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d2c8a53c55
- `ImageType` is now restricted to `results` and `uploads`. - Add a reserved `meta` field to nodes to hold the `is_intermediate` boolean. We can extend it in the future to support other node `meta`. - Add a `is_intermediate` column to the `images` table to hold this. (When `latents`, `conditioning` etc are added to the DB, they will also have this column.) - All nodes default to `*not* intermediate`. Nodes must explicitly be marked `intermediate` for their outputs to be `intermediate`. - When building a graph, you can set `node.meta.is_intermediate=True` and it will be handled as an intermediate. - Add a new `update()` method to the `ImageService`, and a route to call it. Updates have a strict model, currently only `session_id` and `image_category` may be updated. - Add a new `update()` method to the `ImageRecordStorageService` to update the image record using the model.
74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
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from typing import Literal
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import cv2 as cv
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import numpy
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from PIL import Image, ImageOps
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from pydantic import BaseModel, Field
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from invokeai.app.models.image import ImageCategory, ImageField, ImageType
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from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
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from .image import ImageOutput
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class CvInvocationConfig(BaseModel):
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"""Helper class to provide all OpenCV invocations with additional config"""
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# Schema customisation
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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"tags": ["cv", "image"],
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},
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}
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class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
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"""Simple inpaint using opencv."""
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# fmt: off
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type: Literal["cv_inpaint"] = "cv_inpaint"
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# Inputs
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image: ImageField = Field(default=None, description="The image to inpaint")
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mask: ImageField = Field(default=None, description="The mask to use when inpainting")
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# fmt: on
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.services.images.get_pil_image(
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self.image.image_type, self.image.image_name
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)
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mask = context.services.images.get_pil_image(
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self.mask.image_type, self.mask.image_name
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)
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# Convert to cv image/mask
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# TODO: consider making these utility functions
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cv_image = cv.cvtColor(numpy.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
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cv_mask = numpy.array(ImageOps.invert(mask.convert("L")))
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# Inpaint
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cv_inpainted = cv.inpaint(cv_image, cv_mask, 3, cv.INPAINT_TELEA)
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# Convert back to Pillow
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# TODO: consider making a utility function
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image_inpainted = Image.fromarray(cv.cvtColor(cv_inpainted, cv.COLOR_BGR2RGB))
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image_dto = context.services.images.create(
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image=image_inpainted,
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image_type=ImageType.RESULT,
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image_category=ImageCategory.GENERAL,
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node_id=self.id,
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session_id=context.graph_execution_state_id,
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)
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return ImageOutput(
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image=ImageField(
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image_name=image_dto.image_name,
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image_type=image_dto.image_type,
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),
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width=image_dto.width,
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height=image_dto.height,
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)
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