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