nodes/TTPlanetPig-Comfyui_Object_Migration
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update node.py for more extend direction function

Browse Source
This commit is contained in:
TTPlanetPig
2024-11-21 00:51:57 +08:00
committed by GitHub
parent c554872754
commit 4dca67f77a
1 changed files with 123 additions and 52 deletions
Download Patch File Download Diff File Expand all files Collapse all files
node.py
+123 -52
View File
@@ -11,23 +11,32 @@ def tensor2pil(t_image: torch.Tensor) -> Image:
return Image.fromarray(np.clip(255.0 * t_image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
class TTP_Expand_And_Mask:
"""
这是一个节点类，用于将输入图片在指定方向扩展一定数量的块并创建相应蒙版。
功能：
1. 支持同时在多个方向上扩展图像。
2. 分别控制每个方向的扩展块数量。
3. 将输入图像的透明通道（Alpha 通道）信息转换为蒙版，并与新创建的蒙版合并。
4. 添加一个布尔参数 fill_alpha_decision 来决定是否将输出图片中的透明区域填充为白色，并输出 RGB 图像。
"""
def __init__(self, *args, **kwargs):
pass
@classmethod
def INPUT_TYPES(cls):
directions = ["left", "right", "top", "bottom"]
return {
"required": {
"image": ("IMAGE",), # 输入一张图片
"fill_mode": (["duplicate", "white"], {"default": "duplicate", "label": "Fill Mode"}),
# fill_mode是一个字符串列表参数，可以选择"duplicate"或"white"
"direction": (["left", "right", "top", "bottom"], {"default": "right", "label": "Expansion Direction"}),
# direction是一个字符串列表参数，可以选择扩展方向："left", "right", "top", "bottom"
"num_blocks": ("INT", {"default": 1, "min": 1, "max": 2, "step": 1, "label": "Number of Blocks"}),
# num_blocks表示要添加扩展块的数量，可选择1或2
"fill_alpha_decision": ("BOOLEAN", {"default": False, "label": "Fill Alpha with White"}),
# fill_alpha_decision为一个布尔值参数，用来决定是否将输出图像透明区域填充为白色
},
"optional": {
**{f"expand_{dir}": ("BOOLEAN", {"default": False, "label": f"Expand {dir.capitalize()}"}) for dir in directions},
**{f"num_blocks_{dir}": ("INT", {"default": 1, "min": 0, "max": 3, "step": 1, "label": f"Blocks {dir.capitalize()}"}) for dir in directions},
}
}
@@ -36,19 +45,32 @@ class TTP_Expand_And_Mask:
FUNCTION = "expand_and_mask"
CATEGORY = "TTP/Image"
def expand_and_mask(self, image, fill_mode="duplicate", direction="right", num_blocks=1, fill_alpha_decision=False):
def expand_and_mask(self, image, fill_mode="duplicate", fill_alpha_decision=False, **kwargs):
pil_image = tensor2pil(image)
width, height = pil_image.size
orig_width, orig_height = pil_image.size
has_alpha = (pil_image.mode == 'RGBA')
# 解析方向和块数
directions = ["left", "right", "top", "bottom"]
expand_directions = {dir: kwargs.get(f"expand_{dir}", False) for dir in directions}
num_blocks = {dir: kwargs.get(f"num_blocks_{dir}", 0) if expand_directions[dir] else 0 for dir in directions}
# 计算扩展后的尺寸
total_width = orig_width + orig_width * (num_blocks["left"] + num_blocks["right"])
total_height = orig_height + orig_height * (num_blocks["top"] + num_blocks["bottom"])
# 创建扩展后的图像
expanded_image_mode = pil_image.mode
expanded_image = Image.new(expanded_image_mode, (total_width, total_height))
# 根据 fill_mode 创建填充图像
def create_fill_image():
if pil_image.mode == 'RGBA':
return Image.new("RGBA", (width, height), color=(255, 255, 255, 255))
return Image.new("RGBA", (orig_width, orig_height), color=(255, 255, 255, 255))
elif pil_image.mode == 'RGB':
return Image.new("RGB", (width, height), color=(255, 255, 255))
return Image.new("RGB", (orig_width, orig_height), color=(255, 255, 255))
elif pil_image.mode == 'L':
return Image.new("L", (width, height), color=255)
return Image.new("L", (orig_width, orig_height), color=255)
else:
raise ValueError(f"Unsupported image mode for fill: {pil_image.mode}")
@@ -59,61 +81,110 @@ class TTP_Expand_And_Mask:
else:
fill_image = pil_image.copy()
if direction in ["left", "right"]:
new_width = width + width * num_blocks
new_height = height
else: # direction in ["top", "bottom"]
new_width = width
new_height = height + height * num_blocks
# 计算原图在扩展图像中的位置
left_offset = orig_width * num_blocks["left"]
top_offset = orig_height * num_blocks["top"]
expanded_image_mode = pil_image.mode
expanded_image = Image.new(expanded_image_mode, (new_width, new_height))
# 粘贴原始图像
expanded_image.paste(pil_image, (left_offset, top_offset))
if direction == "left":
for i in range(num_blocks):
expanded_image.paste(fill_image, (width * i, 0))
expanded_image.paste(pil_image, (width * num_blocks, 0))
elif direction == "right":
