Vehicle-Anti-Theft-Face-Recognition-System/venv/Lib/site-packages/skimage/registration/_optical_flow.py

# coding: utf-8
"""TV-L1 optical flow algorithm implementation.

"""

from functools import partial
import numpy as np
from scipy import ndimage as ndi
from skimage.transform import warp

from ._optical_flow_utils import coarse_to_fine


def _tvl1(reference_image, moving_image, flow0, attachment, tightness,
          num_warp, num_iter, tol, prefilter):
    """TV-L1 solver for optical flow estimation.

    Parameters
    ----------
    reference_image : ndarray, shape (M, N[, P[, ...]])
        The first gray scale image of the sequence.
    moving_image : ndarray, shape (M, N[, P[, ...]])
        The second gray scale image of the sequence.
    flow0 : ndarray, shape (image0.ndim, M, N[, P[, ...]])
        Initialization for the vector field.
    attachment : float
        Attachment parameter. The smaller this parameter is,
        the smoother is the solutions.
    tightness : float
        Tightness parameter. It should have a small value in order to
        maintain attachement and regularization parts in
        correspondence.
    num_warp : int
        Number of times image1 is warped.
    num_iter : int
        Number of fixed point iteration.
    tol : float
        Tolerance used as stopping criterion based on the L² distance
        between two consecutive values of (u, v).
    prefilter : bool
        Whether to prefilter the estimated optical flow before each
        image warp.

    Returns
    -------
    flow : ndarray, shape ((image0.ndim, M, N[, P[, ...]])
        The estimated optical flow components for each axis.

    """

    dtype = reference_image.dtype
    grid = np.meshgrid(*[np.arange(n, dtype=dtype)
                         for n in reference_image.shape],
                       indexing='ij')

    dt = 0.5 / reference_image.ndim
    reg_num_iter = 2
    f0 = attachment * tightness
    f1 = dt / tightness
    tol *= reference_image.size

    flow_current = flow_previous = flow0

    g = np.zeros((reference_image.ndim,) + reference_image.shape, dtype=dtype)
    proj = np.zeros((reference_image.ndim, reference_image.ndim,)
                    + reference_image.shape, dtype=dtype)

    s_g = [slice(None), ] * g.ndim
    s_p = [slice(None), ] * proj.ndim
    s_d = [slice(None), ] * (proj.ndim-2)

    for _ in range(num_warp):
        if prefilter:
            flow_current = ndi.median_filter(flow_current,
                                             [1] + reference_image.ndim * [3])

        image1_warp = warp(moving_image, grid + flow_current, mode='nearest')
        grad = np.array(np.gradient(image1_warp))
        NI = (grad*grad).sum(0)
        NI[NI == 0] = 1

        rho_0 = image1_warp - reference_image - (grad * flow_current).sum(0)

        for _ in range(num_iter):

            # Data term

            rho = rho_0 + (grad*flow_current).sum(0)

            idx = abs(rho) <= f0 * NI

            flow_auxiliary = flow_current

            flow_auxiliary[:, idx] -= rho[idx]*grad[:, idx]/NI[idx]

            idx = ~idx
            srho = f0 * np.sign(rho[idx])
            flow_auxiliary[:, idx] -= srho*grad[:, idx]

            # Regularization term
            flow_current = flow_auxiliary.copy()

            for idx in range(reference_image.ndim):
                s_p[0] = idx
                for _ in range(reg_num_iter):
                    for ax in range(reference_image.ndim):
                        s_g[0] = ax
                        s_g[ax+1] = slice(0, -1)
                        g[tuple(s_g)] = np.diff(flow_current[idx], axis=ax)
                        s_g[ax+1] = slice(None)

                    norm = np.sqrt((g ** 2).sum(0))[np.newaxis, ...]
                    norm *= f1
                    norm += 1.
                    proj[idx] -= dt * g
                    proj[idx] /= norm

