Python Image.check_inversion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 check_inversion [as 別名]

#.........這裏部分代碼省略.........
        self.load_image(path)

    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):
        """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 an "action" is necessary on the part of the physicist to
            resolve the issue.
        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.
        """
        if action_tolerance is not None and tolerance < action_tolerance:
            raise ValueError("Tolerance cannot be lower than the action tolerance")

        """Pre-analysis"""
        self._clear_attrs()
        self._action_lvl = action_tolerance
        self.image.check_inversion()
        self._threshold()
        self._find_orientation()

        """Analysis"""
        self._construct_pickets(tolerance, action_tolerance)
        leaf_centers = self._find_leaf_centers(hdmlc)
        self._calc_mlc_positions(leaf_centers)
        self._calc_mlc_error()

    def _construct_pickets(self, tolerance, action_tolerance):
        """Construct the Picket instances."""
        if self.orientation == orientations['UD']:
            leaf_prof = np.median(self._analysis_array, 0)
        else:
            leaf_prof = np.median(self._analysis_array, 1)
        leaf_prof = Profile(leaf_prof)
        _, peak_idxs = leaf_prof.find_peaks(min_peak_distance=0.01, min_peak_height=0.5)
        for peak in range(len(peak_idxs)):
            self.pickets.append(Picket(self.image, tolerance, self.orientation, action_tolerance))

    def _find_leaf_centers(self, hdmlc):
        """Return the leaf centers perpendicular to the leaf motion."""
        # generate some settings
        sm_lf_wdth = 5 * self.image.dpmm
        bg_lf_wdth = sm_lf_wdth * 2
        if hdmlc:
            sm_lf_wdth /= 2
            bg_lf_wdth /= 2
        self._sm_lf_meas_wdth = slmw = int(round(sm_lf_wdth*3/4))
        self._bg_lf_meas_wdth = blmw = int(round(bg_lf_wdth*3/4))
        bl_ex = int(bg_lf_wdth/4)
        sm_ex = int(sm_lf_wdth/4)

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

#.........這裏部分代碼省略.........
        """Return the number of pickets determined."""
        return len(self.pickets)

    @classmethod
    def from_demo_image(cls, filter=None):
        """Construct a PicketFence instance using the demo image.

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

    def load_demo_image(self, filter=None):
        """Load the demo image that is included with pylinac."""
        im_open_path = osp.join(osp.dirname(__file__), 'demo_files', 'picket_fence', 'EPID-PF-LR.dcm')
        self.load_image(im_open_path, filter=filter)

    def load_image(self, file_path, filter=None):
        """Load the image

        Parameters
        ----------
        file_path : str
            Path to the image file.
        filter : int, None
            If None (default), no filtering will be done to the image.
            If an int, will perform median filtering over image of size *filter*.
        """
        self.image = Image(file_path)
        if isinstance(filter, int):
            self.image.median_filter(size=filter)
        self._check_for_noise()
        self.image.check_inversion()

    @classmethod
    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):

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

#.........這裏部分代碼省略.........
        """Return the number of pickets determined."""
        return len(self.pickets)

    @classmethod
    def from_demo_image(cls, filter=None):
        """Construct a PicketFence instance using the demo image.

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

    def load_demo_image(self, filter=None):
        """Load the demo image that is included with pylinac."""
        im_open_path = osp.join(osp.dirname(__file__), 'demo_files', 'picket_fence', 'EPID-PF-LR.dcm')
        self.load_image(im_open_path, filter=filter)

    def load_image(self, file_path, filter=None):
        """Load the image

        Parameters
        ----------
        file_path : str
            Path to the image file.
        filter : int, None
            If None (default), no filtering will be done to the image.
            If an int, will perform median filtering over image of size *filter*.
        """
        self.image = Image(file_path)
        if isinstance(filter, int):
            self.image.median_filter(size=filter)
        self._check_for_noise()
        self.image.check_inversion()

    @classmethod
    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)

    @classmethod
    def from_multiple_images(cls, path_list):
        """Load and superimpose multiple images and instantiate a Starshot object.

        .. versionadded:: 0.9

        Parameters
        ----------
        path_list : iterable
            An iterable of path locations to the files to be loaded/combined.
        """
        obj = cls()
        obj.load_multiple_images(path_list)
        return obj

    def load_multiple_images(self, path_list):