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# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from multiprocessing.pool import Pool
import numpy as np
import SimpleITK as sitk
from nnunet.utilities.task_name_id_conversion import convert_task_name_to_id, convert_id_to_task_name
from batchgenerators.utilities.file_and_folder_operations import *
from nnunet.paths import *
color_cycle = (
"000000",
"4363d8",
"f58231",
"3cb44b",
"e6194B",
"911eb4",
"ffe119",
"bfef45",
"42d4f4",
"f032e6",
"000075",
"9A6324",
"808000",
"800000",
"469990",
)
def hex_to_rgb(hex: str):
assert len(hex) == 6
return tuple(int(hex[i:i + 2], 16) for i in (0, 2, 4))
def generate_overlay(input_image: np.ndarray, segmentation: np.ndarray, mapping: dict = None, color_cycle=color_cycle,
overlay_intensity=0.6):
"""
image must be a color image, so last dimension must be 3. if image is grayscale, tile it first!
Segmentation must be label map of same shape as image (w/o color channels)
mapping can be label_id -> idx_in_cycle or None
returned image is scaled to [0, 255]!!!
"""
# assert len(image.shape) == len(segmentation.shape)
# assert all([i == j for i, j in zip(image.shape, segmentation.shape)])
# create a copy of image
image = np.copy(input_image)
if len(image.shape) == 2:
image = np.tile(image[:, :, None], (1, 1, 3))
elif len(image.shape) == 3:
assert image.shape[2] == 3, 'if 3d image is given the last dimension must be the color channels ' \
'(3 channels). Only 2D images are supported'
else:
raise RuntimeError("unexpected image shape. only 2D images and 2D images with color channels (color in "
"last dimension) are supported")
# rescale image to [0, 255]
image = image - image.min()
image = image / image.max() * 255
# create output
if mapping is None:
uniques = np.unique(segmentation)
mapping = {i: c for c, i in enumerate(uniques)}
for l in mapping.keys():
image[segmentation == l] += overlay_intensity * np.array(hex_to_rgb(color_cycle[mapping[l]]))
# rescale result to [0, 255]
image = image / image.max() * 255
return image.astype(np.uint8)
def plot_overlay(image_file: str, segmentation_file: str, output_file: str, overlay_intensity: float = 0.6):
import matplotlib.pyplot as plt
image = sitk.GetArrayFromImage(sitk.ReadImage(image_file))
seg = sitk.GetArrayFromImage(sitk.ReadImage(segmentation_file))
assert all([i == j for i, j in zip(image.shape, seg.shape)]), "image and seg do not have the same shape: %s, %s" % (
image_file, segmentation_file)
assert len(image.shape) == 3, 'only 3D images/segs are supported'
fg_mask = seg != 0
fg_per_slice = fg_mask.sum((1, 2))
selected_slice = np.argmax(fg_per_slice)
overlay = generate_overlay(image[selected_slice], seg[selected_slice], overlay_intensity=overlay_intensity)
plt.imsave(output_file, overlay)
def plot_overlay_preprocessed(case_file: str, output_file: str, overlay_intensity: float = 0.6, modality_index=0):
import matplotlib.pyplot as plt
data = np.load(case_file)['data']
assert modality_index < (data.shape[0] - 1), 'This dataset only supports modality index up to %d' % (data.shape[0] - 2)
image = data[modality_index]
seg = data[-1]
seg[seg < 0] = 0
fg_mask = seg > 0
fg_per_slice = fg_mask.sum((1, 2))
selected_slice = np.argmax(fg_per_slice)
overlay = generate_overlay(image[selected_slice], seg[selected_slice], overlay_intensity=overlay_intensity)
plt.imsave(output_file, overlay)
def multiprocessing_plot_overlay(list_of_image_files, list_of_seg_files, list_of_output_files, overlay_intensity,
num_processes=8):
p = Pool(num_processes)
r = p.starmap_async(plot_overlay, zip(
list_of_image_files, list_of_seg_files, list_of_output_files, [overlay_intensity] * len(list_of_output_files)
))
r.get()
p.close()
p.join()
def multiprocessing_plot_overlay_preprocessed(list_of_case_files, list_of_output_files, overlay_intensity,
num_processes=8, modality_index=0):
p = Pool(num_processes)
r = p.starmap_async(plot_overlay_preprocessed, zip(
list_of_case_files, list_of_output_files, [overlay_intensity] * len(list_of_output_files),
[modality_index] * len(list_of_output_files)
))
r.get()
p.close()
p.join()
def generate_overlays_for_task(task_name_or_id, output_folder, num_processes=8, modality_idx=0, use_preprocessed=True,
data_identifier=default_data_identifier):
if isinstance(task_name_or_id, str):
if not task_name_or_id.startswith("Task"):
task_name_or_id = int(task_name_or_id)
task_name = convert_id_to_task_name(task_name_or_id)
else:
task_name = task_name_or_id
else:
task_name = convert_id_to_task_name(int(task_name_or_id))
if not use_preprocessed:
folder = join(nnUNet_raw_data, task_name)
identifiers = [i[:-7] for i in subfiles(join(folder, 'labelsTr'), suffix='.nii.gz', join=False)]
image_files = [join(folder, 'imagesTr', i + "_%04.0d.nii.gz" % modality_idx) for i in identifiers]
seg_files = [join(folder, 'labelsTr', i + ".nii.gz") for i in identifiers]
assert all([isfile(i) for i in image_files])
assert all([isfile(i) for i in seg_files])
maybe_mkdir_p(output_folder)
output_files = [join(output_folder, i + '.png') for i in identifiers]
multiprocessing_plot_overlay(image_files, seg_files, output_files, 0.6, num_processes)
else:
folder = join(preprocessing_output_dir, task_name)
if not isdir(folder): raise RuntimeError("run preprocessing for that task first")
matching_folders = subdirs(folder, prefix=data_identifier + "_stage")
if len(matching_folders) == 0: "run preprocessing for that task first (use default experiment planner!)"
matching_folders.sort()
folder = matching_folders[-1]
identifiers = [i[:-4] for i in subfiles(folder, suffix='.npz', join=False)]
maybe_mkdir_p(output_folder)
output_files = [join(output_folder, i + '.png') for i in identifiers]
image_files = [join(folder, i + ".npz") for i in identifiers]
maybe_mkdir_p(output_folder)
multiprocessing_plot_overlay_preprocessed(image_files, output_files, overlay_intensity=0.6,
num_processes=num_processes, modality_index=modality_idx)
def entry_point_generate_overlay():
import argparse
parser = argparse.ArgumentParser("Plots png overlays of the slice with the most foreground. Note that this "
"disregards spacing information!")
parser.add_argument('-t', type=str, help="task name or task ID", required=True)
parser.add_argument('-o', type=str, help="output folder", required=True)
parser.add_argument('-num_processes', type=int, default=8, required=False, help="number of processes used. Default: 8")
parser.add_argument('-modality_idx', type=int, default=0, required=False,
help="modality index used (0 = _0000.nii.gz). Default: 0")
parser.add_argument('--use_raw', action='store_true', required=False, help="if set then we use raw data. else "
"we use preprocessed")
args = parser.parse_args()
generate_overlays_for_task(args.t, args.o, args.num_processes, args.modality_idx, use_preprocessed=not args.use_raw) |