                    # d will be the (negative) divergence of proj[idx]
                    d = -proj[idx].sum(0)
                    for ax in range(reference_image.ndim):
                        s_p[1] = ax
                        s_p[ax+2] = slice(0, -1)
                        s_d[ax] = slice(1, None)
                        d[tuple(s_d)] += proj[tuple(s_p)]
                        s_p[ax+2] = slice(None)
                        s_d[ax] = slice(None)

                    flow_current[idx] = flow_auxiliary[idx] + d

        flow_previous -= flow_current  # The difference as stopping criteria
        if (flow_previous*flow_previous).sum() < tol:
            break

        flow_previous = flow_current

    return flow_current


def optical_flow_tvl1(reference_image, moving_image,
                      *,
                      attachment=15, tightness=0.3, num_warp=5, num_iter=10,
                      tol=1e-4, prefilter=False, dtype=np.float32):
    r"""Coarse to fine optical flow estimator.

    The TV-L1 solver is applied at each level of the image
    pyramid. TV-L1 is a popular algorithm for optical flow estimation
    introduced by Zack et al. [1]_, improved in [2]_ and detailed in [3]_.

    Parameters
    ----------
    reference_image : ndarray, shape (M, N[, P[, ...]])
        The first gray scale image of the sequence.
    moving_image : ndarray, shape (M, N[, P[, ...]])
        The second gray scale image of the sequence.
    attachment : float, optional
        Attachment parameter (:math:`\lambda` in [1]_). The smaller
        this parameter is, the smoother the returned result will be.
    tightness : float, optional
        Tightness parameter (:math:`\tau` in [1]_). It should have
        a small value in order to maintain attachement and
        regularization parts in correspondence.
    num_warp : int, optional
        Number of times image1 is warped.
    num_iter : int, optional
        Number of fixed point iteration.
    tol : float, optional
        Tolerance used as stopping criterion based on the L² distance
        between two consecutive values of (u, v).
    prefilter : bool, optional
        Whether to prefilter the estimated optical flow before each
        image warp. This helps to remove the potential outliers.
    dtype : dtype, optional
        Output data type: must be floating point. Single precision
        provides good results and saves memory usage and computation
        time compared to double precision.

    Returns
    -------
    flow : ndarray, shape ((image0.ndim, M, N[, P[, ...]])
        The estimated optical flow components for each axis.

    Notes
    -----
    Color images are not supported.

    References
    ----------
    .. [1] Zach, C., Pock, T., & Bischof, H. (2007, September). A
       duality based approach for realtime TV-L 1 optical flow. In Joint
       pattern recognition symposium (pp. 214-223). Springer, Berlin,
       Heidelberg. :DOI:`10.1007/978-3-540-74936-3_22`
    .. [2] Wedel, A., Pock, T., Zach, C., Bischof, H., & Cremers,
       D. (2009). An improved algorithm for TV-L 1 optical flow. In
       Statistical and geometrical approaches to visual motion analysis
       (pp. 23-45). Springer, Berlin, Heidelberg.
       :DOI:`10.1007/978-3-642-03061-1_2`
    .. [3] Pérez, J. S., Meinhardt-Llopis, E., & Facciolo,
       G. (2013). TV-L1 optical flow estimation. Image Processing On
       Line, 2013, 137-150. :DOI:`10.5201/ipol.2013.26`

    Examples
    --------
    >>> from skimage.color import rgb2gray
    >>> from skimage.data import stereo_motorcycle
    >>> from skimage.registration import optical_flow_tvl1
    >>> image0, image1, disp = stereo_motorcycle()
    >>> # --- Convert the images to gray level: color is not supported.
    >>> image0 = rgb2gray(image0)
    >>> image1 = rgb2gray(image1)
    >>> flow = optical_flow_tvl1(image1, image0)

    """

    solver = partial(_tvl1, attachment=attachment,
                     tightness=tightness, num_warp=num_warp, num_iter=num_iter,
                     tol=tol, prefilter=prefilter)

    return coarse_to_fine(reference_image, moving_image, solver, dtype=dtype)