259 lines
10 KiB
Python
259 lines
10 KiB
Python
# nodes.py
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import torch
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import numpy as np
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from sklearn.cluster import KMeans
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import colorsys
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import json
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import random
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from PIL import Image, ImageDraw, ImageFont
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import io
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import time
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class ColorshiftColorNode:
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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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"color_count": ("INT", {"default": 8, "min": 2, "max": 32}),
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},
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"optional": {
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"lock_mask": ("MASK", {"default": None}),
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"palette_override": ("PALETTE", {"default": None}),
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"font_size": ("INT", {"default": 20, "min": 10, "max": 50}),
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}
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}
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RETURN_TYPES = ("IMAGE", "PALETTE", "MASK", "IMAGE")
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RETURN_NAMES = ("image", "palette", "index_map", "palette_preview")
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FUNCTION = "process"
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CATEGORY = "Image/Color"
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def process(self, image, color_count, lock_mask=None, palette_override=None, font_size=20):
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# Convert torch tensor to numpy array
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img_np = image.numpy().squeeze()
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height, width = img_np.shape[:2]
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pixels = img_np.reshape(-1, 3)
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if palette_override is not None:
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if hasattr(palette_override, 'numpy'):
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palette = palette_override.numpy()
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else:
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palette = palette_override
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labels = self._match_palette(pixels, palette)
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else:
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kmeans = KMeans(n_clusters=color_count, random_state=42)
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labels = kmeans.fit_predict(pixels)
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palette = kmeans.cluster_centers_
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# Generate palette preview
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preview_img = self.generate_palette_preview(palette, font_size)
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# Get the unique index map first
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index_map = labels.reshape(height, width)
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index_map_tensor = torch.from_numpy(index_map.astype(np.float32))
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if lock_mask is not None:
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if hasattr(lock_mask, 'numpy'):
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mask = lock_mask.numpy()
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else:
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mask = lock_mask
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pixel_mask = np.zeros_like(labels, dtype=bool)
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for i, is_locked in enumerate(mask):
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if is_locked:
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pixel_mask[labels == i] = True
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original_pixels = pixels.copy()
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new_pixels = palette[labels]
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pixels[~pixel_mask] = new_pixels[~pixel_mask]
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pixels[pixel_mask] = original_pixels[pixel_mask]
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else:
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pixels = palette[labels]
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processed_image = torch.from_numpy(pixels.reshape((1, height, width, 3))).float()
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return (processed_image, torch.from_numpy(palette), index_map_tensor, preview_img)
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def generate_palette_preview(self, palette, font_size):
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if isinstance(palette, torch.Tensor):
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colors = palette.numpy()
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else:
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colors = palette
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patch_size = 100
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cols = 8
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rows = (len(colors) + cols - 1) // cols
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img = Image.new("RGB", (cols*patch_size, rows*patch_size), (40,40,40))
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draw = ImageDraw.Draw(img)
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try:
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font = ImageFont.truetype("arial.ttf", font_size)
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except:
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font = ImageFont.load_default()
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for i, color in enumerate(colors):
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x = (i % cols) * patch_size
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y = (i // cols) * patch_size
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draw.rectangle([x, y, x+patch_size, y+patch_size],
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fill=tuple((color * 255).astype(int)))
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text = str(i)
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bbox = draw.textbbox((x, y), text, font=font)
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text_w = bbox[2] - bbox[0]
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text_h = bbox[3] - bbox[1]
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draw.text((x + (patch_size-text_w)//2, y + (patch_size-text_h)//2),
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text, fill="white", font=font)
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img_tensor = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0)
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return img_tensor
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def _match_palette(self, pixels, palette):
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distances = np.linalg.norm(pixels[:, np.newaxis] - palette, axis=2)
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return np.argmin(distances, axis=1)
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class PaletteEditorNode:
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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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"palette": ("PALETTE",),
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"index_map": ("MASK",),
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},
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"optional": {
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"hue_random_enable": ("BOOLEAN", {"default": False}),
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"hue_shift": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
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"saturation_random_enable": ("BOOLEAN", {"default": False}),
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"saturation_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 5.0}),
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"value_random_enable": ("BOOLEAN", {"default": False}),
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"value_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 5.0}),
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"lock_color_num": ("STRING", {"default": "0,", "placeholder": "例: 0,1,2,3,4"}),
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"mask_enable": ("BOOLEAN", {"default": False}),
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"invert_mask": ("BOOLEAN", {"default": False}),
