Bouletto
4e5b534b04
Complete package with Core, Creative, Vintage, Deformation, Light Effects, and Geometric categories
74 lines
3.1 KiB
Python
74 lines
3.1 KiB
Python
import numpy as np
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import torch
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class ShadowHighlightNode:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"shadow_amount": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step": 1.0}),
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"highlight_amount": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step": 1.0}),
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"shadow_width": ("FLOAT", {"default": 50.0, "min": 0.0, "max": 100.0, "step": 1.0}),
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"highlight_width": ("FLOAT", {"default": 50.0, "min": 0.0, "max": 100.0, "step": 1.0}),
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"radius": ("FLOAT", {"default": 30.0, "min": 0.0, "max": 100.0, "step": 1.0}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply_shadow_highlight"
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CATEGORY = "Image Effects"
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def apply_shadow_highlight(self, image, shadow_amount, highlight_amount, shadow_width, highlight_width, radius):
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if len(image.shape) == 4:
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img_tensor = image[0]
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else:
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img_tensor = image
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image_np = img_tensor.cpu().numpy()
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result = image_np.copy()
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# Calculer la luminance
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luminance = 0.299 * result[:,:,0] + 0.587 * result[:,:,1] + 0.114 * result[:,:,2]
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# Créer les masques pour ombres et hautes lumières
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shadow_threshold = shadow_width / 100.0
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highlight_threshold = 1.0 - (highlight_width / 100.0)
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# Masque des ombres (transition douce)
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shadow_mask = np.where(luminance < shadow_threshold,
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1.0 - (luminance / shadow_threshold),
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0.0)
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# Masque des hautes lumières (transition douce)
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highlight_mask = np.where(luminance > highlight_threshold,
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(luminance - highlight_threshold) / (1.0 - highlight_threshold),
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0.0)
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# Appliquer un flou gaussien pour adoucir les transitions
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if radius > 0:
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import cv2
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kernel_size = int(radius / 10) * 2 + 1
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shadow_mask = cv2.GaussianBlur(shadow_mask, (kernel_size, kernel_size), radius/30)
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highlight_mask = cv2.GaussianBlur(highlight_mask, (kernel_size, kernel_size), radius/30)
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# Appliquer les corrections
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shadow_factor = 1.0 + (shadow_amount / 100.0)
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highlight_factor = 1.0 + (highlight_amount / 100.0)
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# Correction des ombres
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if shadow_amount != 0:
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shadow_mask_3d = np.expand_dims(shadow_mask, axis=2)
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shadow_correction = result * shadow_factor
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result = result * (1 - shadow_mask_3d) + shadow_correction * shadow_mask_3d
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# Correction des hautes lumières
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if highlight_amount != 0:
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highlight_mask_3d = np.expand_dims(highlight_mask, axis=2)
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highlight_correction = result * highlight_factor
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result = result * (1 - highlight_mask_3d) + highlight_correction * highlight_mask_3d
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result = np.clip(result, 0, 1)
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result_tensor = torch.from_numpy(result).unsqueeze(0)
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return (result_tensor,)
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