feat(nodes): add CannyEdgeDetectionInvocation

Similar to the existing node, but without the resolution fields.

invokeai/app/invocations/canny.py

@@ -0,0 +1,34 @@
+import cv2
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.fields import ImageField, InputField, WithBoard, WithMetadata
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.image_util.util import cv2_to_pil, pil_to_cv2
+
+
+@invocation(
+    "canny_edge_detection",
+    title="Canny Edge Detection",
+    tags=["controlnet", "canny"],
+    category="controlnet",
+    version="1.0.0",
+)
+class CannyEdgeDetectionInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Geneartes an edge map using a cv2's Canny algorithm."""
+
+    image: ImageField = InputField(description="The image to process")
+    low_threshold: int = InputField(
+        default=100, ge=0, le=255, description="The low threshold of the Canny pixel gradient (0-255)"
+    )
+    high_threshold: int = InputField(
+        default=200, ge=0, le=255, description="The high threshold of the Canny pixel gradient (0-255)"
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.images.get_pil(self.image.image_name, "RGB")
+        np_img = pil_to_cv2(image)
+        edge_map = cv2.Canny(np_img, self.low_threshold, self.high_threshold)
+        edge_map_pil = cv2_to_pil(edge_map)
+        image_dto = context.images.save(image=edge_map_pil)
+        return ImageOutput.build(image_dto)