




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,
)
UpperCAmelCase__				     = logging.getLogger(__name__)







def 		_A(       UpperCamelCase__							:			str				)    -> List[str]:
							'''simple docstring'''


							__lowercase      =    git.Repo(search_parent_directories=_lowerCamelCase				)
							__lowercase      =    {
							    "repo_id": str(_lowerCamelCase				),
							    "repo_sha": str(repo.head.object.hexsha				),
							    "repo_branch": str(repo.active_branch				),
							}

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







def 		_A(       UpperCamelCase__							:			List[str]				)    -> int:
							'''simple docstring'''


							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 		_A(       UpperCamelCase__							:			List[Any]				)    -> List[Any]:
							'''simple docstring'''


							np.random.seed(args.seed				)
							torch.manual_seed(args.seed				)
							if args.n_gpu > 0:
														torch.cuda.manual_seed_all(args.seed				)







import cva
import numpy as np







class a :
						"""simple docstring"""

						def __init__( self :     Dict				,							lowerCamelCase__ :     float				,							lowerCamelCase__ :     int				)    ->						Dict:
													"""simple docstring"""


													if k in (0.0_4, 0.0_6):
																				__lowercase      =    k
																				__lowercase      =    window_size
													else:
																				raise ValueError('''invalid k value'''				)






						def __str__( self :     int				)    ->						str:
													"""simple docstring"""


													return str(self.k				)






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     str				)    ->						tuple[cva.Mat, list[list[int]]]:
													"""simple docstring"""


													__lowercase      =    cva.imread(lowerCamelCase__				,							0				)
													__lowercase     ,     __lowercase      =    img.shape
													__lowercase      =    []
													__lowercase      =    img.copy()
													__lowercase      =    cva.cvtColor(lowerCamelCase__				,							cva.COLOR_GRAY2RGB				)
													__lowercase     ,     __lowercase      =    np.gradient(lowerCamelCase__				)
													__lowercase      =    dx**2
													__lowercase      =    dy**2
													__lowercase      =    dx * dy
													__lowercase      =    0.0_4
													__lowercase      =    self.window_size // 2
													for y in range(lowerCamelCase__				,							h - offset				):
																				for x in range(lowerCamelCase__				,							w - offset				):
																											__lowercase      =    ixx[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()
																											__lowercase      =    iyy[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()
																											__lowercase      =    ixy[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()

																											__lowercase      =    (wxx * wyy) - (wxy**2)
																											__lowercase      =    wxx + wyy
																											__lowercase      =    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__":
			UpperCAmelCase__				     = HarrisCorner(0.04, 3)
			UpperCAmelCase__     ,							UpperCAmelCase__				     = edge_detect.detect("path_to_image")
			cva.imwrite("detect.png", color_img)






