from sklearn.cluster import KMeans
import gc
def nextpow2(x):
x = int(x)
return 1 << (x-1).bit_length()
def ispow2(x):
x = int(x)
return x > 0 and (x & (x - 1))
args.raw_train_pth = 'data/sunnybrook/WSI'
ufs.make_folder('../' + args.train_image_pth, True)
wsipaths = glob.glob('../{}/*.svs'.format(args.raw_train_pth))
' check if metadata gt.npy already exists to append to it '
metadata_pth = '../{}/gt.npy'.format(args.train_image_pth)
metadata = ufs.fetch_metadata(metadata_pth)
pwhs = {
np.maximum(args.tile_w, args.tile_h): 0
}
wsipaths = sorted(wsipaths)
patch_id = 0
num_iters = 1 # each iter randomizes the centers of objects
for _ in range(num_iters):
def gallery(array, ncols):
nindex, height, width, intensity = array.shape
nrows = nindex//ncols
assert nindex == nrows*ncols
# want result.shape = (height*nrows, width*ncols, intensity)
result = (array.reshape(nrows, ncols, height, width, intensity)
.swapaxes(1, 2)
.reshape(height*nrows, width * ncols, intensity))
return result
args.patch_folder = '/home/ozan/ICIAR2018_BACH_Challenge/Photos'
if __name__ == '__main__':
ufs.make_folder('../' + args.train_image_pth, False)
' map class names to codes '
cls_codes = {
'Normal': 0,
'Benign': 1,
'InSitu': 2,
'Invasive': 3
}
' check if metadata gt.npy already exists to append to it '
metadata_pth = '../{}/gt.npy'.format(args.train_image_pth)
metadata = ufs.fetch_metadata(metadata_pth)
cls_folders = glob.glob('{}/*/'.format(args.patch_folder))
train_hr_pths = [
args.train_hr_image_pth,
'data/val_hr',
]
raw_train_pths = [
args.raw_train_pth,
'data/val',
]
us_kmeans = 8 # undersample the gt for faster processing
scan_level = 2 # of which we extract coordinates
min_center_points = dhr.HR_NUM_CNT_SAMPLES
for ij in range(2):
ufs.make_folder('../' + train_hr_pths[ij], True)
wsipaths = sorted(glob.glob('../{}/*.svs'.format(raw_train_pths[ij])))
' check if metadata gt.npy already exists to append to it '
metadata_pth = '../{}/gt.npy'.format(train_hr_pths[ij])
metadata = ufs.fetch_metadata(metadata_pth)
for wsipath in tqdm(wsipaths):
filename = os.path.basename(wsipath)
'open scan'
scan = openslide.OpenSlide(wsipath)
wsi = scan.read_region(
(0, 0), scan_level,
scan.level_dimensions[scan_level]).convert('RGB')
args.patch_folder = '/home/ozan/Downloads/breastpathq/datasets/validation'
args.label_csv_path = '/home/ozan/Downloads/breastpathq-test/val_labels.csv'
#args.patch_folder = '/home/ozan/Downloads/breastpathq/datasets (copy)/validation'
#args.label_csv_path = '/home/ozan/Downloads/breastpathq/datasets (copy)/val_labels.csv'
savepath = args.val_image_pth
else:
args.patch_folder = '/home/ozan/Downloads/breastpathq/datasets/train'
args.label_csv_path = '/home/ozan/Downloads/breastpathq/datasets/train_labels.csv'
#args.patch_folder = '/home/ozan/Downloads/breastpathq/datasets (copy)/train'
#args.label_csv_path = '/home/ozan/Downloads/breastpathq/datasets (copy)/train_labels.csv'
savepath = args.train_image_pth
if __name__ == '__main__':
'train'
ufs.make_folder('../' + savepath, is_spie)
metadata_pth_train = '../{}/gt.npy'.format(savepath)
metadata = ufs.fetch_metadata(metadata_pth_train)
raw_gt = {}
cc = []
with open('{}'.format(args.label_csv_path)) as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
next(csv_reader)
for row in csv_reader:
image_id = int(row[0])
region_id = int(row[1])
cellularity = float(row[2])
if image_id not in raw_gt:
root_dir = osp.join(cur_dir, '..')
# data_dir = osp.join(root_dir, 'data')
output_dir = osp.join(root_dir, 'output')
model_dir = osp.join(output_dir, 'model_dump')
vis_dir = osp.join(output_dir, 'vis')
log_dir = osp.join(output_dir, 'log')
result_dir = osp.join(output_dir, 'result')
## others
num_thread = 8
# gpu_ids = '0'
# num_gpus = 1
# continue_train = False
# def set_args(self, gpu_ids, continue_train=False):
# self.gpu_ids = gpu_ids
# self.num_gpus = len(self.gpu_ids.split(','))
# self.continue_train = continue_train
# os.environ["CUDA_VISIBLE_DEVICES"] = self.gpu_ids
# print('>>> Using GPU: {}'.format(self.gpu_ids))
cfg = Config()
sys.path.insert(0, osp.join(cfg.root_dir, 'common'))
from utils.filesystem import add_pypath, make_folder
# add_pypath(osp.join(cfg.data_dir))
# add_pypath(osp.join(cfg.data_dir, cfg.dataset))
make_folder(cfg.model_dir)
make_folder(cfg.vis_dir)
make_folder(cfg.log_dir)
make_folder(cfg.result_dir)
