forked from mateuszbuda/duke-dbt-detection
/
dataset.py
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/
dataset.py
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import numpy as np
import os
import pandas as pd
import torch
from glob import glob
from scipy.ndimage.morphology import binary_fill_holes
from skimage.io import imread
from skimage.morphology import disk, binary_erosion, label
from skimage.transform import downscale_local_mean
from torch.utils.data import Dataset
from subsets import data_frame_subset
class TomoDetectionDataset(Dataset):
"""Duke Digital Breast Tomosythesis (DBT) detection dataset"""
cell_size = 96
img_width = cell_size * 7
img_height = cell_size * 11
out_channels = 5
grid_size = (img_height // cell_size, img_width // cell_size)
anchor = (256, 256)
def __init__(
self,
csv_views,
csv_bboxes,
root_dir,
transform=None,
skip_preprocessing=False,
downscale=2,
subset="train",
random=False,
only_biopsied=False,
max_slice_offset=0,
seed=42
):
"""
:param csv_views: (string) path to csv file with views (see: data/data_train.py)
:param csv_bboxes: (string) path to csv file with bounding boxes (manual annotations)
:param root_dir: (string) root folder with PNG images containing folders for patients
:param transform: transformation to apply to samples (see: transform.py)
:param skip_preprocessing: set to True if root_dir is set to preprocess.py output folder
:param downscale: even if skip_proprocessing is set to True, boxes are still downscaled
:param subset: [test|train|validation]
:param random: ensure that the same slice is sampled for the same case (useful for validation set)
:param only_biopsied: filters for cases with boxes
:param max_slice_offset: range of slices to sample from the central one (0 uses a formula based on box size)
:param seed: random seed for training-validation set split
"""
assert subset in ["test", "train", "validation"]
self.random = random
self.data_frame = data_frame_subset(
csv_views, csv_bboxes, subset, seed=seed
)
self.df_bboxes = pd.read_csv(csv_bboxes)
if not only_biopsied:
self.data_frame = self.data_frame[
self.data_frame["StudyUID"].isin(self.df_bboxes["StudyUID"])
]
self.root_dir = root_dir
self.transform = transform
self.skip_preprocessing = skip_preprocessing
self.downscale = downscale
# coordinate conv channels
self.in_channels = 1
if max_slice_offset == 0:
self.df_bboxes["SliceOffset"] = self.df_bboxes.apply(
lambda row: int(np.sqrt((row["Width"] + row["Height"]) / 2)), axis=1
)
if subset == "validation":
self.df_bboxes["SliceOffset"] = self.df_bboxes["SliceOffset"] // 2
else:
self.df_bboxes["SliceOffset"] = int(max_slice_offset)
self.df_bboxes = self.df_bboxes[
self.df_bboxes["StudyUID"].isin(set(self.data_frame["StudyUID"]))
]
print(
"{} boxes for {} studies in {} set".format(
len(self.df_bboxes), len(set(self.df_bboxes["StudyUID"])), subset
)
)
def __len__(self):
return len(self.data_frame)
def __getitem__(self, idx):
# read sample data
pid = str(self.data_frame.iloc[idx]["PatientID"]).zfill(5)
sid = self.data_frame.iloc[idx]["StudyUID"]
view = str(self.data_frame.iloc[idx]["View"])
# filter bboxes related to sample
df_view_bboxes = self.df_bboxes[
(self.df_bboxes["StudyUID"] == sid)
& (self.df_bboxes["View"] == view.lower())
]
# find the number of slices
max_slice = self._max_slice(pid, sid, view)
if not self.random:
# assure the same slice for samples if random is set to False
np.random.seed(idx)
slice_n = np.random.randint(max_slice + 1)
# sample slice for positive case
if len(df_view_bboxes) > 0:
box = df_view_bboxes.sample()
slice_n = box.iloc[0]["Slice"] # GT central slice
max_slice_offset = box.iloc[0]["SliceOffset"]
