Python morphology.disk方法代碼示例

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def addNoiseAndGray(surf):
    # https://stackoverflow.com/questions/34673424/how-to-get-numpy-array-of-rgb-colors-from-pygame-surface
    imgdata = pygame.surfarray.array3d(surf)
    imgdata = imgdata.swapaxes(0, 1)
    # print('imgdata shape %s' % imgdata.shape)  # shall be IMG_HEIGHT * IMG_WIDTH
    imgdata2 = noise_generator('s&p', imgdata)

    img2 = Image.fromarray(np.uint8(imgdata2))
    # img2.save('/home/zhichyu/Downloads/2sp.jpg')
    grayscale2 = ImageOps.grayscale(img2)
    # grayscale2.save('/home/zhichyu/Downloads/2bw2.jpg')
    # return grayscale2

    array = np.asarray(np.uint8(grayscale2))
    # print('array.shape %s' % array.shape)
    selem = disk(random.randint(0, 1))
    eroded = erosion(array, selem)
    return eroded 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def dilation(x, radius=3):
    """ Return greyscale morphological dilation of an image,
    see `skimage.morphology.dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.dilation>`_.

    Parameters
    -----------
    x : 2D array image.
    radius : int for the radius of mask.
    """
    from skimage.morphology import disk, dilation
    mask = disk(radius)
    x = dilation(x, selem=mask)
    return x




## Sequence 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def dilate(data, size, shape, dim=None):
    """
    Dilate data using ball structuring element
    :param data: Image or numpy array: 2d or 3d array
    :param size: int: If shape={'square', 'cube'}: Corresponds to the length of an edge (size=1 has no effect).
    If shape={'disk', 'ball'}: Corresponds to the radius, not including the center element (size=0 has no effect).
    :param shape: {'square', 'cube', 'disk', 'ball'}
    :param dim: {0, 1, 2}: Dimension of the array which 2D structural element will be orthogonal to. For example, if
    you wish to apply a 2D disk kernel in the X-Y plane, leaving Z unaffected, parameters will be: shape=disk, dim=2.
    :return: numpy array: data dilated
    """
    if isinstance(data, Image):
        im_out = data.copy()
        im_out.data = dilate(data.data, size, shape, dim)
        return im_out
    else:
        return dilation(data, selem=_get_selem(shape, size, dim), out=None) 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def erode(data, size, shape, dim=None):
    """
    Dilate data using ball structuring element
    :param data: Image or numpy array: 2d or 3d array
    :param size: int: If shape={'square', 'cube'}: Corresponds to the length of an edge (size=1 has no effect).
    If shape={'disk', 'ball'}: Corresponds to the radius, not including the center element (size=0 has no effect).
    :param shape: {'square', 'cube', 'disk', 'ball'}
    :param dim: {0, 1, 2}: Dimension of the array which 2D structural element will be orthogonal to. For example, if
    you wish to apply a 2D disk kernel in the X-Y plane, leaving Z unaffected, parameters will be: shape=disk, dim=2.
    :return: numpy array: data dilated
    """
    if isinstance(data, Image):
        im_out = data.copy()
        im_out.data = erode(data.data, size, shape, dim)
        return im_out
    else:
        return erosion(data, selem=_get_selem(shape, size, dim), out=None) 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def get_diffraction_test_image(self, dtype=np.float32):
        image_x, image_y = self.image_x, self.image_y
        cx, cy = image_x / 2, image_y / 2
        image = np.zeros((image_y, image_x), dtype=np.float32)
        iterator = zip(self._x_list, self._y_list, self._intensity_list)
        for x, y, i in iterator:
            if self.diff_intensity_reduction is not False:
                dr = np.hypot(x - cx, y - cy)
                i = self._get_diff_intensity_reduction(dr, i)
            image[y, x] = i
        disk = morphology.disk(self.disk_r, dtype=dtype)
        image = convolve2d(image, disk, mode="same")
        if self.rotation != 0:
            image = rotate(image, self.rotation, reshape=False)
        if self.blur != 0:
            image = gaussian_filter(image, self.blur)
        if self._background_lorentz_width is not False:
            image += self._get_background_lorentz()
        if self.intensity_noise is not False:
            noise = np.random.random((image_y, image_x)) * self.intensity_noise
            image += noise
        return image 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def test_correct_disk_x_y_and_radius_random(self):
        x, y, px, py = 56, 48, 4, 5
        x, y = randint(45, 55, size=(py, px)), randint(45, 55, size=(py, px))
        r = randint(20, 40, size=(py, px))
        s = mdtd.generate_4d_data(
            probe_size_x=px,
            probe_size_y=py,
            image_size_x=120,
            image_size_y=100,
            disk_x=x,
            disk_y=y,
            disk_r=5,
            disk_I=20,
            ring_x=x,
            ring_y=y,
            ring_r=r,
            ring_I=5,
            blur=True,
            downscale=False,
        )
        s_com = s.center_of_mass()
        s_r = s.radial_average(centre_x=s_com.inav[0].data, centre_y=s_com.inav[1].data)
        s_r = s_r.isig[15:]  # Do not include the disk
        r -= 15  # Need to shift the radius, due to not including the disk
        assert (s_r.data.argmax(axis=-1) == r).all() 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def masked(img, gt, mask, alpha=1):
    """Returns image with GT lung field outlined with red, predicted lung field
    filled with blue."""
    rows, cols = img.shape[:2]
    color_mask = np.zeros((rows, cols, 3))
    boundary = morphology.dilation(gt, morphology.disk(3)) ^ gt
    color_mask[mask == 1] = [0, 0, 1]
    color_mask[boundary == 1] = [1, 0, 0]
    
