103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
import qrcode
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from math import ceil
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from PIL import ImageOps
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import numpy as np
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import torch
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from custom_nodes.DTAIComfyVariables import variables
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class QrCodeNode:
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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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"""
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Return a dictionary which contains config for all input fields.
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Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
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Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
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The type can be a list for selection.
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Returns: `dict`:
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- Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
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- Value input_fields (`dict`): Contains input fields config:
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* Key field_name (`string`): Name of a entry-point method's argument
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* Value field_config (`tuple`):
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+ First value is a string indicate the type of field or a list for selection.
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+ Secound value is a config for type "INT", "STRING" or "FLOAT".
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"""
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return {
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"required": {
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"link": ("STRING", {
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"multiline": False, # True if you want the field to look like the one on the ClipTextEncode node
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"default": "https://doubtech.ai",
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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 = ("image_output_name",)
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FUNCTION = "create_qr_code"
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# OUTPUT_NODE = False
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CATEGORY = "DoubTech/Loaders"
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def create_qr_code(self, link):
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# Data to encode
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data = variables.apply(link)
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# Desired size in pixels
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size = 768
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# Size of the border in blocks
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border = 5
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# Version of the QR code, could vary depending on the length of data
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version = 1
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# Number of modules (blocks) based on the version
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modules = version * 4 + 17
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# Calculate box size so that (box_size * modules + 2 * border * box_size) is close to desired size
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box_size = ceil(size / (modules + 2 * border))
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qr = qrcode.QRCode(
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version=version,
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error_correction=qrcode.constants.ERROR_CORRECT_H,
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box_size=box_size,
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border=border,
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)
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qr.add_data(data)
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qr.make(fit=True)
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# Generate QR Code
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i = qr.make_image(fill='black', back_color='white')
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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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return (image, mask)
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return (image,)
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# A dictionary that contains all nodes you want to export with their names
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# NOTE: names should be globally unique
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NODE_CLASS_MAPPINGS = {
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"QRCode": QrCodeNode,
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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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"QRCode": "QR Code"
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}
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