YOLOv8 get predicted bounding box

Question

I want to integrate OpenCV with YOLOv8 from ultralytics, so I want to obtain the bounding box coordinates from the model prediction. How do I do this?

from ultralytics import YOLO
import cv2

model = YOLO('yolov8n.pt')
cap = cv2.VideoCapture(0)
cap.set(3, 640)
cap.set(4, 480)

while True:
    _, frame = cap.read()
    
    img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    results = model.predict(img)

    for r in results:
        for c in r.boxes.cls:
            print(model.names[int(c)])

    cv2.imshow('YOLO V8 Detection', frame)
    if cv2.waitKey(1) & 0xFF == ord(' '):
        break

cap.release()
cv2.destroyAllWindows()

I want to display the YOLO annotated image in OpenCV. I know I can use the stream parameter in model.predict(source='0', show=True). But I want to continuously monitor the predicted class names for my program, at the same time displaying the image output.

Answer 1

This will:

1. Loop through each frame in the video
2. Pass each frame to Yolov8 which will generate bounding boxes
3. Draw the bounding boxes on the frame using the built in ultralytics' annotator:

from ultralytics import YOLO
import cv2
from ultralytics.utils.plotting import Annotator  # ultralytics.yolo.utils.plotting is deprecated

model = YOLO('yolov8n.pt')
cap = cv2.VideoCapture(0)
cap.set(3, 640)
cap.set(4, 480)

while True:
    _, img = cap.read()
    
    # BGR to RGB conversion is performed under the hood
    # see: https://github.com/ultralytics/ultralytics/issues/2575
    results = model.predict(img)

    for r in results:
        
        annotator = Annotator(img)
        
        boxes = r.boxes
        for box in boxes:
            
            b = box.xyxy[0]  # get box coordinates in (left, top, right, bottom) format
            c = box.cls
            annotator.box_label(b, model.names[int(c)])
          
    img = annotator.result()  
    cv2.imshow('YOLO V8 Detection', img)     
    if cv2.waitKey(1) & 0xFF == ord(' '):
        break

cap.release()
cv2.destroyAllWindows()

Answer 2

You can get all the information using the next code:

for result in results:
    # detection
    result.boxes.xyxy   # box with xyxy format, (N, 4)
    result.boxes.xywh   # box with xywh format, (N, 4)
    result.boxes.xyxyn  # box with xyxy format but normalized, (N, 4)
    result.boxes.xywhn  # box with xywh format but normalized, (N, 4)
    result.boxes.conf   # confidence score, (N, 1)
    result.boxes.cls    # cls, (N, 1)

    # segmentation
    result.masks.masks     # masks, (N, H, W)
    result.masks.segments  # bounding coordinates of masks, List[segment] * N

    # classification
    result.probs     # cls prob, (num_class, )

you can read furthermore in the documentation.