supervision-0.21.0

[SkalskiP]

📅 Timeline

The supervision-0.21.0 release is around the corner. Here is the timeline:

• 5 Jun 2024 08:00 PM CEST (UTC +2) / 5 Jun 2024 11:00 AM PDT (UTC -7) - merge develop into main - closing list supervision-0.21.0 features
• 6 Jun 2024 11:00 AM CEST (UTC +2) / 6 Jun 2024 02:00 AM PDT (UTC -7) - release supervision-0.21.0

🪵 Changelog

🚀 Added

• sv.Detections.with_nmm to perform non-maximum merging on the current set of object detections. (#500)

• sv.Detections.from_lmm allowing to parse Large Multimodal Model (LMM) text result into sv.Detections object. For now from_lmm supports only PaliGemma result parsing. (#1221)

import supervision as sv

paligemma_result = "<loc0256><loc0256><loc0768><loc0768> cat"
detections = sv.Detections.from_lmm(
    sv.LMM.PALIGEMMA,
    paligemma_result,
    resolution_wh=(1000, 1000),
    classes=['cat', 'dog']
)
detections.xyxy
# array([[250., 250., 750., 750.]])

detections.class_id
# array([0])

• sv.VertexLabelAnnotator allowing to annotate every vertex of a keypoint skeleton with custom text and color. (#1236)

import supervision as sv

image = ...
key_points = sv.KeyPoints(...)

LABELS = [
    "nose", "left eye", "right eye", "left ear",
    "right ear", "left shoulder", "right shoulder", "left elbow",
    "right elbow", "left wrist", "right wrist", "left hip",
    "right hip", "left knee", "right knee", "left ankle",
    "right ankle"
]

COLORS = [
    "#FF6347", "#FF6347", "#FF6347", "#FF6347",
    "#FF6347", "#FF1493", "#00FF00", "#FF1493",
    "#00FF00", "#FF1493", "#00FF00", "#FFD700",
    "#00BFFF", "#FFD700", "#00BFFF", "#FFD700",
    "#00BFFF"
]
COLORS = [sv.Color.from_hex(color_hex=c) for c in COLORS]

vertex_label_annotator = sv.VertexLabelAnnotator(
    color=COLORS,
    text_color=sv.Color.BLACK,
    border_radius=5
)
annotated_frame = vertex_label_annotator.annotate(
    scene=image.copy(),
    key_points=key_points,
    labels=labels
)

• sv.KeyPoints.from_inference and sv.KeyPoints.from_yolo_nas allowing to create sv.KeyPoints from Inference and YOLO-NAS result. (#1147 and #1138)

• sv.mask_to_rle and sv.rle_to_mask allowing for easy conversion between mask and rle formats. (#1163)

🌱 Changed

• sv.InferenceSlicer allowing to select overlap filtering strategy (NONE, NON_MAX_SUPPRESSION and NON_MAX_MERGE). (#1236)

• sv.InferenceSlicer adding instance segmentation model support. (#1178)

import cv2
import numpy as np
import supervision as sv
from inference import get_model

model = get_model(model_id="yolov8x-seg-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)

def callback(image_slice: np.ndarray) -> sv.Detections:
    results = model.infer(image_slice)[0]
    return sv.Detections.from_inference(results)

slicer = sv.InferenceSlicer(callback = callback)
detections = slicer(image)

mask_annotator = sv.MaskAnnotator()
label_annotator = sv.LabelAnnotator()

annotated_image = mask_annotator.annotate(
    scene=image, detections=detections)
annotated_image = label_annotator.annotate(
    scene=annotated_image, detections=detections)

• sv.LineZone making it 10-20 times faster, depending on the use case. (#1228)

• sv.DetectionDataset.from_coco and sv.DetectionDataset.as_coco adding support for run-length encoding (RLE) mask format. (#1163)

🏆 Contributors

@onuralpszr (Onuralp SEZER), @LinasKo (Linas Kondrackis), @rolson24 (Raif Olson), @xaristeidou (Christoforos Aristeidou), @ManzarIMalik (Manzar Iqbal Malik), @tc360950 (Tomasz Cąkała), @emsko, @SkalskiP (Piotr Skalski)

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This discussion was created from the release supervision-0.21.0.