
"""simple docstring"""



import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					bytes     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			f"""{sampling_rate}"""
							_lowerCAmelCase  	=			'1'
							_lowerCAmelCase  	=			'f32le'
							_lowerCAmelCase  	=			[
							    'ffmpeg',
							    '-i',
							    'pipe:0',
							    '-ac',
							    ac,
							    '-ar',
							    ar,
							    '-f',
							    format_for_conversion,
							    '-hide_banner',
							    '-loglevel',
							    'quiet',
							    'pipe:1',
							]

							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,  stdin=subprocess.PIPE     ,  stdout=subprocess.PIPE						) as ffmpeg_process:
																					_lowerCAmelCase  	=			ffmpeg_process.communicate(SCREAMING_SNAKE_CASE						)
							except FileNotFoundError as error:
														raise ValueError('ffmpeg was not found but is required to load audio files from filename'						) from error
							_lowerCAmelCase  	=			output_stream[0]
							_lowerCAmelCase  	=			np.frombuffer(SCREAMING_SNAKE_CASE     ,  np.floataa						)
							if audio.shape[0] == 0:
														raise ValueError('Malformed soundfile'						)
							return audio






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					float     ,  SCREAMING_SNAKE_CASE:					str = "f32le"     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			f"""{sampling_rate}"""
							_lowerCAmelCase  	=			'1'
							if format_for_conversion == "s16le":
														_lowerCAmelCase  	=			2
							elif format_for_conversion == "f32le":
														_lowerCAmelCase  	=			4
							else:
														raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""						)

							_lowerCAmelCase  	=			platform.system()
							if system == "Linux":
														_lowerCAmelCase  	=			'alsa'
														_lowerCAmelCase  	=			'default'
							elif system == "Darwin":
														_lowerCAmelCase  	=			'avfoundation'
														_lowerCAmelCase  	=			':0'
							elif system == "Windows":
														_lowerCAmelCase  	=			'dshow'
														_lowerCAmelCase  	=			'default'

							_lowerCAmelCase  	=			[
							    'ffmpeg',
							    '-f',
							    format_,
							    '-i',
							    input_,
							    '-ac',
							    ac,
							    '-ar',
							    ar,
							    '-f',
							    format_for_conversion,
							    '-fflags',
							    'nobuffer',
							    '-hide_banner',
							    '-loglevel',
							    'quiet',
							    'pipe:1',
							]
							_lowerCAmelCase  	=			int(round(sampling_rate * chunk_length_s						)						) * size_of_sample
							_lowerCAmelCase  	=			_ffmpeg_stream(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							for item in iterator:
														yield item






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					float     ,  SCREAMING_SNAKE_CASE:					Optional[int] = None     ,  SCREAMING_SNAKE_CASE:					Optional[Union[Tuple[float, float], float]] = None     ,  SCREAMING_SNAKE_CASE:					str = "f32le"     ,  ):






							"""simple docstring"""



							if stream_chunk_s is not None:
														_lowerCAmelCase  	=			stream_chunk_s
							else:
														_lowerCAmelCase  	=			chunk_length_s

							_lowerCAmelCase  	=			ffmpeg_microphone(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  format_for_conversion=SCREAMING_SNAKE_CASE						)
							if format_for_conversion == "s16le":
														_lowerCAmelCase  	=			np.intaa
														_lowerCAmelCase  	=			2
							elif format_for_conversion == "f32le":
														_lowerCAmelCase  	=			np.floataa
														_lowerCAmelCase  	=			4
							else:
														raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""						)

							if stride_length_s is None:
														_lowerCAmelCase  	=			chunk_length_s / 6
							_lowerCAmelCase  	=			int(round(sampling_rate * chunk_length_s						)						) * size_of_sample
							if isinstance(SCREAMING_SNAKE_CASE     ,  (int, float)						):
														_lowerCAmelCase  	=			[stride_length_s, stride_length_s]

							_lowerCAmelCase  	=			int(round(sampling_rate * stride_length_s[0]						)						) * size_of_sample
							_lowerCAmelCase  	=			int(round(sampling_rate * stride_length_s[1]						)						) * size_of_sample
							_lowerCAmelCase  	=			datetime.datetime.now()
							_lowerCAmelCase  	=			datetime.timedelta(seconds=SCREAMING_SNAKE_CASE						)
							for item in chunk_bytes_iter(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  stride=(stride_left, stride_right)     ,  stream=SCREAMING_SNAKE_CASE						):
														# Put everything back in numpy scale
														_lowerCAmelCase  	=			np.frombuffer(item['raw']     ,  dtype=SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			(
														    item['stride'][0] // size_of_sample,
														    item['stride'][1] // size_of_sample,
														)
														_lowerCAmelCase  	=			sampling_rate
														audio_time += delta
														if datetime.datetime.now() > audio_time + 10 * delta:
																					# We're late !! SKIP
																					continue
														yield item






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					Tuple[int, int]     ,  SCREAMING_SNAKE_CASE:					bool = False						):






							"""simple docstring"""



							_lowerCAmelCase  	=			B''
							_lowerCAmelCase	,		_lowerCAmelCase  	=			stride
							if stride_left + stride_right >= chunk_len:
														raise ValueError(
														    f"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"""						)
							_lowerCAmelCase  	=			0
							for raw in iterator:
														acc += raw
														if stream and len(SCREAMING_SNAKE_CASE						) < chunk_len:
																					_lowerCAmelCase  	=			(_stride_left, 0)
																					yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
														else:
																					while len(SCREAMING_SNAKE_CASE						) >= chunk_len:
																												# We are flushing the accumulator
																												_lowerCAmelCase  	=			(_stride_left, stride_right)
																												_lowerCAmelCase  	=			{'raw': acc[:chunk_len], 'stride': stride}
																												if stream:
																																			_lowerCAmelCase  	=			False
																												yield item
																												_lowerCAmelCase  	=			stride_left
																												_lowerCAmelCase  	=			acc[chunk_len - stride_left - stride_right :]
    # Last chunk
							if len(SCREAMING_SNAKE_CASE						) > stride_left:
														_lowerCAmelCase  	=			{'raw': acc, 'stride': (_stride_left, 0)}
														if stream:
																					_lowerCAmelCase  	=			False
														yield item






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			2**24  # 16Mo
							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,  stdout=subprocess.PIPE     ,  bufsize=SCREAMING_SNAKE_CASE						) as ffmpeg_process:
																					while True:
																												_lowerCAmelCase  	=			ffmpeg_process.stdout.read(SCREAMING_SNAKE_CASE						)
																												if raw == b"":
																																			break
																												yield raw
							except FileNotFoundError as error:
														raise ValueError('ffmpeg was not found but is required to stream audio files from filename'						) from error








"""simple docstring"""



from pathlib import Path

import numpy as np
from PIL import Image






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							return (gray > 127) & (gray <= 255)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray     ,  SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase  	=			np.zeros_like(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)						)

							# Copy image to padded image
							_lowerCAmelCase  	=			image

							# Iterate over image & apply kernel
							for x in range(image.shape[1]						):
														for y in range(image.shape[0]						):
																					_lowerCAmelCase  	=			(
																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																					).sum()
																					_lowerCAmelCase  	=			int(summation > 0						)
							return output


if __name__ == "__main__":
	# read original image
	_snake_case          =						Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
	_snake_case          =						np.array(Image.open(lena_path))
	# kernel to be applied
	_snake_case          =						np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
	_snake_case          =						dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
	# Save the output image
	_snake_case          =						Image.fromarray(output).convert('''RGB''')
	pil_img.save('''result_dilation.png''')






