Python Image.median_filter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 median_filter [as 別名]
class Starshot:
    """Class that can determine the wobble in a "starshot" image, be it gantry, collimator,
        couch or MLC. The image can be DICOM or a scanned film (TIF, JPG, etc).

    Attributes
    ----------
    image : :class:`~pylinac.core.image.Image`
    circle_profile : :class:`~pylinac.starshot.StarProfile`
    lines : list of :class:`~pylinac.core.geometry.Line` instances
    wobble : :class:`~pylinac.starshot.Wobble`

    Examples
    --------
    Run the demo:
        >>> Starshot().run_demo()

    Typical session:
        >>> img_path = r"C:/QA/Starshots/Coll.jpeg"
        >>> mystar = Starshot(img_path)
        >>> mystar.analyze()
        >>> print(mystar.return_results())
        >>> mystar.plot_analyzed_image()
    """
    def __init__(self, filepath=None):
        # self.image = Image  # The image array and image property structure
        self.circle_profile = StarProfile()  # a circular profile which will detect radiation line locations
        self.lines = []  # a list which will hold Line instances representing radiation lines.
        self.wobble = Wobble()  # A Circle representing the radiation wobble
        self.tolerance = Tolerance(1, 'pixels')
        if filepath is not None:
            self.load_image(filepath)

    @classmethod
    def from_demo_image(cls):
        """Construct a Starshot instance and load the demo image.

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

    def load_demo_image(self):
        """Load the starshot demo image.

        The Pylinac package comes with compressed demo images.
        When called, the function unpacks the demo image and loads it.

        Parameters
        ----------
        cleanup : boolean
            If True (default), the extracted demo file is deleted (but not the compressed version).
            If False, leaves the extracted file alone after loading. Useful when using the demo image a lot,
            or you don't mind using the extra space.
        """
        demo_folder = osp.join(osp.dirname(__file__), 'demo_files', 'starshot')
        demo_file = osp.join(demo_folder, '10X_collimator.tif')
        # demo_file = osp.join(demo_folder, 'DHMC_starshot.dcm')
        self.load_image(demo_file)

    def load_image(self, filepath):
        """Load the image via the file path.

        Parameters
        ----------
        filepath : str
            Path to the file to be loaded.
        """
        self.image = Image(filepath)
        # apply filter if it's a large image to reduce noise
        if self.image.shape[0] > 1100:
            self.image.median_filter(0.002)

    @classmethod
    def from_multiple_images(cls, filepath_list):
        """Construct a Starshot instance and load in and combine multiple images.

        .. versionadded:: 0.6
        """
        obj = cls()
        obj.load_multiple_images(filepath_list)
        return obj

    def load_multiple_images(self, filepath_list):
        """Load multiple images via the file path.

        .. versionadded:: 0.5.1

        Parameters
        ----------
        filepath_list : sequence
            An iterable sequence of filepath locations.
        """
        self.image = Image.from_multiples(filepath_list)

    @classmethod
    def from_multiple_images_UI(cls):
        """Construct a Starshot instance and load in and combine multiple images via a UI dialog box.

        .. versionadded:: 0.6
#.........這裏部分代碼省略.........

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

#.........這裏部分代碼省略.........
    @property
    def num_pickets(self):
        """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._clear_attrs()
        self._check_for_noise()

    @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

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

#.........這裏部分代碼省略.........
    @property
    def num_pickets(self):
        """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

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

#.........這裏部分代碼省略.........
    @property
    def num_pickets(self):
        """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