275 lines
7.9 KiB
Python
275 lines
7.9 KiB
Python
import numpy as np
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import torch
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from PIL import Image
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class BrightnessTransparency:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images": (
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"IMAGE",
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{
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"tooltip": "Input images to adjust transparency. Bright areas will become transparent."
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},
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),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "run"
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CATEGORY = "ComfyUI-Image-Toolkit"
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def run(self, images):
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is_tensor = isinstance(images, torch.Tensor)
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if is_tensor:
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if images.device.type != "cpu":
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images = images.cpu()
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images_np = images.numpy()
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else:
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images_np = images
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result_images = []
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for image in images_np:
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if image.shape[2] == 3:
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rgba_image = np.zeros(
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(image.shape[0], image.shape[1], 4), dtype=np.float32
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)
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rgba_image[:, :, :3] = image
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rgba_image[:, :, 3] = 1.0 # Range 0.0-1.0
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else:
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rgba_image = image.copy()
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# Set alpha values inversely proportional to brightness (bright=transparent, dark=opaque)
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brightness = np.mean(rgba_image[:, :, :3], axis=2)
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alpha = 1.0 - brightness
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rgba_image[:, :, 3] = alpha
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result_images.append(rgba_image)
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result_np = np.stack(result_images)
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result_tensor = torch.from_numpy(result_np)
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if is_tensor and images.device.type != "cpu":
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result_tensor = result_tensor.to(images.device)
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return (result_tensor,)
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class BinarizeImage:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images": ("IMAGE", {"tooltip": "Images to binarize."}),
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"threshold": (
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"INT",
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{
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"default": 127,
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"min": 0,
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"max": 255,
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"step": 1,
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"tooltip": "Values above threshold become white (1.0), below become black (0.0).",
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},
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),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "run"
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CATEGORY = "ComfyUI-Image-Toolkit"
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def run(self, images, threshold):
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is_tensor = isinstance(images, torch.Tensor)
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if is_tensor:
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if images.device.type != "cpu":
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images = images.cpu()
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images_np = images.numpy()
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else:
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images_np = images
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result_images = []
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normalized_threshold = threshold / 255.0
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for image in images_np:
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processed_image = image.copy()
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grayscale = np.mean(image[:, :, :3], axis=2)
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binary_mask = np.where(grayscale >= normalized_threshold, 1.0, 0.0)
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for i in range(3):
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processed_image[:, :, i] = binary_mask
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result_images.append(processed_image)
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result_np = np.stack(result_images)
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result_tensor = torch.from_numpy(result_np)
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if is_tensor and images.device.type != "cpu":
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result_tensor = result_tensor.to(images.device)
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return (result_tensor,)
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class GrayscaleImage:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images": (
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"IMAGE",
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{"tooltip": "Converts images to grayscale."},
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),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "run"
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CATEGORY = "ComfyUI-Image-Toolkit"
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def run(self, images):
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is_tensor = isinstance(images, torch.Tensor)
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if is_tensor:
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if images.device.type != "cpu":
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images = images.cpu()
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images_np = images.numpy()
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else:
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images_np = images
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result_images = []
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# 常に均等な重みを使用
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r_weight, g_weight, b_weight = 1 / 3, 1 / 3, 1 / 3
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for image in images_np:
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# グレースケール値を計算
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grayscale = (
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r_weight * image[:, :, 0]
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+ g_weight * image[:, :, 1]
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+ b_weight * image[:, :, 2]
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)
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# 出力画像を作成
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result_image = np.zeros_like(image)
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for i in range(3):
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result_image[:, :, i] = grayscale
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# アルファチャンネルがある場合は保持
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if image.shape[2] == 4:
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result_image[:, :, 3] = image[
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:, :, 3
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] # アルファチャンネルをコピー
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result_images.append(result_image)
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result_np = np.stack(result_images)
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result_tensor = torch.from_numpy(result_np)
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if is_tensor and images.device.type != "cpu":
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result_tensor = result_tensor.to(images.device)
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return (result_tensor,)
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class AntialiasingImage:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images": (
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"IMAGE",
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{"tooltip": "Images to apply antialiasing effect."},
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),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "run"
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CATEGORY = "ComfyUI-Image-Toolkit"
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def run(self, images):
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is_tensor = isinstance(images, torch.Tensor)
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device = images.device if is_tensor else None
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# 画像をCPUに移動し、NumPy配列に変換
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if is_tensor:
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if images.device.type != "cpu":
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images = images.cpu()
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images_np = images.numpy()
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else:
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images_np = images
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# LANCZOSフィルターを固定で使用
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pil_filter = (
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Image.LANCZOS
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if hasattr(Image, "LANCZOS")
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else Image.Resampling.LANCZOS
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)
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result_images = []
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# 固定のスケール倍率を2.0に設定
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scale_factor = 2.0
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for image in images_np:
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# NumPy配列をPIL画像に変換（0-1の範囲を0-255に変換）
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pil_image = Image.fromarray(
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(image[:, :, :3] * 255).astype(np.uint8)
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)
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# 元のサイズを取得
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original_size = pil_image.size
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# 固定倍率2.0に拡大
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upscaled = pil_image.resize(
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(
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int(original_size[0] * scale_factor),
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int(original_size[1] * scale_factor),
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),
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pil_filter,
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)
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# 元のサイズに縮小（アンチエイリアス効果）
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processed = upscaled.resize(original_size, pil_filter)
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# PIL画像をNumPy配列に戻す（0-255の範囲を0-1に変換）
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processed_np = np.array(processed).astype(np.float32) / 255.0
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# 結果画像の作成
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result_image = np.zeros_like(image)
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result_image[:, :, :3] = processed_np
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# アルファチャンネルがある場合は保持
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if image.shape[2] == 4:
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result_image[:, :, 3] = image[:, :, 3]
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result_images.append(result_image)
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# 処理結果をスタックしてテンソルに変換
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result_np = np.stack(result_images)
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result_tensor = torch.from_numpy(result_np)
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# 元のデバイスに戻す
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if is_tensor and device and device.type != "cpu":
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result_tensor = result_tensor.to(device)
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return (result_tensor,)
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NODE_CLASS_MAPPINGS = {
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"BrightnessTransparency": BrightnessTransparency,
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"BinarizeImage": BinarizeImage,
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"GrayscaleImage": GrayscaleImage,
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"AntialiasingImage": AntialiasingImage,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"BrightnessTransparency": "BrightnessTransparency",
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"BinarizeImage": "BinarizeImage",
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"GrayscaleImage": "GrayscaleImage",
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"AntialiasingImage": "AntialiasingImage",
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}
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