Python scipy.ndimage方法代碼示例

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def predict_multiscale(net, image, tile_size, scales, classes, flip_evaluation, recurrence):
    """
    Predict an image by looking at it with different scales.
        We choose the "predict_whole_img" for the image with less than the original input size,
        for the input of larger size, we would choose the cropping method to ensure that GPU memory is enough.
    """
    image = image.data
    N_, C_, H_, W_ = image.shape
    full_probs = np.zeros((H_, W_, classes))  
    for scale in scales:
        scale = float(scale)
        print("Predicting image scaled by %f" % scale)
        scale_image = ndimage.zoom(image, (1.0, 1.0, scale, scale), order=1, prefilter=False)
        scaled_probs = predict_whole(net, scale_image, tile_size, recurrence)
        if flip_evaluation == True:
            flip_scaled_probs = predict_whole(net, scale_image[:,:,:,::-1].copy(), tile_size, recurrence)
            scaled_probs = 0.5 * (scaled_probs + flip_scaled_probs[:,::-1,:])
        full_probs += scaled_probs
    full_probs /= len(scales)
    return full_probs 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def bwdist(bwvol):
    """
    positive distance transform from positive entries in logical image

    Parameters
    ----------
    bwvol : nd array
        The logical volume

    Returns
    -------
    possdtrf : nd array
        the positive distance transform

    See Also
    --------
    bw2sdtrf
    """

    # reverse volume to run scipy function
    revbwvol = np.logical_not(bwvol)

    # get distance
    return scipy.ndimage.morphology.distance_transform_edt(revbwvol) 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def predict_multiscale(net, image, tile_size, scales, classes, flip_evaluation, recurrence):
    """
    Predict an image by looking at it with different scales.
        We choose the "predict_whole_img" for the image with less than the original input size,
        for the input of larger size, we would choose the cropping method to ensure that GPU memory is enough.
    """
    image = image.data
    N_, C_, H_, W_ = image.shape
    full_probs = np.zeros((H_, W_, classes))  
    for scale in scales:
        scale = float(scale)
        #print("Predicting image scaled by %f" % scale)
        scale_image = ndimage.zoom(image, (1.0, 1.0, scale, scale), order=1, prefilter=False)
        scaled_probs = predict_whole(net, scale_image, tile_size, recurrence)
        if flip_evaluation == True:
            flip_scaled_probs = predict_whole(net, scale_image[:,:,:,::-1].copy(), tile_size, recurrence)
            scaled_probs = 0.5 * (scaled_probs + flip_scaled_probs[:,::-1,:])
        full_probs += scaled_probs
    full_probs /= len(scales)
    return full_probs 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def scipy_conv_c01b(self, images, filters):
        """
        Emulate c01b convolution with scipy
        """
        assert images.ndim == 4
        assert filters.ndim == 4
        in_chans, rows, cols, bs = images.shape
        in_chans_, rows_, cols_, out_chans = filters.shape
        assert in_chans_ == in_chans

        out_bc01 = [
            [sum(scipy.ndimage.filters.convolve(images[c, :, :, b],
                                                filters[c, ::-1, ::-1, i])
                 for c in xrange(in_chans))
             for i in xrange(out_chans)]
            for b in xrange(bs)]
        out_c01b = numpy.array(out_bc01).transpose(1, 2, 3, 0)
        return out_c01b 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def test_binary_ops(funcname,
                    input,
                    structure,
                    origin):
    da_func = getattr(da_ndm, funcname)
    sp_func = getattr(spnd, funcname)

    da_result = da_func(
        input,
        structure=structure,
        origin=origin
    )

    sp_result = sp_func(
        input,
        structure=structure,
        origin=origin
    )

    dau.assert_eq(sp_result, da_result) 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def test_binary_ops_iter(funcname,
                         input,
                         structure,
                         iterations,
                         origin):
    da_func = getattr(da_ndm, funcname)
    sp_func = getattr(spnd, funcname)

    da_result = da_func(
        input,
        structure=structure,
        iterations=iterations,
        origin=origin
    )

    sp_result = sp_func(
        input,
        structure=structure,
        iterations=iterations,
        origin=origin
    )

    dau.assert_eq(sp_result, da_result) 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def applyFilter():
    global imageRaw

    # Repeat array four times for Lepton2
    if leptonVersion == 0:
        array2d = leptonValues.reshape(60, 80)
        array2dBig = array2d.repeat(4, axis=0).repeat(4, axis=1)
        imageRaw = numpy.transpose(array2dBig)

    # Repeat array two times for Lepton3
    else:
        array2d = leptonValues.reshape(120, 160)
        array2dBig = array2d.repeat(2, axis=0).repeat(2, axis=1)
        imageRaw = numpy.transpose(array2dBig)

