def __init__(self):
self.dataHandler = DataHandler()
self.dataHandler.show_info()
self.__log_path = LOG_PATH + LOG_NAME
self.__save_path = DUMP_IMAGE + SAVE_VECTOR + "/"
self.__make_dir(self.save_path, have_to_make_labels=True)
self.__vector_dict = {
'train': {
'alive': list(),
'death': list()
},
'valid': {
'alive': list(),
'death': list()
},
'test': {
'alive': list(),
'death': list()
},
'count_total_train': int(),
'count_alive_train': int(),
'count_death_train': int(),
'count_total_valid': int(),
'count_alive_valid': int(),
'count_death_valid': int(),
'count_total_test': int(),
'count_alive_test': int(),
'count_death_test': int()
}
# -*- coding: utf-8 -*-
import sys
from os import path
try:
import DMP
except ImportError:
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
from DMP.learning.dataHandler import DataHandler
if __name__ == '__main__':
handler = DataHandler()
handler.vector2txt()
# -*- coding: utf-8 -*-
import sys
from os import path
try:
import DMP
except ImportError:
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
from DMP.learning.dataClassifier import DataClassifier
from DMP.learning.dataHandler import DataHandler
if __name__ == '__main__':
classifier = DataClassifier(DataHandler())
classifier.predict_tf_record()
class ImageConverter:
def __init__(self):
self.dataHandler = DataHandler()
self.dataHandler.show_info()
self.__log_path = LOG_PATH + LOG_NAME
self.__save_path = DUMP_IMAGE + SAVE_VECTOR + "/"
self.__make_dir(self.save_path, have_to_make_labels=True)
self.__vector_dict = {
'train': {
'alive': list(),
'death': list()
},
'valid': {
'alive': list(),
'death': list()
},
'test': {
'alive': list(),
'death': list()
},
'count_total_train': int(),
'count_alive_train': int(),
'count_death_train': int(),
'count_total_valid': int(),
'count_alive_valid': int(),
'count_death_valid': int(),
'count_total_test': int(),
'count_alive_test': int(),
'count_death_test': int()
}
@property
def log_path(self):
return self.__log_path
@property
def save_path(self):
return self.__save_path
@property
def vector_dict(self):
return self.__vector_dict
@staticmethod
def __make_dir(_path, have_to_make_labels=False):
if os.path.isdir(_path):
shutil.rmtree(_path)
os.mkdir(_path)
if have_to_make_labels:
if CONVERT_VERSION == 1:
print("Convert Version 1\n")
os.mkdir(_path + ALIVE_DIR)
os.mkdir(_path + DEATH_DIR)
elif CONVERT_VERSION == 2:
print("Convert Version 2\n")
os.mkdir(_path + DUMP_TRAIN)
os.mkdir(_path + DUMP_TRAIN + ALIVE_DIR)
os.mkdir(_path + DUMP_TRAIN + DEATH_DIR)
os.mkdir(_path + DUMP_VALID)
os.mkdir(_path + DUMP_VALID + ALIVE_DIR)
os.mkdir(_path + DUMP_VALID + DEATH_DIR)
os.mkdir(_path + DUMP_TEST)
os.mkdir(_path + DUMP_TEST + ALIVE_DIR)
os.mkdir(_path + DUMP_TEST + DEATH_DIR)
else:
print("Error] Convert version option!")
print(" You must input 1 or 2\n\n")
exit(-1)
def convert(self):
x_train = self.dataHandler.x_train
x_valid = self.dataHandler.x_valid
x_test = self.dataHandler.x_test
y_train = self.dataHandler.y_train
y_valid = self.dataHandler.y_valid
y_test = self.dataHandler.y_test
# expand 1d to 2d which is a square matrix
self.dataHandler.expand4square_matrix(x_train,
x_valid,
x_test,
use_origin=True)
self.show_img_size(int(math.sqrt(len(x_train[0]))))
if CONVERT_VERSION == 1:
self.__version_1(x_train, y_train, key="train")
self.__version_1(x_valid, y_valid, key="valid")
self.__version_1(x_test, y_test, key="test")
elif CONVERT_VERSION == 2:
self.__version_2(x_train, y_train, key="train")
self.__version_2(x_valid, y_valid, key="valid")
self.__version_2(x_test, y_test, key="test")
@staticmethod
def show_img_size(img_size):
print("\n\nImage size -", img_size)
if IMAGE_SIZE:
print("Image resize -", IMAGE_SIZE, "\n\n")
def __version_1(self, x_data, y_data, key):
"""
# convert version 0
file name:
train_1~N
valid_1~M
test_1~K
arg.output/
alive/
train_1.jpg
train_2.jpg
....
