0.27.0

# Description

## 🚀 Added

- Added [`sv.filter_segments_by_distance`](https://supervision.roboflow.com/0.27.0/detection/utils/masks/#supervision.detection.utils.masks.filter_segments_by_distance) to keep the largest connected component and any nearby components within an absolute or relative distance threshold. This helps you clean up predictions from segmentation models like SAM, SAM2, YOLO segmentation, and RF-DETR segmentation. ([#2008](https://github.com/roboflow/supervision/pull/2008))

https://github.com/user-attachments/assets/2bdfd45d-b235-414b-91a3-6544d7c2b4ec


- Added [`sv.edit_distance`](https://supervision.roboflow.com/0.27.0/detection/utils/vlms/#supervision.detection.utils.vlms.edit_distance) for Levenshtein distance between two strings. Supports insert, delete, substitute. ([#1912](https://github.com/roboflow/supervision/pull/1912))

    ```python
    import supervision as sv

    sv.edit_distance("hello", "hello")
    # 0

    sv.edit_distance("hello world", "helloworld")
    # 1
    
    sv.edit_distance("YOLO", "yolo", case_sensitive=True)
    # 4
    ```

- Added [`sv.fuzzy_match_index`](https://supervision.roboflow.com/0.27.0/detection/utils/vlms/#supervision.detection.utils.vlms.fuzzy_match_index) to find the first close match in a list using edit distance. ([#1912](https://github.com/roboflow/supervision/pull/1912))
    
    ```python
    import supervision as sv
    
    sv.fuzzy_match_index(["cat", "dog", "rat"], "dat", threshold=1)
    # 0
    
    sv.fuzzy_match_index(["alpha", "beta", "gamma"], "bata", threshold=1)
    # 1
    
    sv.fuzzy_match_index(["one", "two", "three"], "ten", threshold=2)
    # None
    ```

- Added [`sv.get_image_resolution_wh`](https://supervision.roboflow.com/0.27.0/utils/image/#supervision.utils.image.get_image_resolution_wh) as a unified way to read image width and height from NumPy and PIL inputs. ([#2014](https://github.com/roboflow/supervision/pull/2014))

- Added [`sv.tint_image`](https://supervision.roboflow.com/0.27.0/utils/image/#supervision.utils.image.tint_image) to apply a solid color overlay to an image at a specified opacity. Works with both NumPy and PIL inputs. ([#1943](https://github.com/roboflow/supervision/pull/1943))

- Added [`sv.grayscale_image`](https://supervision.roboflow.com/0.27.0/utils/image/#supervision.utils.image.tint_image) to convert an image to 3-channel grayscale for compatibility with color-based drawing utilities. ([#1943](https://github.com/roboflow/supervision/pull/1943))

- Added [`sv.xyxy_to_mask`](https://supervision.roboflow.com/0.27.0/detection/utils/converters/#supervision.detection.utils.converters.xyxy_to_mask) to convert bounding boxes into 2D boolean masks. Each mask corresponds to one bounding box. ([#2006](https://github.com/roboflow/supervision/pull/2006))

## 🌱 Changed

- Added Qwen3-VL support in [`sv.Detections.from_vlm`](https://supervision.roboflow.com/0.27.0/detection/core/#supervision.detection.core.Detections.from_vlm) and legacy `from_lmm` mapping. Use `vlm=sv.QWEN_3_VL`. ([#2015](https://github.com/roboflow/supervision/pull/2015))

    ```python
    import supervision as sv
    
    response = """```json
    [
	    {"bbox_2d": [220, 102, 341, 206], "label": "taxi"},
	    {"bbox_2d": [30, 606, 171, 743], "label": "taxi"},
	    {"bbox_2d": [192, 451, 318, 581], "label": "taxi"},
	    {"bbox_2d": [358, 908, 506, 1000], "label": "taxi"},
	    {"bbox_2d": [735, 359, 873, 480], "label": "taxi"},
	    {"bbox_2d": [758, 508, 885, 617], "label": "taxi"},
	    {"bbox_2d": [857, 263, 988, 374], "label": "taxi"},
	    {"bbox_2d": [735, 243, 838, 351], "label": "taxi"},
	    {"bbox_2d": [303, 291, 434, 417], "label": "taxi"},
	    {"bbox_2d": [426, 273, 552, 382], "label": "taxi"}
    ]
    ```"""

    detections = sv.Detections.from_vlm(
        vlm=sv.VLM.QWEN_3_VL,
        result=response,
        resolution_wh=(1023, 682)
    )

    detections.xyxy
    # array([[ 225.06 ,   69.564,  348.843,  140.492],
    #        [  30.69 ,  413.292,  174.933,  506.726],
    #        [ 196.416,  307.582,  325.314,  396.242],
    #        [ 366.234,  619.256,  517.638,  682.   ],
    #        [ 751.905,  244.838,  893.079,  327.36 ],
    #        [ 775.434,  346.456,  905.355,  420.794],
    #        [ 876.711,  179.366, 1010.724,  255.068],
    #        [ 751.905,  165.726,  857.274,  239.382],
    #        [ 309.969,  198.462,  443.982,  284.394],
    #        [ 435.798,  186.186,  564.696,  260.524]])
    ```

<img width="1023" height="682" alt="supervision-0 27 0-promo-from-qwen-3-vl" src="https://github.com/user-attachments/assets/bbef03fd-5d76-4dbf-a7a9-2fbaef758b6c" />

