Mon Jun 26 04:32:02 PM CST 2023

.gitignore

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+virtualenv/

demo.py

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+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()

torch_check.py

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+import torch
+print(torch.__version__)
+print(torch.cuda.is_available())