




"""simple docstring"""

import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class 	lowerCamelCase__			(     unittest.TestCase	):
			a__       :					Tuple	    =   MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]    	= hf_hub_download(
					    repo_id="nateraw/video-demo" ,       filename="archery.mp4" ,       repo_type="dataset"							)
					snake_case						:  Any    	= VideoClassificationPipeline(model=UpperCAmelCase__ ,       image_processor=UpperCAmelCase__ ,       top_k=2							)
					snake_case						:  List[Any]    	= [
					    example_video_filepath,
					    """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
					]
					return video_classifier, examples


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					for example in examples:
							snake_case						:  Tuple    	= video_classifier(UpperCAmelCase__							)

							self.assertEqual(
							    UpperCAmelCase__ ,       [
							        {"score": ANY(UpperCAmelCase__							), "label": ANY(UpperCAmelCase__							)},
							        {"score": ANY(UpperCAmelCase__							), "label": ANY(UpperCAmelCase__							)},
							    ] ,       )


			@require_torch
			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Optional[Any]    	= """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
					snake_case						:  List[Any]    	= VideoMAEFeatureExtractor(
					    size={"shortest_edge": 10} ,       crop_size={"height": 10, "width": 10}							)
					snake_case						:  int    	= pipeline(
					    "video-classification" ,       model=UpperCAmelCase__ ,       feature_extractor=UpperCAmelCase__ ,       frame_sampling_rate=4							)

					snake_case						:  str    	= hf_hub_download(repo_id="nateraw/video-demo" ,       filename="archery.mp4" ,       repo_type="dataset"							)
					snake_case						:  Tuple    	= video_classifier(UpperCAmelCase__ ,       top_k=2							)
					self.assertEqual(
					    nested_simplify(UpperCAmelCase__ ,       decimals=4							) ,       [{"score": 0.51_99, "label": "LABEL_0"}, {"score": 0.48_01, "label": "LABEL_1"}] ,       )

					snake_case						:  Optional[int]    	= video_classifier(
					    [
					        video_file_path,
					        video_file_path,
					    ] ,       top_k=2 ,       )
					self.assertEqual(
					    nested_simplify(UpperCAmelCase__ ,       decimals=4							) ,       [
					        [{"score": 0.51_99, "label": "LABEL_0"}, {"score": 0.48_01, "label": "LABEL_1"}],
					        [{"score": 0.51_99, "label": "LABEL_0"}, {"score": 0.48_01, "label": "LABEL_1"}],
					    ] ,       )



			@require_tf
			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					pass









"""simple docstring"""




import cva
import numpy as np






class        UpperCamelCase			:


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						float							,						UpperCAmelCase__		:						int							)					->       Dict:

   if k in (0.0_4, 0.0_6):
     _a  :       List[str]             =	k
     _a  :       List[Any]             =	window_size
   else:
     raise ValueError("""invalid k value"""							)


 def __str__(     self		:						Dict							)					->       str:
   return str(self.k							)







 def _lowercase	(     self		:						int							,						UpperCAmelCase__		:						str							)					->       tuple[cva.Mat, list[list[int]]]:
   _a  :       Dict             =	cva.imread(UpperCAmelCase__							,						0							)
   _a      ,  _a  :       List[Any]             =	img.shape
   _a  :       list[list[int]]             =	[]
   _a  :       List[Any]             =	img.copy()
   _a  :       int             =	cva.cvtColor(UpperCAmelCase__							,						cva.COLOR_GRAY2RGB							)
   _a      ,  _a  :       Any             =	np.gradient(UpperCAmelCase__							)
   _a  :       Tuple             =	dx**2
   _a  :       Union[str, Any]             =	dy**2
   _a  :       Union[str, Any]             =	dx * dy
   _a  :       int             =	0.0_4
   _a  :       List[str]             =	self.window_size // 2
   for y in range(UpperCAmelCase__							,						h - offset							):
     for x in range(UpperCAmelCase__							,						w - offset							):
       _a  :       str             =	ixx[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       List[Any]             =	iyy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       Tuple             =	ixy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()

       _a  :       Any             =	(wxx * wyy) - (wxy**2)
       _a  :       Tuple             =	wxx + wyy
       _a  :       Any             =	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__":
 _snake_case          =   HarrisCorner(0.04, 3)
 _snake_case      , _snake_case          =   edge_detect.detect('path_to_image')
 cva.imwrite('detect.png', color_img)

