How to use _apply_filter method in lisa

Best Python code snippet using lisa_python

filter.py

Source: filter.py Github

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...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]])...

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filters.py

Source: filters.py Github

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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)...

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npysNPSFilteredData.py

Source: npysNPSFilteredData.py Github

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...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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