79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
import cv2
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import os
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import numpy as np
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from ultralytics import YOLO
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def predict_obb_best_angle(model_path, image_path, save_path=None):
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"""
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输入：
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model_path: YOLO 权重路径
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image_path: 图片路径
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save_path: 可选，保存带标注图像
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输出：
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angle_deg: 置信度最高两个框的主方向夹角（度），如果检测少于两个目标返回 None
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annotated_img: 可视化图像
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"""
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# 1. 加载模型
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model = YOLO(model_path)
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# 2. 读取图像
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img = cv2.imread(image_path)
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if img is None:
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print(f"无法读取图像: {image_path}")
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return None, None
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# 3. 推理 OBB
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results = model(img, save=False, imgsz=640, conf=0.3, mode='obb')
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result = results[0]
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print(result)
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# 4. 可视化
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annotated_img = result.plot()
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if save_path:
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os.makedirs(os.path.dirname(save_path), exist_ok=True)
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cv2.imwrite(save_path, annotated_img)
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print(f"推理结果已保存至: {save_path}")
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# 5. 提取旋转角度和置信度
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boxes = result.obb
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if boxes is None or len(boxes) < 2:
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print("检测到少于两个目标，无法计算夹角。")
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return None, annotated_img
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box_info = []
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for box in boxes:
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conf = box.conf.cpu().numpy()[0]
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cx, cy, w, h, r_rad = box.xywhr.cpu().numpy()[0]
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direction = r_rad if w >= h else r_rad + np.pi/2
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direction = direction % np.pi
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box_info.append((conf, direction))
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# 6. 取置信度最高两个框
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box_info = sorted(box_info, key=lambda x: x[0], reverse=True)
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dir1, dir2 = box_info[0][1], box_info[1][1]
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# 7. 计算夹角（最小夹角，0~90°）
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diff = abs(dir1 - dir2)
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diff = min(diff, np.pi - diff)
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angle_deg = np.degrees(diff)
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print(f"置信度最高两个框主方向夹角: {angle_deg:.2f}°")
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return angle_deg, annotated_img
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# ------------------- 测试 -------------------
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if __name__ == "__main__":
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weight_path = r'obb.pt'
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#weight_path = r'obb.pt'
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image_path = r"1.png"
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save_path = "./inference_results/detected_3.jpg"
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#angle_deg, annotated_img = predict_obb_best_angle(weight_path, image_path, save_path)
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angle_deg,_ = predict_obb_best_angle(weight_path, image_path, save_path)
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annotated_img = None
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print(angle_deg)
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if annotated_img is not None:
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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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