
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase_						: Union[str, Any]   =  2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowerCamelCase_						: Union[str, Any]   =  50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowerCamelCase_						: List[Any]   =  0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	tuple[str, float]:
				UpperCamelCase_:							Optional[int]	=						len([g for position, g in enumerate(lowerCamelCase						) if g == main_target[position]]						)
				return (item, float(lowerCamelCase						))







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	tuple[str, str]:
				UpperCamelCase_:							Optional[int]	=						random.randint(0     ,    len(lowerCamelCase						) - 1						)
				UpperCamelCase_:							Dict	=						parent_a[:random_slice] + parent_a[random_slice:]
				UpperCamelCase_:							Tuple	=						parent_a[:random_slice] + parent_a[random_slice:]
				return (child_a, child_a)







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	str:
				UpperCamelCase_:							Optional[int]	=						list(lowerCamelCase						)
				if random.uniform(0     ,    1						) < MUTATION_PROBABILITY:
								UpperCamelCase_:							Optional[Any]	=						random.choice(lowerCamelCase						)
				return "".join(lowerCamelCase						)







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    )  ->	list[str]:
				UpperCamelCase_:							Optional[Any]	=						[]
				# Generate more children proportionally to the fitness score.
				UpperCamelCase_:							List[str]	=						int(parent_a[1] * 1_00						) + 1
				UpperCamelCase_:							List[str]	=						10 if child_n >= 10 else child_n
				for _ in range(lowerCamelCase						):
								UpperCamelCase_:							List[str]	=						population_score[random.randint(0     ,    lowerCamelCase						)][0]

								UpperCamelCase_:							Tuple	=						crossover(parent_a[0]     ,    lowerCamelCase						)
								# Append new string to the population list.
								pop.append(mutate(lowerCamelCase     ,    lowerCamelCase						)						)
								pop.append(mutate(lowerCamelCase     ,    lowerCamelCase						)						)
				return pop







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = True						)  ->	tuple[int, int, str]:

				# Verify if N_POPULATION is bigger than N_SELECTED
				if N_POPULATION < N_SELECTED:
								UpperCamelCase_:							Union[str, Any]	=						F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
								raise ValueError(lowerCamelCase						)
				# Verify that the target contains no genes besides the ones inside genes variable.
				UpperCamelCase_:							str	=						sorted({c for c in target if c not in genes}						)
				if not_in_genes_list:
								UpperCamelCase_:							Union[str, Any]	=						F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
								raise ValueError(lowerCamelCase						)

				# Generate random starting population.
				UpperCamelCase_:							Union[str, Any]	=						[]
				for _ in range(lowerCamelCase						):
								population.append("""""".join([random.choice(lowerCamelCase						) for i in range(len(lowerCamelCase						)						)]						)						)

				# Just some logs to know what the algorithms is doing.
				UpperCamelCase_:							Union[str, Any]	=						0, 0

				# This loop will end when we find a perfect match for our target.
				while True:
								generation += 1
								total_population += len(lowerCamelCase						)

								# Random population created. Now it's time to evaluate.

								# Adding a bit of concurrency can make everything faster,
								#
								# import concurrent.futures
								# population_score: list[tuple[str, float]] = []
								# with concurrent.futures.ThreadPoolExecutor(
								#                                   max_workers=NUM_WORKERS) as executor:
								#     futures = {executor.submit(evaluate, item) for item in population}
								#     concurrent.futures.wait(futures)
								#     population_score = [item.result() for item in futures]
								#
								# but with a simple algorithm like this, it will probably be slower.
								# We just need to call evaluate for every item inside the population.
								UpperCamelCase_:							Dict	=						[evaluate(lowerCamelCase     ,    lowerCamelCase						) for item in population]

								# Check if there is a matching evolution.
								UpperCamelCase_:							Any	=						sorted(lowerCamelCase     ,    key=lambda lowerCamelCase						: x[1]     ,    reverse=lowerCamelCase						)
								if population_score[0][0] == target:
												return (generation, total_population, population_score[0][0])

