def convertcsv(bboxfname, bboxpath, detp):
resolution = np.array([1, 1, 1])
origin = np.load(sideinfopath+bboxfname[:-8]+'_origin.npy', mmap_mode='r')
spacing = np.load(sideinfopath+bboxfname[:-8]+'_spacing.npy', mmap_mode='r')
extendbox = np.load(sideinfopath+bboxfname[:-8]+'_extendbox.npy', mmap_mode='r')
pbb = np.load(bboxpath+bboxfname, mmap_mode='r')
diam = pbb[:,-1]
check = sigmoid(pbb[:,0]) > detp
pbbold = np.array(pbb[check])
# pbbold = np.array(pbb[pbb[:,0] > detp])
pbbold = np.array(pbbold[pbbold[:,-1] > 3]) # add new 9 15
pbb = nms(pbbold, nmsthresh)
pbb = np.array(pbb[:, :-1])
pbb[:, 1:] = np.array(pbb[:, 1:] + np.expand_dims(extendbox[:,0], 1).T)
pbb[:, 1:] = np.array(pbb[:, 1:] * np.expand_dims(resolution, 1).T / np.expand_dims(spacing, 1).T)
pos = VoxelToWorldCoord(pbb[:, 1:], origin, spacing)
rowlist = []
for nk in range(pos.shape[0]): # pos[nk, 2], pos[nk, 1], pos[nk, 0]
rowlist.append([bboxfname[:-8], pos[nk, 2], pos[nk, 1], pos[nk, 0], diam[nk], 1/(1+np.exp(-pbb[nk,0]))])
return rowlist
def __init__(self, split, config, phase='train'):
assert (phase == 'train' or phase == 'val' or phase == 'test')
self.random_sample = config['random_sample'] #True
self.T = config['T'] #1
self.topk = config['topk'] #5
self.crop_size = config['crop_size'] #[96,96,96]
self.stride = config['stride'] #4
self.augtype = config[
'augtype'] #{'flip':True,'swap':False,'rotate':False,'scale':False}
#self.labels = np.array(pandas.read_csv(config['labelfile']))
datadir = config['datadir'] #preprocess_result_path
bboxpath = config['bboxpath'] #'../detector/results/res18/bbox/',
self.phase = phase
self.candidate_box = []
self.pbb_label = []
split = [str(f, 'utf-8') for f in split]
idcs = split
self.filenames = [
os.path.join(datadir, '%s_clean.npy' % idx) for idx in idcs
]
labels = np.array(pandas.read_csv(
config['labelfile'])) #'./full_label.csv'
if phase != 'test':
# print('0',labels[:,0])
# print('0=', [str(f,'utf-8') for f in split])
# self.yset = np.array([labels[labels[:,0]==str(f,'utf-8'),1] for f in split]).astype('int')
self.yset = np.array([
labels[labels[:, 0] == f.split('-')[0].split('_')[0], 1]
for f in split
]).astype('int')
# print([str(f,'utf-8') for f in idcs])
# idcs = [str(f,'utf-8') for f in idcs]
for idx in idcs:
pbb = np.load(os.path.join(bboxpath, idx + '_pbb.npy'))
# print('pbb.shape',pbb.shape)#(~,5)
pbb = pbb[pbb[:, 0] > config['conf_th']] #-1
pbb = nms(pbb, config['nms_th']) #0.05
lbb = np.load(os.path.join(bboxpath, idx + '_lbb.npy'))
pbb_label = []
for p in pbb:
isnod = False
for l in lbb:
score = iou(p[1:5], l)
if score > config['detect_th']: #0.05
isnod = True
break
pbb_label.append(isnod)
# if idx.startswith()
self.candidate_box.append(pbb)
self.pbb_label.append(np.array(pbb_label))
self.crop = simpleCrop(config, phase)
def __init__(self, split, config, phase='train'):
assert (phase == 'train' or phase == 'val' or phase == 'test')
self.random_sample = config['random_sample']
self.T = config['T']
self.topk = config['topk']
self.crop_size = config['crop_size']
self.stride = config['stride']
self.augtype = config['augtype']
# self.labels = np.array(pandas.read_csv(config['labelfile']))
datadir = config['datadir']
bboxpath = config['bboxpath']
self.phase = phase
self.candidate_box = []
self.pbb_label = []
idcs = split
self.filenames = [
os.path.join(datadir, '%s_clean.npy' % idx) for idx in idcs
]
labels = np.array(pandas.read_csv(config['labelfile']))
if phase != 'test':
self.yset = np.array([
labels[labels[:, 0] == f.split('-')[0].split('_')[0], 1]
for f in split
]).astype('int')
idcs = [f.split('-')[0] for f in idcs]
'''
