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)