expanded_image.paste(pil_image, (0, 0))
for i in range(num_blocks):
expanded_image.paste(fill_image, (width + width * i, 0))
elif direction == "top":
for i in range(num_blocks):
expanded_image.paste(fill_image, (0, height * i))
expanded_image.paste(pil_image, (0, height * num_blocks))
elif direction == "bottom":
expanded_image.paste(pil_image, (0, 0))
for i in range(num_blocks):
expanded_image.paste(fill_image, (0, height + height * i))
else:
raise ValueError(f"Unsupported direction: {direction}")
# 粘贴填充区域
for dir in directions:
blocks = num_blocks[dir]
for i in range(blocks):
if dir == "left":
x = left_offset - orig_width * (i + 1)
y = top_offset
elif dir == "right":
x = left_offset + orig_width * (i + 1)
y = top_offset
elif dir == "top":
x = left_offset
y = top_offset - orig_height * (i + 1)
elif dir == "bottom":
x = left_offset
y = top_offset + orig_height * (i + 1)
else:
continue
expanded_image.paste(fill_image, (x, y))
mask_array = np.zeros((new_height, new_width), dtype=np.float32)
# 粘贴角落填充区域（处理同时选择多个方向的情况）
corner_positions = []
if expand_directions["left"] and expand_directions["top"]:
for i in range(num_blocks["left"]):
for j in range(num_blocks["top"]):
x = left_offset - orig_width * (i + 1)
y = top_offset - orig_height * (j + 1)
corner_positions.append((x, y))
if expand_directions["left"] and expand_directions["bottom"]:
for i in range(num_blocks["left"]):
for j in range(num_blocks["bottom"]):
x = left_offset - orig_width * (i + 1)
y = top_offset + orig_height * (j + 1)
corner_positions.append((x, y))
if expand_directions["right"] and expand_directions["top"]:
for i in range(num_blocks["right"]):
for j in range(num_blocks["top"]):
x = left_offset + orig_width * (i + 1)
y = top_offset - orig_height * (j + 1)
corner_positions.append((x, y))
if expand_directions["right"] and expand_directions["bottom"]:
for i in range(num_blocks["right"]):
for j in range(num_blocks["bottom"]):
x = left_offset + orig_width * (i + 1)
y = top_offset + orig_height * (j + 1)
corner_positions.append((x, y))
for pos in corner_positions:
expanded_image.paste(fill_image, pos)
# 创建蒙版
mask_array = np.zeros((total_height, total_width), dtype=np.float32)
# 原始图像区域蒙版处理
if has_alpha:
alpha_array = np.array(pil_image.getchannel("A"), dtype=np.float32) / 255.0
alpha_mask_array = 1.0 - alpha_array
mask_array[top_offset:top_offset + orig_height, left_offset:left_offset + orig_width] = alpha_mask_array
if direction == "left":
mask_array[:, width * num_blocks:width * num_blocks + width] = alpha_mask_array
elif direction == "right":
mask_array[:, :width] = alpha_mask_array
elif direction == "top":
mask_array[height * num_blocks:height * num_blocks + height, :] = alpha_mask_array
elif direction == "bottom":
mask_array[:height, :] = alpha_mask_array
# 填充区域蒙版设置为1.0
# 左右扩展区域
for dir in ["left", "right"]:
blocks = num_blocks[dir]
for i in range(blocks):
if dir == "left":
x_start = left_offset - orig_width * (i + 1)
x_end = left_offset - orig_width * i
elif dir == "right":
x_start = left_offset + orig_width * (i + 1)
x_end = left_offset + orig_width * (i + 2)
else:
continue
mask_array[top_offset:top_offset + orig_height, x_start:x_end] = 1.0
if direction == "left":
mask_array[:, :width * num_blocks] = 1.0
elif direction == "right":
mask_array[:, width:] = 1.0
elif direction == "top":
mask_array[:height * num_blocks, :] = 1.0
elif direction == "bottom":
mask_array[height:, :] = 1.0
# 上下扩展区域
for dir in ["top", "bottom"]:
blocks = num_blocks[dir]
for i in range(blocks):
if dir == "top":
y_start = top_offset - orig_height * (i + 1)
y_end = top_offset - orig_height * i
elif dir == "bottom":
y_start = top_offset + orig_height * (i + 1)
y_end = top_offset + orig_height * (i + 2)
else:
continue
mask_array[y_start:y_end, left_offset:left_offset + orig_width] = 1.0
# 角落区域蒙版设置为1.0
for pos in corner_positions:
x, y = pos
mask_array[y:y + orig_height, x:x + orig_width] = 1.0
# 创建蒙版张量 (1, 1, height, width)
mask_tensor = torch.from_numpy(mask_array).unsqueeze(0).unsqueeze(0)
# 根据 fill_alpha_decision 参数决定是否将输出图像中的透明区域填充为白色
if fill_alpha_decision and has_alpha:
expanded_image = expanded_image.convert('RGBA') # 确保图像是RGBA模式
background = Image.new('RGBA', expanded_image.size, (255, 255, 255, 255))