746 lines
28 KiB
Python
746 lines
28 KiB
Python
import os
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import csv
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import torch
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import shutil
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import requests
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import chardet
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import pathlib
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import openpyxl
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import folder_paths
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import node_helpers
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import numpy as np
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from PIL import Image, ImageOps, ImageSequence
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from pathlib import Path
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#======加载重置图像
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class LoadAndAdjustImage:
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@classmethod
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def INPUT_TYPES(s):
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input_dir = folder_paths.get_input_directory()
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files = [f.name for f in Path(input_dir).iterdir() if f.is_file()]
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return {
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"required": {
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"image": (sorted(files), {"image_upload": True}),
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"max_dimension": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}),
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"size_option": (["No Change", "Custom", "Million Pixels", "Small", "Medium", "Large",
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"480P-H(vid 4:3)", "480P-V(vid 3:4)", "720P-H(vid 16:9)", "720P-V(vid 9:16)", "832×480", "480×832"],
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{"default": "No Change"})
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK", "STRING")
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RETURN_NAMES = ("image", "mask", "info")
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FUNCTION = "load_image"
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CATEGORY = "Meeeyo/File"
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DESCRIPTION = "如需更多帮助或商务需求(For tech and business support)+VX/WeChat: meeeyo"
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def IS_CHANGED():
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return float("NaN")
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def load_image(self, image, max_dimension, size_option):
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image_path = folder_paths.get_annotated_filepath(image)
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img = Image.open(image_path)
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W, H = img.size
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aspect_ratio = W / H
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def get_target_size():
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if size_option == "No Change":
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# No resizing or cropping, just return the original size
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return W, H
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elif size_option == "Million Pixels":
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return self._resize_to_million_pixels(W, H)
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elif size_option == "Custom":
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ratio = min(max_dimension / W, max_dimension / H)
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return round(W * ratio), round(H * ratio)
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size_options = {
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"Small": (
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(768, 512) if aspect_ratio >= 1.23 else
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(512, 768) if aspect_ratio <= 0.82 else
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(768, 768)
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),
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"Medium": (
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(1216, 832) if aspect_ratio >= 1.23 else
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(832, 1216) if aspect_ratio <= 0.82 else
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(1216, 1216)
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),
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"Large": (
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(1600, 1120) if aspect_ratio >= 1.23 else
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(1120, 1600) if aspect_ratio <= 0.82 else
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(1600, 1600)
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),
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"Million Pixels": self._resize_to_million_pixels(W, H), # Million Pixels option
