Add ImageTransform module
This commit is contained in:
Nourepide
@@ -38,18 +38,20 @@ class Loader:
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def setup_override(self):
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override_nodes_len = 0
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if self.config()["override"]["postprocessing"]:
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def override(function):
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start_len = nodes.NODE_CLASS_MAPPINGS.__len__()
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nodes.NODE_CLASS_MAPPINGS = dict(
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filter(
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lambda item: not item[1].CATEGORY.startswith("image/postprocessing"),
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nodes.NODE_CLASS_MAPPINGS.items()
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)
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filter(function, nodes.NODE_CLASS_MAPPINGS.items())
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)
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end_len = nodes.NODE_CLASS_MAPPINGS.__len__()
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override_nodes_len += start_len - end_len
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return start_len - nodes.NODE_CLASS_MAPPINGS.__len__()
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if self.config()["override"]["postprocessing"]:
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override_nodes_len += override(lambda item: not item[1].CATEGORY.startswith("image/postprocessing"))
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if self.config()["override"]["transform"]:
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override_nodes_len += override(lambda item: not item[0] == "ImageScale" and not item[0] == "ImageInvert")
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self.__log(str(override_nodes_len) + " standard nodes was overridden.")
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@@ -88,6 +90,10 @@ class Loader:
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from .modules import ImageText
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modules.update(ImageText.NODE_CLASS_MAPPINGS)
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if self.config()["modules"]["ImageTransform"]:
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from .modules import ImageTransform
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modules.update(ImageTransform.NODE_CLASS_MAPPINGS)
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modules_len = dict(
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filter(
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lambda item: item[1],
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@@ -639,6 +639,79 @@ You also can change the fonts folder in config.
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</details>
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---
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### Image Transform
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> Transform your images.
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<details>
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<summary>Nodes:</summary>
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### Resize
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> Change size of images.
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<details>
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<summary>Params:</summary>
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#### Absolute
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* [width, height] `[1 - *]` - New size of images.
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#### Relative
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* scale_[width, height] `[0.0 - 1.0]` - New size of images.
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</details>
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### Crop
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> Returns area from images.
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<details>
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<summary>Params:</summary>
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#### Absolute
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* start_[x, y] `[1 - *]` - Start of rectangle point.
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* end_[x, y] `[1 - *]` - End of rectangle point.
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#### Relative
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* start_[x, y] `[0.0 - 1.0]` - Start of rectangle point.
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* end_[x, y] `[0.0 - 1.0]` - End of rectangle point.
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</details>
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### Crop Corners
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> Round corners of your images.
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<details>
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<summary>Params:</summary>
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* radius `[0 -360]` - Radius of the corners.
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* [top_left, top_right, bottom_right, bottom_left]_corner `[boolean]` - The ability to choose for which angle to apply the radius.
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* SSAO [1 - 16] - [Super Sampling Anti-Aliasing](https://en.wikipedia.org/wiki/Supersampling). The figure is drawn initially at a higher resolution, and then compressed to the specified resolution.
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</details>
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### Rotate
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> Rotate your images.
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<details>
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<summary>Params:</summary>
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* angle `[0 -360]` - Angle in degrees. Angles are measured from 3 o’clock, increasing clockwise.
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* expand `[boolean]` - If "true" when rotating, change the size of the image to fit into it.
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* SSAO [1 - 16] - [Super Sampling Anti-Aliasing](https://en.wikipedia.org/wiki/Supersampling). The figure is drawn initially at a higher resolution, and then compressed to the specified resolution.
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</details>
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### Transpose
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> Transpose your images.
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</details>
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---
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### Clamp
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@@ -670,6 +743,8 @@ You can configurate Allor with `config.json`.
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* fonts_folder_path - Array with a relative path to fonts folder, by default is `["comfy_extras", "fonts"]`. (Converted to `ComfyUI/comfy_extras/fonts` for Unix and `ComfyUI\comfy_extras\fonts` for Windows).
