

'''simple docstring'''





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     int	)						->				np.ndarray:




	'''simple docstring'''




	if (ksize % 2) == 0:
		_UpperCamelCase   :		Dict			=						ksize + 1
	_UpperCamelCase   :		Optional[Any]			=						np.zeros((ksize, ksize)							,       dtype=np.floataa	)

	# each value
	for y in range(UpperCAmelCase_	):
		for x in range(UpperCAmelCase_	):
			# distance from center
			_UpperCamelCase   :		Union[str, Any]			=						x - ksize // 2
			_UpperCamelCase   :		int			=						y - ksize // 2

			# degree to radiant
			_UpperCamelCase   :		List[Any]			=						theta / 1_8_0 * np.pi
			_UpperCamelCase   :		Any			=						np.cos(_theta	)
			_UpperCamelCase   :		List[str]			=						np.sin(_theta	)

			# get kernel x
			_UpperCamelCase   :		List[str]			=						cos_theta * px + sin_theta * py

			# get kernel y
			_UpperCamelCase   :		int			=						-sin_theta * px + cos_theta * py

			# fill kernel
			_UpperCamelCase   :		List[str]			=						np.exp(
			    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)	) * np.cos(2 * np.pi * _x / lambd + psi	)

	return gabor


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							# read original image
							lowerCAmelCase__								=		imread("""../image_data/lena.jpg""")
							# turn image in gray scale value
							lowerCAmelCase__								=		cvtColor(img, COLOR_BGR2GRAY)

							# Apply multiple Kernel to detect edges
							lowerCAmelCase__								=		np.zeros(gray.shape[:2])
							for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
														lowerCAmelCase__								=		gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
														out += filteraD(gray, CV_8UC3, kernel_aa)
							lowerCAmelCase__								=		out / out.max() * 2_5_5
							lowerCAmelCase__								=		out.astype(np.uinta)

							imshow("""Original""", gray)
							imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

							waitKey(0)







from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCAmelCase__								=		numpy.array([0, 0])
lowerCAmelCase__								=		numpy.array([0.5, 0.8_66_02_54])
lowerCAmelCase__								=		numpy.array([1, 0])
lowerCAmelCase__								=		[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]							,       UpperCAmelCase_							:     int	)						->				list[numpy.ndarray]:




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=						initial_vectors
	for _ in range(UpperCAmelCase_	):
		_UpperCamelCase   :		str			=						iteration_step(UpperCAmelCase_	)
	return vectors






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]	)						->				list[numpy.ndarray]:




	'''simple docstring'''




	_UpperCamelCase   :		int			=						[]
	for i, start_vector in enumerate(vectors[:-1]	):
		_UpperCamelCase   :		Union[str, Any]			=						vectors[i + 1]
		new_vectors.append(UpperCAmelCase_	)
		_UpperCamelCase   :		Tuple			=						end_vector - start_vector
		new_vectors.append(start_vector + difference_vector / 3	)
		new_vectors.append(
		    start_vector + difference_vector / 3 + rotate(difference_vector / 3							,       6_0	)	)
		new_vectors.append(start_vector + difference_vector * 2 / 3	)
	new_vectors.append(vectors[-1]	)
	return new_vectors






def 					lowerCamelCase_     (							UpperCAmelCase_							:     numpy.ndarray							,       UpperCAmelCase_							:     float	)						->				numpy.ndarray:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						numpy.radians(UpperCAmelCase_	)
	_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						numpy.cos(UpperCAmelCase_	), numpy.sin(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						numpy.array(((c, -s), (s, c))	)
	return numpy.dot(UpperCAmelCase_							,       UpperCAmelCase_	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]	)						->				None:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						plt.gca()
	axes.set_aspect('equal'	)

	# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
	# y-coordinates as inputs, which are constructed from the vector-list using
	# zip()
	_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						zip(*UpperCAmelCase_	)
	plt.plot(UpperCAmelCase_							,       UpperCAmelCase_	)
	plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							lowerCAmelCase__								=		iterate(INITIAL_VECTORS, 5)
							plot(processed_vectors)