								# Print the best result every 10 generation.
								# Just to know that the algorithm is working.
								if debug and generation % 10 == 0:
												print(
												    F'''\nGeneration: {generation}'''
												    F'''\nTotal Population:{total_population}'''
												    F'''\nBest score: {population_score[0][1]}'''
												    F'''\nBest string: {population_score[0][0]}'''						)

								# Flush the old population, keeping some of the best evolutions.
								# Keeping this avoid regression of evolution.
								UpperCamelCase_:							List[Any]	=						population[: int(N_POPULATION / 3						)]
								population.clear()
								population.extend(lowerCamelCase						)
								# Normalize population score to be between 0 and 1.
								UpperCamelCase_:							Optional[Any]	=						[
								    (item, score / len(lowerCamelCase						)) for item, score in population_score
								]

								# This is selection
								for i in range(lowerCamelCase						):
												population.extend(select(population_score[int(lowerCamelCase						)]     ,    lowerCamelCase     ,    lowerCamelCase						)						)
												# Check if the population has already reached the maximum value and if so,
												# break the cycle.  If this check is disabled, the algorithm will take
												# forever to compute large strings, but will also calculate small strings in
												# a far fewer generations.
												if len(lowerCamelCase						) > N_POPULATION:
																break


if __name__ == "__main__":
							lowerCamelCase_						: Any   =  (
							    """This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
							)
							lowerCamelCase_						: str   =  list(
							    """ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
							    """nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
							)
							lowerCamelCase_						: Optional[Any]   =  basic(target_str, genes_list)
							print(
							    F"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
							)








import cva
import numpy as np

class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       Dict						,	snake_case_ :       float						,	snake_case_ :       int      ):

							if k in (0.04, 0.06):
											UpperCamelCase_:							Union[str, Any]	=						k
											UpperCamelCase_:							Union[str, Any]	=						window_size
							else:
											raise ValueError("""invalid k value"""      )

			def __str__(       self :       int      ):
							return str(self.k      )




			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       str      ):
							UpperCamelCase_:							int	=						cva.imread(snake_case_						,	0      )
							UpperCamelCase_,   UpperCamelCase_:							List[Any]	=						img.shape
							UpperCamelCase_:							list[list[int]]	=						[]
							UpperCamelCase_:							int	=						img.copy()
							UpperCamelCase_:							Any	=						cva.cvtColor(snake_case_						,	cva.COLOR_GRAY2RGB      )
							UpperCamelCase_,   UpperCamelCase_:							List[Any]	=						np.gradient(snake_case_      )
							UpperCamelCase_:							Optional[Any]	=						dx**2
							UpperCamelCase_:							Dict	=						dy**2
							UpperCamelCase_:							Optional[Any]	=						dx * dy
							UpperCamelCase_:							str	=						0.04
							UpperCamelCase_:							int	=						self.window_size // 2
							for y in range(snake_case_						,	h - offset      ):
											for x in range(snake_case_						,	w - offset      ):
															UpperCamelCase_:							List[Any]	=						ixx[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()
															UpperCamelCase_:							int	=						iyy[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()
															UpperCamelCase_:							List[str]	=						ixy[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()

															UpperCamelCase_:							List[str]	=						(wxx * wyy) - (wxy**2)
															UpperCamelCase_:							Optional[int]	=						wxx + wyy
															UpperCamelCase_:							Dict	=						det - k * (trace**2)
															# Can change the value
															if r > 0.5:
																			corner_list.append([x, y, r]      )
																			color_img.itemset((y, x, 0)						,	0      )
																			color_img.itemset((y, x, 1)						,	0      )
																			color_img.itemset((y, x, 2)						,	255      )
							return color_img, corner_list


if __name__ == "__main__":
							lowerCamelCase_						: Optional[Any]         =  HarrisCorner(0.04, 3)
							lowerCamelCase_   ,       lowerCamelCase_						: Any         =  edge_detect.detect("""path_to_image""")
							cva.imwrite("""detect.png""", color_img)






