Python Rectangle.y1方法代码示例

# 需要导入模块: from matplotlib.patches import Rectangle [as 别名]
# 或者: from matplotlib.patches.Rectangle import y1 [as 别名]
def align_img(img_one, img_two, method = 'imgreg', show=False, roi=True, sb_filtering=False, filt_size= 200,
              binning=2, feducial=1, manualxy = None, **kwargs):
    '''
    Function to align images or holograms using X-correlation
    Parameters
    ----------
    img_one : ndarray
        The refrence image
    img_two : ndarray
        An image to align
    method : string
        Either 'imgreg' to use image registartion from skimage,
        or 'xcorr' to use crosscorrelation in real space from scipy,
        or 'feducial' to use feducial markers,
        or 'manual' to displace the images manually
#        or 'gui_shift' to shift image intractively
    show : boolean or string
        Set True to plot the results, set 'diff' to show difference of the images
    roi : boolean
        Set true to do alignament on ROI instead of whole image
    sb_filtering : boolean
        Set True, to apply for holograms
    filt_size : int
        Size of the filter for main band filtering
        used only for alignment of holograms
    binning : int
        Binning for the images during alignment used in 'xcorr' method
    feducial : int
        Number of feducial markers for 'feducial' method
    manualxy : tuple of 2 int
        Coordiantes x,y for manual alignment

    Returns
    -------
    img_algn : ndarray
        Aligned image img_two
    (xdrift, ydrift): tuple
        drift correction coordinates

    Notes
    -----
    * Manual alignamnt method requiers cooridnates provided using 'manualxy' parameter
    * X-correlation method is slow! Use only small images and small ROI (use of binning is recomended)
    * Binning is implemented for "xcorr" method only

    See Also
    --------

    '''

    (ry,cx) = img_one.shape
#    img_one = img_one.astype(float)
#    img_two = img_two.astype(float)

    # --- IFFT main band:
    if sb_filtering:
        fft_img_one = fftshift(fft2(img_one))
        (xx,yy) = np.meshgrid(np.linspace(-ry/2, ry/2-1, ry), np.linspace(-cx/2, cx/2-1, cx))
        rr = np.sqrt(xx**2+yy**2)
        mask = np.zeros((ry,cx))
        mask[rr<filt_size] = 1
        img_one_m = np.absolute(ifft2(ifftshift(fft_img_one*mask)))
        # --- Processing second image
        fft_img_two = fftshift(fft2(img_two))
        img_two_m = np.absolute(ifft2(ifftshift(fft_img_two*mask)))
    else:
        img_one_m = img_one
        img_two_m = img_two

    # --- Use ROI if True
    if roi:
        # --- GUI based assignment of ROI
        f, ax = plt.subplots(1, 1)
        ax.imshow(img_one_m, cmap=cm.binary_r)
        rect = utils.RoiRect()
        if hasattr(f.canvas.manager, 'window'): f.canvas.manager.window.raise_()
        plt.waitforbuttonpress(100)
        plt.waitforbuttonpress(5)
        plt.close(f)
    else:
        rect = Rectangle((0,0), 1, 1,fc='none', ec='r')
        rect.x0 = 0
        rect.y0 = 0
        rect.y1 = ry-1
        rect.x1 = cx-1

    # --- Select allignment method
    if method is 'imgreg':

        img_one_roi = img_one_m[rect.y0:rect.y1, rect.x0:rect.x1]
        img_two_roi = img_two_m[rect.y0:rect.y1, rect.x0:rect.x1]

#        px_rescale_y = np.float(img_one_m.shape[0])/np.float(img_one_roi.shape[0])
#        px_rescale_x = np.float(img_one_m.shape[1])/np.float(img_one_roi.shape[1])
        upsample = 4
        # --- Upsampled image registration for ROI
        shift, error, diffphase = register_translation(img_one_roi, img_two_roi, upsample)

        ydrift = shift[0]
        xdrift = shift[1]
#.........这里部分代码省略.........