
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class 			UpperCamelCase_			:




       '''simple docstring'''

       def __init__(				self     ,						UpperCamelCase     ,						UpperCamelCase) ->	Dict:
          UpperCamelCase__    :		Optional[int]     =				0
          # max greyscale value for #FFFFFF
          UpperCamelCase__    :		Tuple     =				int(self.get_greyscale(2_55     ,						2_55     ,						2_55))

          if not self.min_threshold < threshold < self.max_threshold:
             UpperCamelCase__    :		List[Any]     =				F"""Factor value should be from 0 to {self.max_threshold}"""
             raise ValueError(__a)

          UpperCamelCase__    :		Optional[int]     =				input_img
          UpperCamelCase__    :		str     =				threshold
          UpperCamelCase__    :		Any     =				self.input_img.shape[1], self.input_img.shape[0]

          # error table size (+4 columns and +1 row) greater than input image because of
          # lack of if statements
          UpperCamelCase__    :		Dict     =				[
              [0 for _ in range(self.height + 4)] for __ in range(self.width + 1)
          ]
          UpperCamelCase__    :		str     =				np.ones((self.width, self.height, 3)     ,						np.uinta) * 2_55
       @classmethod
       def        lowerCAmelCase__ (				cls     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	Tuple:
          return 0.114 * blue + 0.587 * green + 0.2126 * red






       def        lowerCAmelCase__ (				self) ->	Any:





          for y in range(self.height):
             for x in range(self.width):
                UpperCamelCase__    :		Optional[int]     =				int(self.get_greyscale(*self.input_img[y][x]))
                if self.threshold > greyscale + self.error_table[y][x]:
                   UpperCamelCase__    :		Tuple     =				(0, 0, 0)
                   UpperCamelCase__    :		List[Any]     =				greyscale + self.error_table[x][y]
                else:
                   UpperCamelCase__    :		Dict     =				(2_55, 2_55, 2_55)
                   UpperCamelCase__    :		Tuple     =				greyscale + self.error_table[x][y] - 2_55
                self.error_table[y][x + 1] += int(8 / 32 * current_error)
                self.error_table[y][x + 2] += int(4 / 32 * current_error)
                self.error_table[y + 1][x] += int(8 / 32 * current_error)
                self.error_table[y + 1][x + 1] += int(4 / 32 * current_error)
                self.error_table[y + 1][x + 2] += int(2 / 32 * current_error)
                self.error_table[y + 1][x - 1] += int(4 / 32 * current_error)
                self.error_table[y + 1][x - 2] += int(2 / 32 * current_error)


if __name__ == "__main__":
       # create Burke's instances with original images in greyscale
       UpperCAmelCase__				:  Union[str, Any]       					=				[
           Burkes(imread('''image_data/lena.jpg''', 1), threshold)
           for threshold in (1, 126, 130, 140)
       ]

       for burkes in burkes_instances:
              burkes.process()

       for burkes in burkes_instances:
              imshow(
                  f'''Original image with dithering threshold: {burkes.threshold}''',
                  burkes.output_img,
              )

       waitKey(0)
       destroyAllWindows()





import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

UpperCAmelCase__				:  str       					=				180



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Tuple:
			UpperCamelCase__    :		Dict     =				k_size // 2
			UpperCamelCase__   ,			UpperCamelCase__    :		Any     =				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
			UpperCamelCase__    :		int     =				(
			    1
			    / (2 * np.pi * sigma)
			    * np.exp(-(np.square(__SCREAMING_SNAKE_CASE ) + np.square(__SCREAMING_SNAKE_CASE )) / (2 * np.square(__SCREAMING_SNAKE_CASE )) )
			)
			return g



