48 lines
1.1 KiB
Python
48 lines
1.1 KiB
Python
import numpy as np
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from PIL import Image
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from torchvision import transforms
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class ObjectCrop:
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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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"Original_image": ("IMAGE",),
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"Chosen_ID": ("INT", {
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"default": 0,
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}),
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"BBOX": ("STRING", {
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"multiline": True,
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})
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("Chosen_Obj",)
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FUNCTION = "run"
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CATEGORY = "CROP_OBJECT"
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def run(self, Original_image, Chosen_ID, BBOX):
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try:
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bboxes = BBOX.split(';\n')[Chosen_ID].split(' - ')[-1].split(',')
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bboxes = list(map(float, bboxes))
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cropped = Original_image[:, int(bboxes[1]):int(bboxes[3]), int(bboxes[0]):int(bboxes[2]), :]
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except:
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cropped = Original_image
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return (cropped, )
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NODE_CLASS_MAPPINGS = {
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"ObjectCrop": ObjectCrop
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}
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# A dictionary that contains the friendly/humanly readable titles for the nodes
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NODE_DISPLAY_NAME_MAPPINGS = {
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"ObjectCrop": "Object Cropping"
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}
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