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import os |
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import torch |
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import numpy as np |
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from PIL import Image, ImageOps |
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from .utils import BIGMAX |
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from .logger import logger |
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class LoadImagesFromDirectory: |
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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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"directory": ("STRING", {"default": ""}), |
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}, |
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"optional": { |
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"image_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}), |
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"start_index": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}), |
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} |
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} |
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RETURN_TYPES = ("IMAGE", "MASK", "INT") |
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FUNCTION = "load_images" |
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CATEGORY = "" |
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def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0): |
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if not os.path.isdir(directory): |
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raise FileNotFoundError(f"Directory '{directory} cannot be found.'") |
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dir_files = os.listdir(directory) |
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if len(dir_files) == 0: |
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raise FileNotFoundError(f"No files in directory '{directory}'.") |
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dir_files = sorted(dir_files) |
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dir_files = [os.path.join(directory, x) for x in dir_files] |
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dir_files = dir_files[start_index:] |
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images = [] |
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masks = [] |
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limit_images = False |
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if image_load_cap > 0: |
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limit_images = True |
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image_count = 0 |
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for image_path in dir_files: |
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if os.path.isdir(image_path): |
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continue |
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if limit_images and image_count >= image_load_cap: |
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break |
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i = Image.open(image_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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if 'A' in i.getbands(): |
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mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 |
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mask = 1. - torch.from_numpy(mask) |
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else: |
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mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") |
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images.append(image) |
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masks.append(mask) |
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image_count += 1 |
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if len(images) == 0: |
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raise FileNotFoundError(f"No images could be loaded from directory '{directory}'.") |
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return (torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count) |
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