2018-Chip-Classifiers/outlet_classifier.py at main · lukasdumasius/2018-Chip-Classifiers · GitHub

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# A classifier that determines whether an image of a microfluidic chip
# contains an outlet or not

import os, sys
import keras #machine learning
from keras import backend as K
import numpy as np #math
import numpy
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 PIL import Image

img_width, img_height = 100, 500 # resized image dimensions
nb_train_samples = 10000 # total
nb_validation_samples = 10000 # total
epochs = 10
batch_size = 20

# collect data
training_data = os.path.normpath(os.getcwd()+"/outlet_classifier_data/training")
validation_data = os.path.normpath(os.getcwd()+"/outlet_classifier_data/testing")
prediction_data = os.path.normpath(os.getcwd()+"/outlet_classifier_data/predictions")

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

# convolutional layers
model = Sequential()
model.add(Conv2D(32, (3,3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(32, (3,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, (3,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

# fully connected layer
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

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

train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0,
    zoom_range=0.1,
    rotation_range=5,
    width_shift_range=0.05,
    height_shift_range=0.05,
    vertical_flip=False,
    horizontal_flip=False,
    fill_mode='nearest'
    )

img = load_img(training_data+os.path.normpath('/contains_outlet/IMG0335.png'))  # this is a PIL image
x = img_to_array(img)
x = x.reshape((1,) + x.shape)

# data augmentation example
i = 0
for batch in train_datagen.flow(x,
                                batch_size=1,
                                save_to_dir=os.getcwd()+os.path.normpath('/outlet_preview'),
                                save_prefix='transformed',
                                save_format='png'
                                ):
    i += 1
    if i > 20:
        break  # otherwise the generator would loop indefinitely

test_datagen = ImageDataGenerator(
    rescale=1. / 255
    )

train_generator = train_datagen.flow_from_directory(
    training_data,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary'
    )

validation_generator = test_datagen.flow_from_directory(
    validation_data,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary'
    )

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size
    )

# saves the model and its weights
model.save('outlet_model.h5')
model.save_weights("outlet_CNN.h5")


def predict_image(mod, img):
    img = load_img(img)
    img = img.resize((500, 100), Image.ANTIALIAS)
    x = img_to_array(img)
    x = x.reshape((1,) + x.shape)
    print(mod.predict_proba(x))

# print("Contains Outlet")
# predict_image(model, training_data+os.path.normpath('/contains_outlet/IMG0655.png'))
# predict_image(model, training_data+os.path.normpath('/contains_outlet/IMG0663.png'))
# predict_image(model, training_data+os.path.normpath('/contains_outlet/IMG0696.png'))
# predict_image(model, training_data+os.path.normpath('/contains_outlet/IMG0707.png'))
# predict_image(model, training_data+os.path.normpath('/contains_outlet/IMG0740.png'))
# predict_image(model, training_data+os.path.normpath('/contains_outlet/IMG0775.png'))
# predict_image(model, training_data+os.path.normpath('/contains_outlet/IMG2405.png'))
# predict_image(model, training_data+os.path.normpath('/contains_outlet/IMG2459.png'))
# predict_image(model, training_data+os.path.normpath('/contains_outlet/IMG2495.png'))
# print("No Outlet")
# predict_image(model, training_data+os.path.normpath('/no_outlet/IMG0779.png'))
# predict_image(model, training_data+os.path.normpath('/no_outlet/IMG0812.png'))
# predict_image(model, training_data+os.path.normpath('/no_outlet/IMG0839.png'))
# predict_image(model, training_data+os.path.normpath('/no_outlet/IMG0471.png'))