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)
