moonwhaler
d4388020c1
Adds a caption/arrow/border overlay on top or bottom of a loaded image without cropping the photo, plus a Preview button backed by a shared /moonpack/label_preview route so the editor preview matches execution output exactly.
105 lines
3.9 KiB
Python
105 lines
3.9 KiB
Python
import numpy as np
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try:
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from PIL import Image, ImageOps
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except ImportError: # pragma: no cover - PIL ships with ComfyUI
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Image = None
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ImageOps = None
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try:
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from ._label_ops import compose_labeled_image
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except ImportError: # importable for tests without package context
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from _label_ops import compose_labeled_image
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CATEGORY = "MoonPack/image"
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class LabeledLoadImage:
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DESCRIPTION = (
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"Loads an image and bakes an editable caption bar into it (e.g. 'Front View', "
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"'Character A'), so a reference stays identifiable once it's combined into a "
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"bigger Reference Concat/Grid layout. The bar is added as extra space at the "
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"top or bottom - the photo itself is never cropped or resized. A bold arrow in "
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"the bar points back at the photo; text and arrow can each be turned off "
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"independently. An optional border (same color as the bar) frames the whole "
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"result. Use the node's Preview button for an instant, exact look at the result "
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"before queuing."
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)
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SEARCH_ALIASES = ["load image", "label", "caption", "annotate", "reference", "text overlay"]
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@classmethod
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def INPUT_TYPES(cls):
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import os
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import folder_paths
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input_dir = folder_paths.get_input_directory()
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files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
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files = folder_paths.filter_files_content_types(files, ["image"])
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return {
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"required": {
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"image": (sorted(files), {"image_upload": True}),
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"text": ("STRING", {
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"default": "", "multiline": False,
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"tooltip": "Caption drawn in the label bar. Auto-shrinks to fit, then truncates with an ellipsis.",
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}),
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"text_position": (["top", "bottom"], {
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"default": "top",
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"tooltip": "Which edge of the image the label bar is added to.",
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}),
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"show_label": ("BOOLEAN", {
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"default": True,
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"tooltip": "Draw the caption text in the bar.",
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}),
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"show_arrow": ("BOOLEAN", {
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"default": True,
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"tooltip": "Draw a bold arrow in the bar, pointing back at the photo.",
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}),
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"background_color": ("STRING", {
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"default": "#000000",
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"tooltip": "Label bar fill color and border color, as a hex code.",
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}),
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"text_color": ("STRING", {
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"default": "#ffffff",
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"tooltip": "Caption text and arrow color, as a hex code.",
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}),
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"border_width": ("INT", {
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"default": 0, "min": 0, "max": 512, "step": 1,
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"tooltip": "Inset border thickness in pixels, drawn in background_color around the whole result. 0 = no border.",
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "load_image"
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CATEGORY = CATEGORY
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def load_image(self, image, text, text_position, show_label, show_arrow,
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background_color, text_color, border_width):
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import folder_paths
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image_path = folder_paths.get_annotated_filepath(image)
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img = Image.open(image_path)
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img = ImageOps.exif_transpose(img).convert("RGB")
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composed = compose_labeled_image(
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img, text, text_position, show_label, show_arrow,
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background_color, text_color, border_width,
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)
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arr = np.array(composed).astype(np.float32) / 255.0
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tensor = arr[None, ...]
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import torch
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return (torch.from_numpy(tensor),)
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NODE_CLASS_MAPPINGS = {
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"MoonPack_LabeledLoadImage": LabeledLoadImage,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"MoonPack_LabeledLoadImage": "Load Image (Labeled)",
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}
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