




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
 from PIL import Image
class        __snake_case  (_a		):
    lowerCAmelCase__				=     "Salesforce/blip-image-captioning-base"
    lowerCAmelCase__				=     (
        "This is a tool that generates a description of an image. It takes an input named `image` which should be the "
        "image to caption, and returns a text that contains the description in English."
    )
    lowerCAmelCase__				=     "image_captioner"
    lowerCAmelCase__				=     AutoModelForVisionaSeq

    lowerCAmelCase__				=     ["image"]
    lowerCAmelCase__				=     ["text"]



    def __init__(      self      :		List[str]		,	*_UpperCAmelCase      :		Optional[int]		,	**_UpperCAmelCase      :		Optional[int]      )  ->    Optional[Any]:





        '''simple docstring'''




        requires_backends(self		,	["""vision"""]      )
        super().__init__(*_UpperCAmelCase		,	**_UpperCAmelCase      )



    def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		"Image"      )  ->    Tuple:





        '''simple docstring'''




        return self.pre_processor(images=_UpperCAmelCase		,	return_tensors="""pt"""      )



    def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	_UpperCAmelCase      :		List[Any]      )  ->    Any:





        '''simple docstring'''




        return self.model.generate(**_UpperCAmelCase      )



    def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	_UpperCAmelCase      :		int      )  ->    Tuple:





        '''simple docstring'''




        return self.pre_processor.batch_decode(_UpperCAmelCase		,	skip_special_tokens=_UpperCAmelCase      )[0].strip()







import cva
import numpy as np
class        __snake_case  :



				def __init__(      self      :		List[str]		,	_UpperCAmelCase      :		float		,	_UpperCAmelCase      :		int      )  ->    int:





								'''simple docstring'''




								if k in (0.04, 0.06):
												_lowerCAmelCase    :    str										=      k
												_lowerCAmelCase    :    Optional[Any]										=      window_size
								else:
												raise ValueError("""invalid k value"""      )



				def __str__(      self      :		int      )  ->    str:





								'''simple docstring'''




								return str(self.k      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      cva.imread(_UpperCAmelCase		,	0      )
								_lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      img.shape
								_lowerCAmelCase    :    list[list[int]]										=      []
								_lowerCAmelCase    :    int										=      img.copy()
								_lowerCAmelCase    :    str										=      cva.cvtColor(_UpperCAmelCase		,	cva.COLOR_GRAY2RGB      )
								_lowerCAmelCase					,    _lowerCAmelCase    :    int										=      np.gradient(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      dx**2
								_lowerCAmelCase    :    Optional[int]										=      dy**2
								_lowerCAmelCase    :    Optional[Any]										=      dx * dy
								_lowerCAmelCase    :    Dict										=      0.04
								_lowerCAmelCase    :    Tuple										=      self.window_size // 2
								for y in range(_UpperCAmelCase		,	h - offset      ):
												for x in range(_UpperCAmelCase		,	w - offset      ):
																_lowerCAmelCase    :    Optional[int]										=      ixx[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()
																_lowerCAmelCase    :    int										=      iyy[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()
																_lowerCAmelCase    :    Any										=      ixy[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()

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

