a  d @sNddlZddlZddlZddlmZddZd dd ZGd d d ej j Z dS) N) functionalc Cs|d}|\}}}}|ddkrPtj||d|d|d|dfdd}ntd|dd\}}|d dkr|||d||}|dd||}tj||d d ||||S|d||||}||d||d|dd||d||}tj||||d ||||SdS) zuPyTorch version of cv2.filter2D Args: img (Tensor): (b, c, h, w) kernel (Tensor): (b, k, k) Zreflect)modezWrong kernel sizeNr)padding)groups)sizeFpad ValueErrorviewZconv2drepeat) imgkernelkbchwphpwrg/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/basicsr/utils/img_process_util.pyfilter2Ds  *,r?2 c Cs|ddkr|d7}t|||fd}||}t|d|k}|d}t|||fd}|||}t|dd}||d||S)aUSM sharpening. Input image: I; Blurry image: B. 1. sharp = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > threshold, else: 0 3. Blur mask: 4. Out = Mask * sharp + (1 - Mask) * I Args: img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. weight (float): Sharp weight. Default: 1. radius (float): Kernel size of Gaussian blur. Default: 50. threshold (int): rrrfloat32)cv2Z GaussianBlurnpabsZastypeclip) rweightradius thresholdblurresidualmask soft_masksharprrr usm_sharp"s   r-cs(eZdZd fdd Zd ddZZS) USMSharprrcs`tt||ddkr"|d7}||_t||}tt ||  d}| d|dS)Nrrrr) superr.__init__r&r!ZgetGaussianKerneltorchZ FloatTensorr"dotZ transposeZ unsqueeze_Zregister_buffer)selfr&sigmar __class__rrr0As  zUSMSharp.__init__rrc Csht||j}||}t|d|k}|}t||j}|||}t|dd}||d||S)Nrrr)rrr1r#floatr$) r3rr%r'r(r)r*r+r,rrrforwardJs   zUSMSharp.forward)rr)rr)__name__ __module__ __qualname__r0r8 __classcell__rrr5rr.?s r.)rrr) r!numpyr"r1Ztorch.nnrr rr-nnModuler.rrrrs