    img_hsv = color.rgb2hsv(img)
    color_mask_hsv = color.rgb2hsv(color_mask)

    img_hsv[..., 0] = color_mask_hsv[..., 0]
    img_hsv[..., 1] = color_mask_hsv[..., 1] * alpha

    img_masked = color.hsv2rgb(img_hsv)
    return img_masked 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def _get_blob_mask(ROI_image, thresh, thresh_block_size, is_light_background, analysis_type):
    # get binary image, 
    if is_light_background:
        ## apply a median filter to reduce rough edges / sharpen the boundary btw worm and background
        ROI_image_th = cv2.medianBlur(ROI_image, 3)
        ROI_mask = ROI_image_th < thresh
    else:
        if analysis_type == "PHARYNX":
            # for fluorescent pharynx labeled images, refine the threshold with a local otsu (http://scikit-image.org/docs/dev/auto_examples/plot_local_otsu.html)
            # this compensates for local variations in brightness in high density regions, when many worms are close to each other
            ROI_rank_otsu = skf.rank.otsu(ROI_image, skm.disk(thresh_block_size))
            ROI_mask = (ROI_image>ROI_rank_otsu)
            # as a local threshold introcudes artifacts at the edge of the mask, also use a global threshold to cut these out
            ROI_mask &= (ROI_image>=thresh)
        else:
            # this case applies for example to worms where the whole body is fluorecently labeled
            ROI_image_th = cv2.medianBlur(ROI_image, 3)
            ROI_mask = ROI_image_th >= thresh
        
    ROI_mask &= (ROI_image != 0)
    ROI_mask = ROI_mask.astype(np.uint8)

    return ROI_mask, thresh # returning thresh here seems redundant, as it isn't actually changed 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def get_dark_mask(full_data):
    #get darker objects that are unlikely to be worm
    if full_data.shape[0] < 2:
        #nothing to do here returning
        return np.zeros((full_data.shape[1], full_data.shape[2]), np.uint8)
    
    #this mask shoulnd't contain many worms
    img_h = cv2.medianBlur(np.max(full_data, axis=0), 5)
    #this mask is likely to contain a lot of worms
    img_l = cv2.medianBlur(np.min(full_data, axis=0), 5)
    
    #this is the difference (the tagged pixels should be mostly worms)
    img_del = img_h-img_l
    th_d = threshold_otsu(img_del)
    
    #this is the maximum of the minimum pixels of the worms...
    th = np.max(img_l[img_del>th_d])
    #this is what a darkish mask should look like
    dark_mask = cv2.dilate((img_h<th).astype(np.uint8), disk(11))
    
