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.

Asked By: Louis

||

Source

Answer 1

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.

Answered By: Roy Amoyal

Answer 2

This will draw the bboxes with their respective label names on the original BGR frame, not the RGB img fed to the model:


from ultralytics import YOLO
import cv2
from ultralytics.yolo.utils.plotting import Annotator

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

cap.release()
cv2.destroyAllWindows()

Answered By: Mike B

Answer 3

it is not work!!!!

img= cv2.imread(‘dog.jpeg’)
model.predict(source=img)

source should be str?
Error:
source = str(source or self.args.source)

Answered By: julio

Answer 4

where this error?

from ultralytics import YOLO
import cv2
img_source = cv2.imread('dog.jpeg')
model = YOLO('yolov8n.pt')
img = cv2.cvtColor(img_source, cv2.COLOR_BGR2RGB)
results = model.predict(source=img)

it is not possible to get results when the source is not a path. How could I make predictions of an image loaded through opencv?

work on anaconda on windows 10

Answered By: julio

YOLOv8 get predicted bounding box

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