rknn替换，板子是3568的

ailai_pc/detet_pc_f.py

@@ -0,0 +1,135 @@
+from ultralytics import YOLO
+from ultralytics.utils.ops import non_max_suppression
+import torch
+import cv2
+import os
+import time
+from pathlib import Path
+
+# ======================
+# 配置参数
+# ======================
+MODEL_PATH = 'detect.pt'  # 你的模型路径
+INPUT_FOLDER = '/home/hx/开发/ailai_image_obb/ailai_pc/train'  # 输入图片文件夹
+OUTPUT_FOLDER = '/home/hx/开发/ailai_image_obb/ailai_pc/results'  # 输出结果文件夹（自动创建）
+CONF_THRESH = 0.5
+IOU_THRESH = 0.45
+CLASS_NAMES = ['bag']
+DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+IMG_SIZE = 640
+SHOW_IMAGE = False  # 是否逐张显示图像（适合调试）
+
+# 支持的图像格式
+IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'}
+
+
+# ======================
+# 获取文件夹中所有图片路径
+# ======================
+def get_image_paths(folder):
+    folder = Path(folder)
+    if not folder.exists():
+        raise FileNotFoundError(f"输入文件夹不存在: {folder}")
+    paths = [p for p in folder.iterdir() if p.suffix.lower() in IMG_EXTENSIONS]
+    if not paths:
+        print(f"⚠️  在 {folder} 中未找到图片")
+    return sorted(paths)  # 按名称排序
+
+
+# ======================
+# 主函数（批量推理）
+# ======================
+def main():
+    print(f"✅ 使用设备: {DEVICE}")
+
+    # 创建输出文件夹
+    os.makedirs(OUTPUT_FOLDER, exist_ok=True)
+    print(f"📁 输出结果将保存到: {OUTPUT_FOLDER}")
+
+    # 加载模型
+    print("➡️ 加载 YOLO 模型...")
+    model = YOLO(MODEL_PATH)
+    model.to(DEVICE)
+
+    # 获取图片列表
+    img_paths = get_image_paths(INPUT_FOLDER)
+    if not img_paths:
+        return
+
+    print(f"📸 共找到 {len(img_paths)} 张图片，开始批量推理...\n")
+
+    total_start_time = time.time()
+
+    for idx, img_path in enumerate(img_paths, 1):
+        print(f"{'=' * 50}")
+        print(f"🖼️  处理第 {idx}/{len(img_paths)} 张: {img_path.name}")
+
+        # 手动计时
+        start_time = time.time()
+
+        # 推理（verbose=True 输出内部耗时）
+        results = model(str(img_path), imgsz=IMG_SIZE, conf=CONF_THRESH, device=DEVICE, verbose=True)
+        inference_time = time.time() - start_time
+
+        # 获取结果
+        r = results[0]
+        pred = r.boxes.data  # GPU 上的原始输出
+
+        # 在 GPU 上做 NMS
+        det = non_max_suppression(
+            pred.unsqueeze(0),
+            conf_thres=CONF_THRESH,
+            iou_thres=IOU_THRESH,
+            classes=None,
+            agnostic=False,
+            max_det=100
+        )[0]
+
+        # 拷贝到 CPU（仅一次）
+        if det is not None and len(det):
+            det = det.cpu().numpy()
+        else:
+            det = []
+
+        # 读取图像并绘制
+        img = cv2.imread(str(img_path))
+        if img is None:
+            print(f"❌ 无法读取图像: {img_path}")
+            continue
+
+        print(f"\n📋 检测结果:")
+        for *xyxy, conf, cls_id in det:
+            x1, y1, x2, y2 = map(int, xyxy)
+            cls_name = CLASS_NAMES[int(cls_id)]
+            print(f"  类别: {cls_name}, 置信度: {conf:.3f}, 框: [{x1}, {y1}, {x2}, {y2}]")
+            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            label = f"{cls_name} {conf:.2f}"
+            cv2.putText(img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+        # 保存结果
+        output_path = os.path.join(OUTPUT_FOLDER, f"result_{img_path.name}")
+        cv2.imwrite(output_path, img)
+        print(f"\n✅ 结果已保存: {output_path}")
+
+        # 显示（可选）
+        if SHOW_IMAGE:
+            cv2.imshow("Detection", img)
+            if cv2.waitKey(1) & 0xFF == ord('q'):  # 按 Q 退出
+                break
+
+        # 输出总耗时
+        total_infer_time = time.time() - start_time
+        print(f"⏱️  总处理时间: {total_infer_time * 1000:.1f}ms (推理+后处理)")
+
+    # 结束
+    total_elapsed = time.time() - total_start_time
+    print(f"\n🎉 批量推理完成！共处理 {len(img_paths)} 张图片，总耗时: {total_elapsed:.2f} 秒")
+    print(
+        f"🚀 平均每张: {total_elapsed / len(img_paths) * 1000:.1f} ms ({1 / (total_elapsed / len(img_paths)):.1f} FPS)")
+
+    if SHOW_IMAGE:
+        cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    main()