nodes/MeeeyoAI-ComfyUI_StringOps
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Meeeyo
2025-04-28 18:19:20 +08:00
committed by GitHub
parent db71d026f7
commit 79ed250c5d
1 changed files with 75 additions and 9 deletions
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meyo_node_File.py
+75 -9
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@@ -159,7 +159,6 @@ class LoadAndAdjustImage:
return width, height
#======重置图像
class ImageAdjuster:
@classmethod
@@ -167,6 +166,7 @@ class ImageAdjuster:
return {
"required": {
"image": ("IMAGE",),
"mask": ("MASK",), # 添加遮罩输入
"max_dimension": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}),
"size_option": ([
"Custom", "Million Pixels", "Small", "Medium", "Large",
@@ -176,33 +176,60 @@ class ImageAdjuster:
}
}
RETURN_TYPES = ("IMAGE", "INT", "INT")
RETURN_NAMES = ("image", "width", "height")
RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT")
RETURN_NAMES = ("image", "mask", "width", "height")
FUNCTION = "process_image"
CATEGORY = "Meeeyo/File"
DESCRIPTION = "如需更多帮助或商务需求(For tech and business support)+VX/WeChat: meeeyo"
def IS_CHANGED():
def IS_CHANGED(self):
return float("NaN")
<<<<<<< HEAD
def process_image(self, image, mask, max_dimension=1024, size_option="Custom"):
batch_size = image.shape[0]
processed_images = []
processed_masks = []
=======
def process_image(self, image, max_dimension=1024, size_option="Custom"):
batch_size = image.shape[0]
processed_images = []
>>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8
widths = []
heights = []
for i in range(batch_size):
current_image = image[i]
<<<<<<< HEAD
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))
=======
input_pil_image = Image.fromarray((current_image.numpy() * 255).astype(np.uint8))
>>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8
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)
=======
>>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8
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")
=======
>>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8
if size_option != "Custom":
aspect_ratio = W / H
@@ -235,6 +262,11 @@ 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))
=======
>>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8
else:
ratio = min(max_dimension / W, max_dimension / H)
adjusted_width = round(W * ratio)
@@ -242,11 +274,23 @@ 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)
=======
>>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8
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
processed_mask = np.array(processed_mask_pil).astype(np.float32) / 255.0
processed_mask = torch.from_numpy(processed_mask)
processed_masks.append(processed_mask)
=======
>>>>>>> db71d026f74322a6e66276701198bfe8b5d44ad8
if size_option != "Custom":
widths.append(target_width)
heights.append(target_height)
@@ -255,11 +299,20 @@ class ImageAdjuster:
heights.append(adjusted_height)
output_image = torch.stack(processed_images)
<<<<<<< HEAD
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])
=======
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
def _resize_to_million_pixels(self, W, H):
aspect_ratio = W / H
@@ -286,13 +339,14 @@ class CustomCrop:
return {
"required": {
"image": ("IMAGE",),
"mask": ("MASK",), # 新增遮罩输入
"width": ("INT", {"default": 768, "min": 0, "max": 4096, "step": 8}),
"height": ("INT", {"default": 768, "min": 0, "max": 4096, "step": 8}),
}
}
RETURN_TYPES = ("IMAGE", "INT", "INT")
RETURN_NAMES = ("image", "width", "height")
RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT") # 新增遮罩输出
RETURN_NAMES = ("image", "mask", "width", "height")
FUNCTION = "process_image"
CATEGORY = "Meeeyo/File"
DESCRIPTION = "如需更多帮助或商务需求(For tech and business support)+VX/WeChat: meeeyo"
@@ -300,11 +354,14 @@ class CustomCrop:
def IS_CHANGED():
return float("NaN")
def process_image(self, image, width=768, height=768):
def process_image(self, image, mask, width=768, height=768):
input_image = Image.fromarray((image.squeeze(0).numpy() * 255).astype(np.uint8))
input_mask = Image.fromarray((mask.squeeze(0).numpy() * 255).astype(np.uint8)) # 转换遮罩为PIL图像
W, H = input_image.size
processed_images = []
processed_masks = [] # 新增遮罩处理列表
for frame in [input_image]:
frame = ImageOps.exif_transpose(frame)
@@ -321,9 +378,18 @@ class CustomCrop:
processed_image = torch.from_numpy(processed_image)[None,]
processed_images.append(processed_image)
output_image = torch.cat(processed_images, dim=0) if len(processed_images) > 1 else processed_images[0]
# 处理遮罩
input_mask = ImageOps.exif_transpose(input_mask)
processed_mask = input_mask.convert("L") # 确保遮罩是灰度图像
processed_mask = ImageOps.fit(processed_mask, (width, height), method=Image.Resampling.BILINEAR, centering=(0.5, 0.5))
processed_mask = np.array(processed_mask).astype(np.float32) / 255.0
processed_mask = torch.from_numpy(processed_mask)[None,]
processed_masks.append(processed_mask)
return (output_image, width, height)
output_image = torch.cat(processed_images, dim=0) if len(processed_images) > 1 else processed_images[0]
output_mask = torch.cat(processed_masks, dim=0) if len(processed_masks) > 1 else processed_masks[0]
return (output_image, output_mask, width, height)
#======保存图像