
"""simple docstring"""




from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
						import numpy as np
						from decord import VideoReader


if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

lowerCAmelCase__							=				logging.get_logger(__name__)





@add_end_docstrings(__lowercase   )
class  snake_case     (							__lowercase   ):

							def __init__(self       ,       *SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__(*SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
									requires_backends(self       ,       '''decord'''    )
									self.check_model_type(SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {}
									if frame_sampling_rate is not None:
											SCREAMING_SNAKE_CASE_						 = frame_sampling_rate
									if num_frames is not None:
											SCREAMING_SNAKE_CASE_						 = num_frames

									SCREAMING_SNAKE_CASE_						 = {}
									if top_k is not None:
											SCREAMING_SNAKE_CASE_						 = top_k
									return preprocess_params, {}, postprocess_params

							def __call__(self       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return super().__call__(SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=1    ):

									"""simple docstring"""







									if num_frames is None:
											SCREAMING_SNAKE_CASE_						 = self.model.config.num_frames

									if video.startswith('''http://'''    ) or video.startswith('''https://'''    ):
											SCREAMING_SNAKE_CASE_						 = BytesIO(requests.get(SCREAMING_SNAKE_CASE_    ).content    )

									SCREAMING_SNAKE_CASE_						 = VideoReader(SCREAMING_SNAKE_CASE_    )
									videoreader.seek(0    )

									SCREAMING_SNAKE_CASE_						 = 0
									SCREAMING_SNAKE_CASE_						 = num_frames * frame_sampling_rate - 1
									SCREAMING_SNAKE_CASE_						 = np.linspace(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num=SCREAMING_SNAKE_CASE_       ,       dtype=np.intaa    )

									SCREAMING_SNAKE_CASE_						 = videoreader.get_batch(SCREAMING_SNAKE_CASE_    ).asnumpy()
									SCREAMING_SNAKE_CASE_						 = list(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.image_processor(SCREAMING_SNAKE_CASE_       ,       return_tensors=self.framework    )
									return model_inputs

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model(**SCREAMING_SNAKE_CASE_    )
									return model_outputs







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=5    ):

									"""simple docstring"""







									if top_k > self.model.config.num_labels:
											SCREAMING_SNAKE_CASE_						 = self.model.config.num_labels

									if self.framework == "pt":
											SCREAMING_SNAKE_CASE_						 = model_outputs.logits.softmax(-1    )[0]
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = probs.topk(SCREAMING_SNAKE_CASE_    )
									else:
											raise ValueError(f'Unsupported framework: {self.framework}'    )

									SCREAMING_SNAKE_CASE_						 = scores.tolist()
									SCREAMING_SNAKE_CASE_						 = ids.tolist()
									return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )]

"""simple docstring"""




import cva
import numpy as np





class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""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    ):

									"""simple docstring"""







									return str(self.k    )







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = cva.imread(SCREAMING_SNAKE_CASE_       ,       0    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = img.shape
									SCREAMING_SNAKE_CASE_						 = []
									SCREAMING_SNAKE_CASE_						 = img.copy()
									SCREAMING_SNAKE_CASE_						 = cva.cvtColor(SCREAMING_SNAKE_CASE_       ,       cva.COLOR_GRAY2RGB    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = np.gradient(SCREAMING_SNAKE_CASE_    )
									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(SCREAMING_SNAKE_CASE_       ,       h - offset    ):
											for x in range(SCREAMING_SNAKE_CASE_       ,       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)       ,       2_55    )
									return color_img, corner_list


if __name__ == "__main__":
						lowerCAmelCase__							=				HarrisCorner(0.04, 3)
						lowerCAmelCase__,  lowerCAmelCase__							=				edge_detect.detect('path_to_image')
						cva.imwrite('detect.png', color_img)