    # Apply the gaussian blur filter
    if filterType == 1:
        imageRaw = scipy.ndimage.filters.gaussian_filter(imageRaw, 1.33, mode='nearest')
    # Apply box blur filter
    if filterType == 2:
        imageRaw = scipy.ndimage.filters.uniform_filter(imageRaw, 3)


# Converts the Lepton raw values to RGB colors 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def __call__(self, image):
        angle = self.random_state.uniform(
            self.angle_range[0], self.angle_range[1])
        if isinstance(image, np.ndarray):
            mi, ma = image.min(), image.max()
            image = scipy.ndimage.interpolation.rotate(
                image, angle, reshape=False, axes=self.axes, mode=self.mode)
            return np.clip(image, mi, ma)
        elif isinstance(image, Image.Image):
            return image.rotate(angle)
        else:
            raise Exception('unsupported type') 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def transform_array(self, X, y, w):
    """Transform the data in a set of (X, y, w) arrays."""
    from PIL import Image
    images = [scipy.ndimage.imread(x, mode='RGB') for x in X]
    images = [Image.fromarray(x).resize(self.size) for x in images]
    return np.array(images), y, w 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def rotate(self, angle=0):
    """ Rotates the image

    Parameters
    ----------
    angle: float (default = 0 i.e no rotation)
	Denotes angle by which the image should be rotated (in Degrees)

    Returns
    ----------
    The rotated imput array
    """
    return scipy.ndimage.rotate(self.Image, angle) 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def gaussian_blur(self, sigma=0.2):
    """ Adds gaussian noise to the image
          Parameters:
            sigma - std dev. of the gaussian distribution
    """
    return scipy.ndimage.gaussian_filter(self.Image, sigma) 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def shift(self, width, height, mode='constant', order=3):
    """Shifts the image
        Parameters:
          width - amount of width shift(positive values shift image right )
          height - amount of height shift(positive values shift image lower)
          mode - Points outside the boundaries of the input are filled according to the given mode
          (‘constant’, ‘nearest’, ‘reflect’ or ‘wrap’). Default is ‘constant’
          order - The order of the spline interpolation, default is 3. The order has to be in the range 0-5.
          """
    if len(self.Image.shape) == 2:
      return scipy.ndimage.shift(
          self.Image, [height, width], order=order, mode=mode)
    if len(self.Image.shape == 3):
      return scipy.ndimage.shift(
          self.Image, [height, width, 0], order=order, mode=mode) 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def apply_transform(x,
                    transform_matrix,
                    channel_axis=0,
                    fill_mode='nearest',
                    cval=0.):
  """Apply the image transformation specified by a matrix.

  Arguments:
      x: 2D numpy array, single image.
      transform_matrix: Numpy array specifying the geometric transformation.
      channel_axis: Index of axis for channels in the input tensor.
      fill_mode: Points outside the boundaries of the input
          are filled according to the given mode
          (one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
      cval: Value used for points outside the boundaries
          of the input if `mode='constant'`.

  Returns:
      The transformed version of the input.
  """
  x = np.rollaxis(x, channel_axis, 0)
  final_affine_matrix = transform_matrix[:2, :2]
  final_offset = transform_matrix[:2, 2]
  channel_images = [
      ndi.interpolation.affine_transform(
          x_channel,
          final_affine_matrix,
          final_offset,
          order=0,
          mode=fill_mode,
          cval=cval) for x_channel in x
  ]
  x = np.stack(channel_images, axis=0)
  x = np.rollaxis(x, 0, channel_axis + 1)
  return x 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def transform_array(self, X, y, w):
    """Transform the data in a set of (X, y, w) arrays."""
    images = [scipy.ndimage.imread(x, mode='RGB') for x in X]
    images = [scipy.misc.imresize(x, size=self.size) for x in images]
    return np.array(images), y, w 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import ndimage [as 別名]
def rotation(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2,
                    fill_mode='nearest', cval=0.):
    """Rotate an image randomly or non-randomly.

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    rg : int or float
        Degree to rotate, usually 0 ~ 180.
    is_random : boolean, default False
        If True, randomly rotate.
    row_index, col_index, channel_index : int
        Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
    fill_mode : string
        Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
    cval : scalar, optional
        Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_

    Examples
    ---------
    >>> x --> [row, col, 1] greyscale
    >>> x = rotation(x, rg=40, is_random=False)
    >>> tl.visualize.frame(x[:,:,0], second=0.01, saveable=True, name='temp',cmap='gray')
    """
    if is_random:
        theta = np.pi / 180 * np.random.uniform(-rg, rg)
    else:
        theta = np.pi /180 * rg
    rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
                                [np.sin(theta), np.cos(theta), 0],
                                [0, 0, 1]])

    h, w = x.shape[row_index], x.shape[col_index]
    transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w)
    x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
    return x