Python Image.invert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 invert [as 別名]

#.........這裏部分代碼省略.........

    @classmethod
    def from_image_UI(cls):
        """Construct a Starshot instance and get the image via a UI dialog box.

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

    def load_image_UI(self):
        """Load the image by using a UI dialog box."""
        path = get_filepath_UI()
        if path:
            self.load_image(path)

    @property
    def start_point(self):
        """The start point of the wobble search algorithm.

        After analysis this point is the wobble center.
        """
        return self.circle_profile.center

    def _check_image_inversion(self):
        """Check the image for proper inversion, i.e. that pixel value increases with dose.

        Notes
        -----
        Inversion is checked by the following:
        - Summing the image along both horizontal and vertical directions.
        - If the maximum point of both horizontal and vertical is in the middle 1/3, the image is assumed to be correct.
        - Otherwise, invert the image.
        """

        # sum the image along each axis
        x_sum = np.sum(self.image.array, 0)
        y_sum = np.sum(self.image.array, 1)

        # determine the point of max value for each sum profile
        xmaxind = np.argmax(x_sum)
        ymaxind = np.argmax(y_sum)

        # If that maximum point isn't near the center (central 1/3), invert image.
        center_in_central_third = ((xmaxind > len(x_sum) / 3 and xmaxind < len(x_sum) * 2 / 3) and
                               (ymaxind > len(y_sum) / 3 and ymaxind < len(y_sum) * 2 / 3))

        if not center_in_central_third:
            self.image.invert()

    def _auto_set_start_point(self):
        """Set the algorithm starting point automatically.

        Notes
        -----
        The determination of an automatic start point is accomplished by finding the Full-Width-80%-Max.
        Finding the maximum pixel does not consistently work, esp. in the presence of a pin prick. The
        FW80M is a more consistent metric for finding a good start point.
        """
        # sum the image along each axis within the central 1/3 (avoids outlier influence from say, gantry shots)
        top_third = int(self.image.array.shape[0]/3)
        bottom_third = int(top_third * 2)
        left_third = int(self.image.array.shape[1]/3)
        right_third = int(left_third * 2)
        central_array = self.image.array[top_third:bottom_third, left_third:right_third]

# 需要導入模塊: from pylinac.core.image import Image [as 別名]
# 或者: from pylinac.core.image.Image import invert [as 別名]
class Test_Image_Methods(unittest.TestCase):

    def setUp(self):
        self.img = Image(img_path)
        self.dcm = Image(dcm_path)
        small_array = np.arange(42).reshape(6,7)
        self.sm_arr = Image.from_array(small_array)

    def test_remove_edges(self):
        """Remove the edges from a pixel array."""
        crop = 15
        orig_shape = self.img.shape
        orig_dpi = self.img.dpi
        self.img.remove_edges(crop)
        new_shape = self.img.shape
        new_dpi = self.img.dpi
        self.assertEqual(new_shape[0]+crop*2, orig_shape[0])
        # ensure original metadata is still the same
        self.assertEqual(new_dpi, orig_dpi)

    def test_median_filter(self):
        filter_size = 3
        self.sm_arr.median_filter(filter_size)
        self.assertEqual(self.sm_arr.array[0, 0], 1)
        filter_size = 0.03
        self.sm_arr.median_filter(filter_size)

        self.assertRaises(ValueError, self.img.median_filter, 1.1)

    def test_ground(self):
        old_min_val = copy.copy(self.dcm.array.min())
        ground_val = self.dcm.ground()
        self.assertEqual(old_min_val, ground_val)
        # test that array was also changed
        self.assertAlmostEqual(self.dcm.array.min(), 0)

    def test_resize(self):
        new_size = (200, 300)
        self.img.resize(new_size)
        self.assertEqual(self.img.shape, new_size)

    def test_invert(self):
        self.img.invert()

    def test_dist2edge_min(self):
        dist = self.sm_arr.dist2edge_min(Point(1,3))
        self.assertEqual(dist, 1)

        dist = self.sm_arr.dist2edge_min((1,3))
        self.assertEqual(dist, 1)

