113 lines
4.2 KiB
Python
113 lines
4.2 KiB
Python
# ComfyUI - CRZ Image Selector
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import os
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import hashlib
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from PIL import Image, ImageOps
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import numpy as np
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import torch
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from pathlib import Path
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import folder_paths
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class CRZImageSelector:
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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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"image_1": ("STRING", {"default": ""}),
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"image_2": ("STRING", {"default": ""}),
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"image_3": ("STRING", {"default": ""}),
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"image_4": ("STRING", {"default": ""}),
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"image_5": ("STRING", {"default": ""}),
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"image_6": ("STRING", {"default": ""}),
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}
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}
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RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "IMAGE", "IMAGE", "IMAGE", "INT", "IMAGE")
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RETURN_NAMES = ("image_1", "image_2", "image_3", "image_4", "image_5", "image_6", "image_count", "image_batch")
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FUNCTION = "load_images"
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CATEGORY = "CRZ"
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def load_image(self, image_path):
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"""Load a single image and return image tensor"""
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if not image_path or image_path == "":
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# Return a blank 64x64 black image if no path provided
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blank_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
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return blank_image
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try:
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input_dir = folder_paths.get_input_directory()
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full_path = os.path.join(input_dir, image_path)
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if not os.path.exists(full_path):
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# Return blank image if file doesn't exist
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blank_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
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return blank_image
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# Load image
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i = Image.open(full_path)
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i = ImageOps.exif_transpose(i)
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image = i.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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return image
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except Exception as e:
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print(f"Error loading image {image_path}: {e}")
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# Return blank image on error
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blank_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
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return blank_image
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def load_images(self, image_1, image_2, image_3, image_4, image_5, image_6):
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"""Load all six images and count them"""
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image_paths = [image_1, image_2, image_3, image_4, image_5, image_6]
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# Count non-empty image paths
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image_count = sum(1 for path in image_paths if path and path.strip() != "")
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img1 = self.load_image(image_1)
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img2 = self.load_image(image_2)
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img3 = self.load_image(image_3)
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img4 = self.load_image(image_4)
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img5 = self.load_image(image_5)
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img6 = self.load_image(image_6)
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# Create image batch from non-empty images
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images = [img1, img2, img3, img4, img5, img6]
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non_empty_images = []
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for i, (img, path) in enumerate(zip(images, image_paths)):
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if path and path.strip() != "":
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non_empty_images.append(img)
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# Create batch similar to MakeImageBatch logic
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if len(non_empty_images) == 0:
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# Return a blank 64x64 black image if no images
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image_batch = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
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elif len(non_empty_images) == 1:
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image_batch = non_empty_images[0]
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else:
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# Concatenate images, handling different sizes
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image1 = non_empty_images[0]
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for image2 in non_empty_images[1:]:
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if image1.shape[1:] != image2.shape[1:]:
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# Resize image2 to match image1 dimensions
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import comfy.utils
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image2 = comfy.utils.common_upscale(
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image2.movedim(-1, 1),
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image1.shape[2],
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image1.shape[1],
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"lanczos",
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"center"
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).movedim(1, -1)
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image1 = torch.cat((image1, image2), dim=0)
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image_batch = image1
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return (img1, img2, img3, img4, img5, img6, image_count, image_batch)
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NODE_CLASS_MAPPINGS = {
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"CRZImageSelector": CRZImageSelector,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"CRZImageSelector": "CRZ Image Selector",
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} |