valid_2.jpg
valid_4.jpg
...
test_1.jpg
test_3.jpg
death/
train_3.jpg
train_4.jpg
....
valid_1.jpg
valid_3.jpg
...
test_2.jpg
test_4.jpg
"""
size = int(math.sqrt(len(x_data[0])))
for i, data in enumerate(zip(x_data, y_data)):
x, y = data[0], data[1]
file_name = key + '_' + str(i + 1)
if y == [1]:
self.vector_dict[key]["death"].append(file_name +
EXTENSION_OF_IMAGE)
self.vector_dict['count_death_' + key] += 1
save_path = self.save_path + DEATH_DIR
else:
self.vector_dict[key]["alive"].append(file_name +
EXTENSION_OF_IMAGE)
self.vector_dict['count_alive_' + key] += 1
save_path = self.save_path + ALIVE_DIR
self.vector_dict['count_total_' + key] += 1
img = self.__init_img(x, size)
self.__save_img(img, save_path, file_name)
if DO_TRANSFORM:
self.__save_img(img.rotate(90), save_path,
file_name + '_ROTATE')
def __version_2(self, x_data, y_data, key):
"""
# convert version 2
file name:
train_1~N
valid_1~M
test_1~K
arg.output/
train/
alive/
train_1.jpg
train_2.jpg
train_3.jpg
death/
train_4.jpg
valid/
alive/
valid_2.jpg
valid_3.jpg
valid_4.jpg
death/
valid_1.jpg
test/
alive/
test_1.jpg
test_2.jpg
test_4.jpg
death/
test_3.jpg
"""
size = int(math.sqrt(len(x_data[0])))
for i, data in enumerate(zip(x_data, y_data)):
x, y = data[0], data[1]
file_name = key + '_' + str(i + 1)
if y == [1]:
self.vector_dict[key]["death"].append(file_name +
EXTENSION_OF_IMAGE)
self.vector_dict['count_death_' + key] += 1
save_path = self.save_path + key + "/" + DEATH_DIR
else:
self.vector_dict[key]["alive"].append(file_name +
EXTENSION_OF_IMAGE)
self.vector_dict['count_alive_' + key] += 1
save_path = self.save_path + key + "/" + ALIVE_DIR
self.vector_dict['count_total_' + key] += 1
img = self.__init_img(x, size)
self.__save_img(img, save_path, file_name)
if DO_TRANSFORM:
self.__save_img(img.rotate(90), save_path,
file_name + '_ROTATE')
@staticmethod
def __init_img(x, size):
x = np.array(x)
x = np.reshape(x, (-1, size))
img = Image.fromarray(x)
if img.mode != 'RGB':
img = img.convert('RGB')
if IMAGE_SIZE:
img = img.resize((IMAGE_SIZE, IMAGE_SIZE), resample=Image.BILINEAR)
return img
@staticmethod
def __save_img(img, save_path, file_name):
img.save(save_path + file_name + EXTENSION_OF_IMAGE)
if DO_TRANSFORM:
img.transpose(
Image.FLIP_LEFT_RIGHT).save(save_path + file_name +
'_FLIP_LR' + EXTENSION_OF_IMAGE)
img.transpose(
Image.FLIP_TOP_BOTTOM).save(save_path + file_name +
'_FLIP_TB' + EXTENSION_OF_IMAGE)
img.transpose(Image.FLIP_LEFT_RIGHT).transpose(
Image.FLIP_TOP_BOTTOM).save(save_path + file_name +
'_FLIP_LR_TB' + EXTENSION_OF_IMAGE)
def save_log(self):
with open(self.save_path + LOG_NAME, 'w') as outfile:
json.dump(self.vector_dict, outfile, indent=4)
print(
"\n=========================================================")
print("\nsuccess make dump file! - file name is",
self.save_path + LOG_NAME, "\n\n")
# -*- coding: utf-8 -*-
# Extract importance feature using Random Forest
import sys
from os import path
try:
import DMP
except ImportError:
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
from DMP.learning.dataHandler import DataHandler
if __name__ == '__main__':
dataHandler = DataHandler()
dataHandler.show_importance_feature(reverse=True)
dataHandler.show_importance_feature()
dataHandler.dump()