


'''simple docstring'''







import cva
import numpy as np


class 			lowerCAmelCase_    :





  """simple docstring"""



  def __init__(       self  :				Optional[int]					,				SCREAMING_SNAKE_CASE__  :				int					,				SCREAMING_SNAKE_CASE__  :				List[str]    ):





       '''simple docstring'''

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

  def __str__(       self  :				Optional[int]    ):





       '''simple docstring'''

       return str(self.k    )


  def __a    (       self  :				List[str]					,				SCREAMING_SNAKE_CASE__  :				Any    ):





       '''simple docstring'''

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

                 __a   			= (wxx * wyy) - (wxy**2)
                 __a   			= wxx + wyy
                 __a   			= 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)					,				2_5_5    )
       return color_img, corner_list


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_   =      HarrisCorner(0.0_4, 3)
 SCREAMING_SNAKE_CASE_   =      edge_detect.detect('path_to_image')
 cva.imwrite('detect.png', color_img)









import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_UpperCAmelCase						:  List[Any]									=			{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
					_UpperCAmelCase						:  List[str]									=			"""hopper-medium-v2"""
					_UpperCAmelCase						:  Any									=			gym.make(env_name)

					_UpperCAmelCase						:  Tuple									=			ValueGuidedRLPipeline.from_pretrained(
					    """bglick13/hopper-medium-v2-value-function-hor32""",
					    env=env,
					)

					env.seed(0)
					_UpperCAmelCase						:  Tuple									=			env.reset()
					_UpperCAmelCase						:  str									=			0
					_UpperCAmelCase						:  Union[str, Any]									=			0
					_UpperCAmelCase						:  List[str]									=			1000
					_UpperCAmelCase						:  Optional[int]									=			[obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															_UpperCAmelCase						:  int									=			pipeline(obs, planning_horizon=32)

															# execute action in environment
															_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase						:  str									=			env.step(denorm_actions)
															_UpperCAmelCase						:  Tuple									=			env.get_normalized_score(total_reward)

															# update return
															total_reward += reward
															total_score += score
															print(
															    F'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
															    F''' {total_score}'''
															)

															# save observations for rendering
															rollout.append(next_observation.copy())

															_UpperCAmelCase						:  Dict									=			next_observation
					except KeyboardInterrupt:
										pass

					print(F'''Total reward: {total_reward}''')




