mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
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from typing import Literal, Optional
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import numpy
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from PIL import Image, ImageFilter, ImageOps
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from pydantic import BaseModel, Field
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from ..models.image import ImageField, ImageType
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from .baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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InvocationContext,
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InvocationConfig,
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)
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from controlnet_aux import CannyDetector, HEDdetector, LineartDetector
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from .image import ImageOutput, build_image_output, PILInvocationConfig
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# Canny Image Processor
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class CannyProcessorInvocation(BaseInvocation, PILInvocationConfig):
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"""Applies Canny edge detection to image"""
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# fmt: off
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type: Literal["canny"] = "canny"
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# Inputs
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image: ImageField = Field(default=None, description="image to process")
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low_threshold: float = Field(default=100, ge=0, description="low threshold of Canny pixel gradient")
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high_threshold: float = Field(default=200, ge=0, description="high threshold of Canny pixel gradient")
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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(
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self.image.image_type, self.image.image_name
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)
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canny_processor = CannyDetector()
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processed_image = canny_processor(image, self.low_threshold, self.high_threshold)
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image_type = ImageType.INTERMEDIATE
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image_name = context.services.images.create_name(
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context.graph_execution_state_id, self.id
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)
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metadata = context.services.metadata.build_metadata(
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session_id=context.graph_execution_state_id, node=self
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)
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context.services.images.save(image_type, image_name, processed_image, metadata)
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return build_image_output(
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image_type=image_type, image_name=image_name, image=processed_image
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)
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