43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
from ultralytics import YOLO
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import cv2
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# 1. 加载模型
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model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_obb2/weights/best.pt')
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# 2. 读取图像
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img_path = r"/home/hx/桌面/image/images/test/1.jpg"
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img = cv2.imread(img_path)
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# ✅ 检查图像是否加载成功
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if img is None:
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print(f"❌ 错误：无法读取图像！请检查路径：{img_path}")
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print("💡 提示：可能是文件不存在、路径错误或图像损坏")
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exit(1)
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# 3. 预测（使用 OBB 模式！）
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results = model(
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img,
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save=False,
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imgsz=1280, # 必须和训练时一致（你训练日志中是 1280）
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conf=0.25,
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mode='obb' # 👈👈👈 关键！必须加这一行才能启用旋转框模式
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)
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# 4. 在原图上绘制检测结果
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result = results[0]
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annotated_img = result.plot() # OBB 模式下会自动画旋转框
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# 5. 显示图像
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cv2.imshow("YOLO OBB Prediction", annotated_img)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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# ✅ 可选：打印检测结果
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if result.boxes is None or len(result.boxes) == 0:
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print("❌ No objects detected.")
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else:
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print(f"✅ Detected {len(result.boxes)} object(s):")
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for box in result.boxes:
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cls = int(box.cls.cpu().numpy()[0])
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conf = box.conf.cpu().numpy()[0]
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print(f" Class: {cls}, Confidence: {conf:.3f}") |