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"480P-H(vid 4:3)": (640, 480), # 480P-H, 640x480
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"480P-V(vid 3:4)": (480, 640), # 480P-V, 480x640
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"720P-H(vid 16:9)": (1280, 720), # 720P-H, 1280x720
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"720P-V(vid 9:16)": (720, 1280), # 720P-V, 720x1280
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"832×480": (832, 480), # 832x480
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"480×832": (480, 832), # 480x832
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}
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return size_options[size_option]
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target_width, target_height = get_target_size()
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output_images = []
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output_masks = []
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for frame in ImageSequence.Iterator(img):
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frame = ImageOps.exif_transpose(frame)
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if frame.mode == 'P':
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frame = frame.convert("RGBA")
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elif 'A' in frame.getbands():
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frame = frame.convert("RGBA")
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if size_option == "No Change":
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# No resizing, just use the original frame
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image_frame = frame.convert("RGB")
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else:
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if size_option == "Custom" or size_option == "Million Pixels":
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ratio = min(target_width / W, target_height / H)
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adjusted_width = round(W * ratio)
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adjusted_height = round(H * ratio)
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image_frame = frame.convert("RGB").resize((adjusted_width, adjusted_height), Image.Resampling.BILINEAR)
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else:
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image_frame = frame.convert("RGB")
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image_frame = ImageOps.fit(image_frame, (target_width, target_height), method=Image.Resampling.BILINEAR, centering=(0.5, 0.5))
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image_array = np.array(image_frame).astype(np.float32) / 255.0
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output_images.append(torch.from_numpy(image_array)[None,])
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# Process the mask if available
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if 'A' in frame.getbands():
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mask_frame = frame.getchannel('A')
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if size_option == "Custom" or size_option == "Million Pixels":
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mask_frame = mask_frame.resize((adjusted_width, adjusted_height), Image.Resampling.BILINEAR)
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else:
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mask_frame = ImageOps.fit(mask_frame, (target_width, target_height), method=Image.Resampling.BILINEAR, centering=(0.5, 0.5))
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mask = np.array(mask_frame).astype(np.float32) / 255.0
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mask = 1. - mask
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else:
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if size_option == "Custom" or size_option == "Million Pixels":
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mask = np.zeros((adjusted_height, adjusted_width), dtype=np.float32)
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else:
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mask = np.zeros((target_height, target_width), dtype=np.float32)
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output_masks.append(torch.from_numpy(mask).unsqueeze(0))
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output_image = torch.cat(output_images, dim=0) if len(output_images) > 1 else output_images[0]
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output_mask = torch.cat(output_masks, dim=0) if len(output_masks) > 1 else output_masks[0]
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info = f"Image Path: {image_path}\nOriginal Size: {W}x{H}\nAdjusted Size: {target_width}x{target_height}"
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return (output_image, output_mask, info)
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@classmethod
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def VALIDATE_INPUTS(s, image):
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if not folder_paths.exists_annotated_filepath(image):
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return f"Invalid image file: {image}"
