

'''simple docstring'''
import cva
import numpy as np







class 							A__    :





  def __init__(		self      :	Tuple							,					_a      :	float							,					_a      :	int )	->    List[Any]:





       '''simple docstring'''






       if k in (0.04, 0.06):
            _SCREAMING_SNAKE_CASE							    =k
            _SCREAMING_SNAKE_CASE							    =window_size
       else:
            raise ValueError('invalid k value' )





  def __str__(		self      :	Any )	->    str:





       '''simple docstring'''






       return str(self.k )





  def 						A  (		self      :	Optional[int]							,					_a      :	str )	->    tuple[cva.Mat, list[list[int]]]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =cva.imread(_a							,					0 )
       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =img.shape
       _SCREAMING_SNAKE_CASE							    =[]
       _SCREAMING_SNAKE_CASE							    =img.copy()
       _SCREAMING_SNAKE_CASE							    =cva.cvtColor(_a							,					cva.COLOR_GRAY2RGB )
       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =np.gradient(_a )
       _SCREAMING_SNAKE_CASE							    =dx**2
       _SCREAMING_SNAKE_CASE							    =dy**2
       _SCREAMING_SNAKE_CASE							    =dx * dy
       _SCREAMING_SNAKE_CASE							    =0.04
       _SCREAMING_SNAKE_CASE							    =self.window_size // 2
       for y in range(_a							,					h - offset ):
            for x in range(_a							,					w - offset ):
                 _SCREAMING_SNAKE_CASE							    =ixx[
                     y - offset : y + offset + 1, x - offset : x + offset + 1
                 ].sum()
                 _SCREAMING_SNAKE_CASE							    =iyy[
                     y - offset : y + offset + 1, x - offset : x + offset + 1
                 ].sum()
                 _SCREAMING_SNAKE_CASE							    =ixy[
                     y - offset : y + offset + 1, x - offset : x + offset + 1
                 ].sum()

                 _SCREAMING_SNAKE_CASE							    =(wxx * wyy) - (wxy**2)
                 _SCREAMING_SNAKE_CASE							    =wxx + wyy
                 _SCREAMING_SNAKE_CASE							    =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 :      str					 =     HarrisCorner(0.0_4, 3)
     lowerCamelCase ,    lowerCamelCase :      Union[str, Any]					 =     edge_detect.detect("path_to_image")
     cva.imwrite("detect.png", color_img)



'''simple docstring'''
import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     Any=False				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =OmegaConf.load(_UpperCamelCase				)
     if display:
          print(yaml.dump(OmegaConf.to_container(_UpperCamelCase				)				)				)
     return config


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Optional[Any]=None							, _UpperCamelCase						:     Union[str, Any]=None				)      ->			Optional[Any]:
     """simple docstring"""




     if conf_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.yaml'
     _SCREAMING_SNAKE_CASE							    =load_config(_UpperCamelCase							, display=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =VQModel(**config.model.params				)
     if ckpt_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.pt'
     _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location=_UpperCamelCase				)
     if ".ckpt" in ckpt_path:
          _SCREAMING_SNAKE_CASE							    =sd['state_dict']
     model.load_state_dict(_UpperCamelCase							, strict=_UpperCamelCase				)
     model.to(_UpperCamelCase				)
     del sd
     return model


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     int				)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =model.encode(_UpperCamelCase				)
     print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"				)
     _SCREAMING_SNAKE_CASE							    =model.decode(_UpperCamelCase				)
     return xrec


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     List[str]=False				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =string.rsplit('.'							, 1				)
     if reload:
          _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)
          importlib.reload(_UpperCamelCase				)
     return getattr(importlib.import_module(_UpperCamelCase							, package=_UpperCamelCase				)							, cls				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			List[str]:
     """simple docstring"""




     if "target" not in config:
          raise KeyError('Expected key `target` to instantiate.'				)
     return get_obj_from_str(config['target']				)(**config.get('params'							, {}				)				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     List[str]=True							, _UpperCamelCase						:     int=True				)      ->			Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =instantiate_from_config(_UpperCamelCase				)
     if sd is not None:
          model.load_state_dict(_UpperCamelCase				)
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}







def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[int]				)      ->			List[Any]:
     """simple docstring"""




     if ckpt:
          _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location='cpu'				)
          _SCREAMING_SNAKE_CASE							    =pl_sd['global_step']
          print(f"loaded model from global step {global_step}."				)
     else:
          _SCREAMING_SNAKE_CASE							    ={'state_dict': None}
          _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =load_model_from_config(config.model							, pl_sd['state_dict']							, gpu=_UpperCamelCase							, eval_mode=_UpperCamelCase				)['model']
     return model, global_step