    def test_center(self):
        self.assertIsInstance(self.img.center, Point)
        img_known_center = Point(512, 1702)
        dcm_known_center = Point(512, 384)
        self.assertEqual(self.img.center.x, img_known_center.x)
        self.assertEqual(self.dcm.center.y, dcm_known_center.y)

    def test_SID(self):
        self.assertEqual(self.dcm.SID, 1050)
        self.assertRaises(TypeError, setattr, self.dcm, 'SID', '105')

    def test_combine_multiples(self):
        bad_img_path = [dcm_path, img_path]
        self.assertRaises(AttributeError, Image.from_multiples, bad_img_path)

        good_img_path = [img_path, img_path]
        combined_img = Image.from_multiples(good_img_path)
        self.assertIsInstance(combined_img, Image)

    def test_plot(self):
        self.img.plot()  # shouldn't raise

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

#.........這裏部分代碼省略.........
        if path_list:
            self.load_multiple_images(path_list)

    @classmethod
    def from_image_UI(cls):
        """Construct a Starshot instance and get the image via a UI dialog box.

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

    def load_image_UI(self):
        """Load the image by using a UI dialog box."""
        path = get_filepath_UI()
        if path:
            self.load_image(path)

    def _check_image_inversion(self):
        """Check the image for proper inversion, i.e. that pixel value increases with dose."""
        # sum the image along each axis
        x_sum = np.sum(self.image.array, 0)
        y_sum = np.sum(self.image.array, 1)

        # determine the point of max value for each sum profile
        xmaxind = np.argmax(x_sum)
        ymaxind = np.argmax(y_sum)

        # If that maximum point isn't near the center (central 1/3), invert image.
        center_in_central_third = ((xmaxind > len(x_sum) / 3 and xmaxind < len(x_sum) * 2 / 3) and
                               (ymaxind > len(y_sum) / 3 and ymaxind < len(y_sum) * 2 / 3))
        if not center_in_central_third:
            self.image.invert()

    def _get_reasonable_start_point(self):
        """Set the algorithm starting point automatically.

        Notes
        -----
        The determination of an automatic start point is accomplished by finding the Full-Width-80%-Max.
        Finding the maximum pixel does not consistently work, esp. in the presence of a pin prick. The
        FW80M is a more consistent metric for finding a good start point.
        """
        # sum the image along each axis within the central 1/3 (avoids outlier influence from say, gantry shots)
        top_third = int(self.image.array.shape[0]/3)
        bottom_third = int(top_third * 2)
        left_third = int(self.image.array.shape[1]/3)
        right_third = int(left_third * 2)
        central_array = self.image.array[top_third:bottom_third, left_third:right_third]

        x_sum = np.sum(central_array, 0)
        y_sum = np.sum(central_array, 1)

        # Calculate Full-Width, 80% Maximum
        fwxm_x_point = SingleProfile(x_sum).get_FWXM_center(80) + left_third
        fwxm_y_point = SingleProfile(y_sum).get_FWXM_center(80) + top_third

        # find maximum points
        x_max = np.unravel_index(np.argmax(central_array), central_array.shape)[1] + left_third
        y_max = np.unravel_index(np.argmax(central_array), central_array.shape)[0] + top_third

        # which one is closer to the center
        fwxm_dist = Point(fwxm_x_point, fwxm_y_point).dist_to(self.image.center)
        max_dist = Point(x_max, y_max).dist_to(self.image.center)

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

#.........這裏部分代碼省略.........
            for mlc_num, mlc in enumerate(self.pickets[0].mlc_meas):

                below_tol = True
                if self._action_lvl_set:
                    below_action = True
                for picket in self.pickets:
                    if not picket.mlc_passed(mlc_num):
                        below_tol = False
                    if self._action_lvl_set and not picket.mlc_passed_action(mlc_num):
                        below_action = False
                if below_tol:
                    if self._action_lvl_set and not below_action:
                        color = 'm'
                    else:
                        color = 'g'
                else:
                    color = 'r'
                if self.orientation == orientations['UD']:
                    r = Rectangle(max(self.image.shape)*2, self._sm_lf_meas_wdth, (mlc.center.x, mlc.center.y))
                else:
                    r = Rectangle(self._sm_lf_meas_wdth, max(self.image.shape) * 2, (mlc.center.x, mlc.center.y))
                r.add_to_axes(ax.axes, edgecolor='none', fill=True, alpha=0.1, facecolor=color)