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"operations": ("STRING", {"default": "[]", "multiline": True}),
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}
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}
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RETURN_TYPES = ("IMAGE", "PALETTE", "MASK")
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RETURN_NAMES = ("image", "palette", "mask")
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FUNCTION = "process_palette"
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CATEGORY = "Image/Color"
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def rgb_to_hsv(self, rgb):
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return np.array([colorsys.rgb_to_hsv(*c) for c in rgb])
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def hsv_to_rgb(self, hsv):
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return np.array([colorsys.hsv_to_rgb(*c) for c in hsv])
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def process_palette(self, image, palette, index_map,
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seed,
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hue_random_enable, hue_shift,
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saturation_random_enable, saturation_scale,
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value_random_enable, value_scale,
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lock_color_num="0,", mask_enable=False,
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invert_mask=False, mask_type="lock", operations="[]"):
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# If mask is not enabled, apply changes to the entire palette
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if not mask_enable:
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mask = np.zeros(len(palette), dtype=bool)
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else:
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try:
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indices = [int(idx.strip()) for idx in lock_color_num.split(",") if idx.strip()]
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mask = np.zeros(len(palette), dtype=bool)
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for idx in indices:
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if 0 <= idx < len(palette):
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mask[idx] = True
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if invert_mask:
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mask = ~mask
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except:
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mask = np.zeros(len(palette), dtype=bool)
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current_hue = random.uniform(-180, 180) if hue_random_enable else hue_shift
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current_saturation = random.uniform(0, 5) if saturation_random_enable else saturation_scale
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current_value = random.uniform(0, 5) if value_random_enable else value_scale
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modified_palette = self.edit_palette(palette, operations, mask,
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current_hue, current_saturation, current_value)
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# Apply the modified palette to the image
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processed_image = self.apply_palette(image, modified_palette, index_map, mask, mask_type)
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random.seed(None)
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return (processed_image, modified_palette, mask)
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def edit_palette(self, palette, operations, mask,
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hue_shift, saturation_scale, value_scale):
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palette_np = palette.numpy()
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hsv_palette = self.rgb_to_hsv(palette_np)
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# Apply HSV adjustments to unmasked colors
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hsv_palette[~mask, 0] = (hsv_palette[~mask, 0] + hue_shift/360.0) % 1.0
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hsv_palette[~mask, 1] = np.clip(hsv_palette[~mask, 1] * saturation_scale, 0, 1)
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hsv_palette[~mask, 2] = np.clip(hsv_palette[~mask, 2] * value_scale, 0, 1)
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try:
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ops = json.loads(operations)
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if isinstance(ops, list):
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for op in ops:
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if isinstance(op, dict) and "index" in op:
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idx = op["index"]
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if 0 <= idx < len(palette_np):
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if "color" in op and len(op["color"]) == 3:
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new_color = np.array(op["color"], dtype=np.float32)
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palette_np[idx] = new_color
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hsv_palette[idx] = self.rgb_to_hsv(new_color.reshape(1,3))[0]
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if "hsv" in op and len(op["hsv"]) == 3:
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hsv_palette[idx] = np.clip(op["hsv"], [0,0,0], [1,1,1])
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except Exception as e:
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print(f"Operation Error: {str(e)}")
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modified_palette = self.hsv_to_rgb(hsv_palette)
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return torch.from_numpy(modified_palette).float()
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def apply_palette(self, image, palette, index_map, mask, mask_type="lock"):
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img_np = image.numpy().squeeze()
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height, width = img_np.shape[:2]
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pixels = img_np.reshape(-1, 3)
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if hasattr(palette, 'numpy'):
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palette = palette.numpy()
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index_map = index_map.numpy() if hasattr(index_map, 'numpy') else index_map
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color_mask = mask.numpy() if hasattr(mask, 'numpy') else mask
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labels = index_map.reshape(-1)
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new_pixels = palette[labels.astype(int)]
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original_pixels = pixels.copy()
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# Check if mask is entirely False (mask disabled)
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if np.all(~color_mask):
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# Apply new_pixels to all pixels
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pixels = new_pixels
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else:
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# Create pixel_mask based on color_mask
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pixel_mask = np.zeros_like(labels, dtype=bool)
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for i, is_masked in enumerate(color_mask):
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if is_masked:
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pixel_mask[labels == i] = True
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if mask_type == "change":
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# Apply new_pixels to masked areas
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pixels = np.where(pixel_mask[:, np.newaxis], new_pixels, original_pixels)
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else:
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# Apply new_pixels to non-masked areas
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pixels = np.where(pixel_mask[:, np.newaxis], original_pixels, new_pixels)
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processed_image = torch.from_numpy(pixels.reshape((1, height, width, 3))).float()
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return processed_image
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NODE_CLASS_MAPPINGS = {
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"ColorshiftColor": ColorshiftColorNode,
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"CsCPaletteEditor": PaletteEditorNode,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"ColorshiftColor": "ColorshiftColor",
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"CsCPaletteEditor": "CsCPaletteEditor",
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} |