93 lines
2.5 KiB
Python
93 lines
2.5 KiB
Python
import cv2
|
|
import numpy as np
|
|
import torch
|
|
|
|
|
|
class CannyEdgeDetection:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"lower_threshold": ("INT", {
|
|
"default": 100,
|
|
"min": 0,
|
|
"max": 500,
|
|
"step": 10
|
|
}),
|
|
"upper_threshold": ("INT", {
|
|
"default": 200,
|
|
"min": 0,
|
|
"max": 500,
|
|
"step": 10
|
|
}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "canny"
|
|
|
|
CATEGORY = "postprocessing"
|
|
|
|
def canny(self, image: torch.Tensor, lower_threshold: int, upper_threshold: int):
|
|
batch_size, height, width, _ = image.shape
|
|
result = torch.zeros(batch_size, height, width)
|
|
|
|
for b in range(batch_size):
|
|
tensor_image = image[b].numpy().copy()
|
|
gray_image = (cv2.cvtColor(tensor_image, cv2.COLOR_RGB2GRAY) * 255).astype(np.uint8)
|
|
canny = cv2.Canny(gray_image, lower_threshold, upper_threshold)
|
|
tensor = torch.from_numpy(canny)
|
|
result[b] = tensor
|
|
|
|
return (result,)
|
|
|
|
class GaussianBlur:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"kernel_size": ("INT", {
|
|
"default": 5,
|
|
"min": 1,
|
|
"max": 31,
|
|
"step": 1
|
|
}),
|
|
"sigma": ("FLOAT", {
|
|
"default": 1.0,
|
|
"min": 0.1,
|
|
"max": 10.0,
|
|
"step": 0.1
|
|
}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "blur"
|
|
|
|
CATEGORY = "postprocessing"
|
|
|
|
def blur(self, image: torch.Tensor, kernel_size: int, sigma: float):
|
|
batch_size, height, width, _ = image.shape
|
|
result = torch.zeros_like(image)
|
|
|
|
for b in range(batch_size):
|
|
tensor_image = image[b].numpy()
|
|
blurred = cv2.GaussianBlur(tensor_image, (kernel_size, kernel_size), sigma)
|
|
tensor = torch.from_numpy(blurred).unsqueeze(0)
|
|
result[b] = tensor
|
|
|
|
return (result,)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"CannyEdgeDetection": CannyEdgeDetection,
|
|
"GaussianBlur": GaussianBlur,
|
|
}
|