How to use _apply_filter method in lisa

Best Python code snippet using lisa_python

filter.py

Source:filter.py

...14    else:15        w_pad_r = w_pad_l16    kernel_padded = np.pad(kernel, ((h_pad_l, h_pad_r), (w_pad_l, w_pad_r)))17    return kernel_padded18def _apply_filter(image_grey, kernel):19    kernel = _pad_kernel(image_grey, kernel)20    result = signal.fftconvolve(image_grey, kernel, mode='same')21    # result = signal.convolve2d(image_grey, kernel, mode='same', boundary='sym')22    result[0, :] = np.zeros_like(result[0, :])23    result[-1, :] = np.zeros_like(result[-1, :])24    result[:, 0] = np.zeros_like(result[:, 0])25    result[:, -1] = np.zeros_like(result[:, -1])26    return result27# brenner variations28def brenner_y(image_grey):29    f = image_grey[:-2, :]30    f = np.pad(f, ((1, 1), (0, 0)))31    f_padded = image_grey[2:, :]32    f_padded = np.pad(f_padded, ((1, 1), (0, 0)))33    return f**2 + f_padded**2 - 2*f*f_padded34def brenner_x(image_grey):35    f = image_grey[:, :-2]36    f = np.pad(f, ((0, 0), (1, 1)))37    f_padded = image_grey[:, 2:]38    f_padded = np.pad(f_padded, ((0, 0), (1, 1)))39    return f**2 + f_padded**2 - 2*f*f_padded40def brenner_xy(image_grey):41    x = brenner_x(image_grey)42    y = brenner_y(image_grey)43    return (x + y)/244def squared_gradient_y(image_grey):45    f = image_grey[:-1, :]46    f = np.pad(f, ((1, 0), (0, 0)))47    f_padded = image_grey[1:, :]48    f_padded = np.pad(f_padded, ((1, 0), (0, 0)))49    return f ** 2 + f_padded ** 2 - 2 * f * f_padded50def squared_gradient_x(image_grey):51    f = image_grey[:, :-1]52    f = np.pad(f, ((0, 0), (1, 0)))53    f_padded = image_grey[:, 1:]54    f_padded = np.pad(f_padded, ((0, 0), (1, 0)))55    return f**2 + f_padded**2 - 2*f*f_padded56# first order derivative operators57def different_h(image_grey):58    kernel = np.array([[0, 0, 0],59                      [-1, 0, 1],60                      [0, 0, 0]])61    return _apply_filter(image_grey, kernel)62def different_v(image_grey):63    kernel = np.array([[0, -1, 0],64                      [0, 0, 0],65                      [0, 1, 0]])66    return _apply_filter(image_grey, kernel)67def sobel_h(image_grey):68    kernel = np.array([[-1, 0, 1],69                      [-2, 0, 2],70                      [-1, 0, 1]])71    return _apply_filter(image_grey, kernel)72def sobel_v(image_grey):73    kernel = np.array([[-1, -2, -1],74                      [0, 0 ,0],75                      [1, 2, 1]])76    return _apply_filter(image_grey, kernel)77def scharr_h(image_grey):78    kernel = np.array([[-3, 0, 3],79                      [-10, 0, 10],80                      [-3, 0, 3]])81    return _apply_filter(image_grey, kernel)82def scharr_v(image_grey):83    kernel = np.array([[-3, -10, -3],84                      [0, 0, 0],85                      [3, 10, 3]])86    return _apply_filter(image_grey, kernel)87def roberts_h(image_grey):88    kernel = np.array([[0, 0, 0],89                      [0, 1, 0],90                      [-1, 0, 0]])91    return _apply_filter(image_grey, kernel)92def roberts_v(image_grey):93    kernel = np.array([[0, 0, 0],94                      [0, 1, 0],95                      [0, 0, -1]])96    return _apply_filter(image_grey, kernel)97def prewitt_h(image_grey):98    kernel = np.array([[-1, 0, 1],99                      [-1, 0, 1],100                      [-1, 0, 1]])101    return _apply_filter(image_grey, kernel)102def prewitt_v(image_grey):103    kernel = np.array([[-1, -1, -1],104                      [0, 0, 0],105                      [1, 1, 1]])106    return _apply_filter(image_grey, kernel)107# second order derivative operators108def laplacian(image_grey):109    kernel = np.array([[-1, -1, -1],110                       [-1, 8, -1],111                       [-1, -1, -1]])112    return _apply_filter(image_grey, kernel)113def sobel2_h(image_grey):114    kernel = np.array([[1, 2, 1],115                      [-2, -4, -2],116                      [1, 2, 1]])117    return _apply_filter(image_grey, kernel)118def sobel2_v(image_grey):119    kernel = np.array([[1, -2, 1],120                      [2, -4, 2],121                      [1, -2, 1]])122    return _apply_filter(image_grey, kernel)123def cross_sobel(image_grey):124    kernel = np.array([[-1, 0, 1],125                      [0, 0, 0],126                      [1, 0, -1]])...

