






'''simple docstring'''


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







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      int						):

						# prepare kernel
						# the kernel size have to be odd
						if (ksize % 2) == 0:
												lowercase        =							ksize + 1
						lowercase        =							np.zeros((ksize, ksize) ,  dtype=np.floataa						)

						# each value
						for y in range(lowercase_						):
												for x in range(lowercase_						):
																		# distance from center
																		lowercase        =							x - ksize // 2
																		lowercase        =							y - ksize // 2

																		# degree to radiant
																		lowercase        =							theta / 180 * np.pi
																		lowercase        =							np.cos(_theta						)
																		lowercase        =							np.sin(_theta						)

																		# get kernel x
																		lowercase        =							cos_theta * px + sin_theta * py

																		# get kernel y
																		lowercase        =							-sin_theta * px + cos_theta * py

																		# fill kernel
																		lowercase        =							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
						lowercase_      :				List[Any]   					=    imread('''../image_data/lena.jpg''')
						# turn image in gray scale value
						lowercase_      :				str   					=    cvtColor(img, COLOR_BGR2GRAY)

						# Apply multiple Kernel to detect edges
						lowercase_      :				Union[str, Any]   					=    np.zeros(gray.shape[:2])
						for theta in [0, 30, 60, 90, 120, 150]:
												lowercase_      :				Dict   					=    gabor_filter_kernel(10, 8, theta, 10, 0, 0)
												out += filteraD(gray, CV_8UC3, kernel_aa)
						lowercase_      :				Tuple   					=    out / out.max() * 255
						lowercase_      :				Dict   					=    out.astype(np.uinta)

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

						waitKey(0)







'''simple docstring'''


import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
lowercase_      :				Tuple   					=    logging.getLogger(__name__)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						lowercase        =							git.Repo(search_parent_directories=lowercase_						)
						lowercase        =							{
						    """repo_id""": str(lowercase_						),
						    """repo_sha""": str(repo.head.object.hexsha						),
						    """repo_branch""": str(repo.active_branch						),
						}

						with open(os.path.join(lowercase_ ,  """git_log.json"""						) ,  """w"""						) as f:
												json.dump(lowercase_ ,  lowercase_ ,  indent=4						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						if params.n_gpu <= 0:
												lowercase        =							0
												lowercase        =							-1
												lowercase        =							True
												lowercase        =							False
												return

						assert torch.cuda.is_available()

						logger.info("""Initializing GPUs"""						)
						if params.n_gpu > 1:
												assert params.local_rank != -1

												lowercase        =							int(os.environ["""WORLD_SIZE"""]						)
												lowercase        =							int(os.environ["""N_GPU_NODE"""]						)
												lowercase        =							int(os.environ["""RANK"""]						)

												# number of nodes / node ID
												lowercase        =							params.world_size // params.n_gpu_per_node
												lowercase        =							params.global_rank // params.n_gpu_per_node
												lowercase        =							True

												assert params.n_nodes == int(os.environ["""N_NODES"""]						)
												assert params.node_id == int(os.environ["""NODE_RANK"""]						)

						# local job (single GPU)
						else:
												assert params.local_rank == -1

												lowercase        =							1
												lowercase        =							0
												lowercase        =							0
												lowercase        =							0
												lowercase        =							1
												lowercase        =							1
												lowercase        =							False

						# sanity checks
						assert params.n_nodes >= 1
						assert 0 <= params.node_id < params.n_nodes
						assert 0 <= params.local_rank <= params.global_rank < params.world_size
						assert params.world_size == params.n_nodes * params.n_gpu_per_node

						# define whether this is the master process / if we are in multi-node distributed mode
						lowercase        =							params.node_id == 0 and params.local_rank == 0
						lowercase        =							params.n_nodes > 1

						# summary
						lowercase        =							F"""--- Global rank: {params.global_rank} - """
						logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes						)
						logger.info(PREFIX + """Node ID        : %i""" % params.node_id						)
						logger.info(PREFIX + """Local rank     : %i""" % params.local_rank						)
						logger.info(PREFIX + """World size     : %i""" % params.world_size						)
						logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node						)
						logger.info(PREFIX + """Master         : %s""" % str(params.is_master						)						)
						logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node						)						)
						logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu						)						)
						logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()						)

						# set GPU device
						torch.cuda.set_device(params.local_rank						)

						# initialize multi-GPU
						if params.multi_gpu:
												logger.info("""Initializing PyTorch distributed"""						)
												torch.distributed.init_process_group(
												    init_method="""env://""" ,  backend="""nccl""" ,  )







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any]						):
						np.random.seed(args.seed						)
						torch.manual_seed(args.seed						)
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed						)


