Bouletto
4e5b534b04
Complete package with Core, Creative, Vintage, Deformation, Light Effects, and Geometric categories
91 lines
3.3 KiB
Python
91 lines
3.3 KiB
Python
import numpy as np
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import torch
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import cv2
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class PinchNode:
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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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"strength": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 2.0, "step": 0.1}),
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"radius": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.05}),
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},
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"optional": {
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"center_x": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"center_y": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"falloff": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply_pinch"
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CATEGORY = "Image Effects"
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def apply_pinch(self, image, strength, radius, center_x=0.5, center_y=0.5, falloff=0.5):
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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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img_np = (img_tensor.cpu().numpy() * 255).astype(np.uint8)
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h, w, c = img_np.shape
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# Centre de l'effet
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cx = w * center_x
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cy = h * center_y
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# Rayon effectif
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effect_radius = min(w, h) / 2 * radius
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# Créer les grilles de coordonnées
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map_x = np.zeros((h, w), dtype=np.float32)
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map_y = np.zeros((h, w), dtype=np.float32)
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for y in range(h):
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for x in range(w):
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# Distance du centre
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dx = x - cx
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dy = y - cy
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distance = np.sqrt(dx*dx + dy*dy)
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if distance == 0 or distance > effect_radius:
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map_x[y, x] = x
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map_y[y, x] = y
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continue
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# Facteur de distance normalisé
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norm_distance = distance / effect_radius
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# Calcul du facteur de pincement avec falloff
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if falloff > 0:
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# Falloff doux
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falloff_factor = 1 - np.power(norm_distance, 1 / falloff)
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else:
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# Falloff linéaire
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falloff_factor = 1 - norm_distance
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# Facteur de pincement
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if strength > 0:
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# Pincement vers l'intérieur
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pinch_factor = 1 - (strength * falloff_factor)
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else:
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# Étirement vers l'extérieur
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pinch_factor = 1 + (abs(strength) * falloff_factor)
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# Nouvelles coordonnées
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new_distance = distance * pinch_factor
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angle = np.arctan2(dy, dx)
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new_x = cx + new_distance * np.cos(angle)
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new_y = cy + new_distance * np.sin(angle)
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map_x[y, x] = np.clip(new_x, 0, w - 1)
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map_y[y, x] = np.clip(new_y, 0, h - 1)
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# Appliquer la transformation
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result = cv2.remap(img_np, map_x, map_y, cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
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result_tensor = torch.from_numpy(result.astype(np.float32) / 255.0).unsqueeze(0)
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return (result_tensor,)
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