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return True
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def _resize_to_million_pixels(self, W, H):
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# Calculate the aspect ratio of the original image
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aspect_ratio = W / H
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target_area = 1000000 # 1 million pixels
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# Calculate the new width and height while maintaining the aspect ratio
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if aspect_ratio > 1: # Landscape
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width = int(np.sqrt(target_area * aspect_ratio))
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height = int(target_area / width)
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else: # Portrait
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height = int(np.sqrt(target_area / aspect_ratio))
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width = int(target_area / height)
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# Round width and height to the nearest multiple of 8
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width = (width + 7) // 8 * 8
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height = (height + 7) // 8 * 8
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return width, height
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#======重置图像
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class ImageAdjuster:
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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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"max_dimension": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}),
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"size_option": ([
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"Custom", "Million Pixels", "Small", "Medium", "Large",
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"480P-H(vid 4:3)", "480P-V(vid 3:4)", "720P-H(vid 16:9)", "720P-V(vid 9:16)", "832×480", "480×832"],
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{"default": "Million Pixels"})
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}
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}
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RETURN_TYPES = ("IMAGE", "INT", "INT")
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RETURN_NAMES = ("image", "width", "height")
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FUNCTION = "process_image"
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CATEGORY = "Meeeyo/File"
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DESCRIPTION = "如需更多帮助或商务需求(For tech and business support)+VX/WeChat: meeeyo"
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def IS_CHANGED():
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return float("NaN")
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def process_image(self, image, max_dimension=1024, size_option="Custom"):
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input_image = Image.fromarray((image.squeeze(0).numpy() * 255).astype(np.uint8))
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W, H = input_image.size
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processed_images = []
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for frame in [input_image]:
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frame = ImageOps.exif_transpose(frame)
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if frame.mode == 'P':
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frame = frame.convert("RGBA")
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elif 'A' in frame.getbands():
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frame = frame.convert("RGBA")
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if size_option != "Custom":
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aspect_ratio = W / H
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size_options = {
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"Small": (
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(768, 512) if aspect_ratio >= 1.23 else
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(512, 768) if aspect_ratio <= 0.82 else
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(768, 768)
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),
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"Medium": (
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(1216, 832) if aspect_ratio >= 1.23 else
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(832, 1216) if aspect_ratio <= 0.82 else
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(1216, 1216)
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),
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"Large": (
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(1600, 1120) if aspect_ratio >= 1.23 else
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(1120, 1600) if aspect_ratio <= 0.82 else
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(1600, 1600)
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),
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"Million Pixels": self._resize_to_million_pixels(W, H), # Million Pixels option