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* modules - You can disable (or enable) modules at will. Nodes from disabled modules will not be loaded during the start of ComfyUI.
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* override - If the nested value is set to `true`, similar functionality from the original nodes is disabled.
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* * postprocessing - Disable `image/postprocessing` nodes. (See `ImageFilter` module)
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* * transform - Disable `ImageScale` and `ImageInvert` nodes. (See `ImageTransform` module)
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## Examples:
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+4
-2
@@ -8,9 +8,11 @@
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"ImageDraw": true,
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"ImageFilter": true,
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"ImageSegmentation": true,
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"ImageText": true
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"ImageText": true,
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"ImageTransform": true
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},
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"override" : {
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"postprocessing": true
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"postprocessing": true,
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"transform": true
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}
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}
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@@ -161,7 +161,7 @@ class ImageContainerInheritanceScale:
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CATEGORY = "image/container"
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def image_container_inheritance_scale(self, images, scale_width, scale_height, red, green, blue, alpha, method):
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width, height = images[0, :, :, 0].shape
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height, width = images[0, :, :, 0].shape
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width = int((width * scale_width) - width)
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height = int((height * scale_height) - height)
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@@ -1520,7 +1520,7 @@ class ImageDrawRectangleRounded:
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(width * start_x * SSAA, height * start_y * SSAA),
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(width * end_x * SSAA, height * end_y * SSAA)
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),
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radius,
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radius * SSAA,
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(fill_red, fill_green, fill_blue, int(fill_alpha * 255)),
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(outline_red, outline_green, outline_blue, int(outline_alpha * 255)),
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outline_size * SSAA,
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@@ -0,0 +1,393 @@
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import torch
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from PIL import Image, ImageDraw
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class ImageTransformResizeAbsolute:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"width": ("INT", {
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"default": 256,
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"min": 1,
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"step": 1
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}),
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"height": ("INT", {
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"default": 256,
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"min": 1,
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"step": 1
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}),
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"method": (["lanczos", "bicubic", "hamming", "bilinear", "box", "nearest"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "image_transform_resize_absolute"
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CATEGORY = "image/transform"
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def image_transform_resize_absolute(self, images, width, height, method):
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def resize_tensor(tensor):
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if method == "lanczos":
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sampler = Image.LANCZOS
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elif method == "bicubic":
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sampler = Image.BICUBIC
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elif method == "hamming":
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sampler = Image.HAMMING
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elif method == "bilinear":
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sampler = Image.BILINEAR
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elif method == "box":
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sampler = Image.BOX
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elif method == "nearest":
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sampler = Image.NEAREST
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else:
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raise ValueError()
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return tensor.tensor_to_image().resize((width, height), sampler).image_to_tensor()
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return (torch.stack([
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resize_tensor(images[i]) for i in range(len(images))
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]),)
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class ImageTransformResizeRelative:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"scale_width": ("FLOAT", {
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"default": 1.0,
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"step": 0.1
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}),
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"scale_height": ("FLOAT", {
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"default": 1.0,
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"step": 0.1
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}),
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"method": (["lanczos", "bicubic", "hamming", "bilinear", "box", "nearest"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "image_transform_resize_relative"
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CATEGORY = "image/transform"
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def image_transform_resize_relative(self, images, scale_width, scale_height, method):
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height, width = images[0, :, :, 0].shape
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width = int(width * scale_width)
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height = int(height * scale_height)
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return ImageTransformResizeAbsolute().image_transform_resize_absolute(images, width, height, method)
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class ImageTransformCropAbsolute:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"start_x": ("INT", {
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"default": 0,
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"step": 1
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}),
