Python Image.roll方法代碼示例

# 需要導入模塊: from pylinac.core.image import Image [as 別名]
# 或者: from pylinac.core.image.Image import roll [as 別名]

#.........這裏部分代碼省略.........
    def from_image_UI(cls, filter=None):
        """Construct a PicketFence instance and load an image using a dialog box.

        .. versionadded:: 0.6
        """
        obj = cls()
        obj.load_image_UI(filter=filter)
        return obj

    def load_image_UI(self, filter=None):
        """Load the image using a UI dialog box."""
        path = get_filepath_UI()
        self.load_image(path, filter=filter)

    def _check_for_noise(self):
        """Check if the image has extreme noise (dead pixel, etc) by comparing
        min/max to 1/99 percentiles and smoothing if need be."""
        while self._has_noise():
            self.image.median_filter()

    def _has_noise(self):
        """Helper method to determine if there is spurious signal in the image."""
        min = self.image.array.min()
        max = self.image.array.max()
        near_min, near_max = np.percentile(self.image.array, [0.5, 99.5])
        max_is_extreme = max > near_max * 2
        min_is_extreme = (min < near_min) and (abs(near_min - min) > 0.2 * near_max)
        return max_is_extreme or min_is_extreme

    def _adjust_for_sag(self, sag):
        """Roll the image to adjust for EPID sag."""
        sag_pixels = int(round(sag * self.settings.dpmm))
        direction = 'y' if self.orientation == orientations['UD'] else 'x'
        self.image.roll(direction, sag_pixels)

    def run_demo(self, tolerance=0.5):
        """Run the Picket Fence demo using the demo image. See analyze() for parameter info."""
        self.load_demo_image()
        self.analyze(tolerance)
        print(self.return_results())
        self.plot_analyzed_image()

    def analyze(self, tolerance=0.5, action_tolerance=None, hdmlc=False, num_pickets=None, sag_adjustment=0):
        """Analyze the picket fence image.

        Parameters
        ----------
        tolerance : int, float
            The tolerance of difference in mm between an MLC pair position and the
            picket fit line.
        action_tolerance : int, float, None
            If None (default), no action tolerance is set or compared to.
            If an int or float, the MLC pair measurement is also compared to this
            tolerance. Must be lower than tolerance. This value is usually meant
            to indicate that a physicist should take an "action" to reduce the error,
            but should not stop treatment.
        hdmlc : bool
            If False (default), a standard (5/10mm leaves) Millennium MLC model is assumed.
            If True, an HD (2.5/5mm leaves) Millennium is assumed.
        num_pickets : int, None

            .. versionadded:: 0.8

            The number of pickets in the image. A helper parameter to limit the total number of pickets,
            only needed if analysis is catching things that aren't pickets.
        sag_adjustment : float, int

# 需要導入模塊: from pylinac.core.image import Image [as 別名]
# 或者: from pylinac.core.image.Image import roll [as 別名]

#.........這裏部分代碼省略.........
    def load_multiple_images(self, path_list):
        """Load and superimpose multiple images.

        .. versionadded:: 0.9

        Parameters
        ----------
        path_list : iterable
            An iterable of path locations to the files to be loaded/combined.
        """
        self.image = Image.from_multiples(path_list, method='mean')
        self._check_for_noise()
        self.image.check_inversion()

    def _check_for_noise(self):
        """Check if the image has extreme noise (dead pixel, etc) by comparing
        min/max to 1/99 percentiles and smoothing if need be."""
        while self._has_noise():
            self.image.median_filter()

    def _has_noise(self):
        """Helper method to determine if there is spurious signal in the image."""
        min = self.image.array.min()
        max = self.image.array.max()
        near_min, near_max = np.percentile(self.image.array, [0.5, 99.5])
        max_is_extreme = max > near_max * 2
        min_is_extreme = (min < near_min) and (abs(near_min - min) > 0.2 * near_max)
        return max_is_extreme or min_is_extreme

    def _adjust_for_sag(self, sag):
        """Roll the image to adjust for EPID sag."""
        sag_pixels = int(round(sag * self.settings.dpmm))
        direction = 'y' if self.orientation == orientations['UD'] else 'x'
        self.image.roll(direction, sag_pixels)

    def run_demo(self, tolerance=0.5, action_tolerance=0.25, interactive=False):
        """Run the Picket Fence demo using the demo image. See analyze() for parameter info."""
        self.load_demo_image()
        self.analyze(tolerance, action_tolerance=action_tolerance)
        print(self.return_results())
        self.plot_analyzed_image(interactive=interactive, leaf_error_subplot=True)

    def analyze(self, tolerance=0.5, action_tolerance=None, hdmlc=False, num_pickets=None, sag_adjustment=0):
        """Analyze the picket fence image.

        Parameters
        ----------
        tolerance : int, float
            The tolerance of difference in mm between an MLC pair position and the
            picket fit line.
        action_tolerance : int, float, None
            If None (default), no action tolerance is set or compared to.
            If an int or float, the MLC pair measurement is also compared to this
            tolerance. Must be lower than tolerance. This value is usually meant
            to indicate that a physicist should take an "action" to reduce the error,
            but should not stop treatment.
        hdmlc : bool
            If False (default), a standard (5/10mm leaves) Millennium MLC model is assumed.
            If True, an HD (2.5/5mm leaves) Millennium is assumed.
        num_pickets : int, None

            .. versionadded:: 0.8

            The number of pickets in the image. A helper parameter to limit the total number of pickets,
            only needed if analysis is catching more pickets than there really are.
        sag_adjustment : float, int