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"480P-H(vid 4:3)": (640, 480), # 480P-H, 640x480
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"480P-V(vid 3:4)": (480, 640), # 480P-V, 480x640
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"720P-H(vid 16:9)": (1280, 720), # 720P-H, 1280x720
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"720P-V(vid 9:16)": (720, 1280), # 720P-V, 720x1280
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"832×480": (832, 480), # 832x480
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"480×832": (480, 832), # 480x832
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}
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target_width, target_height = size_options[size_option]
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processed_image = frame.convert("RGB")
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processed_image = ImageOps.fit(processed_image, (target_width, target_height), method=Image.Resampling.BILINEAR, centering=(0.5, 0.5))
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else:
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ratio = min(max_dimension / W, max_dimension / H)
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adjusted_width = round(W * ratio)
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adjusted_height = round(H * ratio)
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processed_image = frame.convert("RGB")
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processed_image = processed_image.resize((adjusted_width, adjusted_height), Image.Resampling.BILINEAR)
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processed_image = np.array(processed_image).astype(np.float32) / 255.0
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processed_image = torch.from_numpy(processed_image)[None,]
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processed_images.append(processed_image)
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output_image = torch.cat(processed_images, dim=0) if len(processed_images) > 1 else processed_images[0]
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if size_option != "Custom":
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return (output_image, target_width, target_height)
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else:
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return (output_image, adjusted_width, adjusted_height)
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def _resize_to_million_pixels(self, W, H):
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# Calculate the aspect ratio of the original image
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aspect_ratio = W / H
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target_area = 1000000 # 1 million pixels
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||
|
||
# Calculate the new width and height while maintaining the aspect ratio
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||
if aspect_ratio > 1: # Landscape
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||
width = int(np.sqrt(target_area * aspect_ratio))
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height = int(target_area / width)
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else: # Portrait
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||
height = int(np.sqrt(target_area / aspect_ratio))
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width = int(target_area / height)
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||
|
||
# Round width and height to the nearest multiple of 8
|
||
width = (width + 7) // 8 * 8
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height = (height + 7) // 8 * 8
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return width, height
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||
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#======裁剪图像
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class CustomCrop:
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@classmethod
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||
def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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||
"width": ("INT", {"default": 768, "min": 0, "max": 4096, "step": 8}),
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"height": ("INT", {"default": 768, "min": 0, "max": 4096, "step": 8}),
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}
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||
}
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||
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RETURN_TYPES = ("IMAGE", "INT", "INT")
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RETURN_NAMES = ("image", "width", "height")
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||
FUNCTION = "process_image"
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||
CATEGORY = "Meeeyo/File"
|
||
DESCRIPTION = "如需更多帮助或商务需求(For tech and business support)+VX/WeChat: meeeyo"
|
||
|
||
def IS_CHANGED(self):
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return float("NaN")
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def process_image(self, image, width=768, height=768):
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input_image = Image.fromarray((image.squeeze(0).numpy() * 255).astype(np.uint8))
