mpietrowskyandSisyphus
22d2ade987
Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
132 lines
4.4 KiB
Python
132 lines
4.4 KiB
Python
import hashlib
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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 comfy.model_management
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import folder_paths
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import node_helpers
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class LoadImageCropped:
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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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"x1": ("INT", {"default": 0, "min": 0, "advanced": True}),
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"y1": ("INT", {"default": 0, "min": 0, "advanced": True}),
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"x2": ("INT", {"default": 0, "min": 0, "advanced": True}),
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"y2": ("INT", {"default": 0, "min": 0, "advanced": True}),
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},
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}
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CATEGORY = "image"
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RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT")
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RETURN_NAMES = ("image", "mask", "width", "height")
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FUNCTION = "load_image"
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@staticmethod
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def _normalize_crop(x1, y1, x2, y2, width, height):
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left = max(0, min(int(x1), int(x2)))
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top = max(0, min(int(y1), int(y2)))
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right = min(width, max(int(x1), int(x2)))
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bottom = min(height, max(int(y1), int(y2)))
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if right <= left or bottom <= top:
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return (0, 0, width, height)
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return (left, top, right, bottom)
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def load_image(self, image, x1=0, y1=0, x2=0, y2=0):
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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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w, h = None, None
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dtype = comfy.model_management.intermediate_dtype()
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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 value: value * (1 / 255))
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image_rgb = i.convert("RGB")
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if len(output_images) == 0:
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w = image_rgb.size[0]
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h = image_rgb.size[1]
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if image_rgb.size[0] != w or image_rgb.size[1] != h:
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continue
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image_np = np.array(image_rgb).astype(np.float32) / 255.0
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image_tensor = torch.from_numpy(image_np)[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 = 1.0 - torch.from_numpy(mask)
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elif i.mode == "P" and "transparency" in i.info:
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mask = np.array(i.convert("RGBA").getchannel("A")).astype(np.float32) / 255.0
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mask = 1.0 - torch.from_numpy(mask)
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else:
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mask = torch.zeros((h, w), dtype=torch.float32, device="cpu")
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output_images.append(image_tensor.to(dtype=dtype))
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output_masks.append(mask.unsqueeze(0).to(dtype=dtype))
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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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left, top, right, bottom = self._normalize_crop(x1, y1, x2, y2, w, h)
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output_image = output_image[:, top:bottom, left:right, :]
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output_mask = output_mask[:, top:bottom, left:right]
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crop_width = right - left
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crop_height = bottom - top
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return (output_image, output_mask, crop_width, crop_height)
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@classmethod
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def IS_CHANGED(cls, image, x1=0, y1=0, x2=0, y2=0):
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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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m.update(f"|{int(x1)}|{int(y1)}|{int(x2)}|{int(y2)}".encode("utf-8"))
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return m.digest().hex()
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@classmethod
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def VALIDATE_INPUTS(cls, image, x1=0, y1=0, x2=0, y2=0):
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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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for value_name, value in (("x1", x1), ("y1", y1), ("x2", x2), ("y2", y2)):
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if int(value) < 0:
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return f"{value_name} must be >= 0"
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return True
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NODE_CLASS_MAPPINGS = {
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"LoadImageCropped": LoadImageCropped,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LoadImageCropped": "Load Image Cropped",
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}
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