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galaxy_learning.py
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from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.core import Activation
from keras.layers.core import Dense
from keras.layers.core import Dropout
from keras.layers.core import Flatten
from keras.models import Sequential, load_model
from keras.utils import np_utils
from keras.utils.np_utils import to_categorical
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
from keras.utils import plot_model
from astropy.io import fits
from PIL import Image
import os
import numpy
import sys
import csv
import random
import numpy as np
from pandas import DataFrame
from sklearn.model_selection import train_test_split
class_num = 2 # the number of classes for classification
img_chanels = 1
#img_channels = 3
#input_shape = (1, 239, 239) # ( channels, cols, rows )
raw_size = (239, 239, 1)
#raw_size = (48, 48, img_channels)
input_shape = (50, 50, 1)
#input_shape = (24, 24, img_channels)
train_test_split_rate = 0.8
nb_epoch = 20
batch_size = 10
#validation_split = 0.1
validation_split = 0.0
path_to_home = "./"
class DatasetLoader:
def __init__(self, input_file_path):
self.train_image_set, self.train_label_set, self.test_image_set, self.test_label_set \
= self.__import_dataset(input_file_path)
def zoom_img(self, img, original_size, pickup_size):
startpos = int(original_size / 2) - int(pickup_size / 2)
img = img[startpos:startpos+pickup_size, startpos:startpos+pickup_size]
return img
def __import_dataset(self, input_file_path):
dataset = []
def load_and_resize(filepath):
hdulist = fits.open(filepath)
raw_image = hdulist[0].data
if( raw_image == None ):
raw_image = hdulist[1].data
print("height:%s, width:%s" % (len(raw_image), len(raw_image[0])))
image = np.resize(raw_image, [raw_size[0], raw_size[1]])
image = self.zoom_img(image, raw_size[0], input_shape[0])
return image
def combine_images(images):
(rows, cols) = (images[0].shape[0], images[0].shape[1])
combined_image = np.zeros((rows, cols, img_channels))
for i in range(0, rows):
for j in range(0, cols):
for k in range(0, img_channels):
combined_image[i, j, k] = images[k][i, j]
return combined_image
def normalize(image):
return (image - image.min()).astype(float)*255 / (image.max() - image.min()).astype(float)
def save_as_image(image, output_path):
image = normalize(image)
pil_img = Image.fromarray(numpy.uint8(image))
pil_img.save(output_path)
with open(input_file_path, 'r') as f:
reader = csv.reader(f)
header = next(reader)
for row in reader:
label = int(row[2])
path = path_to_home + row[1]
if os.path.isdir(path):
files = [f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
images = [load_and_resize(f) for f in files]
image = combine_images(images)
else:
image = load_and_resize(path)
dataset.append( (label, image) )
train_image_set = []
train_label_set = []
test_image_set = []
test_label_set = []
for i in range(0, class_num):
images = list(map(lambda x: x[1], list(filter(lambda x: x[0] == i, dataset))))
labels = len(images)*[i]
train_X, test_X, train_Y, test_Y = train_test_split(images, labels, train_size=train_test_split_rate)
train_image_set.extend(train_X)
train_label_set.extend(train_Y)
test_image_set.extend(test_X)
test_label_set.extend(test_Y)
return ( train_image_set, train_label_set, test_image_set, test_label_set )
class GalaxyClassifier:
def __init__(self):
self.model = Sequential()
#self.build_model()
def build_model_lbg(self):
self.model.add(Conv2D(10, 3, 3, border_mode='same', input_shape=(input_shape[0], input_shape[1], input_shape[2])))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="tf"))
self.model.add(Conv2D(64, 3, 3))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="tf"))
#self.model.add(Dropout(0.25))
self.model.add(Flatten())
self.model.add(Dense(256))
self.model.add(Activation('relu'))
self.model.add(Dropout(0.5))
self.model.add(Dense(class_num))
self.model.add(Activation('softmax'))
def build_model_lae(self):
self.model.add(Conv2D(10, 3, 3, border_mode='same', input_shape=(input_shape[0], input_shape[1], input_shape[2])))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="tf"))
#self.model.add(Conv2D(64, 3, 3))
#self.model.add(Activation('relu'))
#self.model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="tf"))
#self.model.add(Dropout(0.25))
self.model.add(Flatten())
self.model.add(Dense(16))
self.model.add(Activation('relu'))
self.model.add(Dropout(0.5))
self.model.add(Dense(class_num))
self.model.add(Activation('softmax'))
def train(self, train_image_set, train_label_set):
optimizer = Adam(lr=0.001)
self.model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
train_image_set = np.array(train_image_set)
print(train_image_set.shape)
train_image_set = train_image_set.reshape(train_image_set.shape[0], input_shape[0], input_shape[1], input_shape[2])
train_label_set = to_categorical(train_label_set)
#early_stopping = EarlyStopping(monitor='val_loss', patience=5)
#self.model.fit(train_image_set, train_label_set, nb_epoch=20, batch_size=10, validation_split=0.1, callbacks=[early_stopping])
self.model.fit(train_image_set, train_label_set, nb_epoch=nb_epoch, batch_size=batch_size, validation_split=validation_split)
def evaluate(self, test_image_set, test_label_set):
test_image_set = np.array(test_image_set)
test_label_set = np.array(test_label_set)
test_image_set = test_image_set.reshape(test_image_set.shape[0], input_shape[0], input_shape[1], input_shape[2])
test_label_set = to_categorical(test_label_set)
score = self.model.evaluate(test_image_set, test_label_set, verbose=0)
print("%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100))
#plot_model(self.model, to_file='model.png')
if __name__ == "__main__":
argv = sys.argv
if len(argv) != 2:
print('Usage: python %s input_file_path' %argv[0])
quit()
dataset = DatasetLoader(argv[1])
galaxyClassifier = GalaxyClassifier()
galaxyClassifier.build_model_lbg()
#galaxyClassifier.build_model_lae()
galaxyClassifier.train(dataset.train_image_set, dataset.train_label_set)
galaxyClassifier.evaluate(dataset.test_image_set, dataset.test_label_set)