Bouletto
4e5b534b04
Complete package with Core, Creative, Vintage, Deformation, Light Effects, and Geometric categories
85 lines
3.3 KiB
Python
85 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 RippleNode:
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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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"amplitude": ("FLOAT", {"default": 20.0, "min": 0.0, "max": 100.0, "step": 1.0}),
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"frequency": ("FLOAT", {"default": 0.02, "min": 0.001, "max": 0.1, "step": 0.001}),
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"wave_type": (["sine", "cosine", "both"], {"default": "sine"}),
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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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"phase": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 6.28, "step": 0.1}),
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"decay": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply_ripple"
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CATEGORY = "Image Effects"
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def apply_ripple(self, image, amplitude, frequency, wave_type,
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center_x=0.5, center_y=0.5, phase=0.0, decay=0.0):
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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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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 des ondulations
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cx = w * center_x
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cy = h * center_y
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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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# Facteur de décroissance
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decay_factor = 1.0
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if decay > 0:
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max_dist = np.sqrt(w*w + h*h) / 2
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decay_factor = np.exp(-decay * distance / max_dist)
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# Calcul de l'ondulation
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if wave_type == "sine":
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ripple = np.sin(distance * frequency + phase)
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elif wave_type == "cosine":
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ripple = np.cos(distance * frequency + phase)
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else: # both
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ripple = (np.sin(distance * frequency + phase) + np.cos(distance * frequency + phase)) / 2
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# Amplitude avec décroissance
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effective_amplitude = amplitude * decay_factor * ripple
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# Direction de l'ondulation (radiale)
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if distance > 0:
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angle = np.arctan2(dy, dx)
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offset_x = effective_amplitude * np.cos(angle)
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offset_y = effective_amplitude * np.sin(angle)
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else:
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offset_x = offset_y = 0
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map_x[y, x] = np.clip(x + offset_x, 0, w - 1)
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map_y[y, x] = np.clip(y + offset_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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