62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
from .SmartCrop import SmartCrop
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from PIL import Image, ImageDraw
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import torch
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import numpy as np
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# Tensor to PIL
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def tensor2pil(image):
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return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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# Convert PIL to Tensor
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def pil2tensor(image):
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
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class ImageSmartCrop:
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def __init__(self):
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pass
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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": ("IMAGE",),
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"width": ("INT", {
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"default": 1024,
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"min": 0, #Minimum value
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"max": 2048, #Maximum value
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"step": 1, #Slider's step
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"display": "number" # Cosmetic only: display as "number" or "slider"
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}),
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"height": ("INT", {
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"default": 1024,
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"min": 0, #Minimum value
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"max": 2048, #Maximum value
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"step": 1, #Slider's step
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"display": "number" # Cosmetic only: display as "number" or "slider"
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}),
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},
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}
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CATEGORY = "👽 ComfyLab/📐 SmartCrop 智能裁剪"
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "imageSmartCrop"
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def imageSmartCrop(self, image, width, height):
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cropper = SmartCrop()
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image = tensor2pil(image)
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result = cropper.crop(image, width, height)
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box = (
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result['top_crop']['x'],
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result['top_crop']['y'],
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result['top_crop']['width'] + result['top_crop']['x'],
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result['top_crop']['height'] + result['top_crop']['y']
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)
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cropped_image = image.crop(box)
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cropped_image.thumbnail((width, height), Image.Resampling.LANCZOS)
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# cropped_image.save(options.outputfile, 'JPEG', quality=90)
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image_tensor = pil2tensor(cropped_image)
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return (image_tensor,)
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