From f14d0c29f81a549b32997f82e610ad71cdb4404c Mon Sep 17 00:00:00 2001 From: Meeeyo Date: Tue, 29 Apr 2025 00:40:09 +0800 Subject: [PATCH] update --- __init__.py | 5 ++ meyo_node_File.py | 117 ++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 122 insertions(+) diff --git a/__init__.py b/__init__.py index ea3d752..2601dc9 100644 --- a/__init__.py +++ b/__init__.py @@ -55,6 +55,7 @@ NODE_CLASS_MAPPINGS = { #文件处理：meyo_node_File "LoadAndAdjustImage": LoadAndAdjustImage, + "GenericImageLoader": GenericImageLoader, "ImageAdjuster": ImageAdjuster, "CustomCrop": CustomCrop, "SaveImagEX": SaveImagEX, @@ -123,6 +124,10 @@ NODE_DISPLAY_NAME_MAPPINGS = { #文件处理：meyo_node_File "LoadAndAdjustImage": "加载重置图像🐠meeeyo.com", +<<<<<<< HEAD + "GenericImageLoader": "全能加载图像🐠meeeyo.com", +======= +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 "ImageAdjuster": "重置图像🐠meeeyo.com", "CustomCrop": "裁剪图像🐠meeeyo.com", "SaveImagEX": "保存图像🐠meeeyo.com", diff --git a/meyo_node_File.py b/meyo_node_File.py index 8ba6c95..ad42928 100644 --- a/meyo_node_File.py +++ b/meyo_node_File.py @@ -1,3 +1,8 @@ +<<<<<<< HEAD +import os, re,io, base64, csv, torch +import shutil, requests, chardet, pathlib +import openpyxl, folder_paths, node_helpers +======= import os import re import csv @@ -9,6 +14,7 @@ import pathlib import openpyxl import folder_paths import node_helpers +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 import numpy as np from PIL import Image, ImageOps, ImageSequence from pathlib import Path @@ -17,6 +23,71 @@ from PIL import Image as PILImage from io import BytesIO from . import AnyType, any_typ +<<<<<<< HEAD + + + +#======全能加载图像 +class GenericImageLoader: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "path_or_input": ("STRING", {"default": ""}), + } + } + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "load_image" + OUTPUT_NODE = False + 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 load_image(self, path_or_input): + path = path_or_input.strip() + for ctrl in ['\u202a', '\u202b', '\u202c', '\u202d', '\u202e']: + while path.startswith(ctrl): + path = path.lstrip(ctrl) + + source = None + lower = path.lower() + if lower.startswith('http://') or lower.startswith('https://'): + source = 'network' + elif os.path.isfile(path): + source = 'local' + else: + source = 'base64' + if source == 'local': + img = Image.open(path) + elif source == 'network': + resp = requests.get(path) + resp.raise_for_status() + img = Image.open(io.BytesIO(resp.content)) + else: # 'base64' + if ',' in path and path.startswith('data:'): + _, data = path.split(',', 1) + else: + data = path + decoded = base64.b64decode(data) + img = Image.open(io.BytesIO(decoded)) + + img = img.convert('RGBA') + has_alpha = img.mode == 'RGBA' + if has_alpha: + alpha_channel = np.array(img)[:, :, 3] + mask = (alpha_channel > 0).astype(np.float32) + mask_tensor = torch.from_numpy(mask).unsqueeze(0).unsqueeze(0) + else: + mask_tensor = torch.zeros((1, 1, img.size[1], img.size[0]), dtype=torch.float32) + np_img = np.array(img).astype(np.float32) / 255.0 + img_tensor = torch.from_numpy(np_img).unsqueeze(0) + + return img_tensor, mask_tensor + +======= +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 #======加载重置图像 class LoadAndAdjustImage: @@ -186,29 +257,41 @@ class ImageAdjuster: return float("NaN") <<<<<<< HEAD +======= +<<<<<<< HEAD +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 def process_image(self, image, mask, max_dimension=1024, size_option="Custom"): batch_size = image.shape[0] processed_images = [] processed_masks = [] +<<<<<<< HEAD +======= ======= def process_image(self, image, max_dimension=1024, size_option="Custom"): batch_size = image.shape[0] processed_images = [] >>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 widths = [] heights = [] for i in range(batch_size): current_image = image[i] <<<<<<< HEAD +======= +<<<<<<< HEAD +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 current_mask = mask[i] input_pil_image = Image.fromarray((current_image.numpy() * 255).astype(np.uint8)) input_pil_mask = Image.fromarray((current_mask.numpy() * 255).astype(np.uint8)) +<<<<<<< HEAD +======= ======= input_pil_image = Image.fromarray((current_image.numpy() * 255).astype(np.uint8)) >>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 W, H = input_pil_image.size processed_image_pil = input_pil_image.copy() @@ -218,8 +301,14 @@ class ImageAdjuster: processed_mask_pil = input_pil_mask.copy() processed_mask_pil = ImageOps.exif_transpose(processed_mask_pil) +======= +<<<<<<< HEAD + processed_mask_pil = input_pil_mask.copy() + processed_mask_pil = ImageOps.exif_transpose(processed_mask_pil) + ======= >>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 if processed_image_pil.mode == 'P': processed_image_pil = processed_image_pil.convert("RGBA") elif 'A' in processed_image_pil.getbands(): @@ -229,7 +318,13 @@ class ImageAdjuster: if processed_mask_pil.mode != "L": processed_mask_pil = processed_mask_pil.convert("L") ======= +<<<<<<< HEAD + + if processed_mask_pil.mode != "L": + processed_mask_pil = processed_mask_pil.convert("L") +======= >>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 if size_option != "Custom": aspect_ratio = W / H @@ -266,7 +361,12 @@ class ImageAdjuster: processed_mask_pil = ImageOps.fit(processed_mask_pil, (target_width, target_height), method=Image.Resampling.BILINEAR, centering=(0.5, 0.5)) ======= +<<<<<<< HEAD + + processed_mask_pil = ImageOps.fit(processed_mask_pil, (target_width, target_height), method=Image.Resampling.BILINEAR, centering=(0.5, 0.5)) +======= >>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 else: ratio = min(max_dimension / W, max_dimension / H) adjusted_width = round(W * ratio) @@ -278,19 +378,30 @@ class ImageAdjuster: processed_mask_pil = processed_mask_pil.resize((adjusted_width, adjusted_height), Image.Resampling.BILINEAR) ======= +<<<<<<< HEAD + + processed_mask_pil = processed_mask_pil.resize((adjusted_width, adjusted_height), Image.Resampling.BILINEAR) +======= >>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 processed_image = np.array(processed_image_pil).astype(np.float32) / 255.0 processed_image = torch.from_numpy(processed_image) processed_images.append(processed_image) <<<<<<< HEAD +======= +<<<<<<< HEAD +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 processed_mask = np.array(processed_mask_pil).astype(np.float32) / 255.0 processed_mask = torch.from_numpy(processed_mask) processed_masks.append(processed_mask) +<<<<<<< HEAD +======= ======= >>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 if size_option != "Custom": widths.append(target_width) heights.append(target_height) @@ -300,12 +411,17 @@ class ImageAdjuster: output_image = torch.stack(processed_images) <<<<<<< HEAD +======= +<<<<<<< HEAD +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 output_mask = torch.stack(processed_masks) if all(w == widths[0] for w in widths) and all(h == heights[0] for h in heights): return (output_image, output_mask, widths[0], heights[0]) else: return (output_image, output_mask, widths[0], heights[0]) +<<<<<<< HEAD +======= ======= if all(w == widths[0] for w in widths) and all(h == heights[0] for h in heights): @@ -313,6 +429,7 @@ class ImageAdjuster: else: return (output_image, widths[0], heights[0]) >>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 +>>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 def _resize_to_million_pixels(self, W, H): aspect_ratio = W / H