133 lines
4.2 KiB
Python
133 lines
4.2 KiB
Python
import torch
|
|
import numpy as np
|
|
from PIL import Image
|
|
|
|
class DDImageSplitter:
|
|
"""
|
|
DD 图像切分器
|
|
支持按比例切分图像并选择输出指定部分
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"图像": ("IMAGE",),
|
|
"切分方向": (["左右", "上下"], {"default": "左右"}),
|
|
"切分份数": ("INT", {"default": 2, "min": 2, "max": 10, "step": 1}),
|
|
"输出位置": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}),
|
|
"切分比例": ("STRING", {"default": "1:1", "multiline": False}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
RETURN_NAMES = ("图像",)
|
|
FUNCTION = "split_image"
|
|
CATEGORY = "🍺DD系列节点"
|
|
|
|
def split_image(self, 图像, 切分方向, 切分份数, 输出位置, 切分比例):
|
|
"""
|
|
切分图像并返回指定位置的部分
|
|
"""
|
|
# 确保输出位置在有效范围内
|
|
if 输出位置 > 切分份数:
|
|
输出位置 = 切分份数
|
|
|
|
# 转换tensor到PIL Image
|
|
image_tensor = 图像[0] if len(图像.shape) == 4 else 图像
|
|
image_np = (image_tensor.cpu().numpy() * 255).astype(np.uint8)
|
|
image_pil = Image.fromarray(image_np)
|
|
|
|
width, height = image_pil.size
|
|
|
|
# 解析切分比例
|
|
ratios = self.parse_ratios(切分比例, 切分份数)
|
|
|
|
if 切分方向 == "左右":
|
|
# 左右切分
|
|
split_image = self.split_horizontal(image_pil, ratios, 输出位置)
|
|
else:
|
|
# 上下切分
|
|
split_image = self.split_vertical(image_pil, ratios, 输出位置)
|
|
|
|
# 转换回tensor
|
|
result_np = np.array(split_image).astype(np.float32) / 255.0
|
|
result_tensor = torch.from_numpy(result_np).unsqueeze(0)
|
|
|
|
return (result_tensor,)
|
|
|
|
def parse_ratios(self, 切分比例, 切分份数):
|
|
"""
|
|
解析切分比例字符串
|
|
"""
|
|
try:
|
|
# 解析比例字符串，如 "1:1" 或 "2:1:3"
|
|
parts = 切分比例.strip().split(':')
|
|
if len(parts) == 切分份数:
|
|
ratios = [float(p) for p in parts]
|
|
elif len(parts) == 2 and 切分份数 == 2:
|
|
ratios = [float(parts[0]), float(parts[1])]
|
|
else:
|
|
# 如果比例数量不匹配，使用均等比例
|
|
ratios = [1.0] * 切分份数
|
|
except:
|
|
# 解析失败时使用均等比例
|
|
ratios = [1.0] * 切分份数
|
|
|
|
# 标准化比例
|
|
total = sum(ratios)
|
|
return [r / total for r in ratios]
|
|
|
|
def split_horizontal(self, image, ratios, 输出位置):
|
|
"""
|
|
水平切分（左右切分）
|
|
"""
|
|
width, height = image.size
|
|
|
|
# 计算每部分的宽度
|
|
widths = [int(width * ratio) for ratio in ratios]
|
|
|
|
# 调整最后一个宽度以确保总和等于原宽度
|
|
widths[-1] = width - sum(widths[:-1])
|
|
|
|
# 计算切分位置的起始x坐标
|
|
start_x = sum(widths[:输出位置-1])
|
|
end_x = start_x + widths[输出位置-1]
|
|
|
|
# 裁剪图像
|
|
cropped_image = image.crop((start_x, 0, end_x, height))
|
|
|
|
return cropped_image
|
|
|
|
def split_vertical(self, image, ratios, 输出位置):
|
|
"""
|
|
垂直切分（上下切分）
|
|
"""
|
|
width, height = image.size
|
|
|
|
# 计算每部分的高度
|
|
heights = [int(height * ratio) for ratio in ratios]
|
|
|
|
# 调整最后一个高度以确保总和等于原高度
|
|
heights[-1] = height - sum(heights[:-1])
|
|
|
|
# 计算切分位置的起始y坐标
|
|
start_y = sum(heights[:输出位置-1])
|
|
end_y = start_y + heights[输出位置-1]
|
|
|
|
# 裁剪图像
|
|
cropped_image = image.crop((0, start_y, width, end_y))
|
|
|
|
return cropped_image
|
|
|
|
|
|
# 节点类映射
|
|
NODE_CLASS_MAPPINGS = {
|
|
"DD-ImageSplitter": DDImageSplitter
|
|
}
|
|
|
|
# 节点显示名称映射
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"DD-ImageSplitter": "DD Image Splitter"
|
|
}
|