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W, H = input_image.size
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||
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||
processed_images = []
|
||
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||
for frame in [input_image]:
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||
frame = ImageOps.exif_transpose(frame)
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||
|
||
if frame.mode == 'P':
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||
frame = frame.convert("RGBA")
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||
elif 'A' in frame.getbands():
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frame = frame.convert("RGBA")
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||
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processed_image = frame.convert("RGB")
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||
processed_image = ImageOps.fit(processed_image, (width, height), method=Image.Resampling.BILINEAR, centering=(0.5, 0.5))
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||
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||
processed_image = np.array(processed_image).astype(np.float32) / 255.0
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||
processed_image = torch.from_numpy(processed_image)[None,]
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||
processed_images.append(processed_image)
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||
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||
output_image = torch.cat(processed_images, dim=0) if len(processed_images) > 1 else processed_images[0]
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||
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return (output_image, width, height)
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||
|
||
|
||
#======保存图像
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||
class SaveImagEX:
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||
def __init__(self):
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||
self.output_dir = folder_paths.get_output_directory()
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||
|
||
@classmethod
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||
def INPUT_TYPES(cls):
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||
return {
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||
"required": {
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||
"image": ("IMAGE",),
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||
"save_path": ("STRING", {"default": "./output"}),
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"image_name": ("STRING", {"default": "ComfyUI"}),
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||
"image_format": (["PNG", "JPG"], {"default": "JPG"})
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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",)
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||
FUNCTION = "save_image"
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||
OUTPUT_NODE = True
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||
CATEGORY = "Meeeyo/File"
|
||
DESCRIPTION = "如需更多帮助或商务需求(For tech and business support)+VX/WeChat: meeeyo"
|
||
|
||
def IS_CHANGED():
|
||
return float("NaN")
|
||
|
||
def save_image(self, image, save_path, image_name, image_format):
|
||
if not isinstance(image, torch.Tensor):
|
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raise ValueError("Invalid image tensor format")
|
||
if save_path == "./output":
|
||
save_path = self.output_dir
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||
elif not os.path.isabs(save_path):
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||
save_path = os.path.join(self.output_dir, save_path)
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||
os.makedirs(save_path, exist_ok=True)
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||
|
||
# 移除可能存在的扩展名，然后添加用户选择的格式对应的扩展名
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base_name = os.path.splitext(image_name)[0]
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||
|
||
batch_size = image.shape[0]
|
||
channel_to_mode = {1: "L", 3: "RGB", 4: "RGBA"}
|
||
|
||
for i in range(batch_size):
|
||
# 根据选择的格式动态设置扩展名
|
||
if image_format == "PNG":
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||
filename = f"{base_name}.png" if batch_size == 1 else f"{base_name}_{i:05d}.png"
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||
save_format = "PNG"
|
||
save_params = {"compress_level": 0}
|
||
else: # JPG
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||
filename = f"{base_name}.jpg" if batch_size == 1 else f"{base_name}_{i:05d}.jpg"
|
||
save_format = "JPEG"
|
||
save_params = {"quality": 100}
|
||
|
||
full_path = os.path.join(save_path, filename)
|
||
single_image = image[i].cpu().numpy()
|
||
single_image = (single_image * 255).astype('uint8')
|
||
channels = single_image.shape[2]
|
||
if channels not in channel_to_mode:
|
||
raise ValueError(f"Unsupported channel number: {channels}")
|
||
mode = channel_to_mode[channels]
|
||
if channels == 1:
|
||
single_image = single_image[:, :, 0]
|
||
pil_image = Image.fromarray(single_image, mode)
|
||
|
||
# 如果是JPG格式，需要转换为RGB模式
|