    return dark_mask 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def local_max_roll(fm, k0, k1, diff):
    max_ls = []
    for ksize in range(k0, k1):
        selem = disk(ksize)
        fm_max = local_max(fm, selem, diff)
        max_ls.append(fm_max)
    lmax = (np.stack(max_ls, axis=0).sum(axis=0) > 0).astype(np.uint8)
    nlab, max_lab = cv2.connectedComponents(lmax)
    max_res = np.zeros_like(lmax)
    for lb in range(1, nlab):
        area = max_lab == lb
        if np.sum(area) > 1:
            crds = tuple(int(np.median(c)) for c in np.where(area))
            max_res[crds] = 1
        else:
            max_res[np.where(area)] = 1
    return max_res 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def finalProcessingSpur(s, params):
    logging.info(f"{s['filename']} - \tfinalProcessingSpur")
    disk_radius = int(params.get("disk_radius", "25"))
    selem = disk(disk_radius)
    mask = s["img_mask_use"]
    mask_opened = binary_opening(mask, selem)
    mask_spur = ~mask_opened & mask

    io.imsave(s["outdir"] + os.sep + s["filename"] + "_spur.png", img_as_ubyte(mask_spur))

    prev_mask = s["img_mask_use"]
    s["img_mask_use"] = mask_opened

    s.addToPrintList("spur_pixels",
                     printMaskHelper(params.get("mask_statistics", s["mask_statistics"]), prev_mask, s["img_mask_use"]))

    if len(s["img_mask_use"].nonzero()[0]) == 0:  # add warning in case the final tissue is empty
        logging.warning(
            f"{s['filename']} - After BasicModule.finalProcessingSpur NO tissue remains detectable! Downstream modules likely to be incorrect/fail")
        s["warnings"].append(
            f"After BasicModule.finalProcessingSpur NO tissue remains detectable! Downstream modules likely to be incorrect/fail") 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def ps_disk(radius):
    r"""
    Creates circular disk structuring element for morphological operations

    Parameters
    ----------
    radius : float or int
        The desired radius of the structuring element

    Returns
    -------
    strel : 2D-array
        A 2D numpy bool array of the structring element
    """
    rad = int(np.ceil(radius))
    other = np.ones((2 * rad + 1, 2 * rad + 1), dtype=bool)
    other[rad, rad] = False
    disk = spim.distance_transform_edt(other) < radius
    return disk 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def make_boundaries(label, thickness=None):
        """
        Input is an image label, output is a numpy array mask encoding the boundaries of the objects
        Extract pixels at the true boundary by dilation - erosion of label.
        Don't just pick the void label as it is not exclusive to the boundaries.
        """
        assert(thickness is not None)
        import skimage.morphology as skm
        void = 255
        mask = np.logical_and(label > 0, label != void)[0]
        selem = skm.disk(thickness)
        boundaries = np.logical_xor(skm.dilation(mask, selem),
                                    skm.erosion(mask, selem))
        return boundaries 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def save_array_as_image(array, path, geotransform, projection, format = "GTiff"):
    """
    Saves a given array as a geospatial image to disk in the format 'format'. The datatype will be of one corresponding
    to
    Array must be gdal format: [bands, y, x].

    Parameters
    ----------
    array
        A Numpy array containing the values to be saved to a raster
    path
        The path to the location to save the output raster to
    geotransform
        The geotransform of the image to be saved. See note.
    projection
        The projection, as wkt, of the image to be saved. See note.
    format

    Returns
    -------

    """
    driver = gdal.GetDriverByName(format)
    type_code = gdal_array.NumericTypeCodeToGDALTypeCode(array.dtype)
    # If array is 2d, give it an extra dimension.
    if len(array.shape) == 2:
        array = np.expand_dims(array, axis=0)
    out_dataset = driver.Create(
        path,
        xsize=array.shape[2],
        ysize=array.shape[1],
        bands=array.shape[0],
        eType=type_code
    )
    out_dataset.SetGeoTransform(geotransform)
    out_dataset.SetProjection(projection)
    out_array = out_dataset.GetVirtualMemArray(eAccess=gdal.GA_Update).squeeze()
    out_array[...] = array
    out_array = None
    out_dataset = None
    return path 

# 需要導入模塊: from skimage import morphology [as 別名]
# 或者: from skimage.morphology import disk [as 別名]
def buffer_mask_in_place(mask_path, buffer_size):
    """Expands a mask in-place, overwriting the previous mask"""
    log = logging.getLogger(__name__)
    log.info("Buffering {} with buffer size {}".format(mask_path, buffer_size))
    mask = gdal.Open(mask_path, gdal.GA_Update)
    mask_array = mask.GetVirtualMemArray(eAccess=gdal.GA_Update)
    cache = morph.binary_erosion(mask_array.squeeze(), selem=morph.disk(buffer_size))
    np.copyto(mask_array, cache)
    mask_array = None
    mask = None