find.py 文件源码

def find_ellipse(image, mode='ellipse_aligned', min_length=24):
    """ Find bright ellipse contours on a black background.

    This routine thresholds the image (using the Otsu threshold), finds the
    longest contour and fits an ellipse to this contour.

    Parameters
    ----------
    image : ndarray, 2d
    mode : {'ellipse', 'ellipse_aligned', 'circle'}
        'ellipse' or None finds an arbitrary ellipse (default)
        'circle' finds a circle
        'ellipse_aligned' finds an ellipse with its axes aligned along [x y] axes
    min_length : number, optional
        minimum length of the ellipse contour, in pixels. Default 24.

    Returns
    -------
    yr, xr, yc, xc when dimension order was y, x (most common)
    xr, yr, xc, yc when dimension order was x, y
    """
    assert image.ndim == 2
    # Threshold the image
    thresh = threshold_otsu(image)
    binary = image > thresh

    # Find the contours of 0.5 value. For a thresholded ellipse contour, this
    # likely finds 2 contours: the inner and the outer.
    contours = find_contours(binary, 0.5, fully_connected='high')
    if len(contours) == 0:
        raise ValueError('No contours found')

    # Eliminate short contours
    contours = [c for c in contours if len(c) >= min_length]

    # fit circles to the rest, keep the one with lowest residual deviation
    result = [np.nan] * 4
    residual = None
    for c in contours:
        try:
            (xr, yr), (xc, yc), _ = fit_ellipse(c.T, mode=mode)
            if np.any(np.isnan([xr, yr, xc, yc])):
                continue
            x, y = c.T
            r = np.sum((((xc - x)/xr)**2 + ((yc - y)/yr)**2 - 1)**2)/len(c)
            if residual is None or r < residual:
                result = xr, yr, xc, yc
                residual = r
        except np.linalg.LinAlgError:
            pass

    return result