vignettingFromRandomSteps.py 文件源码

def __next__(self):
        # THE IMAGED OBJECT WILL BE AVERAGED FROM ALL
        # INDIVITUAL IMAGES SHOWING THIS OBJECT FROM DIFFERENT POSITIONS:
        obj = MaskedMovingAverage(shape=self.obj_shape)

        with np.errstate(divide='ignore', invalid='ignore'):
            for f, h in zip(self.fits, self.Hinvs):
                warpedflatField = cv2.warpPerspective(self.flatField,
                                                      h, (f.shape[1], f.shape[0]))
                obj.update(f / warpedflatField, warpedflatField != 0)

        self.object = obj.avg

        # THE NEW flatField WILL BE OBTAINED FROM THE WARPED DIVIDENT
        # BETWEEN ALL IMAGES THE THE ESTIMATED IMAGE OOBJECT
        sh = self.flatField.shape
        s = MaskedMovingAverage(shape=sh)

        for f, mask, h in zip(self.fits, self._fit_masks, self.Hs):
            div = f / self.object
            # ->do not interpolate between background and image border
            div[mask] = np.nan
            div = cv2.warpPerspective(div, h, (sh[1], sh[0]),  # borderMode=cv2.BORDER_TRANSPARENT
                                      )
            div = np.nan_to_num(div)
            s.update(div, div != 0)

        new_flatField = s.avg

        # STOP ITERATION?
        # RMSE excluding NaNs:
        dev = np.nanmean((new_flatField[::10, ::10] -
                          self.flatField[::10, ::10])**2)**0.5
        print('residuum: %s' % dev)
        if self.n >= self.maxIter or (self._last_dev and (
                (self.n > 4 and dev > self._last_dev) or
                dev < self.maxDev)):
            raise StopIteration

        # remove erroneous values:
        self.flatField = np.clip(new_flatField, 0, 1)

        self.n += 1
        self._last_dev = dev
        return self.n