SEGM Detector (combined)

Documentation

• Class name: SegmDetectorCombined_v2
• Category: ImpactPack/Detector
• Output node: False

The SegmDetectorCombined_v2 node is designed for image segmentation tasks, combining detection and segmentation processes to output a mask representing the segmented areas of the input image. It abstracts the complexity of underlying segmentation models and detection algorithms, providing a streamlined interface for generating segmentation masks.

Input types

Required

• segm_detector
  • The segmentation detector model used for detecting and segmenting objects within the image. It plays a crucial role in the overall segmentation process.
  • Comfy dtype: SEGM_DETECTOR
  • Python dtype: object
• image
  • The input image to be processed for segmentation. The image is analyzed to identify and segment relevant objects or areas.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• threshold
  • A threshold value used to determine the sensitivity of the segmentation detection. It influences the segmentation outcome by filtering out detections below this threshold.
  • Comfy dtype: FLOAT
  • Python dtype: float
• dilation
  • Specifies the dilation level applied to the segmentation masks, affecting the mask's boundary smoothness and size.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• mask
  • Comfy dtype: MASK
  • The output segmentation mask, indicating the segmented areas of the input image.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class SegmDetectorCombined:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
                        "segm_detector": ("SEGM_DETECTOR", ),
                        "image": ("IMAGE", ),
                        "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
                        "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
                      }
                }

    RETURN_TYPES = ("MASK",)
    FUNCTION = "doit"

    CATEGORY = "ImpactPack/Detector"

    def doit(self, segm_detector, image, threshold, dilation):
        mask = segm_detector.detect_combined(image, threshold, dilation)

        if mask is None:
            mask = torch.zeros((image.shape[2], image.shape[1]), dtype=torch.float32, device="cpu")

        return (mask.unsqueeze(0),)