58 lines
1.5 KiB
Python
58 lines
1.5 KiB
Python
import cv2
|
|
import numpy as np
|
|
import torch
|
|
|
|
|
|
class Sharpen:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"kernel_size": ("INT", {
|
|
"default": 5,
|
|
"min": 1,
|
|
"max": 31,
|
|
"step": 1
|
|
}),
|
|
"alpha": ("FLOAT", {
|
|
"default": 1.0,
|
|
"min": 0.1,
|
|
"max": 5.0,
|
|
"step": 0.1
|
|
}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "sharpen"
|
|
|
|
CATEGORY = "postprocessing"
|
|
|
|
def sharpen(self, image: torch.Tensor, kernel_size: int, alpha: float):
|
|
batch_size, height, width, _ = image.shape
|
|
result = torch.zeros_like(image)
|
|
|
|
for b in range(batch_size):
|
|
tensor_image = image[b].numpy()
|
|
|
|
kernel = np.ones((kernel_size, kernel_size), dtype=np.float32) * -1
|
|
center = kernel_size // 2
|
|
kernel[center, center] = kernel_size**2
|
|
kernel *= alpha
|
|
|
|
sharpened = cv2.filter2D(tensor_image, -1, kernel)
|
|
|
|
tensor = torch.from_numpy(sharpened).unsqueeze(0)
|
|
tensor = torch.clamp(tensor, 0, 1)
|
|
result[b] = tensor
|
|
|
|
return (result,)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"Sharpen": Sharpen
|
|
}
|