filters.py

Source:filters.py

1from PIL import Image, ImageFilter2def _apply_filter(image: Image.Image, filter) -> Image.Image:3    return image.filter(filter)4def blur(image: Image.Image) -> Image.Image:5    return _apply_filter(image, ImageFilter.BLUR())6def contour(image: Image.Image) -> Image.Image:7    return _apply_filter(image, ImageFilter.CONTOUR())8def detail(image: Image.Image) -> Image.Image:9    return _apply_filter(image, ImageFilter.DETAIL())10def edge_enhance(image: Image.Image) -> Image.Image:11    return _apply_filter(image, ImageFilter.EDGE_ENHANCE())12def edge_enhance_plus(image: Image.Image) -> Image.Image:13    return _apply_filter(image, ImageFilter.EDGE_ENHANCE_MORE())14def emboss(image: Image.Image) -> Image.Image:15    return _apply_filter(image, ImageFilter.EMBOSS())16def find_edges(image: Image.Image) -> Image.Image:17    return _apply_filter(image, ImageFilter.FIND_EDGES())18def sharpen(image: Image.Image) -> Image.Image:19    return _apply_filter(image, ImageFilter.SHARPEN())20def smooth(image: Image.Image) -> Image.Image:21    return _apply_filter(image, ImageFilter.SMOOTH())22def smooth_plus(image: Image.Image) -> Image.Image:23    return _apply_filter(image, ImageFilter.SMOOTH_MORE())24def filter_image(image: Image.Image, filter: str) -> Image.Image:25    filtered_image = None26    if filter == "blur":27        filtered_image = blur(image)28    elif filter == "contour":29        filtered_image = contour(image)30    elif filter == "detail":31        filtered_image = detail(image)32    elif filter == "edge_enhance":33        filtered_image = edge_enhance(image)34    elif filter == "edge_enhance_plus":35        filtered_image = edge_enhance_plus(image)36    elif filter == "emboss":37        filtered_image = emboss(image)...

npysNPSFilteredData.py

Source:npysNPSFilteredData.py

...9    def get_all_values(self):10        return self._values11    def set_filter(self, this_filter):12        self._filter = this_filter13        self._apply_filter()14    def filter_data(self):15        # should set self._filtered_values to the filtered values16        raise Exception("You need to define the way the filter operates")17    18    def get(self):19        self._apply_filter()20        return self._filtered_values21    def _apply_filter(self):22        # Could do some caching here - but the default definition does not.23        self._filtered_values = self.filter_data()24            25class NPSFilteredDataList(NPSFilteredDataBase):26    def filter_data(self):27        if self._filter and self.get_all_values():28            return [x for x in self.get_all_values() if self._filter in x]29        else:30            return self.get_all_values()31    ...

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