vignettingFromRandomSteps.py 文件源码

def __init__(self, img, bg=None, maxDev=1e-4, maxIter=10, remove_border_size=0,
                 # feature_size=5,
                 cameraMatrix=None, distortionCoeffs=None):  # 20
        """
        Args:
            img (path or array): Reference image
        Kwargs:
            bg (path or array): background image - same for all given images
            maxDev (float): Relative deviation between the last two iteration steps
                            Stop iterative refinement, if deviation is smaller
            maxIter (int): Stop iterative refinement after maxIter steps
        """
        self.lens = None
        if cameraMatrix is not None:
            self.lens = LensDistortion()
            self.lens._coeffs['distortionCoeffs'] = distortionCoeffs
            self.lens._coeffs['cameraMatrix'] = cameraMatrix

        self.maxDev = maxDev
        self.maxIter = maxIter
        self.remove_border_size = remove_border_size
        #self.feature_size = feature_size
        img = imread(img, 'gray')

        self.bg = bg
        if bg is not None:
            self.bg = getBackground(bg)
            if not isinstance(self.bg, np.ndarray):
                self.bg = np.full_like(img, self.bg, dtype=img.dtype)
            else:
                self.bg = self.bg.astype(img.dtype)
            img = cv2.subtract(img, self.bg)

        if self.lens is not None:
            img = self.lens.correct(img, keepSize=True)
        # CREATE TEMPLATE FOR PATTERN COMPARISON:
        pos = self._findObject(img)
        self.obj_shape = img[pos].shape

        PatternRecognition.__init__(self, img[pos])

        self._ff_mma = MaskedMovingAverage(shape=img.shape,
                                           dtype=np.float64)

        self.object = None

        self.Hs = []    # Homography matrices of all fitted images
        self.Hinvs = []  # same, but inverse
        self.fits = []  # all imaged, fitted to reference
        self._fit_masks = []

        self._refined = False

    # TODO: remove that property?