Plant/segment/segment_test_sv.py

import cv2
import supervision as sv
from ultralytics import YOLO

cap = cv2.VideoCapture(0)
#model = YOLO('best_magent_block.pt')
model = YOLO('yolov8n-seg.pt')
#model = YOLO('best0312.pt')
byte_tracker = sv.ByteTrack()

while True:
    ret, frame = cap.read()
    result = model(frame)[0]
    detections = sv.Detections.from_ultralytics(result)
    detections_tracker = byte_tracker.update_with_detections(detections=detections)

    #print(detections)
    if ret:

        polygon_annotator = sv.PolygonAnnotator()
        annotated_frame = polygon_annotator.annotate(
            scene=frame.copy(), detections=detections)

        mask_annotator = sv.MaskAnnotator()
        annotated_frame = mask_annotator.annotate(
            scene=annotated_frame,detections=detections
        )

        dot_annotator = sv.DotAnnotator()
        annotated_frame = dot_annotator.annotate(
            scene=annotated_frame, detections=detections_tracker)


        # bounding_box_annotator = sv.BoundingBoxAnnotator()
        # annotated_frame = bounding_box_annotator.annotate(
        #     scene=annotated_frame, detections=detections)
        #labels = [detections.confidence]

        labels = [
            f"#{tracker_id} {result.names[class_id]} {confidence:.2f}"
            for class_id, tracker_id, confidence
            in zip(detections_tracker.class_id, detections_tracker.tracker_id, detections_tracker.confidence)
        ]
        # labels_con_id = [
        #     f"{result.names[class_id]} {confidence:.2f}"
        #     for class_id, confidence
        #     in zip(detections.class_id, detections.confidence)
        # ]

        print(labels)

        label_annotator = sv.LabelAnnotator()
        annotated_frame = label_annotator.annotate(
            scene=annotated_frame,
            detections=detections_tracker,
            labels=labels
        )
        position = sv.Position.CENTER_OF_MASS
        xy = detections.get_anchors_coordinates(anchor=position)
        for detection_idx in range(len(detections)):
            print(xy)
            center = (int(xy[detection_idx,0]), int(xy[detection_idx,1]))
            cv2.circle(annotated_frame, center,radius=3, color=(255,255,0), thickness=-1)
        cv2.imshow('frame', annotated_frame)

    if cv2.waitKey(1) == ord('q'):
        break