fork-missing-tooth/train_fork.py at master · engineer1982/fork-missing-tooth · GitHub

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# -*- coding: utf-8 -*-
"""
Created on Thu Mar 29 12:51:13 2018

@author: Marcelo Mota de Azevedo Junior
"""

import numpy as np
from PIL import Image
import glob
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import optimizers

def load_images():

    print("loading images...")
    image_list = []
    label_list = []

    #select which folders(labels) will be trained
    #bkg -only background, with no fork image
    #full - images of a fork with all teeth
    #1 - images of a fork missing the 1st tooth (left to right)
    #2 - images of a fork missing the 2nd tooth (left to right)
    #...
    #6 - images of a fork missing the 6th tooth (left to right)
    folders = ["bkg", "full", "1", "2", "3", "4", "5", "6"]
    #folders = ["bkg", "full", "6"]

    size = len(folders)

    #going in every folder, transforming the image in an array (224,224,3), normalizing (./255), appending to a list of examples
    #and respective lables and converting it to arrays
    for k in range(0,size):

        print("sweeping folder " + folders[k])

        for filename in glob.glob('data/train/' + folders[k] + '/*.jpg'): #assuming jpg

            label = np.zeros((size))
            img = load_img(str(filename))  # this is a PIL image
            x = img_to_array(img)  # this is a Numpy array with shape (3, 150, 150)
            x /= 255
            image_list.append(x)
            label[k] = 1
            label_list.append(label)

    imagem = np.array(image_list)
    print("images in array - OK!")
    labels = np.array(label_list)
    print("labels in array - OK!")

    return imagem, labels, size

batch_size = 32

epochs = 150

x_train, y_train, size = load_images()

print("building Convolutional Neural Network")

#close to a VGG-16

model = Sequential()
model.add(Conv2D(64, (3, 3), input_shape=(224, 224, 3)))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

model.add(Conv2D(128, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))


model.add(Conv2D(256, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(256, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(256, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

model.add(Conv2D(512, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

model.add(Conv2D(512, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

# bloco Fully Connected

model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(4096))
model.add(Activation('relu'))
model.add(Dense(4096))
model.add(Activation('relu'))
model.add(Dense(4096))
model.add(Activation('relu'))
#model.add(Dropout(0.2))
model.add(Dense(size))
model.add(Activation('softmax'))

sgd = optimizers.SGD(lr=0.001)

model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
print("model compiled")


print("training model")
hist = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_split=0.2)