||
if image_format == "JPG":
|
||
pil_image = pil_image.convert("RGB")
|
||
|
||
pil_image.save(full_path, format=save_format, **save_params)
|
||
return (image,)
|
||
|
||
|
||
#======文件操作
|
||
class FileCopyCutNode:
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"source_path": ("STRING", {"default": "", "multiline": False}),
|
||
"destination_path": ("STRING", {"default": "", "multiline": False}),
|
||
"operation": (["copy", "cut"], {"default": "copy"}),
|
||
},
|
||
"optional": {
|
||
"any": ("*",),
|
||
},
|
||
}
|
||
|
||
RETURN_TYPES = ("STRING",) # 返回操作结果字符串
|
||
RETURN_NAMES = ("result",)
|
||
FUNCTION = "copy_cut_file"
|
||
CATEGORY = "Meeeyo/File"
|
||
DESCRIPTION = "如需更多帮助或商务需求(For tech and business support)+VX/WeChat: meeeyo"
|
||
|
||
def IS_CHANGED():
|
||
return float("NaN")
|
||
|
||
def copy_cut_file(self, source_path, destination_path, operation, **kwargs):
|
||
result = "执行失败"
|
||
try:
|
||
# 检查文件是否存在
|
||
if not os.path.isfile(source_path):
|
||
raise Exception(f"Source file not found: {source_path}")
|
||
|
||
# 确保目录存在
|
||
os.makedirs(os.path.dirname(destination_path), exist_ok=True)
|
||
|
||
# 执行复制或剪切操作
|
||
if operation == "copy":
|
||
shutil.copy2(source_path, destination_path)
|
||
result = "执行成功: 文件已复制"
|
||
elif operation == "cut":
|
||
shutil.move(source_path, destination_path)
|
||
result = "执行成功: 文件已剪切"
|
||
except Exception as e:
|
||
result = f"执行失败: {str(e)}"
|
||
|
||
return (result,)
|
||
|
||
|
||
#======文件路径和后缀统计
|
||
class FileListAndSuffix:
|
||
def __init__(self):
|
||
pass
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"folder_path": ("STRING",),
|
||
"file_extension": (["jpg", "png", "jpg&png", "txt", "csv", "all"], {"default": "jpg"}),
|
||
},
|
||
"optional": {},
|
||
}
|
||
|
||
RETURN_TYPES = ("STRING", "INT", "LIST")
|
||
FUNCTION = "file_list_and_suffix"
|
||
CATEGORY = "Meeeyo/File"
|
||
DESCRIPTION = "如需更多帮助或商务需求(For tech and business support)+VX/WeChat: meeeyo"
|
||
|
||
def IS_CHANGED():
|
||
return float("NaN")
|
||
|
||
def file_list_and_suffix(self, folder_path, file_extension):
|
||
try:
|
||
if not os.path.isdir(folder_path):
|
||
return ("", 0, [])
|
||
|
||
if file_extension == "all":
|
||
file_paths = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))]
|
||
elif file_extension == "jpg&png":
|
||
file_paths = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.lower().endswith(('.jpg', '.png'))]
|
||
else:
|
||
file_paths = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.lower().endswith('.' + file_extension)]
|
||
|
||
return ('\n'.join(file_paths), len(file_paths), file_paths)
|
||
except Exception as e:
|
||
return ("", 0, [])
|
||
|
||
|
||
#======读取表格数据
|
||
class ReadExcelData:
|
||
def __init__(self):
|
||
pass
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"excel_path": ("STRING", {"default": "path/to/your/file.xlsx"}),
|
||
"sheet_name": ("STRING", {"default": "Sheet1"}),
|
||
"row_range": ("STRING", {"default": "2-3"}),
|
||
"col_range": ("STRING", {"default": "1-4"}),
|
||
}
|
||
}
|
||
|
||
RETURN_TYPES = ("STRING",)
|
||
FUNCTION = "read_excel_data"
|
||
CATEGORY = "Meeeyo/File"
|
||
DESCRIPTION = "如需更多帮助或商务需求(For tech and business support)+VX/WeChat: meeeyo"
|
||
|
||
def IS_CHANGED():
|
||
return float("NaN")
|
||
|
||
def read_excel_data(self, excel_path, sheet_name, row_range, col_range):
|
||
try:
|
||
if "-" in row_range:
|
||
start_row, end_row = map(int, row_range.split("-"))
|
||
else:
|
||
start_row = end_row = int(row_range)
|
||
|
||
if "-" in col_range:
|
||
start_col, end_col = map(int, col_range.split("-"))
|
||
else:
|
||
start_col = end_col = int(col_range)
|
||
|
||
start_row = max(1, start_row)
|
||
start_col = max(1, start_col)
|
||
|
||
workbook = openpyxl.load_workbook(excel_path, read_only=True, data_only=True)
|
||
sheet = workbook[sheet_name]
|
||
|
||
output_lines = []
|
||
for row in range(start_row, end_row + 1):
|
||
row_data = []
|
||
for col in range(start_col, end_col + 1):
|
||
cell_value = sheet.cell(row=row, column=col).value
|
||
row_data.append(str(cell_value) if cell_value is not None else "")
|
||
output_lines.append("|".join(row_data))
|
||
|
||
workbook.close()
|
||
del workbook
|
||
|
||
return ("\n".join(output_lines),)
|
||
|
||
except Exception as e:
|
||
return (f"Error: {str(e)}",)
|
||
|
||
|
||
#======写入表格数据
|
||
class WriteExcelData:
|
||
def __init__(self):
|
||
pass
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"excel_path": ("STRING", {"default": "path/to/your/file.xlsx"}),
|
||
"sheet_name": ("STRING", {"default": "Sheet1"}),
|
||
"row_range": ("STRING", {"default": "2-3"}),
|
||
"col_range": ("STRING", {"default": "1-4"}),
|
||
"data": ("STRING", {"default": "", "multiline": True}),
|
||
}
|
||
}
|
||
|
||
RETURN_TYPES = ("STRING",)
|
||
FUNCTION = "write_excel_data"
|
||
CATEGORY = "Meeeyo/File"
|
||
DESCRIPTION = "如需更多帮助或商务需求(For tech and business support)+VX/WeChat: meeeyo"
|
||
|
||
def IS_CHANGED():
|
||
return float("NaN")
|
||
|
||
def write_excel_data(self, excel_path, sheet_name, row_range, col_range, data):
|
||
try:
|
||
if not os.path.exists(excel_path):
|
||
return (f"Error: File does not exist at path: {excel_path}",)
|
||
|
||
if not os.access(excel_path, os.W_OK):
|
||
return (f"Error: No write permission for file at path: {excel_path}",)
|
||
|
||
