dsb_utils.py 文件源码

def segmenter_data_transform(imb, shift=0, rotate=0, scale=0, normalize_pctwise=(20,95), istest=False):
    if isinstance(imb, tuple) and len(imb) == 2:
        imgs,labels = imb
    else:
        imgs = imb

    # rotate image if training
    if rotate>0:
        for i in xrange(imgs.shape[0]):
            degrees = rotate if istest else np.clip(np.random.normal(),-2,2)*rotate;
            imgs[i,0] = scipy.misc.imrotate(imgs[i,0], degrees, interp='bilinear')
            if isinstance(imb, tuple):
                labels[i,0] = scipy.misc.imrotate(labels[i,0], degrees, interp='bilinear')
    #rescale
    if scale>0:
        assert(scale>0 and scale<=0.5);
        for i in xrange(imgs.shape[0]):
            sc = 1 + (scale if istest else np.clip(np.random.normal(),-2,2)*scale);
            imgs[i,0] = rescale(imgs[i,0],sc);
            if isinstance(imb, tuple):
                labels[i,0] = rescale(labels[i,0], sc);

    #shift
    if shift>0 and not istest:
        for i in xrange(imgs.shape[0]):
            x,y = np.random.randint(-shift,shift,2);
            imgs[i,0] = img_shift(imgs[i,0], (x,y));
            if isinstance(imb, tuple):
                labels[i,0] = img_shift(labels[i,0], (x,y));

    imgs = nn.utils.floatX(imgs)/255.0;
    for i in xrange(imgs.shape[0]):
        pclow, pchigh = normalize_pctwise 
        if isinstance(pclow,tuple):
            pclow = np.random.randint(pclow[0],pclow[1]);
            pchigh = np.random.randint(pchigh[0],pchigh[1]);
        pl,ph = np.percentile(imgs[i],(pclow, pchigh))
        imgs[i] = exposure.rescale_intensity(imgs[i], in_range=(pl, ph));
        imgs[i] = 2*imgs[i]/imgs[i].max() - 1.

    if isinstance(imb,tuple):
        labels = nn.utils.floatX(labels)/255.0;
        return imgs,labels
    else:
        return imgs;