Mon Jun 26 05:12:51 PM CST 2023

demo.py

@@ -1,53 +0,0 @@
-from ultralytics import YOLO
-# from ultralytics.yolo.utils.benchmarks import benchmark 。
-import cv2
-
-# Load a model
-# model = YOLO("yolov8n.yaml")  # build a new model from scratch
-model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
-
-# Use the model
-# model.train(data="coco128.yaml", epochs=3,workers=0)  # train the model,workers=0 if windows
-# metrics = model.val()  # evaluate model performance on the validation set
-'''
-results = model("bus.jpg")  # predict on an image
-print(results)
-# '''
-img_path = "./image/"
-# results = model.predict(img_path, save=True,conf=0.5) # device=0 by default, conf:置信度阈值
-# results = model.predict(img_path,save=True,classes=[0,2],conf=0.5) # i.e. classes=0,classes=[0,3,4]
-
-# save detection results *
-# results = model.predict(img_path,save=True,save_txt=True,classes=0,conf=0.4)
-
-
-
-# predict video
-video_path = "./video/1.mp4"
-cap = cv2.VideoCapture(0)
-
-# Loop through the video frames
-while cap.isOpened():
-    # Read a frame from the video
-    success, frame = cap.read()
-
-    if success:
-        # Run YOLOv8 inference on the frame
-        results = model(frame)
-
-        # Visualize the results on the frame
-        annotated_frame = results[0].plot()
-
-        # Display the annotated frame
-        cv2.imshow("YOLOv8 Inference", annotated_frame)
-
-        # Break the loop if 'q' is pressed
-        if cv2.waitKey(1) & 0xFF == ord("q"):
-            break
-    else:
-        # Break the loop if the end of the video is reached
-        break
-
-# Release the video capture object and close the display window
-cap.release()
-cv2.destroyAllWindows()