offset = np.random.randint(-max_slice_offset, max_slice_offset + 1)
slice_n = slice_n + offset
slice_n = max(0, min(max_slice, slice_n))
# we take all boxes from slices "close" to the sampled one
df_view_bboxes = df_view_bboxes[
abs(slice_n - df_view_bboxes["Slice"]) <= df_view_bboxes["SliceOffset"]
]
# read image
image_name = "{}TomosynthesisReconstruction_{}_.png".format(
view.upper(), slice_n
)
image_path = os.path.join(self.root_dir, pid, sid, image_name)
if self.skip_preprocessing:
img = imread(image_path)
else:
img = self._imread(image_path, flip="R" in view.upper())
# read boxes
boxes = self._df2dict(df_view_bboxes)
if self.transform is not None:
img, boxes = self.transform((img, boxes))
lbl = self._boxes2label(boxes)
# normalize
img = img.astype(np.float32) / np.max(img)
# fix dimensions (N, C, H, W)
img = img[..., np.newaxis]
img = img.transpose((2, 0, 1))
# cast to tensors
img_tensor = torch.from_numpy(img)
lbl_tensor = torch.from_numpy(lbl)
return img_tensor, lbl_tensor
def _max_slice(self, pid, sid, view):
view_template = "{}TomosynthesisReconstruction_*_.png".format(view.upper())
view_files = glob(os.path.join(self.root_dir, pid, sid, view_template))
max_slice = np.max([int(x.split("_")[-2]) for x in view_files])
return max_slice
def _imread(self, imgpath, flip=False):
image = imread(imgpath)
if self.downscale != 1:
image = downscale_local_mean(image, (self.downscale, self.downscale))
if flip:
image = np.fliplr(image).copy()
image = self._preprocess(image)
return image
def _preprocess(self, image, erosion=5):
mask = self._mask(image, erosion=erosion)
image = mask * image
return image
def _mask(self, image, erosion=10):
mask = image > 0
mask = np.pad(mask, ((0, 0), (1, 0)), mode="constant", constant_values=1)
mask = binary_fill_holes(mask)
mask = mask[:, 1:]
mask = binary_erosion(mask, disk(erosion))
cc = label(mask, background=0)
lcc = np.argmax(np.bincount(cc.flat)[1:]) + 1
mask = cc == lcc
return mask
def _mean_filter(self, image, filter_size=4):
fs = filter_size
yy, xx = np.nonzero(image >= np.max(image) * 0.99)
image_out = image
for y, x in zip(yy, xx):
neighborhood = image[max(0, y - fs) : y + fs, max(0, x - fs) : x + fs]
image_out[y, x] = np.mean(neighborhood)
return image_out
def _df2dict(self, df_view_boxes):
df_boxes = df_view_boxes.copy()
df_boxes = df_boxes[["X", "Y", "Width", "Height"]]
df_boxes["Width"] = df_boxes["Width"] // self.downscale
df_boxes["Height"] = df_boxes["Height"] // self.downscale
df_boxes["X"] = df_boxes["X"] // self.downscale
df_boxes["Y"] = df_boxes["Y"] // self.downscale
df_boxes["X"] = df_boxes["X"] + (df_boxes["Width"] // 2)
df_boxes["Y"] = df_boxes["Y"] + (df_boxes["Height"] // 2)
return df_boxes.to_dict(orient="list")
def _boxes2label(self, boxes):
label = np.zeros((self.out_channels,) + self.grid_size, dtype=np.float32)
csz = self.cell_size
box_indices = range(len(boxes["X"]))
if "Points" in boxes:
box_indices = zip(box_indices, boxes["Points"])
box_indices = sorted(box_indices, key=lambda i: i[1])
box_indices = [i[0] for i in box_indices]
for b in box_indices:
# box dimensions
w = boxes["Width"][b]
h = boxes["Height"][b]
# box center point
x = boxes["X"][b]
y = boxes["Y"][b]
# fill label tensor
pos_cell_x = min(self.grid_size[1] - 1, int(x / csz))
pos_cell_y = min(self.grid_size[0] - 1, int(y / csz))
label[0, pos_cell_y, pos_cell_x] = 1.0
y_offset = ((y % csz) - (csz / 2)) / (csz / 2)
x_offset = ((x % csz) - (csz / 2)) / (csz / 2)
label[1, pos_cell_y, pos_cell_x] = y_offset
label[2, pos_cell_y, pos_cell_x] = x_offset
y_scale = np.sqrt(float(h) / self.anchor[0])
x_scale = np.sqrt(float(w) / self.anchor[1])
label[3, pos_cell_y, pos_cell_x] = y_scale
label[4, pos_cell_y, pos_cell_x] = x_scale
return label