def build_dataset(slide_dir,
output_dir,
projects,
background=0.2,
size=255,
reject_rate=0.1,
ignore_repeat=False):
proceed = None
train_path = os.path.join(output_dir, "train.h5")
val_path = os.path.join(output_dir, "val.h5")
test_path = os.path.join(output_dir, "test.h5")
if (os.path.isfile(train_path) and os.path.isfile(val_path)
and os.path.isfile(test_path)):
while not (proceed == "C" or proceed == "A" or proceed == "R"
or proceed == "Q"):
print(
"""A dataset already exists in this directory. Do you want to \n
- Continue to build the datset [C] \n
- Reset the dataset [R] \n
- Quit [Q]
""")
proceed = input().upper()
if proceed == "R":
os.remove(train_path)
os.remove(val_path)
os.remove(test_path)
if proceed == "C":
train_data = load_set_data(train_path)
val_data = load_set_data(val_path)
test_data = load_set_data(test_path)
train_h5 = h5py.File(train_path, 'a')
val_h5 = h5py.File(val_path, 'a')
test_h5 = h5py.File(test_path, 'a')
elif proceed == "R" or proceed == None:
if projects is None:
raise ValueError("Missing list of projects to download.")
data = get_projects_info(projects)
train_h5 = h5py.File(train_path, 'a')
val_h5 = h5py.File(val_path, 'a')
test_h5 = h5py.File(test_path, 'a')
all_cases = list(data['case to images'].keys())
shuffle(all_cases)
#split to train and val+test
train_len = int(0.8 * len(all_cases))
train_set = all_cases[:train_len]
all_cases = all_cases[train_len:]
#split val+test into val and test
val_len = int(0.5 * len(all_cases))
val_set = all_cases[:val_len]
test_set = all_cases[val_len:]
train_data = split_to_sets(train_set, data, train_path)
val_data = split_to_sets(val_set, data, val_path)
test_data = split_to_sets(test_set, data, test_path)
if proceed != "Q":
dataset = [(list(train_data["image to sample"].keys()), train_h5),
(list(val_data["image to sample"].keys()), val_h5),
(list(test_data["image to sample"].keys()), test_h5)]
# train_images = ["TCGA-44-7671-01A-01-BS1.914604a2-de9c-404d-9fa5-23fbd0b76da3.svs"]
# val_images = ["TCGA-FF-8041-01A-01-TS1.b8b69ce3-a325-4864-a5b0-43c450347bc9.svs"]
# test_images = ["TCGA-G8-6326-01A-01-TS1.e0eb24da-6293-4ecb-8345-b70149c84d1e.svs"]
# # # train_images = []
# val_images = []
# test_images = []
# dataset = [
# (train_images, train_h5),
# (val_images, val_h5),
# (test_images, test_h5)
# ]
normalizer = Normalizer()
for images, h5_file in dataset:
image_h5_file = h5_file.require_group("images")
for filename in images:
if proceed != "C" or ".".join(
filename.split(".")[:-1]) not in image_h5_file:
download_image(filename, slide_dir)
Tile(slide_loc=os.path.join(slide_dir, filename),
set_hdf5_file=image_h5_file,
normalizer=normalizer,
background=background,
size=size,
reject_rate=reject_rate,
ignore_repeat=ignore_repeat)
h5_file.close()
normalizer.normalize_dir(output_dir)