1. 对目标检测过的预测结果做nms
2. 提取使用到的label
3. self.pbb_label 预测结果中是否含有目标的label
4. self.candidate_box 候选框pbb 为预测结果的加载，疑似存在目标
5. yset 为分类的annotations
'''
for idx in idcs:
# output shape = n*[conf,z,h,w,d]
pbb = np.load(os.path.join(bboxpath, idx + '_pbb.npy'))
pbb = pbb[pbb[:, 0] > config['conf_th']]
pbb = nms(pbb, config['nms_th'])
lbb = np.load(os.path.join(bboxpath, idx + '_lbb.npy'))
pbb_label = []
for p in pbb:
isnod = False
for l in lbb:
score = iou(p[1:5], l)
if score > config['detect_th']:
isnod = True
break
pbb_label.append(isnod)
# if idx.startswith()
self.candidate_box.append(pbb)
self.pbb_label.append(np.array(pbb_label))
self.crop = simpleCrop(config, phase)
def __init__(self, dirlist, config, phase='train'):
assert (phase in {'train', 'val', 'test'})
self.random_sample = config['random_sample']
self.T = config['T']
self.topk = config['topk']
self.crop_size = config['crop_size']
self.stride = config['stride']
self.augtype = config['augtype']
self.filling_value = config['filling_value']
# self.labels = np.array(pandas.read_csv(config['labelfile']))
datadir = config['datadir']
bboxpath = config['bboxpath']
self.phase = phase
self.candidate_box = []
self.pbb_label = []
idcs = dirlist
self.filenames = [
p.join(datadir, '%s_clean.npy' % idx.split('-')[0]) for idx in idcs
]
if self.phase != 'test':
self.yset = 1 - np.array([f.split('-')[1][2]
for f in idcs]).astype('int')
for idx in idcs:
pbb = np.load(p.join(bboxpath, idx + '_pbb.npy'))
pbb = pbb[pbb[:, 0] > config['conf_th']]
pbb = nms(pbb, config['nms_th'])
lbb = np.load(p.join(bboxpath, idx + '_lbb.npy'))
pbb_label = []
for pb in pbb:
isnod = False
for lb in lbb:
score = iou(pb[1:5], lb)
if score > config['detect_th']:
isnod = True
break
pbb_label.append(isnod)
# if idx.startswith()
self.candidate_box.append(pbb)
self.pbb_label.append(np.array(pbb_label))
self.crop = simpleCrop(config, phase)
def __init__(self, split, config, phase = 'train'):
assert(phase == 'train' or phase == 'val' or phase == 'test')
self.random_sample = config['random_sample']
self.T = config['T']
self.topk = config['topk']
self.crop_size = config['crop_size']
self.stride = config['stride']
#print ('------------stride: ', self.stride)
self.augtype = config['augtype']
self.filling_value = config['filling_value']
#self.labels = np.array(pandas.read_csv(config['labelfile']))
datadir = config['datadir']
bboxpath = config['bboxpath']
self.phase = phase
self.candidate_box = []
self.pbb_label = []
self.subj_name = []
idcs = split
self.filenames = [os.path.join(datadir, '%s_clean.npy' % idx.split('-')[0]) for idx in idcs]
if self.phase!='test':
self.yset = 1-np.array([f.split('-')[1][2] for f in idcs]).astype('int')
for idx in idcs:
#print ('This is ', idx)
try:
pbb = np.load(os.path.join(bboxpath,idx+'_pbb.npy'))
except:
print (idx+'_pbb.npy not existed' )
continue
pbb = pbb[pbb[:,0]>config['conf_th']]
pbb = nms(pbb, config['nms_th'])
lbb = np.load(os.path.join(bboxpath,idx+'_lbb.npy'))
pbb_label = []
for p in pbb:
isnod = False
for l in lbb:
score = iou(p[1:5], l)
if score > config['detect_th']:
isnod = True
break
pbb_label.append(isnod)
# if idx.startswith()
self.subj_name.append(idx)
self.candidate_box.append(pbb)
self.pbb_label.append(np.array(pbb_label))
self.crop = simpleCrop(config,phase)
def __init__(self, split, config, phase = 'train'):
assert(phase == 'train' or phase == 'val' or phase == 'test')
self.random_sample = config['random_sample']
self.T = config['T']
self.topk = config['topk']
self.crop_size = config['crop_size']
self.stride = config['stride']
self.augtype = config['augtype']
#self.labels = np.array(pandas.read_csv(config['labelfile']))
