statistic_plgs.py 文件源码

def run(self, ips, snap, img, para = None):
        intenimg = WindowsManager.get(para['inten']).ips.img
        strc = ndimage.generate_binary_structure(2, 1 if para['con']=='4-connect' else 2)
        buf, n = ndimage.label(snap, strc, output=np.uint16)
        index = range(1, n+1)
        idx = (np.ones(n+1)*para['front']).astype(np.uint8)
        msk = np.ones(n, dtype=np.bool)

        if para['mean']>0: msk *= ndimage.mean(intenimg, buf, index)>=para['mean']
        if para['mean']<0: msk *= ndimage.mean(intenimg, buf, index)<-para['mean']
        if para['max']>0: msk *= ndimage.maximum(intenimg, buf, index)>=para['max']
        if para['max']<0: msk *= ndimage.maximum(intenimg, buf, index)<-para['max']
        if para['min']>0: msk *= ndimage.minimum(intenimg, buf, index)>=para['min']
        if para['min']<0: msk *= ndimage.minimum(intenimg, buf, index)<-para['min']
        if para['sum']>0: msk *= ndimage.sum(intenimg, buf, index)>=para['sum']
        if para['sum']<0: msk *= ndimage.sum(intenimg, buf, index)<-para['sum']
        if para['std']>0: msk *= ndimage.standard_deviation(intenimg, buf, index)>=para['std']
        if para['std']<0: msk *= ndimage.standard_deviation(intenimg, buf, index)<-para['std']


        xy = ndimage.center_of_mass(intenimg, buf, index)
        xy = np.array(xy).round(2).T

        idx[1:][~msk] = para['back']
        idx[0] = 0
        img[:] = idx[buf]

        WindowsManager.get(para['inten']).ips.mark = RGMark((xy.T, msk))
        WindowsManager.get(para['inten']).ips.update = True