skimage.measure.ransac()部分参数使用方法

在求解本质矩阵和内点数据时用到的，记录一下。

    # 求解本质矩阵和内点数据
    model, inliers = ransac((norm_last_kps, norm_curr_kps),
                            EssentialMatrixTransform,
                            min_samples=8,  # 最少需要 8 个点
                            residual_threshold=0.005,  # 参数小于阈值的为异常值
                            max_trials=200)  # 最大迭代次数

官方文档如下：

def ransac(data, model_class, min_samples, residual_threshold,
           is_data_valid=None, is_model_valid=None,
           max_trials=100, stop_sample_num=np.inf, stop_residuals_sum=0,
           stop_probability=1, random_state=None, initial_inliers=None):
    """Fit a model to data with the RANSAC (random sample consensus) algorithm.

    RANSAC is an iterative algorithm for the robust estimation of parameters
    from a subset of inliers from the complete data set. Each iteration
    performs the following tasks:

    1. Select `min_samples` random samples from the original data and check
       whether the set of data is valid (see `is_data_valid`).
    2. Estimate a model to the random subset
       (`model_cls.estimate(*data[random_subset]`) and check whether the
       estimated model is valid (see `is_model_valid`).
    3. Classify all data as inliers or outliers by calculating the residuals
       to the estimated model (`model_cls.residuals(*data)`) - all data samples
       with residuals smaller than the `residual_threshold` are considered as
       inliers.
    4. Save estimated model as best model if number of inlier samples is
       maximal. In case the current estimated model has the same number of
       inliers, it is only considered as the best model if it has less sum of
       residuals.

    These steps are performed either a maximum number of times or until one of
    the special stop criteria are met. The final model is estimated using all
    inlier samples of the previously determined best model.

    Parameters
    ----------
    data : [list, tuple of] (N, ...) array
        Data set to which the model is fitted, where N is the number of data
        points and the remaining dimension are depending on model requirements.
        If the model class requires multiple input data arrays (e.g. source and
        destination coordinates of  ``skimage.transform.AffineTransform``),
        they can be optionally passed as tuple or list. Note, that in this case
        the functions ``estimate(*data)``, ``residuals(*data)``,
        ``is_model_valid(model, *random_data)`` and
        ``is_data_valid(*random_data)`` must all take each data array as
        separate arguments.
    model_class : object
        Object with the following object methods:

         * ``success = estimate(*data)``
         * ``residuals(*data)``

        where `success` indicates whether the model estimation succeeded
        (`True` or `None` for success, `False` for failure).
    min_samples : int in range (0, N)
        The minimum number of data points to fit a model to.
    residual_threshold : float larger than 0
        Maximum distance for a data point to be classified as an inlier.
    is_data_valid : function, optional
        This function is called with the randomly selected data before the
        model is fitted to it: `is_data_valid(*random_data)`.
    is_model_valid : function, optional
        This function is called with the estimated model and the randomly
        selected data: `is_model_valid(model, *random_data)`, .
    max_trials : int, optional
        Maximum number of iterations for random sample selection.
    stop_sample_num : int, optional
        Stop iteration if at least this number of inliers are found.
    stop_residuals_sum : float, optional
        Stop iteration if sum of residuals is less than or equal to this
        threshold.
    stop_probability : float in range [0, 1], optional
        RANSAC iteration stops if at least one outlier-free set of the
        training data is sampled with ``probability >= stop_probability``,
        depending on the current best model's inlier ratio and the number
        of trials. This requires to generate at least N samples (trials):

            N >= log(1 - probability) / log(1 - e**m)

        where the probability (confidence) is typically set to a high value
        such as 0.99, e is the current fraction of inliers w.r.t. the
        total number of samples, and m is the min_samples value.
    random_state : int, RandomState instance or None, optional
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.
    initial_inliers : array-like of bool, shape (N,), optional
        Initial samples selection for model estimation


    Returns
    -------
    model : object
        Best model with largest consensus set.
    inliers : (N, ) array
        Boolean mask of inliers classified as ``True``.

    References
    ----------
    .. [1] "RANSAC", Wikipedia, https://en.wikipedia.org/wiki/RANSAC

    Examples
    --------

    Generate ellipse data without tilt and add noise:

    >>> t = np.linspace(0, 2 * np.pi, 50)
    >>> xc, yc = 20, 30
    >>> a, b = 5, 10
    >>> x = xc + a * np.cos(t)
    >>> y = yc + b * np.sin(t)
    >>> data = np.column_stack([x, y])
    >>> np.random.seed(seed=1234)
    >>> data += np.random.normal(size=data.shape)

    Add some faulty data:

    >>> data[0] = (100, 100)
    >>> data[1] = (110, 120)
    >>> data[2] = (120, 130)
    >>> data[3] = (140, 130)

    Estimate ellipse model using all available data:

    >>> model = EllipseModel()
    >>> model.estimate(data)
    True
    >>> np.round(model.params)  # doctest: +SKIP
    array([ 72.,  75.,  77.,  14.,   1.])

    Estimate ellipse model using RANSAC:

    >>> ransac_model, inliers = ransac(data, EllipseModel, 20, 3, max_trials=50)
    >>> abs(np.round(ransac_model.params))
    array([20., 30.,  5., 10.,  0.])
    >>> inliers # doctest: +SKIP
    array([False, False, False, False,  True,  True,  True,  True,  True,
            True,  True,  True,  True,  True,  True,  True,  True,  True,
            True,  True,  True,  True,  True,  True,  True,  True,  True,
            True,  True,  True,  True,  True,  True,  True,  True,  True,
            True,  True,  True,  True,  True,  True,  True,  True,  True,
            True,  True,  True,  True,  True], dtype=bool)
    >>> sum(inliers) > 40
    True

    RANSAC can be used to robustly estimate a geometric transformation. In this section,
    we also show how to use a proportion of the total samples, rather than an absolute number.

    >>> from skimage.transform import SimilarityTransform
    >>> np.random.seed(0)
    >>> src = 100 * np.random.rand(50, 2)
    >>> model0 = SimilarityTransform(scale=0.5, rotation=1, translation=(10, 20))
    >>> dst = model0(src)
    >>> dst[0] = (10000, 10000)
    >>> dst[1] = (-100, 100)
    >>> dst[2] = (50, 50)
    >>> ratio = 0.5  # use half of the samples
    >>> min_samples = int(ratio * len(src))
    >>> model, inliers = ransac((src, dst), SimilarityTransform, min_samples, 10,
    ...                         initial_inliers=np.ones(len(src), dtype=bool))
    >>> inliers
    array([False, False, False,  True,  True,  True,  True,  True,  True,
            True,  True,  True,  True,  True,  True,  True,  True,  True,
            True,  True,  True,  True,  True,  True,  True,  True,  True,
            True,  True,  True,  True,  True,  True,  True,  True,  True,
            True,  True,  True,  True,  True,  True,  True,  True,  True,
            True,  True,  True,  True,  True])

    """