`sv.Detections.from_ultralytics()` can't handle segmentation results `ultralytics.engine.results.Results` from ultralytics `SAM()` when using `bboxes` or `points` arguments

[xaristeidou]

Search before asking

• [X] I have searched the Supervision issues and found no similar bug report.

Bug

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/christoforos/Documents/supervision/supervision/detection/core.py", line 277, in from_ultralytics
    class_id = ultralytics_results.boxes.cls.cpu().numpy().astype(int)
AttributeError: 'NoneType' object has no attribute 'cls'

Environment

• Supervision: 0.22.0

Minimal Reproducible Example

import cv2
import supervision as sv
from ultralytics import SAM

image = cv2.imread("image.jpg")

model = SAM("mobile_sam.pt")
results = model(
    image,
    bboxes=[[100, 100, 200, 200]],
)

detections = sv.Detections.from_ultralytics(results[0])

Additional

When predicting with any model using SAM() class from ultralytics, if we don't specify bboxes or points arguments in the predict() function of SAM() model, it has expected behaviour.

The problem is when we pass bboxes or points arguments, which will results in ultralytics.engine.results.Results to contain None type for boxes, probs attributes (not sure if probs attribute affects the process but boxes does for sure).

As a results, mask annotators (polygon, halo, mask) also can't be used.

We could slightly modify sv.Detection.from_ultralytics() to handle this problem, by making an extra check and fill in required attributes of the class. I have implemented a fix of this issue, and I could submit a PR for review.

Are you willing to submit a PR?

• [X] Yes I'd like to help by submitting a PR!

[onuralpszr]

@xaristeidou may I see branch + collab to test ?

[onuralpszr]

Let's fix that out quickly

[xaristeidou]

@onuralpszr PR #1454 with colab

[onuralpszr]

Fixed via https://github.com/roboflow/supervision/pull/1454