Bouletto
4e5b534b04
Complete package with Core, Creative, Vintage, Deformation, Light Effects, and Geometric categories
74 lines
2.9 KiB
Python
74 lines
2.9 KiB
Python
import numpy as np
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import torch
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import cv2
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class VibranceNode:
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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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"vibrance": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step": 1.0}),
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"saturation": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step": 1.0}),
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},
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"optional": {
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"protect_skin_tones": ("BOOLEAN", {"default": True}),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply_vibrance"
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CATEGORY = "Image Effects"
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def apply_vibrance(self, image, vibrance, saturation, protect_skin_tones=True, strength=1.0):
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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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# Convertir en HSV pour les calculs de saturation
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hsv = cv2.cvtColor((result * 255).astype(np.uint8), cv2.COLOR_RGB2HSV).astype(np.float32)
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hsv[:,:,1] /= 255.0 # Normaliser la saturation
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hsv[:,:,2] /= 255.0 # Normaliser la valeur
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# Calculer la saturation actuelle
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current_saturation = hsv[:,:,1]
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# Appliquer la vibrance (effet sélectif)
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if vibrance != 0:
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vibrance_factor = vibrance / 100.0
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# La vibrance affecte moins les couleurs déjà saturées
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vibrance_mask = 1.0 - current_saturation
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vibrance_adjustment = vibrance_factor * vibrance_mask * strength
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hsv[:,:,1] = np.clip(current_saturation + vibrance_adjustment, 0, 1)
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# Appliquer la saturation globale
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if saturation != 0:
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saturation_factor = 1.0 + (saturation / 100.0) * strength
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hsv[:,:,1] = np.clip(hsv[:,:,1] * saturation_factor, 0, 1)
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# Protection des tons chair
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if protect_skin_tones:
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# Détecter les tons chair (teinte entre 0-30 et 330-360 degrés)
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hue = hsv[:,:,0] * 2 # Convertir en degrés (0-360)
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skin_mask = ((hue >= 0) & (hue <= 30)) | ((hue >= 330) & (hue <= 360))
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skin_protection = np.where(skin_mask, 0.5, 1.0)
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skin_protection = np.expand_dims(skin_protection, axis=2)
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# Réduire l'effet sur les tons chair
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protected_result = image_np * (1 - skin_protection) + result * skin_protection
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result = protected_result
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# Reconvertir en RGB
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hsv[:,:,1] *= 255
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hsv[:,:,2] *= 255
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result = cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2RGB).astype(np.float32) / 255.0
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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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