Predict a class from the results of a detected object of trained YOLOv8 model in python
I have a trained model and I have detected my required object using following code
import cv2
from PIL import Image
from ultralytics import YOLO
image = cv2.imread("screenshot.png")
model = YOLO('runs/detect/train4/weights/best.pt')
results = model.predict(image, show=True, stream=True, classes=0, imgsz=512)
for result in results:
for box in result.boxes:
class_id = result.names[box.cls[0].item()]
if (class_id == "myclassname"):
cords = box.xyxy[0].tolist()
cords = [round(x) for x in cords]
conf = round(box.conf[0].item(), 2)
print("Object type:", class_id)
print("Coordinates:", cords)
print("Probability:", conf)
print("---")
From this detected portion of image I need to detect an other class how I can do that?
I have searched enough but I could not see any post for this.
Comments
Post a Comment