Added first controlnet preprocessor node for canny edge detection.

invokeai/app/invocations/controlnet_image_processors.py

@@ -0,0 +1,48 @@
+from typing import Literal, Optional
+
+import numpy
+from PIL import Image, ImageFilter, ImageOps
+from pydantic import BaseModel, Field
+
+from ..models.image import ImageField, ImageType
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    InvocationContext,
+    InvocationConfig,
+)
+
+from controlnet_aux import CannyDetector, HEDdetector, LineartDetector
+from .image import ImageOutput, build_image_output, PILInvocationConfig
+
+
+# Canny Image Processor
+class CannyProcessorInvocation(BaseInvocation, PILInvocationConfig):
+    """Applies Canny edge detection to image"""
+
+    # fmt: off
+    type: Literal["canny"] = "canny"
+
+    # Inputs
+    image: ImageField = Field(default=None, description="image to process")
+    low_threshold:  float = Field(default=100, ge=0, description="low threshold of Canny pixel gradient")
+    high_threshold: float = Field(default=200, ge=0, description="high threshold of Canny pixel gradient")
+    # fmt: on
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
+        canny_processor = CannyDetector()
+        processed_image = canny_processor(image, self.low_threshold, self.high_threshold)
+        image_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+        context.services.images.save(image_type, image_name, processed_image, metadata)
+        return build_image_output(
+            image_type=image_type, image_name=image_name, image=processed_image
+        )