- Added DeepSeek-VL2 support in [`sv.Detections.from_vlm`](https://supervision.roboflow.com/0.27.0/detection/core/#supervision.detection.core.Detections.from_vlm) and legacy `from_lmm` mapping. Use `vlm=sv.VLM.DEEPSEEK_VL_2`. ([#1884](https://github.com/roboflow/supervision/pull/1884))

- Improved [`sv.Detections.from_vlm`](https://supervision.roboflow.com/0.27.0/detection/core/#supervision.detection.core.Detections.from_vlm) parsing for Qwen 2.5 VL outputs. The function now handles incomplete or truncated JSON responses. ([#2015](https://github.com/roboflow/supervision/pull/2015))

- [`sv.InferenceSlicer`](https://supervision.roboflow.com/0.27.0/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer) now uses a new offset generation logic that removes redundant tiles and ensures clean border aligned slicing. This reduces the number of tiles processed, lowering inference time without hurting detection quality. ([#2014](https://github.com/roboflow/supervision/pull/2014))

https://github.com/user-attachments/assets/0141ff44-0269-472c-900d-610f47330d57

```python
import supervision as sv
from PIL import Image
from rfdetr import RFDETRMedium

model = RFDETRMedium()

def callback(tile):
    return model.predict(tile)

slicer = sv.InferenceSlicer(callback, slice_wh=512, overlap_wh=128)

image = Image.open("example.png")
detections = slicer(image)
```

- [`sv.Detections`](https://supervision.roboflow.com/0.27.0/detection/core/#supervision.detection.core.Detections) now includes a `box_aspect_ratio` property for vectorized aspect ratio computation. You use it to filter detections based on box shape. ([#2016](https://github.com/roboflow/supervision/pull/2016))

```python
import numpy as np
import supervision as sv

xyxy = np.array([
    [10, 10, 50, 50],
    [60, 10, 180, 50],
    [10, 60, 50, 180],
])

detections = sv.Detections(xyxy=xyxy)

ar = detections.box_aspect_ratio
# array([1.0, 3.0, 0.33333333])

detections[(ar < 2.0) & (ar > 0.5)].xyxy
# array([[10., 10., 50., 50.]])
```

- Improved the performance of [`sv.box_iou_batch`](https://supervision.roboflow.com/0.27.0/detection/utils/iou_and_nms/#supervision.detection.utils.iou_and_nms.box_iou_batch). Processing runs about 2x to 5x faster. ([#2001](https://github.com/roboflow/supervision/pull/2001))

- [`sv.process_video`](https://supervision.roboflow.com/0.27.0/utils/video/#supervision.utils.video.process_video) now uses a threaded reader, processor, and writer pipeline. This removes I/O stalls and improves throughput while keeping the callback single threaded and safe for stateful models. ([#1997](https://github.com/roboflow/supervision/pull/1997))

- [`sv.denormalize_boxes`](https://supervision.roboflow.com/0.27.0/detection/utils/boxes/#supervision.detection.utils.boxes.denormalize_boxes) now supports batch conversion of bounding boxes. The function now accepts arrays of shape `(N, 4)` and returns a batch of absolute pixel coordinates.

- [`sv.LabelAnnotator`](https://supervision.roboflow.com/develop/0.27.0/annotators/#supervision.annotators.core.LabelAnnotator) and [`sv.RichLabelAnnotator`](https://supervision.roboflow.com/develop/0.27.0/annotators/#supervision.annotators.core.LabelAnnotator) now accepts `text_offset=(x, y)` to shift the label relative to `text_position`. Works with smart label position and line wrapping. ([#1917](https://github.com/roboflow/supervision/pull/1917))

## ❌ Removed

- Removed the deprecated `overlap_ratio_wh` argument from `sv.InferenceSlicer`. Use the pixel based `overlap_wh` argument to control slice overlap. ([#2014](https://github.com/roboflow/supervision/pull/2014))

> [!TIP] 
> Convert your old ratio based overlap to pixel based overlap. Multiply each ratio by the slice dimensions.
>
> ```python
> # before
>
> slice_wh = (640, 640)
> overlap_ratio_wh = (0.25, 0.25)
>
> slicer = sv.InferenceSlicer(
>     callback=callback,
>     slice_wh=slice_wh,
>     overlap_ratio_wh=overlap_ratio_wh,
>     overlap_filter=sv.OverlapFilter.NON_MAX_SUPPRESSION,
> )
> 
> # after
>
> overlap_wh = (
>     int(overlap_ratio_wh[0] * slice_wh[0]),
>     int(overlap_ratio_wh[1] * slice_wh[1]),
> )
>
> slicer = sv.InferenceSlicer(
>     callback=callback,
>     slice_wh=slice_wh,
>     overlap_wh=overlap_wh,
>     overlap_filter=sv.OverlapFilter.NON_MAX_SUPPRESSION,
> )
> ```

## 🏆 Contributors

@SkalskiP ([Piotr Skalski](https://www.linkedin.com/in/skalskip92/)), @onuralpszr ([Onuralp SEZER](https://www.linkedin.com/in/osezer/)), @soumik12345 ([Soumik Rakshit](https://www.linkedin.com/in/soumikrakshit/)), @rcvsq, @AlexBodner ([Alex Bodner](https://www.linkedin.com/in/alexanderbodner)), @Ashp116, @kshitijaucharmal ([Kshitij Aucharmal](https://www.linkedin.com/in/kshitijaucharmal21/)), @ernestlwt, @AnonymDevOSS, @jackiehimel ([Jackie Himel ](https://www.linkedin.com/in/jackie-himel)), @dominikWin ([Dominik Winecki](https://www.linkedin.com/in/dominikwin))