if "-" in row_range:
|
||
start_row, end_row = map(int, row_range.split("-"))
|
||
else:
|
||
start_row = end_row = int(row_range)
|
||
|
||
if "-" in col_range:
|
||
start_col, end_col = map(int, col_range.split("-"))
|
||
else:
|
||
start_col = end_col = int(col_range)
|
||
|
||
start_row = max(1, start_row)
|
||
start_col = max(1, start_col)
|
||
|
||
workbook = openpyxl.load_workbook(excel_path, read_only=False, data_only=True)
|
||
sheet = workbook[sheet_name]
|
||
|
||
data_lines = data.strip().split("\n")
|
||
|
||
for row_index, line in enumerate(data_lines, start=start_row):
|
||
if row_index > end_row:
|
||
break
|
||
|
||
cell_values = line.split("|")
|
||
for col_index, cell_value in enumerate(cell_values, start=start_col):
|
||
if col_index > end_col:
|
||
break
|
||
|
||
if cell_value.strip():
|
||
sheet.cell(row=row_index, column=col_index).value = cell_value.strip()
|
||
|
||
workbook.save(excel_path)
|
||
|
||
workbook.close()
|
||
del workbook
|
||
|
||
return ("Data written successfully!",)
|
||
|
||
except PermissionError as pe:
|
||
return (f"Permission Error: {str(pe)}",)
|
||
except Exception as e:
|
||
return (f"Error: {str(e)}",)
|
||
|
||
|
||
#======查找表格数据
|
||
class FindExcelData:
|
||
def __init__(self):
|
||
pass
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"excel_path": ("STRING", {"default": "path/to/your/file.xlsx"}),
|
||
"sheet_name": ("STRING", {"default": "Sheet1"}),
|
||
"search_content": ("STRING", {"default": "查找内容"}),
|
||
"search_mode": (["精确查找", "模糊查找"], {"default": "精确查找"}),
|
||
},
|
||
"optional": {},
|
||
}
|
||
|
||
RETURN_TYPES = ("STRING", "INT", "INT")
|
||
FUNCTION = "find_excel_data"
|
||
CATEGORY = "Meeeyo/File"
|
||
DESCRIPTION = "如需更多帮助或商务需求(For tech and business support)+VX/WeChat: meeeyo"
|
||
|
||
def IS_CHANGED():
|
||
return float("NaN")
|
||
|
||
def find_excel_data(self, excel_path, sheet_name, search_content, search_mode):
|
||
try:
|
||
if not os.path.exists(excel_path):
|
||
return (f"Error: File does not exist at path: {excel_path}", None, None)
|
||
|
||
if not os.access(excel_path, os.R_OK):
|
||
return (f"Error: No read permission for file at path: {excel_path}", None, None)
|
||
|
||
workbook = openpyxl.load_workbook(excel_path, read_only=True, data_only=True)
|
||
sheet = workbook[sheet_name]
|
||
|
||
results = []
|
||
found_row = None
|
||
found_col = None
|
||
|
||
for row in range(1, sheet.max_row + 1):
|
||
for col in range(1, sheet.max_column + 1):
|
||
cell = sheet.cell(row=row, column=col)
|
||
cell_value = cell.value if cell.value is not None else ""
|
||
|
||
if (search_mode == "精确查找" and cell_value == search_content) or \
|
||
(search_mode == "模糊查找" and search_content in cell_value):
|
||
results.append(f"{sheet_name}|{row}|{col}|{cell_value}")
|
||
found_row = row
|
||
found_col = col
|
||
|
||
workbook.close()
|
||
del workbook
|
||
|
||
if not results:
|
||
return ("No results found.", None, None)
|
||
|
||
return ("\n".join(results), found_row, found_col)
|
||
|
||
except Exception as e:
|
||
return (f"Error: {str(e)}", None, None)
|
||
|
||
|
||
#======读取表格数量差
|
||
class ReadExcelRowOrColumnDiff:
|
||
def __init__(self):
|
||
pass
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"excel_path": ("STRING", {"default": "path/to/your/file.xlsx"}),
|
||
"sheet_name": ("STRING", {"default": "Sheet1"}),
|
||
"read_mode": (["读行", "读列"], {"default": "读行"}),
|
||
"indices": ("STRING", {"default": "1,3"}),
|
||
}
|
||
}
|
||
|
||
RETURN_TYPES = ("INT",)
|
||
FUNCTION = "read_excel_row_or_column_diff"
|
||
CATEGORY = "Meeeyo/File"
|
||
DESCRIPTION = "如需更多帮助或商务需求(For tech and business support)+VX/WeChat: meeeyo"
|
||
|
||
def IS_CHANGED():
|
||
return float("NaN")
|
||
|
||
def read_excel_row_or_column_diff(self, excel_path, sheet_name, read_mode, indices):
|
||
try:
|
||
if not os.path.exists(excel_path):
|
||
return (f"Error: File does not exist at path: {excel_path}",)
|
||
|
||
if not os.access(excel_path, os.R_OK):
|
||
return (f"Error: No read permission for file at path: {excel_path}",)
|
||
|
||
workbook = openpyxl.load_workbook(excel_path, read_only=True, data_only=True)
|
||
sheet = workbook[sheet_name]
|
||
|
||
def count_cells(mode, index):
|
||
count = 0
|
||
if mode == "读行":
|
||
for col in range(1, sheet.max_column + 1):
|
||
cell_value = sheet.cell(row=index, column=col).value
|
||
if cell_value is not None:
|
||
count += 1
|
||
else:
|
||
break
|
||
elif mode == "读列":
|
||
for row in range(1, sheet.max_row + 1):
|
||
cell_value = sheet.cell(row=row, column=index).value
|
||
if cell_value is not None:
|
||
count += 1
|
||
else:
|
||
break
|
||
return count
|
||
|
||
indices = indices.strip()
|
||
if "," in indices:
|
||
try:
|
||
index1, index2 = map(int, indices.split(","))
|
||
except ValueError:
|
||
return (f"Error: Invalid indices format. Please use 'number,number' format.",)
|
||
|
||
count1 = count_cells(read_mode, index1)
|
||
count2 = count_cells(read_mode, index2)
|
||
result = count1 - count2
|
||
else:
|
||
try:
|
||
index = int(indices)
|
||
except ValueError:
|
||
return (f"Error: Invalid index format. Please enter a valid number.",)
|
||
|
||
result = count_cells(read_mode, index)
|
||
|
||
workbook.close()
|
||
del workbook
|
||
|
||
return (result,)
|
||
|
||
except Exception as e:
|
||
return (f"Error: {str(e)}",)
|