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get_roi.py
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get_roi.py
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import json
import click
import cv2
import nibabel as nib
import numpy as np
import torch
from pathlib2 import Path
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm
import utils.checkpoint as cp
from dataset import KiTS19
from dataset.transform import MedicalTransform
from network import ResUNet
from utils.vis import imshow
def calc(seg, idx):
bincount = np.bincount(seg.flatten())
area = int(bincount[idx])
value = []
for i in range(seg.shape[0]):
value.append(seg[i].max())
value = np.array(value)
slice_ = np.where(value > idx - 1)[0]
num_slice = len(slice_)
min_z = int(slice_.min())
max_z = int(slice_.max()) + 1
min_x = min_y = 10000
max_x = max_y = -1
for i in range(min_z, max_z):
if seg[i].max() > idx - 1:
mask = np.ma.masked_where(seg[i] > idx - 1, seg[i]).mask
rect = cv2.boundingRect(mask.astype(np.uint8))
min_x = min(min_x, rect[0])
min_y = min(min_y, rect[1])
max_x = max(max_x, rect[0] + rect[2])
max_y = max(max_y, rect[1] + rect[3])
roi = {'min_x': min_x, 'min_y': min_y, 'min_z': min_z,
'max_x': max_x, 'max_y': max_y, 'max_z': max_z,
'area': area, 'slice': num_slice}
return roi
def get_roi_from_gt(data_path, roi_file):
data_path = Path(data_path)
cases = sorted([d for d in data_path.iterdir() if d.is_dir()])
case_idx = 0
rois = {}
for case in tqdm(cases, ascii=True, dynamic_ncols=True):
img_file = case / 'imaging.nii.gz'
assert img_file.exists()
img = nib.load(str(img_file)).get_data()
total_z, total_y, total_x = img.shape
vol = {'total_x': total_x, 'total_y': total_y, 'total_z': total_z}
case_data = {'vol': vol}
seg_file = case / 'segmentation.nii.gz'
if seg_file.exists():
seg = nib.load(str(seg_file)).get_data()
kidney = calc(seg, idx=1)
tumor = calc(seg, idx=2)
case_data.update({'kidney': kidney, 'tumor': tumor})
rois[f'case_{case_idx:05d}'] = case_data
with open(roi_file, 'w') as f:
json.dump(rois, f, indent=4, separators=(',', ': '))
case_idx += 1
def get_roi_from_resunet(batch_size, num_gpu, img_size, data_path, resume, roi_file, vis_intvl, num_workers):
with open(roi_file, 'r') as f:
rois = json.load(f)
data_path = Path(data_path)
transform = MedicalTransform(output_size=img_size, roi_error_range=15, use_roi=False)
dataset = KiTS19(data_path, stack_num=5, spec_classes=[0, 1, 1], img_size=img_size,
use_roi=False, train_transform=transform, valid_transform=transform)
net = ResUNet(in_ch=dataset.img_channels, out_ch=dataset.num_classes, base_ch=64)
if resume:
data = {'net': net}
cp_file = Path(resume)
cp.load_params(data, cp_file, device='cpu')
gpu_ids = [i for i in range(num_gpu)]
torch.cuda.empty_cache()
net = torch.nn.DataParallel(net, device_ids=gpu_ids).cuda()
net.eval()
torch.set_grad_enabled(False)
transform.eval()
subset = dataset.test_dataset
case_slice_indices = dataset.test_case_slice_indices
sampler = SequentialSampler(subset)
data_loader = DataLoader(subset, batch_size=batch_size, sampler=sampler,
num_workers=num_workers, pin_memory=True)
case = 0
vol_output = []
with tqdm(total=len(case_slice_indices) - 1, ascii=True, desc=f'eval/test', dynamic_ncols=True) as pbar:
for batch_idx, data in enumerate(data_loader):
imgs, idx = data['image'].cuda(), data['index']
predicts = net(imgs)
predicts = predicts.argmax(dim=1)
predicts = predicts.cpu().detach().numpy()
idx = idx.numpy()
vol_output.append(predicts)
while case < len(case_slice_indices) - 1 and idx[-1] >= case_slice_indices[case + 1] - 1:
vol_output = np.concatenate(vol_output, axis=0)
vol_num_slice = case_slice_indices[case + 1] - case_slice_indices[case]
vol = vol_output[:vol_num_slice]
kidney = calc(vol, idx=1)
case_roi = {'kidney': kidney}
case_id = dataset.case_idx_to_case_id(case, 'test')
rois[f'case_{case_id:05d}'].update(case_roi)
with open(roi_file, 'w') as f:
json.dump(rois, f, indent=4, separators=(',', ': '))
vol_output = [vol_output[vol_num_slice:]]
case += 1
pbar.update(1)
if vis_intvl > 0 and batch_idx % vis_intvl == 0:
data['predict'] = predicts
data = dataset.vis_transform(data)
imgs, predicts = data['image'], data['predict']
imshow(title=f'eval/test', imgs=(imgs[0, 1], predicts[0]), shape=(1, 2),
subtitle=('image', 'predict'))
@click.command()
@click.option('--org_data', 'org_data_path', help='kits19 data path',
type=click.Path(exists=True, dir_okay=True, resolve_path=True),
default='data', show_default=True)
@click.option('--data', 'data_path', help='Path of kits19 data after conversion',
type=click.Path(exists=True, dir_okay=True, resolve_path=True),
default='data', show_default=True)
@click.option('-o', '--output', 'roi_file', help='Output roi file path',
type=click.Path(file_okay=True, resolve_path=True), default='roi_gt.json', show_default=True)
@click.option('-b', '--batch', 'batch_size', help='Number of batch size', type=int, default=1, show_default=True)
@click.option('-g', '--num_gpu', help='Number of GPU', type=int, default=1, show_default=True)
@click.option('-s', '--size', 'img_size', help='Output image size', type=(int, int),
default=(512, 512), show_default=True)
@click.option('-r', '--resume', help='Resume model',
type=click.Path(exists=True, file_okay=True, resolve_path=True), required=True)
@click.option('--vis_intvl', help='Number of iteration interval of display visualize image. '
'No display when set to 0',
type=int, default=20, show_default=True)
@click.option('--num_workers', help='Number of workers on dataloader. '
'Recommend 0 in Windows. '
'Recommend num_gpu in Linux',
type=int, default=0, show_default=True)
def get_roi(org_data_path, data_path, roi_file, batch_size, num_gpu, img_size, resume, vis_intvl, num_workers):
get_roi_from_gt(org_data_path, roi_file)
get_roi_from_resunet(batch_size, num_gpu, img_size, data_path, resume, roi_file, vis_intvl, num_workers)
if __name__ == '__main__':
get_roi()