51 lines
1.4 KiB
Python
51 lines
1.4 KiB
Python
import cv2
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import numpy as np
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import torch
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class CannyEdgeDetection:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"lower_threshold": ("INT", {
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"default": 100,
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"min": 0,
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"max": 500,
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"step": 10
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}),
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"upper_threshold": ("INT", {
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"default": 200,
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"min": 0,
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"max": 500,
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"step": 10
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "canny"
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CATEGORY = "postprocessing"
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def canny(self, image: torch.Tensor, lower_threshold: int, upper_threshold: int):
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batch_size, height, width, _ = image.shape
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result = torch.zeros(batch_size, height, width)
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for b in range(batch_size):
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tensor_image = image[b].numpy().copy()
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gray_image = (cv2.cvtColor(tensor_image, cv2.COLOR_RGB2GRAY) * 255).astype(np.uint8)
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canny = cv2.Canny(gray_image, lower_threshold, upper_threshold)
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tensor = torch.from_numpy(canny)
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result[b] = tensor
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return (result,)
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NODE_CLASS_MAPPINGS = {
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"CannyEdgeDetection": CannyEdgeDetection
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}
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