
+ model.add(Dropout(0.20))
+
+model.add(Conv2D(32, (3, 3), padding='same'))
+ model.add(Activation('relu'))
+ model.add(Conv2D(32, (3, 3)))
+ model.add(Activation('relu'))
+ model.add(MaxPooling2D(pool_size=(2, 2)))
+ model.add(Dropout(0.20))
+
+model.add(Flatten())
+model.add(Dense(1024))
+model.add(Activation('relu'))
+ model.add(Dense(nb_classes))
+ model.add(Activation('softmax'))
+
+# initiate RMSprop optimizer
+ opt = keras.optimizers.rmsprop(lr=0.001, decay=1e-7)
+
+# Let's train the model using RMSprop
+ model.compile(loss='categorical_crossentropy',optimizer=opt,metrics=['accuracy'])
+
+# checkpoint: save best model during teh training checkpoint
+ filepath=path+"weights.hdf5"
+ checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=0, save_best_only=True, mode='max')
+ callbacks_list = [checkpoint]
+#preprocessing and realtime data augmentation:
+data_generation = ImageDataGenerator(
+ rotation_range=7, # randomly rotate images in the range (degrees, 0 to 180)
+ width_shift_range=0.10, # randomly shift images horizontally (fraction of total width)
+ height_shift_range=0.10, # randomly shift images vertically (fraction of total height)
+ horizontal_flip=True, # randomly flip images
+ vertical_flip=True) # randomly flip images
+data_generation.fit(x_train)
+
+# Fit the model 
+model_param=model.fit_generator(data_generation.flow(x_train, y_train,batch_size=batch_size),
+ steps_per_epoch=x_train.shape[0] // batch_size,
+ epochs=epochs,
+ validation_data=(x_test, y_test), callbacks=callbacks_list)
+