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Any:
			UpperCamelCase__    :		Dict     =				np.zeros(__SCREAMING_SNAKE_CASE )

			for row in range(1				,    image_shape[0] - 1 ):
						for col in range(1				,    image_shape[1] - 1 ):
									UpperCamelCase__    :		Dict     =				gradient_direction[row, col]

									if (
									    0 <= direction < PI / 8
									    or 15 * PI / 8 <= direction <= 2 * PI
									    or 7 * PI / 8 <= direction <= 9 * PI / 8
									):
												UpperCamelCase__    :		List[str]     =				sobel_grad[row, col - 1]
												UpperCamelCase__    :		Dict     =				sobel_grad[row, col + 1]
												if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
															UpperCamelCase__    :		Tuple     =				sobel_grad[row, col]

									elif (
									    PI / 8 <= direction < 3 * PI / 8
									    or 9 * PI / 8 <= direction < 11 * PI / 8
									):
												UpperCamelCase__    :		Tuple     =				sobel_grad[row + 1, col - 1]
												UpperCamelCase__    :		str     =				sobel_grad[row - 1, col + 1]
												if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
															UpperCamelCase__    :		List[str]     =				sobel_grad[row, col]

									elif (
									    3 * PI / 8 <= direction < 5 * PI / 8
									    or 11 * PI / 8 <= direction < 13 * PI / 8
									):
												UpperCamelCase__    :		Optional[Any]     =				sobel_grad[row - 1, col]
												UpperCamelCase__    :		Dict     =				sobel_grad[row + 1, col]
												if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
															UpperCamelCase__    :		Optional[Any]     =				sobel_grad[row, col]

									elif (
									    5 * PI / 8 <= direction < 7 * PI / 8
									    or 13 * PI / 8 <= direction < 15 * PI / 8
									):
												UpperCamelCase__    :		Tuple     =				sobel_grad[row - 1, col - 1]
												UpperCamelCase__    :		Optional[int]     =				sobel_grad[row + 1, col + 1]
												if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
															UpperCamelCase__    :		Optional[Any]     =				sobel_grad[row, col]

			return destination



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Tuple:
			for row in range(1				,    image_shape[0] - 1 ):
						for col in range(1				,    image_shape[1] - 1 ):
									if destination[row, col] >= threshold_high:
												UpperCamelCase__    :		str     =				strong
									elif destination[row, col] <= threshold_low:
												UpperCamelCase__    :		List[str]     =				0
									else:
												UpperCamelCase__    :		int     =				weak



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Any:
			for row in range(1				,    image_shape[0] ):
						for col in range(1				,    image_shape[1] ):
									if destination[row, col] == weak:
												if 255 in (
												    destination[row, col + 1],
												    destination[row, col - 1],
												    destination[row - 1, col],
												    destination[row + 1, col],
												    destination[row - 1, col - 1],
												    destination[row + 1, col - 1],
												    destination[row - 1, col + 1],
												    destination[row + 1, col + 1],
												):
															UpperCamelCase__    :		List[str]     =				strong
												else:
															UpperCamelCase__    :		Union[str, Any]     =				0



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=15				,    __SCREAMING_SNAKE_CASE=30				,    __SCREAMING_SNAKE_CASE=128				,    __SCREAMING_SNAKE_CASE=255 )	->     Any:
			# gaussian_filter
			UpperCamelCase__    :		Optional[int]     =				img_convolve(__SCREAMING_SNAKE_CASE				,    gen_gaussian_kernel(9				,    sigma=1.4 ) )
			# get the gradient and degree by sobel_filter
			UpperCamelCase__   ,			UpperCamelCase__    :		Union[str, Any]     =				sobel_filter(__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Tuple     =				PI + np.radadeg(__SCREAMING_SNAKE_CASE )

			UpperCamelCase__    :		Any     =				suppress_non_maximum(image.shape				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			detect_high_low_threshold(
			    image.shape				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			track_edge(image.shape				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			return destination


if __name__ == "__main__":
							# read original image in gray mode
							UpperCAmelCase__				:  List[str]       					=				cva.imread(r'''../image_data/lena.jpg''', 0)
							# canny edge detection
							UpperCAmelCase__				:  Tuple       					=				canny(lena)
							cva.imshow('''canny''', canny_destination)
							cva.waitKey(0)



