From 8063af7eca6a2ae05d09a6b5cd65da09624dafec Mon Sep 17 00:00:00 2001 From: Meeeyo Date: Tue, 29 Apr 2025 00:48:39 +0800 Subject: [PATCH] Update --- meyo_node_File.py | 163 ---------------------------------------------- 1 file changed, 163 deletions(-) diff --git a/meyo_node_File.py b/meyo_node_File.py index 9160ec8..b25c7e6 100644 --- a/meyo_node_File.py +++ b/meyo_node_File.py @@ -1,26 +1,6 @@ -<<<<<<< HEAD import os, re,io, base64, csv, torch import shutil, requests, chardet, pathlib import openpyxl, folder_paths, node_helpers -======= -<<<<<<< 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 -import torch -import shutil -import requests -import chardet -import pathlib -import openpyxl -import folder_paths -import node_helpers ->>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c import numpy as np from PIL import Image, ImageOps, ImageSequence from pathlib import Path @@ -29,10 +9,6 @@ from PIL import Image as PILImage from io import BytesIO from . import AnyType, any_typ -<<<<<<< HEAD -======= -<<<<<<< HEAD ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c @@ -42,11 +18,7 @@ class GenericImageLoader: def INPUT_TYPES(cls): return { "required": { -<<<<<<< HEAD "image_input": ("STRING", {"default": ""}), -======= - "path_or_input": ("STRING", {"default": ""}), ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c } } RETURN_TYPES = ("IMAGE", "MASK") @@ -58,13 +30,8 @@ class GenericImageLoader: def IS_CHANGED(): return float("NaN") -<<<<<<< HEAD def load_image(self, image_input): path = image_input.strip() -======= - def load_image(self, path_or_input): - path = path_or_input.strip() ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c for ctrl in ['\u202a', '\u202b', '\u202c', '\u202d', '\u202e']: while path.startswith(ctrl): path = path.lstrip(ctrl) @@ -104,11 +71,6 @@ class GenericImageLoader: return img_tensor, mask_tensor -<<<<<<< HEAD -======= -======= ->>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c #======加载重置图像 class LoadAndAdjustImage: @@ -277,99 +239,35 @@ class ImageAdjuster: def IS_CHANGED(self): return float("NaN") -<<<<<<< HEAD -======= -<<<<<<< HEAD -======= -<<<<<<< HEAD ->>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c def process_image(self, image, mask, max_dimension=1024, size_option="Custom"): batch_size = image.shape[0] processed_images = [] processed_masks = [] -<<<<<<< HEAD -======= -<<<<<<< HEAD -======= -======= - def process_image(self, image, max_dimension=1024, size_option="Custom"): - batch_size = image.shape[0] - processed_images = [] ->>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 ->>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c widths = [] heights = [] for i in range(batch_size): current_image = image[i] -<<<<<<< HEAD -======= -<<<<<<< HEAD -======= -<<<<<<< HEAD ->>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c 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 -======= -<<<<<<< HEAD -======= -======= - input_pil_image = Image.fromarray((current_image.numpy() * 255).astype(np.uint8)) ->>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 ->>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c W, H = input_pil_image.size processed_image_pil = input_pil_image.copy() processed_image_pil = ImageOps.exif_transpose(processed_image_pil) -<<<<<<< HEAD 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) - -======= -<<<<<<< HEAD - processed_mask_pil = input_pil_mask.copy() - processed_mask_pil = ImageOps.exif_transpose(processed_mask_pil) - -======= ->>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 ->>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c if processed_image_pil.mode == 'P': processed_image_pil = processed_image_pil.convert("RGBA") elif 'A' in processed_image_pil.getbands(): processed_image_pil = processed_image_pil.convert("RGBA") -<<<<<<< HEAD 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") -======= -<<<<<<< HEAD - - if processed_mask_pil.mode != "L": - processed_mask_pil = processed_mask_pil.convert("L") -======= ->>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 ->>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c if size_option != "Custom": aspect_ratio = W / H @@ -402,21 +300,8 @@ class ImageAdjuster: target_width, target_height = size_options[size_option] processed_image_pil = processed_image_pil.convert("RGB") processed_image_pil = ImageOps.fit(processed_image_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)) -======= -<<<<<<< HEAD - - 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 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c else: ratio = min(max_dimension / W, max_dimension / H) adjusted_width = round(W * ratio) @@ -424,45 +309,17 @@ class ImageAdjuster: processed_image_pil = processed_image_pil.convert("RGB") processed_image_pil = processed_image_pil.resize((adjusted_width, adjusted_height), Image.Resampling.BILINEAR) -<<<<<<< HEAD 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) -======= -<<<<<<< HEAD - - processed_mask_pil = processed_mask_pil.resize((adjusted_width, adjusted_height), Image.Resampling.BILINEAR) -======= ->>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 ->>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c 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 -======= -<<<<<<< HEAD ->>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c 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 -======= -<<<<<<< HEAD -======= -======= ->>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 ->>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c if size_option != "Custom": widths.append(target_width) heights.append(target_height) @@ -471,32 +328,12 @@ class ImageAdjuster: heights.append(adjusted_height) output_image = torch.stack(processed_images) -<<<<<<< HEAD -======= -<<<<<<< HEAD -======= -<<<<<<< HEAD ->>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c 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 -======= -<<<<<<< HEAD -======= -======= - - if all(w == widths[0] for w in widths) and all(h == heights[0] for h in heights): - return (output_image, widths[0], heights[0]) - else: - return (output_image, widths[0], heights[0]) ->>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8 ->>>>>>> 79ed250c5d0d1f11fccbec9087431ff25fc1e810 ->>>>>>> f14d0c29f81a549b32997f82e610ad71cdb4404c def _resize_to_million_pixels(self, W, H): aspect_ratio = W / H