86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
import torch
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from PIL import Image
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import numpy as np
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from .imagefunc import log, tensor2pil, pil2tensor, gradient, Hex_to_RGB
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class GradientMap:
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def __init__(self):
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self.NODE_NAME = 'GradientMap'
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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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"start_color": ("STRING", {"default": "#015A52"}),
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"mid_color": ("STRING", {"default": "#02AF9F"}),
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"end_color": ("STRING", {"default": "#7FFFEC"}),
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"mid_point": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 1.0, "step": 0.01}),
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"opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}),
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},
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"optional": {
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"layer_mask": ("MASK",),
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}
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}
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RETURN_TYPES = ("IMAGE", "IMAGE")
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RETURN_NAMES = ("image", "gradient")
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FUNCTION = 'apply_gradient_map'
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CATEGORY = '😺dzNodes/LayerStyle'
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def apply_gradient_map(self, image, start_color, mid_color, end_color, mid_point, opacity, layer_mask=None):
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def create_gradient_array(start_color, mid_color, end_color, mid_point):
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start_rgb = Hex_to_RGB(start_color)
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mid_rgb = Hex_to_RGB(mid_color)
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end_rgb = Hex_to_RGB(end_color)
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mid_index = int(255 * mid_point)
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gradient1 = np.array([np.linspace(start_rgb[i], mid_rgb[i], mid_index + 1) for i in range(3)]).T
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gradient2 = np.array([np.linspace(mid_rgb[i], end_rgb[i], 256 - mid_index) for i in range(3)]).T
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return np.vstack((gradient1[:-1], gradient2))
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gradient_array = create_gradient_array(start_color, mid_color, end_color, mid_point)
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gradient_image = Image.fromarray(np.uint8(gradient_array.reshape(1, -1, 3).repeat(50, axis=0)))
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gradient_tensor = pil2tensor(gradient_image)
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ret_images = []
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for img in image:
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pil_image = tensor2pil(img)
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# Convert to grayscale to get luminance
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gray_image = np.array(pil_image.convert('L'))
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# Apply gradient map
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gradient_mapped = gradient_array[gray_image]
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# Preserve luminance of original image
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original_array = np.array(pil_image)
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luminance = np.sum(original_array * [0.299, 0.587, 0.114], axis=2, keepdims=True) / 255.0
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gradient_mapped = gradient_mapped * luminance + original_array * (1 - luminance)
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gradient_mapped_image = Image.fromarray(np.uint8(gradient_mapped))
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# Apply opacity
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if opacity < 100:
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gradient_mapped_image = Image.blend(pil_image, gradient_mapped_image, opacity / 100)
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# Apply mask if provided
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if layer_mask is not None:
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mask = tensor2pil(layer_mask).convert('L')
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pil_image.paste(gradient_mapped_image, (0, 0), mask)
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else:
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pil_image = gradient_mapped_image
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ret_images.append(pil2tensor(pil_image))
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log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0), gradient_tensor)
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NODE_CLASS_MAPPINGS = {
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"LayerStyle: Gradient Map": GradientMap
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerStyle: Gradient Map": "LayerStyle: Gradient Map"
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} |