datadir = config['datadir']
bboxpath = config['bboxpath']
self.phase = phase
self.candidate_box = []
self.pbb_label = []
idcs = split
self.filenames = [os.path.join(datadir, '%s_clean.npy' % idx) for idx in idcs]
labels = np.array(pandas.read_csv(config['labelfile']))
if phase !='test':
for tmp in [labels[labels[:, 0] == f.split('-')[0].split('_')[0], 1] for f in split]:
print(tmp.shape)
print([labels[labels[:,0]==f.split('-')[0].split('_')[0],1] for f in split])
self.yset = np.array([labels[labels[:,0]==f.split('-')[0].split('_')[0],1] for f in split]).astype('int')
idcs = [f.split('-')[0] for f in idcs]
for idx in idcs:
pbb = np.load(os.path.join(bboxpath,idx+'_pbb.npy'))
pbb = pbb[pbb[:,0]>config['conf_th']]
pbb = nms(pbb, config['nms_th'])
lbb = np.load(os.path.join(bboxpath,idx+'_lbb.npy'))
pbb_label = []
for p in pbb:
isnod = False
for l in lbb:
score = iou(p[1:5], l)
if score > config['detect_th']:
isnod = True
break
pbb_label.append(isnod)
# if idx.startswith()
self.candidate_box.append(pbb)
self.pbb_label.append(np.array(pbb_label))
self.crop = simpleCrop(config,phase)
def __init__(self, split, config, phase = 'train'):
assert(phase == 'train' or phase == 'val' or phase == 'test')
self.random_sample = config['random_sample']
self.T = config['T']
self.topk = config['topk']
self.crop_size = config['crop_size']
self.stride = config['stride']
self.augtype = config['augtype']
self.filling_value = config['filling_value']
#self.labels = np.array(pandas.read_csv(config['labelfile']))
datadir = config['datadir']
bboxpath = config['bboxpath']
self.phase = phase
self.candidate_box = []
self.pbb_label = []
idcs = split
self.filenames = [os.path.join(datadir, '%s_clean.npy' % idx.split('-')[0]) for idx in idcs]
if self.phase!='test':
self.yset = 1-np.array([f.split('-')[1][2] for f in idcs]).astype('int')
for idx in idcs:
pbb = np.load(os.path.join(bboxpath,idx+'_pbb.npy'))
pbb = pbb[pbb[:,0]>config['conf_th']]
pbb = nms(pbb, config['nms_th'])
lbb = np.load(os.path.join(bboxpath,idx+'_lbb.npy'))
pbb_label = []
for p in pbb:
isnod = False
for l in lbb:
score = iou(p[1:5], l)
if score > config['detect_th']:
isnod = True
break
pbb_label.append(isnod)
# if idx.startswith()
self.candidate_box.append(pbb)
self.pbb_label.append(np.array(pbb_label))
self.crop = simpleCrop(config,phase)
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import sys
sys.path.append('../training/')
sys.path.append('../')
sys.path.append('../preprocessing/')
from layers import nms, iou
img = np.load('./1_clean.npy')
pbb = np.load('./1_pbb.npy')
pbb = pbb[pbb[:, 0] > -1]
pbb = nms(pbb, 0.05)
box = pbb[0].astype('int')[1:]
ax = plt.subplot(1, 1, 1)
plt.imshow(img[0, box[0]], 'gray')
plt.axis('off')
rect = patches.Rectangle((box[2] - box[3], box[1] - box[3]),
box[3] * 2,
box[3] * 2,
linewidth=2,
edgecolor='red',
facecolor='none')
ax.add_patch(rect)
# print('NoduleBox',boxS)#
# ax = plt.subplot(1,1,1)
# plt.imshow(img[0,boxS[0]],'gray')
# plt.axis('off')
# rect = patches.Rectangle((boxS[2]-boxS[3],boxS[1]-boxS[3]),boxS[3]*2,boxS[3]*2,linewidth=2,edgecolor='blue',facecolor='none')
# ax.add_patch(rect)
# plt.savefig(os.path.join(save_dir,imglist[i].split('_')[0] + '---' + str(boxS[0]) + '.png'))
# plt.close()
if pbb.shape[0] != 0:
max = np.max(pbb[:, 0])
thes = -10
# thes = -1
if max < -1:
thes = max
pbb = pbb[pbb[:, 0] >= thes]
pbb = nms(pbb, 0.1)
print('pbb', imglist[i].split('_')[0], pbb.shape, img.shape)
for box_num in range(pbb.shape[0]):
box = pbb[box_num].astype('int')[1:] #取第一个box的xyzd
# if imgLungmask[0,box[0],box[1],box[2]] != 0:
if box[0] < img.shape[1]:
