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https://github.com/invoke-ai/InvokeAI.git
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bc55791db1
Similar to the existing node, but without any resizing and with a revised model loading API that uses the model manager. All code related to the invocation now lives in the Invoke repo.
27 lines
1.3 KiB
Python
27 lines
1.3 KiB
Python
from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
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from invokeai.app.invocations.fields import ImageField, InputField, WithBoard, WithMetadata
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from invokeai.app.invocations.primitives import ImageOutput
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.backend.image_util.mediapipe_face import detect_faces
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@invocation(
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"mediapipe_face_detection",
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title="MediaPipe Face Detection",
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tags=["controlnet", "face"],
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category="controlnet",
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version="1.0.0",
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)
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class MediaPipeFaceDetectionInvocation(BaseInvocation, WithMetadata, WithBoard):
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"""Detects faces using MediaPipe."""
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image: ImageField = InputField(description="The image to process")
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max_faces: int = InputField(default=1, ge=1, description="Maximum number of faces to detect")
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min_confidence: float = InputField(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.images.get_pil(self.image.image_name, "RGB")
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detected_faces = detect_faces(image=image, max_faces=self.max_faces, min_confidence=self.min_confidence)
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image_dto = context.images.save(image=detected_faces)
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return ImageOutput.build(image_dto)
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