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"start_y": ("INT", {
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"default": 0,
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"step": 1
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}),
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"end_x": ("INT", {
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"default": 128,
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"step": 1
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}),
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"end_y": ("INT", {
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"default": 128,
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"step": 1
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "image_transform_crop_absolute"
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CATEGORY = "image/transform"
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def image_transform_crop_absolute(self, images, start_x, start_y, end_x, end_y):
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def resize_tensor(tensor):
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return tensor.tensor_to_image().crop([start_x, start_y, end_x, end_y]).image_to_tensor()
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return (torch.stack([
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resize_tensor(images[i]) for i in range(len(images))
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]),)
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class ImageTransformCropRelative:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"start_x": ("FLOAT", {
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"default": 0.25,
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"max": 1.0,
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"step": 0.01
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}),
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"start_y": ("FLOAT", {
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"default": 0.25,
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"max": 1.0,
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"step": 0.01
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}),
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"end_x": ("FLOAT", {
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"default": 0.75,
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"max": 1.0,
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"step": 0.01
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}),
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"end_y": ("FLOAT", {
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"default": 0.75,
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"max": 1.0,
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"step": 0.01
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "image_transform_crop_relative"
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CATEGORY = "image/transform"
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def image_transform_crop_relative(self, images, start_x, start_y, end_x, end_y):
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height, width = images[0, :, :, 0].shape
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return ImageTransformCropAbsolute().image_transform_crop_absolute(
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images,
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width * start_x,
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height * start_y,
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width * end_x,
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height * end_y
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)
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class ImageTransformCropCorners:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"radius": ("INT", {
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"default": 180,
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"max": 360,
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"step": 1
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}),
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"top_left_corner": (["true", "false"],),
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"top_right_corner": (["true", "false"],),
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"bottom_right_corner": (["true", "false"],),
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"bottom_left_corner": (["true", "false"],),
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"SSAA": ("INT", {
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"default": 4,
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"min": 1,
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"max": 16,
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"step": 1
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}),
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"method": (["lanczos", "bicubic", "hamming", "bilinear", "box", "nearest"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "image_transform_crop_corners"
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CATEGORY = "image/transform"
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# noinspection PyUnresolvedReferences, PyArgumentList
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def image_transform_crop_corners(
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self,
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images,
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radius,
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top_left_corner,
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top_right_corner,
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bottom_right_corner,
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bottom_left_corner,
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SSAA,
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method
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):
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if method == "lanczos":
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sampler = Image.LANCZOS
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elif method == "bicubic":
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sampler = Image.BICUBIC
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elif method == "hamming":
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sampler = Image.HAMMING
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elif method == "bilinear":
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sampler = Image.BILINEAR
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elif method == "box":
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sampler = Image.BOX
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elif method == "nearest":
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sampler = Image.NEAREST
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else:
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raise ValueError()
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height, width = images[0, :, :, 0].shape
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canvas = Image.new("RGBA", (width * SSAA, height * SSAA), (0, 0, 0, 0))
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draw = ImageDraw.Draw(canvas)
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draw.rounded_rectangle(
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((0, 0), (width * SSAA, height * SSAA)),
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radius * SSAA, (255, 255, 255, 255),
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corners=(
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True if top_left_corner == "true" else False,
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True if top_right_corner == "true" else False,
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True if bottom_right_corner == "true" else False,
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True if bottom_left_corner == "true" else False
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)
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)
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canvas = canvas.resize((width, height), sampler)
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mask = 1.0 - canvas.image_to_tensor()[:, :, 3]
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def crop_tensor(tensor):
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return torch.stack([
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tensor[:, :, i] - mask for i in range(tensor.shape[2])
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], dim=2)
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return (torch.stack([
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crop_tensor(images[i]) for i in range(len(images))
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]),)
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class ImageTransformRotate:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"angle": ("FLOAT", {
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"default": 35.0,
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"max": 360.0,
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"step": 0.1
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}),
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"expand": (["true", "false"],),
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"SSAA": ("INT", {
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"default": 4,
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"min": 1,
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"max": 16,
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"step": 1
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}),
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"method": (["lanczos", "bicubic", "hamming", "bilinear", "box", "nearest"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "image_transform_rotate"
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CATEGORY = "image/transform"
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def image_transform_rotate(self, images, angle, expand, SSAA, method):
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height, width = images[0, :, :, 0].shape
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def rotate_tensor(tensor):
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if method == "lanczos":
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resize_sampler = Image.LANCZOS
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rotate_sampler = Image.BICUBIC
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elif method == "bicubic":
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resize_sampler = Image.BICUBIC
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rotate_sampler = Image.BICUBIC
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elif method == "hamming":
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resize_sampler = Image.HAMMING
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rotate_sampler = Image.BILINEAR
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elif method == "bilinear":
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resize_sampler = Image.BILINEAR
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rotate_sampler = Image.BILINEAR
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elif method == "box":
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resize_sampler = Image.BOX
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rotate_sampler = Image.NEAREST
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elif method == "nearest":
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resize_sampler = Image.NEAREST
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rotate_sampler = Image.NEAREST
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else:
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raise ValueError()
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if SSAA > 1:
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img = tensor.tensor_to_image()
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img_us_scaled = img.resize((width * SSAA, height * SSAA), resize_sampler)
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img_rotated = img_us_scaled.rotate(angle, rotate_sampler, expand == "true", fillcolor=(0, 0, 0, 0))
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img_down_scaled = img_rotated.resize((img_rotated.width // SSAA, img_rotated.height // SSAA), resize_sampler)
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result = img_down_scaled.image_to_tensor()
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else:
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img = tensor.tensor_to_image()
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img_rotated = img.rotate(angle, rotate_sampler, expand == "true", fillcolor=(0, 0, 0, 0))
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result = img_rotated.image_to_tensor()
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return result
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if angle == 0.0 or angle == 360.0:
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return (images,)
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else:
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return (torch.stack([
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rotate_tensor(images[i]) for i in range(len(images))
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]),)
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||||
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class ImageTransformTranspose:
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def __init__(self):
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pass
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||||
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@classmethod
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def INPUT_TYPES(cls):
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||||
return {
|
||||
"required": {
|
||||
"images": ("IMAGE",),
|
||||
"method": (["flip_horizontally", "flip_vertically", "rotate_90", "rotate_180", "rotate_270", "transpose", "transverse"],),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "image_transform_transpose"
|
||||
CATEGORY = "image/transform"
|
||||
|
||||
def image_transform_transpose(self, images, method):
|
||||
def transpose_tensor(tensor):
|
||||
if method == "flip_horizontally":
|
||||
transpose = Image.FLIP_LEFT_RIGHT
|
||||
elif method == "flip_vertically":
|
||||
transpose = Image.FLIP_TOP_BOTTOM
|
||||
elif method == "rotate_90":
|
||||
transpose = Image.ROTATE_90
|
||||
elif method == "rotate_180":
|
||||
transpose = Image.ROTATE_180
|
||||
elif method == "rotate_270":
|
||||
transpose = Image.ROTATE_270
|
||||
elif method == "transpose":
|
||||
transpose = Image.TRANSPOSE
|
||||
elif method == "transverse":
|
||||
transpose = Image.TRANSVERSE
|
||||
else:
|
||||
raise ValueError()
|
||||
|
||||
return tensor.tensor_to_image().transpose(transpose).image_to_tensor()
|
||||
|
||||
return (torch.stack([
|
||||
transpose_tensor(images[i]) for i in range(len(images))
|
||||
]),)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"ImageTransformResizeAbsolute": ImageTransformResizeAbsolute,
|
||||
"ImageTransformResizeRelative": ImageTransformResizeRelative,
|
||||
"ImageTransformCropAbsolute": ImageTransformCropAbsolute,
|
||||
"ImageTransformCropRelative": ImageTransformCropRelative,
|
||||
"ImageTransformCropCorners": ImageTransformCropCorners,
|
||||
"ImageTransformRotate": ImageTransformRotate,
|
||||
"ImageTransformTranspose": ImageTransformTranspose
|
||||
}
|
||||
Reference in New Issue
Block a user