print('box', box) #array([169, 153, 69, 13])
ax = plt.subplot(1, 1, 1)
plt.imshow(img[0, box[0]], 'gray')
plt.axis('off')
rect = patches.Rectangle((box[2] - box[3], box[1] - box[3]),
box[3] * 2,
box[3] * 2,
linewidth=2,
# print('boxXYZ,pos',boxXYZ,pos)
# ax = plt.subplot(1,1,1)
# plt.imshow(img[0,boxS[0]],'gray')
# plt.axis('off')
# rect = patches.Rectangle((boxS[2]-boxS[3],boxS[1]-boxS[3]),boxS[3]*2,boxS[3]*2,linewidth=2,edgecolor='blue',facecolor='none')
# ax.add_patch(rect)
# plt.savefig(os.path.join(save_dir,imglist[i].split('_')[0] + '---' + str(boxS[0]) + '.png'))
# plt.close()
if pbb5.shape[0] != 0:
max = np.max(pbb5[:, 0])
thes = -10
# thes = -1
if max < -1:
thes = max
pbb5 = pbb5[pbb5[:, 0] >= thes]
pbb5 = nms(pbb5, 0.1)
# print('pbb5',imglist[i].split('_')[0],pbb5.shape, Image.shape, extendbox.shape)
pbb = np.array(pbb5[:, :-1]) #去掉直径
print('pbb5', pbb5, Spacing)
pbb[:, 1:] = np.array(pbb[:, 1:] + np.expand_dims(
extendbox[:, 0], 1).T) #对输出加上拓展box的坐标，其实就是恢复为原来的坐标，我对这个拓展box深恶痛绝
pbb[:, 1:] = np.array(
pbb[:, 1:] * np.expand_dims(resolution, 1).T /
np.expand_dims(Spacing, 1).T) #将输出恢复为原来的分辨率，这样就对应了原始数据中的体素坐标
pbb5[:, 2:] = np.array(
pbb5[:, 2:] * np.expand_dims(resolution, 1).T /
np.expand_dims(Spacing, 1).T) #将输出恢复为原来的分辨率，这样就对应了原始数据中的体素坐标
pos = VoxelToWorldCoord(pbb[:, 1:], Origin, Spacing)
# print('pos',pos)
def test(data_loader, net, get_pbb, save_dir, config, test_set):
start_time = time.time()
# eg test2_100
save_dir = os.path.join(
save_dir, 'bbox/',
test_set + '_' + os.path.splitext(os.path.basename(args.resume))[0])
if not os.path.exists(save_dir):
os.makedirs(save_dir)
print(save_dir)
net.eval()
namelist = []
split_comber = data_loader.dataset.split_comber
for i_name, (data, target, coord, nzhw) in enumerate(data_loader):
s = time.time()
target = [np.asarray(t, np.float32) for t in target]
lbb = target[0]
nzhw = nzhw[0]
name = os.path.basename(
data_loader.dataset.filenames[i_name]).split('_clean')[0]
data = data[0][0]
coord = coord[0][0]
isfeat = False
if 'output_feature' in config:
if config['output_feature']:
isfeat = True
n_per_run = args.n_test
print(data.size())
splitlist = range(0, len(data) + 1, n_per_run)
if splitlist[-1] != len(data):
splitlist.append(len(data))
outputlist = []
featurelist = []
for i in range(len(splitlist) - 1):
input = Variable(data[splitlist[i]:splitlist[i + 1]],
volatile=True).cuda()
inputcoord = Variable(coord[splitlist[i]:splitlist[i + 1]],
volatile=True).cuda()
if isfeat:
output, feature = net(input, inputcoord)
featurelist.append(feature.data.cpu().numpy())
else:
output = net(input, inputcoord)
outputlist.append(output.data.cpu().numpy())
output = np.concatenate(outputlist, 0)
output = split_comber.combine(output, nzhw=nzhw)
if isfeat:
feature = np.concatenate(featurelist,
0).transpose([0, 2, 3, 4,
1])[:, :, :, :, :,
np.newaxis]
feature = split_comber.combine(feature, sidelen)[..., 0]
# Initially filter by sig(-3) = 0.047
thresh = -3
pbb, mask = get_pbb(output, thresh, ismask=True)
if isfeat:
feature_selected = feature[mask[0], mask[1], mask[2]]
np.save(os.path.join(save_dir, name + '_feature.npy'),
feature_selected)
#tp,fp,fn,_ = acc(pbb,lbb,0,0.1,0.1)
#print([len(tp),len(fp),len(fn)])
print([i_name, name])
e = time.time()
#pbb = nms(pbb[pbb[:,0] > 0], 0.1) # Save only unique predicted bboxes with threshold sig(0) = 0.5
pbb = nms(pbb, 0.1)
np.save(os.path.join(save_dir, name + '_pbb.npy'), pbb)
np.save(os.path.join(save_dir, name + '_lbb.npy'), lbb)
np.save(os.path.join(save_dir, 'namelist.npy'), namelist)
end_time = time.time()
print('elapsed time is %3.2f seconds' % (end_time - start_time))
print
print
