TractGeoNet/hcp.py at main · SlicerDMRI/TractGeoNet · GitHub

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from __future__ import print_function
import numpy
import torch.utils.data as data
import torch
import random
import h5py

class PointSet_pair(data.Dataset):
    def __init__(self,vec,gt,transform=None,n_points=[1024],val=False):
        self.vec=vec
        self.tpvt=gt
        self.transform = transform
        self.n_points=n_points
        self.val=val

    def __getitem__(self, index: int):
        feat_points=self.vec[index]
        tracts=list(feat_points.keys())
        feat=None
        for i,tract in enumerate(tracts):
            feat_tract=feat_points[tract]
            # if self.val:
            #     numpy.random.seed(0)
            #     random.seed(0)
            id1 = numpy.random.randint(feat_tract.shape[0], size=20000)
            id = random.sample(list(numpy.unique(id1)), self.n_points[i])
            feat1 = feat_tract[id]
            # if index==1:
            #     print(id[:3])
            if feat is None:
                feat=feat1
            else:
                feat=numpy.concatenate((feat,feat1),0)
        label = self.tpvt[index]
        feat_1 = torch.tensor(feat.T, dtype=torch.float)
        label_1 = torch.tensor(label, dtype=torch.float)

        index2=random.randint(0,len(self.vec)-1)
        feat_points = self.vec[index2]
        tracts=list(feat_points.keys())
        feat=None
        for i,tract in enumerate(tracts):
            feat_tract=feat_points[tract]
            id1 = numpy.random.randint(feat_tract.shape[0], size=20000)
            id2 = random.sample(list(numpy.unique(id1)), self.n_points[i])
            feat1 = feat_tract[id2]
            if feat is None:
                feat=feat1
            else:
                feat=numpy.concatenate((feat,feat1),0)
        label = self.tpvt[index2]
        feat_2 = torch.tensor(feat.T, dtype=torch.float)
        label_2 = torch.tensor(label, dtype=torch.float)
        return feat_1,label_1,feat_2,label_2,numpy.array(id)

    def __len__(self) -> int:
        return len(self.vec)
class PointSet(data.Dataset):
    def __init__(self,vec,gt,transform=None,n_points=[1024],val=False):
        self.vec=vec
        self.tpvt=gt
        self.transform = transform
        self.n_points=n_points
        self.val = val

    def __getitem__(self, index: int):
        #print(index)
        feat_points=self.vec[index]
        tracts=list(feat_points.keys())
        #n_points_sub=int(self.n_points/len(tracts))
        feat=None
        for i,tract in enumerate(tracts):
            feat_tract=feat_points[tract]
            # if self.val:
            #     numpy.random.seed(0)
            #     random.seed(0)
            id1 = numpy.random.randint(feat_tract.shape[0], size=20000)
            id = random.sample(list(numpy.unique(id1)), self.n_points[i])
            feat1 = feat_tract[id]
            if feat is None:
                feat=feat1
            else:
                feat=numpy.concatenate((feat,feat1),0)
        # id = numpy.random.choice(list(range(feat_points.shape[0])), 1024
        #                          , replace=False)
        #id = random.sample(list(range(20000)), 1024)
        #id = numpy.random.choice(list(range(feat_points.shape[0])), 1024, replace=False)
        label = self.tpvt[index]
        feat = torch.tensor(feat.T, dtype=torch.float)
        label = torch.tensor(label, dtype=torch.float)
        # if self.transform is not None:
        #     img = self.transform(img)
        return feat,label

    def __len__(self) -> int:
        return len(self.vec)
class Fiber_pair_vis(data.Dataset):
    def __init__(self,vec,gt,transform=None,n_points=1024):
        self.vec=vec
        self.tpvt=gt
        self.transform = transform
        self.n_points=n_points

    def __getitem__(self, index: int):
        feat_points=self.vec[index]
        feat=feat_points[self.n_points*index:self.n_points*(index+1),:]
        label = self.tpvt[index]
        feat = torch.tensor(feat.T, dtype=torch.float)
        label = torch.tensor(label, dtype=torch.float)
        # if self.transform is not None:
        #     img = self.transform(img)
        return feat,label

    def __len__(self) -> int:
        return len(self.vec)
class Fiber_sub(data.Dataset):
    def __init__(self,vec,gt,transform=None,n_points=[1024]):
        self.vec=vec
        self.tpvt=gt
        self.transform = transform
        self.n_points=n_points

    def __getitem__(self, index: int):

        #print(index)
        sub_id=self.vec[index]
        ff = h5py.File('/media/annabelchen/DataShare/deepClustering/HCPTestingData/tractography_prediction/tract_feats/subjects/{}.h5'.format(sub_id))
        tracts=list(ff.keys())
        feat=None
        for i,tract in enumerate(tracts):
            feat_tract=ff[tract][:]
            id1 = numpy.random.randint(feat_tract.shape[0], size=20000)
            id = random.sample(list(numpy.unique(id1)), self.n_points[i])
            feat1 = feat_tract[id]
            if feat is None:
                feat=feat1
            else:
                feat=numpy.concatenate((feat,feat1),0)

        label = self.tpvt[index]
        feat = torch.tensor(feat.T, dtype=torch.float)
        label = torch.tensor(label, dtype=torch.float)
        # if self.transform is not None:
        #     img = self.transform(img)
        return feat,label

    def __len__(self) -> int:
        return len(self.vec)