U-Net-Python/data.py at master · jeongyoonlee2015/U-Net-Python · GitHub

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import os
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

## 데이터 불러오기
dir_data = './datasets'

name_label = 'train-labels.tif'
name_input = 'train-volume.tif'
# ---------------------------

img_label = Image.open(os.path.join(dir_data, name_label))
img_input = Image.open(os.path.join(dir_data, name_input))

ny, nx = img_label.size
nframe = img_label.n_frames

##set을 3개로 나눈다.
nframe_train = 24
nframe_val = 3
nframe_test = 3

dir_save_train = os.path.join(dir_data, 'train')
dir_save_val = os.path.join(dir_data, 'val')
dir_save_test = os.path.join(dir_data, 'test')

if not os.path.exists(dir_save_train):
    os.makedirs(dir_save_train)

if not os.path.exists(dir_save_val):
    os.makedirs(dir_save_val)

if not os.path.exists(dir_save_test):
    os.makedirs(dir_save_test)

##각각의 프레임에 대해 아이디로 나눈 후 섞는다.
id_frame = np.arange(nframe)
np.random.shuffle(id_frame)


#이제 세개로 나누어서 넘파이 배열로 저장한다.
offset_nframe = 0

for i in range(nframe_train):
    img_label.seek(id_frame[i + offset_nframe])
    img_input.seek(id_frame[i + offset_nframe])

    label_ = np.asarray(img_label)
    input_ = np.asarray(img_input)

    np.save(os.path.join(dir_save_train, 'label_%03d.npy' % i), label_)
    np.save(os.path.join(dir_save_train, 'input_%03d.npy' % i), input_)

##
offset_nframe = nframe_train

for i in range(nframe_val):
    img_label.seek(id_frame[i + offset_nframe])
    img_input.seek(id_frame[i + offset_nframe])

    label_ = np.asarray(img_label)
    input_ = np.asarray(img_input)

    np.save(os.path.join(dir_save_val, 'label_%03d.npy' % i), label_)
    np.save(os.path.join(dir_save_val, 'input_%03d.npy' % i), input_)

##
offset_nframe = nframe_train + nframe_val

for i in range(nframe_test):
    img_label.seek(id_frame[i + offset_nframe])
    img_input.seek(id_frame[i + offset_nframe])

    label_ = np.asarray(img_label)
    input_ = np.asarray(img_input)

    np.save(os.path.join(dir_save_test, 'label_%03d.npy' % i), label_)
    np.save(os.path.join(dir_save_test, 'input_%03d.npy' % i), input_)

##테스트
plt.subplot(121)
plt.imshow(label_, cmap='gray')
plt.title('label')

plt.subplot(122)
plt.imshow(input_, cmap='gray')
plt.title('input')

plt.show()

# Data 읽는 것 준비 완료! 이제 시작할 준비가 되었다.