statistic_plgs.py 文件源码

def run(self, ips, imgs, para = None):
        inten = WindowsManager.get(para['inten']).ips
        if not para['slice']:
            imgs = [inten.img]
            msks = [ips.img]
        else: 
            msks = ips.imgs
            if len(msks)==1:
                msks *= len(imgs)
        buf = imgs[0].astype(np.uint16)
        strc = ndimage.generate_binary_structure(2, 1 if para['con']=='4-connect' else 2)
        idct = ['Max','Min','Mean','Variance','Standard','Sum']
        key = {'Max':'max','Min':'min','Mean':'mean',
               'Variance':'var','Standard':'std','Sum':'sum'}
        idct = [i for i in idct if para[key[i]]]
        titles = ['Slice', 'ID'][0 if para['slice'] else 1:] 
        if para['center']: titles.extend(['Center-X','Center-Y'])
        if para['extent']: titles.extend(['Min-Y','Min-X','Max-Y','Max-X'])
        titles.extend(idct)
        k = ips.unit[0]
        data, mark = [], []
        for i in range(len(imgs)):
            n = ndimage.label(msks[i], strc, output=buf)
            index = range(1, n+1)
            dt = []
            if para['slice']:dt.append([i]*n)
            dt.append(range(n))

            xy = ndimage.center_of_mass(imgs[i], buf, index)
            xy = np.array(xy).round(2).T
            if para['center']:dt.extend([xy[1]*k, xy[0]*k])

            boxs = [None] * n
            if para['extent']:
                boxs = ndimage.find_objects(buf)
                boxs = [(i[0].start, i[1].start, i[0].stop, i[1].stop) for i in boxs]
                for j in (0,1,2,3):
                    dt.append([i[j]*k for i in boxs])
            if para['max']:dt.append(ndimage.maximum(imgs[i], buf, index).round(2))
            if para['min']:dt.append(ndimage.minimum(imgs[i], buf, index).round(2))        
            if para['mean']:dt.append(ndimage.mean(imgs[i], buf, index).round(2))
            if para['var']:dt.append(ndimage.variance(imgs[i], buf, index).round(2)) 
            if para['std']:dt.append(ndimage.standard_deviation(imgs[i], buf, index).round(2))
            if para['sum']:dt.append(ndimage.sum(imgs[i], buf, index).round(2))      

            mark.append([(center, cov) for center,cov in zip(xy.T, boxs)]) 
            data.extend(list(zip(*dt)))

        IPy.table(inten.title+'-region statistic', data, titles)
        inten.mark = Mark(mark)
        inten.update = True