






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve
def 		_a					(   a				:int				)							->    Optional[int]:
 a							=   np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]				)
 a							=   np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]				)

 a							=   np.abs(img_convolve(a			,							a				)				)
 a							=   np.abs(img_convolve(a			,							a				)				)
 # modify the pix within [0, 255]
 a							=   dst_x * 255 / np.max(a				)
 a							=   dst_y * 255 / np.max(a				)

 a							=   np.sqrt((np.square(a				)) + (np.square(a				))				)
 a							=   dst_xy * 255 / np.max(a				)
 a							=   dst_xy.astype(np.uinta				)

 a							=   np.arctana(a			,							a				)
 return dst, theta


if __name__ == "__main__":
     # read original image
     UpperCAmelCase__            =	imread("../image_data/lena.jpg")
     # turn image in gray scale value
     UpperCAmelCase__            =	cvtColor(img, COLOR_BGR2GRAY)

     UpperCAmelCase__     ,   UpperCAmelCase__            =	sobel_filter(gray)

     # show result images
     imshow("sobel filter", sobel_grad)
     imshow("sobel theta", sobel_theta)
     waitKey(0)










from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
     from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline


