nodes/MeeeyoAI-ComfyUI_StringOps
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MeeeyoAI
2025-04-14 04:27:12 +08:00
parent f85bb593cb
commit dd48c4e46d
2 changed files with 26 additions and 18 deletions
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meyo_node_File.py
+25 -17
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@@ -25,12 +25,15 @@ class LoadAndAdjustImage:
}
}
RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT")
RETURN_NAMES = ("image", "mask", "width", "height")
RETURN_TYPES = ("IMAGE", "MASK", "STRING")
RETURN_NAMES = ("image", "mask", "info")
FUNCTION = "load_image"
CATEGORY = "Meeeyo/File"
def load_image(self, image, max_dimension, size_option):
from PIL import Image, ImageOps, ImageSequence
import numpy as np
import torch
image_path = folder_paths.get_annotated_filepath(image)
img = node_helpers.pillow(Image.open, image_path)
@@ -46,6 +49,7 @@ class LoadAndAdjustImage:
elif 'A' in frame.getbands():
frame = frame.convert("RGBA")
# Process image and mask based on size_option
if size_option != "Custom":
aspect_ratio = W / H
@@ -71,35 +75,35 @@ class LoadAndAdjustImage:
else:
target_width, target_height = 1120, 1600
# Resize image
image = frame.convert("RGB")
image = ImageOps.fit(image, (target_width, target_height), method=Image.Resampling.BILINEAR, centering=(0.5, 0.5))
# Resize mask
if 'A' in frame.getbands():
mask = np.array(frame.getchannel('A')).astype(np.float32) / 255.0
mask = torch.from_numpy(mask)
mask_image = Image.fromarray((mask.numpy() * 255).astype(np.uint8))
mask_image = ImageOps.fit(mask_image, (target_width, target_height), method=Image.Resampling.BILINEAR, centering=(0.5, 0.5))
mask_frame = frame.getchannel('A')
mask_image = ImageOps.fit(mask_frame, (target_width, target_height), method=Image.Resampling.BILINEAR, centering=(0.5, 0.5))
mask = np.array(mask_image).astype(np.float32) / 255.0
mask = torch.from_numpy(mask)
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.ones((target_height, target_width), dtype=torch.float32, device="cpu")
mask = torch.zeros((target_height, target_width), dtype=torch.float32, device="cpu")
else:
ratio = min(max_dimension / W, max_dimension / H)
adjusted_width = round(W * ratio)
adjusted_height = round(H * ratio)
# Resize image
image = frame.convert("RGB")
image = image.resize((adjusted_width, adjusted_height), Image.Resampling.BILINEAR)
# Resize mask
if 'A' in frame.getbands():
mask = np.array(frame.getchannel('A')).astype(np.float32) / 255.0
mask = torch.from_numpy(mask)
mask_image = Image.fromarray((mask.numpy() * 255).astype(np.uint8))
mask_image = mask_image.resize((adjusted_width, adjusted_height), Image.Resampling.BILINEAR)
mask_frame = frame.getchannel('A')
mask_image = mask_frame.resize((adjusted_width, adjusted_height), Image.Resampling.BILINEAR)
mask = np.array(mask_image).astype(np.float32) / 255.0
mask = torch.from_numpy(mask)
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.ones((adjusted_height, adjusted_width), dtype=torch.float32, device="cpu")
mask = torch.zeros((adjusted_height, adjusted_width), dtype=torch.float32, device="cpu")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
@@ -112,17 +116,21 @@ class LoadAndAdjustImage:
else:
output_image = output_images[0]
output_mask = output_masks[0]
# 获取图片信息
if size_option != "Custom":
return (output_image, output_mask, target_width, target_height)
info = f"Image Path: {image_path}\nOriginal Size: {W}x{H}\nAdjusted Size: {target_width}x{target_height}"
else:
return (output_image, output_mask, adjusted_width, adjusted_height)
info = f"Image Path: {image_path}\nOriginal Size: {W}x{H}\nAdjusted Size: {adjusted_width}x{adjusted_height}"
return (output_image, output_mask, info)
@classmethod
def VALIDATE_INPUTS(s, image):
if not folder_paths.exists_annotated_filepath(image):
return f"Invalid image file: {image}"
return True
#======重置图像尺寸
class ImageAdjuster: