mirror of
https://github.com/invoke-ai/InvokeAI
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220 lines
9.5 KiB
Python
220 lines
9.5 KiB
Python
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
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from datetime import datetime, timezone
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from typing import Literal, Optional
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import numpy
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from pydantic import Field, BaseModel
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from PIL import Image, ImageOps, ImageFilter
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from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
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from ..services.image_storage import ImageType
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from ..services.invocation_services import InvocationServices
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class ImageField(BaseModel):
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"""An image field used for passing image objects between invocations"""
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image_type: str = Field(default=ImageType.RESULT, description="The type of the image")
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image_name: Optional[str] = Field(default=None, description="The name of the image")
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class ImageOutput(BaseInvocationOutput):
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"""Base class for invocations that output an image"""
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type: Literal['image'] = 'image'
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image: ImageField = Field(default=None, description="The output image")
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class MaskOutput(BaseInvocationOutput):
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"""Base class for invocations that output a mask"""
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type: Literal['mask'] = 'mask'
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mask: ImageField = Field(default=None, description="The output mask")
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# TODO: this isn't really necessary anymore
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class LoadImageInvocation(BaseInvocation):
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"""Load an image from a filename and provide it as output."""
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type: Literal['load_image'] = 'load_image'
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# Inputs
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image_type: ImageType = Field(description="The type of the image")
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image_name: str = Field(description="The name of the image")
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def invoke(self, context: InvocationContext) -> ImageOutput:
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return ImageOutput(
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image = ImageField(image_type = self.image_type, image_name = self.image_name)
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)
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class ShowImageInvocation(BaseInvocation):
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"""Displays a provided image, and passes it forward in the pipeline."""
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type: Literal['show_image'] = 'show_image'
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# Inputs
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image: ImageField = Field(default=None, description="The image to show")
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.services.images.get(self.image.image_type, self.image.image_name)
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if image:
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image.show()
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# TODO: how to handle failure?
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return ImageOutput(
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image = ImageField(image_type = self.image.image_type, image_name = self.image.image_name)
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)
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class CropImageInvocation(BaseInvocation):
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"""Crops an image to a specified box. The box can be outside of the image."""
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type: Literal['crop'] = 'crop'
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# Inputs
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image: ImageField = Field(default=None, description="The image to crop")
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x: int = Field(default=0, description="The left x coordinate of the crop rectangle")
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y: int = Field(default=0, description="The top y coordinate of the crop rectangle")
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width: int = Field(default=512, gt=0, description="The width of the crop rectangle")
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height: int = Field(default=512, gt=0, description="The height of the crop rectangle")
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.services.images.get(self.image.image_type, self.image.image_name)
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image_crop = Image.new(mode = 'RGBA', size = (self.width, self.height), color = (0, 0, 0, 0))
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image_crop.paste(image, (-self.x, -self.y))
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image_type = ImageType.INTERMEDIATE
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image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
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context.services.images.save(image_type, image_name, image_crop)
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return ImageOutput(
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image = ImageField(image_type = image_type, image_name = image_name)
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)
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class PasteImageInvocation(BaseInvocation):
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"""Pastes an image into another image."""
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type: Literal['paste'] = 'paste'
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# Inputs
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base_image: ImageField = Field(default=None, description="The base image")
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image: ImageField = Field(default=None, description="The image to paste")
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mask: Optional[ImageField] = Field(default=None, description="The mask to use when pasting")
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x: int = Field(default=0, description="The left x coordinate at which to paste the image")
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y: int = Field(default=0, description="The top y coordinate at which to paste the image")
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def invoke(self, context: InvocationContext) -> ImageOutput:
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base_image = context.services.images.get(self.base_image.image_type, self.base_image.image_name)
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image = context.services.images.get(self.image.image_type, self.image.image_name)
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mask = None if self.mask is None else ImageOps.invert(services.images.get(self.mask.image_type, self.mask.image_name))
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# TODO: probably shouldn't invert mask here... should user be required to do it?
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min_x = min(0, self.x)
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min_y = min(0, self.y)
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max_x = max(base_image.width, image.width + self.x)
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max_y = max(base_image.height, image.height + self.y)
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new_image = Image.new(mode = 'RGBA', size = (max_x - min_x, max_y - min_y), color = (0, 0, 0, 0))
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new_image.paste(base_image, (abs(min_x), abs(min_y)))
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new_image.paste(image, (max(0, self.x), max(0, self.y)), mask = mask)
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image_type = ImageType.RESULT
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image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
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context.services.images.save(image_type, image_name, new_image)
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return ImageOutput(
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image = ImageField(image_type = image_type, image_name = image_name)
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)
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class MaskFromAlphaInvocation(BaseInvocation):
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"""Extracts the alpha channel of an image as a mask."""
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type: Literal['tomask'] = 'tomask'
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# Inputs
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image: ImageField = Field(default=None, description="The image to create the mask from")
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invert: bool = Field(default=False, description="Whether or not to invert the mask")
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def invoke(self, context: InvocationContext) -> MaskOutput:
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image = context.services.images.get(self.image.image_type, self.image.image_name)
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image_mask = image.split()[-1]
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if self.invert:
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image_mask = ImageOps.invert(image_mask)
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image_type = ImageType.INTERMEDIATE
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image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
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context.services.images.save(image_type, image_name, image_mask)
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return MaskOutput(
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mask = ImageField(image_type = image_type, image_name = image_name)
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)
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class BlurInvocation(BaseInvocation):
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"""Blurs an image"""
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type: Literal['blur'] = 'blur'
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# Inputs
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image: ImageField = Field(default=None, description="The image to blur")
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radius: float = Field(default=8.0, ge=0, description="The blur radius")
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blur_type: Literal['gaussian', 'box'] = Field(default='gaussian', description="The type of blur")
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.services.images.get(self.image.image_type, self.image.image_name)
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blur = ImageFilter.GaussianBlur(self.radius) if self.blur_type == 'gaussian' else ImageFilter.BoxBlur(self.radius)
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blur_image = image.filter(blur)
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image_type = ImageType.INTERMEDIATE
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image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
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context.services.images.save(image_type, image_name, blur_image)
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return ImageOutput(
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image = ImageField(image_type = image_type, image_name = image_name)
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)
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class LerpInvocation(BaseInvocation):
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"""Linear interpolation of all pixels of an image"""
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type: Literal['lerp'] = 'lerp'
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# Inputs
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image: ImageField = Field(default=None, description="The image to lerp")
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min: int = Field(default=0, ge=0, le=255, description="The minimum output value")
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max: int = Field(default=255, ge=0, le=255, description="The maximum output value")
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.services.images.get(self.image.image_type, self.image.image_name)
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image_arr = numpy.asarray(image, dtype=numpy.float32) / 255
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image_arr = image_arr * (self.max - self.min) + self.max
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lerp_image = Image.fromarray(numpy.uint8(image_arr))
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image_type = ImageType.INTERMEDIATE
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image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
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context.services.images.save(image_type, image_name, lerp_image)
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return ImageOutput(
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image = ImageField(image_type = image_type, image_name = image_name)
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)
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class InverseLerpInvocation(BaseInvocation):
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"""Inverse linear interpolation of all pixels of an image"""
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type: Literal['ilerp'] = 'ilerp'
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# Inputs
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image: ImageField = Field(default=None, description="The image to lerp")
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min: int = Field(default=0, ge=0, le=255, description="The minimum input value")
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max: int = Field(default=255, ge=0, le=255, description="The maximum input value")
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.services.images.get(self.image.image_type, self.image.image_name)
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image_arr = numpy.asarray(image, dtype=numpy.float32)
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image_arr = numpy.minimum(numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1) * 255
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ilerp_image = Image.fromarray(numpy.uint8(image_arr))
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image_type = ImageType.INTERMEDIATE
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image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
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context.services.images.save(image_type, image_name, ilerp_image)
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return ImageOutput(
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image = ImageField(image_type = image_type, image_name = image_name)
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)
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