        plt.xlim([0, self.image.shape[1]])
        plt.ylim([0, self.image.shape[0]])

        plt.axis('off')

        if show:
            plt.show()

    def save_analyzed_image(self, filename, guard_rails=True, mlc_peaks=True, overlay=True, **kwargs):
        """Save the analyzed figure to a file."""
        self.plot_analyzed_image(guard_rails, mlc_peaks, overlay, show=False)
        plt.savefig(filename, **kwargs)

    def return_results(self):
        """Return results of analysis. Use with print()."""
        pass_pct = self.percent_passing
        string = "Picket Fence Results: \n{:2.1f}% " \
                 "Passed\nMedian Error: {:2.3f}mm \n" \
                 "Max Error: {:2.3f}mm on Picket: {}, Leaf: {}".format(pass_pct, self.abs_median_error, self.max_error,
                                                                                                   self.max_error_picket,
                                                                                                  self.max_error_leaf)
        return string

    def _check_inversion(self):
        """Check the image for inversion (pickets are valleys, not peaks) by sampling the 4 image corners.
        If the average value of the four corners is above the average pixel value, then it is very likely inverted.
        """
        outer_edge = 10
        inner_edge = 30
        TL_corner = self.image.array[outer_edge:inner_edge, outer_edge:inner_edge]
        BL_corner = self.image.array[-inner_edge:-outer_edge, -inner_edge:-outer_edge]
        TR_corner = self.image.array[outer_edge:inner_edge, outer_edge:inner_edge]
        BR_corner = self.image.array[-inner_edge:-outer_edge, -inner_edge:-outer_edge]
        corner_avg = np.mean((TL_corner, BL_corner, TR_corner, BR_corner))
        if corner_avg > np.mean(self.image.array.flatten()):
            self.image.invert()

    def _threshold(self):
        """Threshold the image by subtracting the minimum value. Allows for more accurate image orientation determination.
        """
        col_prof = np.median(self.image.array, 0)
        col_prof = Profile(col_prof)
        row_prof = np.median(self.image.array, 1)
        row_prof = Profile(row_prof)
        _, r_peak_idx = row_prof.find_peaks(min_peak_distance=0.01, exclude_lt_edge=0.05, exclude_rt_edge=0.05)
        _, c_peak_idx = col_prof.find_peaks(min_peak_distance=0.01, exclude_lt_edge=0.05, exclude_rt_edge=0.05)
        min_val = self.image.array[r_peak_idx[0]:r_peak_idx[-1], c_peak_idx[0]:c_peak_idx[-1]].min()
        self._analysis_array = self.image.array.copy()
        self._analysis_array[self._analysis_array < min_val] = min_val
        self._analysis_array -= min_val

    # @property
    # def _analysis_array(self):
    #     return getattr(self, '_aa', self.image.array.copy())
    #
    # @_analysis_array.setter
    # def _analysis_array(self, array):
    #     if array.shape != self.image.shape:
    #         raise ValueError("Array size is not the same as the original image")
    #     self._aa = array

    def _find_orientation(self):
        """Determine the orientation of the radiation strips by examining percentiles of the sum of each axes of the image.
        A high standard deviation is a surrogate for the axis the pickets are along.
        """
        row_sum = np.sum(self._analysis_array, 0)
        col_sum = np.sum(self._analysis_array, 1)
        row80, row90 = np.percentile(row_sum, [80, 90])
        col80, col90 = np.percentile(col_sum, [80, 90])
        row_range = row90 - row80
        col_range = col90 - col80
        # The true picket side will have a greater difference in
        # percentiles than will the non-picket size.
        if row_range < col_range:
            self.orientation = orientations['LR']
        else:
            self.orientation = orientations['UD']