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