
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

SCREAMING_SNAKE_CASE							:List[Any]										=    imread(R'digital_image_processing/image_data/lena_small.jpg')
SCREAMING_SNAKE_CASE							:Optional[Any]										=    cvtColor(img, COLOR_BGR2GRAY)


def 						UpperCAmelCase      (     )		->     List[str]:







				"""simple docstring"""



				__A             =			cn.convert_to_negative(a_						)
				# assert negative_img array for at least one True
				assert negative_img.any()


def 						UpperCAmelCase      (     )		->     Dict:







				"""simple docstring"""



				with Image.open("digital_image_processing/image_data/lena_small.jpg"						) as img:
								# Work around assertion for response
								assert str(cc.change_contrast(a_		,       1_1_0						)						).startswith(
								    "<PIL.Image.Image image mode=RGB size=100x100 at"						)


def 						UpperCAmelCase      (     )		->     Dict:







				"""simple docstring"""



				__A             =			canny.gen_gaussian_kernel(9		,       sigma=1.4						)
				# Assert ambiguous array
				assert resp.all()


def 						UpperCAmelCase      (     )		->     Tuple:







				"""simple docstring"""



				__A             =			imread("digital_image_processing/image_data/lena_small.jpg"		,       0						)
				# assert ambiguous array for all == True
				assert canny_img.all()
				__A             =			canny.canny(a_						)
				# assert canny array for at least one True
				assert canny_array.any()


def 						UpperCAmelCase      (     )		->     Tuple:







				"""simple docstring"""



				assert gg.gaussian_filter(a_		,       5		,       sigma=0.9						).all()


def 						UpperCAmelCase      (     )		->     int:







				"""simple docstring"""



				__A             =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]						)
				__A             =			conv.img_convolve(a_		,       a_						).astype(a_						)
				assert res.any()


def 						UpperCAmelCase      (     )		->     str:







				"""simple docstring"""



				assert med.median_filter(a_		,       3						).any()


def 						UpperCAmelCase      (     )		->     List[str]:







				"""simple docstring"""



				__A      ,							__A             =			sob.sobel_filter(a_						)
				assert grad.any() and theta.any()


def 						UpperCAmelCase      (     )		->     str:







				"""simple docstring"""



				__A             =			sp.make_sepia(a_		,       2_0						)
				assert sepia.all()


def 						UpperCAmelCase      (     a_ = "digital_image_processing/image_data/lena_small.jpg"						)		->     Tuple:







				"""simple docstring"""



				__A             =			bs.Burkes(imread(a_		,       1						)		,       1_2_0						)
				burkes.process()
				assert burkes.output_img.any()


def 						UpperCAmelCase      (     a_ = "digital_image_processing/image_data/lena_small.jpg"		,       )		->     Optional[int]:







				"""simple docstring"""



				__A             =			rs.NearestNeighbour(imread(a_		,       1						)		,       4_0_0		,       2_0_0						)
				nn.process()
				assert nn.output.any()


def 						UpperCAmelCase      (     )		->     int:







				"""simple docstring"""



				__A             =			"digital_image_processing/image_data/lena.jpg"

				# Reading the image and converting it to grayscale.
				__A             =			imread(a_		,       0						)

				# Test for get_neighbors_pixel function() return not None
				__A             =			0
				__A             =			0
				__A             =			image[x_coordinate][y_coordinate]

				__A             =			lbp.get_neighbors_pixel(
				    a_		,       a_		,       a_		,       a_						)

				assert neighbors_pixels is not None

				# Test for local_binary_pattern function()
				# Create a numpy array as the same height and width of read image
				__A             =			np.zeros((image.shape[0], image.shape[1])						)

				# Iterating through the image and calculating the local binary pattern value
				# for each pixel.
				for i in range(0		,       image.shape[0]						):
								for j in range(0		,       image.shape[1]						):
												__A             =			lbp.local_binary_value(a_		,       a_		,       a_						)

				assert lbp_image.any()


from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE							:Tuple										=    datasets.load_iris()

SCREAMING_SNAKE_CASE							:Dict										=    np.array(data['data'])
SCREAMING_SNAKE_CASE							:Optional[int]										=    np.array(data['target'])
SCREAMING_SNAKE_CASE							:List[str]										=    data['target_names']

SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE							:List[Any]										=    train_test_split(X, y)


def 						UpperCAmelCase      (     a_		,       a_						)		->     Tuple:







				"""simple docstring"""



				return np.linalg.norm(np.array(a_						) - np.array(a_						)						)


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_=5						)		->     Dict:







				"""simple docstring"""



				__A             =			zip(a_		,       a_						)
				# List of distances of all points from the point to be classified
				__A             =			[]
				for data_point in data:
								__A             =			euclidean_distance(data_point[0]		,       a_						)
								distances.append((distance, data_point[1])						)
				# Choosing 'k' points with the least distances.
				__A             =			[i[1] for i in sorted(a_						)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				__A             =			Counter(a_						).most_common(1						)[0][0]
				return classes[result]


if __name__ == "__main__":
					print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
