fd_tar_train = './data/ISIC/train_18/'
fd_tar_valid = './data/ISIC/valid_18/'
np.random.seed(0)
val_pct = 0.2
for ky,val in dict_label.items():
flist_18 = (Path(fd_18)/val).glob('*.jpg')
fn_18 = [fn.stem for fn in flist_18]
del_mkdirs([str(Path(fd_tar_train)/val),str(Path(fd_tar_valid)/val)])
np.random.shuffle(fn_18)
idx_18 = int(len(fn_18) * val_pct)
fn_18_tr = fn_18[idx_18:]
fn_18_te = fn_18[:idx_18]
print(f'{val} {len(fn_18_tr)} {len(fn_18_te)} ')
for fn in fn_18_tr:
path_dicom = './data/ori/ori_dicom'
path_png = './data/ori/png_ori_test'
path_png_crp_anno = './data/ori/png_crp_anno'
path_png_crp = './data/ori/png_crp_test'
fd_list = [str(Path(path_dicom) / 'pos'),
str(Path(path_dicom) / 'neg')] #,str(Path(path_dicom)/'unknown')]
#fd_list = [str(Path(path_dicom)/'unknown')]
#%% net to DEVICE
use_cuda = torch.cuda.is_available()
device = torch.device('cuda' if use_cuda else 'cpu')
#%%
#fd_list_png = str(Path(fd_list[0].replace(Path(path_dicom).stem,'png_ori_test')).parents[0])
del_mkdirs([path_png])
#del_mkdirs([str(Path(fd_list_png[0]).parents[0])])
for fds in fd_list:
flist = [str(fn) for fn in sorted(Path(fds).glob('*.dcm'))]
for fn in flist:
with pydicom.dcmread(fn) as dc:
img_dicom = dc.pixel_array
print(img_dicom.shape, img_dicom.shape[0] / img_dicom.shape[1])
fname_png = fn.replace('.dcm','.png').replace(Path(path_dicom).stem,Path(path_png).stem).replace('0.png','_AP.png') \
.replace('1.png','_LAT.png').replace('pos/','Pos').replace('neg/','Neg').replace('unknown/', 'Un')
from transforms.data_preprocessing import TestAugmentation_albu
import shutil
#%% convert dicom to png
path_dicom = './data/ori/ori_dicom'
fd_list = [str(Path(path_dicom)/'pos'),str(Path(path_dicom)/'neg')]
fd_list_png = str(Path(fd_list[0].replace('dicom','png')).parents[0])
del_mkdirs([fd_list_png])
#del_mkdirs([str(Path(fd_list_png[0]).parents[0])])
for fds in fd_list:
flist = [str(fn) for fn in sorted(Path(fds).glob('*.dcm'))]
for fn in flist:
with pydicom.dcmread(fn) as dc:
img_dicom = dc.pixel_array
#print(img_dicom.shape, img_dicom.shape[0]/img_dicom.shape[1])
fname_png = fn.replace('.dcm','.png').replace('dicom','png').replace('0.png','_AP.png').replace('1.png','_LAT.png').replace('pos/','Pos').replace('neg/','Neg')
@author: minjie
"""
from pathlib import Path
import cv2
from utils.utils import del_mkdirs
from tqdm import tqdm
fd = 'data/ISIC/train_18'
fd_in = list(Path(fd).glob('*'))
#fd_out = ['./data/bone_aug/train','./data/bone_aug/test']
fd_out = './test_aug_crp'
del_mkdirs([fd_out])
import albumentations as A
aug = A.Compose([
A.Resize(384, 512, interpolation=1, p=1),
#A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.5),
# A.HueSaturationValue(hue_shift_limit=2, sat_shift_limit=15, val_shift_limit=20,p = 0.5),
A.OneOf([
A.Blur(blur_limit=5, p=0.3),
A.GaussNoise(var_limit=(5.0, 10.0), p=0.3),
A.IAASharpen(alpha=(0.1, 0.3), lightness=(0.5, 1.0), p=0.4)
],
p=1.0),
A.Flip(p=0.5),
#A.Transpose(p = 0.5),
#A.RandomRotate90(p = 0.5),
from fastai.vision import *
import pydicom
import matplotlib.pyplot as plt
from utils.utils import del_mkdirs
from tqdm import tqdm
from transforms.data_preprocessing import TestAugmentation_albu
#model = models.LocateBoneModel()
from transforms.data_preprocessing import TestAugmentation_albu
#%% convert dicom to png
path_dicom = './data/ori/ori_dicom'
fd_list = [str(Path(path_dicom) / 'pos'), str(Path(path_dicom) / 'neg')]
fd_list_png = [fd.replace('dicom', 'png') for fd in fd_list]
del_mkdirs(fd_list_png)
#del_mkdirs([str(Path(fd_list_png[0]).parents[0])])
for fds in fd_list:
flist = [str(fn) for fn in sorted(Path(fds).glob('*.dcm'))]
for fn in flist:
with pydicom.dcmread(fn) as dc:
img_dicom = dc.pixel_array
print(img_dicom.shape, img_dicom.shape[0] / img_dicom.shape[1])
fname_png = fn.replace('.dcm', '.png').replace('dicom', 'png')
cv2.imwrite(fname_png, (img_dicom / 16384.0 * 255).astype('uint8'))