115 lines
4.0 KiB
Python
115 lines
4.0 KiB
Python
# "Design-focused node suite for ComfyUI."
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# Copyright 2026 Augment Studio
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# Augmentstudio.app
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import hashlib
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import json
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import os
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import numpy as np
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import torch
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from PIL import Image, ImageOps, ImageSequence
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import folder_paths
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import node_helpers
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class AugmentLoadImage:
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@classmethod
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def INPUT_TYPES(cls):
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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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},
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"optional": {
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"image_override": ("IMAGE",),
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"trigger": ("TRIGGER",),
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},
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}
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RETURN_TYPES = ("IMAGE", "INT", "INT", "MASK", "MASK", "MASK", "MASK", "AUGMENT_JSON", "TRIGGER")
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RETURN_NAMES = ("image", "width", "height", "alpha", "red", "green", "blue", "json", "trigger")
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OUTPUT_NODE = True
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FUNCTION = "load_image"
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CATEGORY = "Augment/Image"
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def load_image(self, image, image_override=None, trigger=None):
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if image_override is not None:
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output_image = image_override
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h, w = output_image.shape[1], output_image.shape[2]
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has_alpha = False
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output_mask = torch.zeros((1, h, w), dtype=torch.float32)
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source_name = "override"
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else:
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image_path = folder_paths.get_annotated_filepath(image)
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img = node_helpers.pillow(Image.open, image_path)
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output_images = []
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output_masks = []
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for i in ImageSequence.Iterator(img):
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i = node_helpers.pillow(ImageOps.exif_transpose, i)
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if i.mode == 'I':
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i = i.point(lambda i: i * (1 / 255))
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has_alpha = 'A' in i.getbands()
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rgba = i.convert("RGBA")
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rgb = i.convert("RGB")
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image_tensor = torch.from_numpy(np.array(rgb).astype(np.float32) / 255.0)[None,]
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output_images.append(image_tensor)
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if has_alpha:
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mask = 1.0 - torch.from_numpy(np.array(rgba.getchannel('A')).astype(np.float32) / 255.0)
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else:
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mask = torch.zeros((rgb.size[1], rgb.size[0]), dtype=torch.float32)
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output_masks.append(mask.unsqueeze(0))
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if img.format == "MPO":
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break
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if len(output_images) > 1:
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output_image = torch.cat(output_images, dim=0)
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output_mask = torch.cat(output_masks, dim=0)
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else:
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output_image = output_images[0]
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output_mask = output_masks[0]
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w = output_image.shape[2]
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h = output_image.shape[1]
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source_name = image
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img_np = output_image[0].numpy()
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red = torch.from_numpy(img_np[:, :, 0]).unsqueeze(0)
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green = torch.from_numpy(img_np[:, :, 1]).unsqueeze(0)
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blue = torch.from_numpy(img_np[:, :, 2]).unsqueeze(0)
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w = output_image.shape[2]
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h = output_image.shape[1]
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json_result = json.dumps({
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"node": "Load Image",
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"file": source_name,
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"width": w,
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"height": h,
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"has_alpha": has_alpha,
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})
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return {
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"ui": {"text": [f"{source_name}\n{w}x{h}"]},
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"result": (output_image, w, h, output_mask, red, green, blue, json_result, "done"),
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}
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@classmethod
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def IS_CHANGED(cls, image, image_override=None, trigger=None):
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image_path = folder_paths.get_annotated_filepath(image)
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m = hashlib.sha256()
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with open(image_path, 'rb') as f:
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m.update(f.read())
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return m.digest().hex()
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@classmethod
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def VALIDATE_INPUTS(cls, image, image_override=None, trigger=None):
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if not folder_paths.exists_annotated_filepath(image):
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return "Invalid image file: {}".format(image)
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return True
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