rknn替换，板子是3568的

ailai_pc/detet_pc.py

@@ -1,72 +1,76 @@
-# detect_pt.py
-import cv2
-import torch
 from ultralytics import YOLO
+from ultralytics.utils.ops import non_max_suppression
+import torch
+import cv2
 
 # ======================
 # 配置参数
 # ======================
-MODEL_PATH = 'best.pt'           # 你的训练模型路径（yolov8n.pt 或你自己训练的）
-#IMG_PATH = '/home/hx/开发/ailai_image_obb/ailai_pc/train/192.168.0.234_01_202510141514352.jpg'            # 测试图像路径
+MODEL_PATH = '/home/hx/开发/ailai_image_obb/ailai_pc/best12.pt'
 IMG_PATH = '1.jpg'
-OUTPUT_PATH = '/home/hx/开发/ailai_image_obb/ailai_pc/output_pt.jpg'    # 可视化结果保存路径
-CONF_THRESH = 0.5                # 置信度阈值
-CLASS_NAMES = ['bag']          # 你的类别名列表（按训练时顺序）
-
-# 是否显示窗口（适合有 GUI 的 PC）
-SHOW_IMAGE = True
+OUTPUT_PATH = 'output_pt.jpg'
+CONF_THRESH = 0.5
+IOU_THRESH = 0.45
+CLASS_NAMES = ['bag']
 
 # ======================
-# 主函数
+# 主函数（优化版）
 # ======================
 def main():
-    # 检查 CUDA
     device = 'cuda' if torch.cuda.is_available() else 'cpu'
     print(f"✅ 使用设备: {device}")
 
     # 加载模型
-    print("➡️  加载 YOLO 模型...")
-    model = YOLO(MODEL_PATH)  # 自动加载架构和权重
+    model = YOLO(MODEL_PATH)
     model.to(device)
 
-    # 推理
-    print("➡️  开始推理...")
-    results = model(IMG_PATH, imgsz=640, conf=CONF_THRESH, device=device)
+    # 推理：获取原始结果（不立即解析）
+    print("➡️ 开始推理...")
+    results = model(IMG_PATH, imgsz=640, conf=CONF_THRESH, device=device, verbose=True)
 
     # 获取第一张图的结果
     r = results[0]
 
-    # 获取原始图像（BGR）
+    # 🚀 关键：使用原始 tensor 在 GPU 上处理
+    # pred: [x1, y1, x2, y2, conf, cls] 形状为 [num_boxes, 6]
+    pred = r.boxes.data  # 已经在 GPU 上，类型: torch.Tensor
+
+    # 🔍 在 GPU 上做 NMS（这才是正确姿势）
+    # 注意：non_max_suppression 输入是 [batch, num_boxes, 6]
+    det = non_max_suppression(
+        pred.unsqueeze(0),      # 增加 batch 维度
+        conf_thres=CONF_THRESH,
+        iou_thres=IOU_THRESH,
+        classes=None,
+        agnostic=False,
+        max_det=100
+    )[0]  # 取第一个（也是唯一一个）batch
+
+    # ✅ 此时所有后处理已完成，现在才从 GPU 拷贝到 CPU
+    if det is not None and len(det):
+        det = det.cpu().numpy()  # ← 只拷贝一次！
+    else:
+        det = []
+
+    # 读取图像
     img = cv2.imread(IMG_PATH)
     if img is None:
         raise FileNotFoundError(f"无法读取图像: {IMG_PATH}")
 
     print("\n📋 检测结果:")
-    for box in r.boxes:
-        # 获取数据
-        xyxy = box.xyxy[0].cpu().numpy()  # [x1, y1, x2, y2]
-        conf = box.conf.cpu().numpy()[0]  # 置信度
-        cls_id = int(box.cls.cpu().numpy()[0])  # 类别 ID
-        cls_name = CLASS_NAMES[cls_id]         # 类别名
-
+    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}]")
 
-        # 画框
+        # 画框和标签
         cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
-        # 画标签
         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
     cv2.imwrite(OUTPUT_PATH, img)
-    print(f"\n🖼️  可视化结果已保存: {OUTPUT_PATH}")
-
-    # 显示（可选）
-    if SHOW_IMAGE:
-        cv2.imshow("YOLOv8 Detection", img)
-        cv2.waitKey(0)
-        cv2.destroyAllWindows()
+    print(f"\n🖼️ 可视化结果已保存: {OUTPUT_PATH}")
 
 if __name__ == '__main__':
     main()

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()

ailai_pc/error_test.py

@@ -0,0 +1,122 @@
+import cv2
+import numpy as np
+import os
+from ultralytics import YOLO
+
+# ====================== 用户配置 ======================
+MODEL_PATH = '11.pt'
+IMAGE_SOURCE_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2/train'   # 验证集图片目录
+LABEL_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2/train'          # 标签目录（与图片同名 .txt）
+OUTPUT_DIR = './output_images'
+
+
+IMG_EXTENSIONS = {'.png', '.jpg', '.jpeg', '.bmp', '.tiff', '.tif', '.webp'}
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# ====================== 可视化函数 ======================
+def draw_keypoints_on_image(image, kpts_xy, colors, label_prefix=''):
+    for j, (x, y) in enumerate(kpts_xy):
+        x, y = int(x), int(y)
+        cv2.circle(image, (x, y), 8, colors[j % len(colors)], -1)
+        cv2.putText(image, f'{label_prefix}{j+1}', (x + 10, y - 10),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, colors[j % len(colors)], 2)
+    return image
+
+# ====================== 标签读取函数 ======================
+def load_keypoints_from_label(label_path, img_shape):
+    """
+    标签格式:
+    <class> xc yc w h x1 y1 v1 x2 y2 v2 x3 y3 v3 x4 y4 v4
+    共 17 项：1 + 4 + 12
+    """
+    if not os.path.exists(label_path):
+        print(f"⚠️ 找不到标签文件: {label_path}")
+        return None
+
+    H, W = img_shape[:2]
+    with open(label_path, 'r') as f:
+        line = f.readline().strip().split()
+
+    if len(line) < 17:
+        print(f"⚠️ 标签长度不足: {label_path} ({len(line)}项)")
+        return None
+
+    floats = [float(x) for x in line[5:]]  # 跳过前5个（class + bbox）
+    coords = np.array(floats).reshape(-1, 3)[:, :2]  # (4,2)
+    coords[:, 0] *= W
+    coords[:, 1] *= H
+    return coords
+
+# ====================== 主程序 ======================
+if __name__ == "__main__":
+    print("🚀 开始验证集关键点误差计算")
+
+    model = YOLO(MODEL_PATH)
+    print(f"✅ 模型加载完成: {MODEL_PATH}")
+
+    image_files = [
+        f for f in os.listdir(IMAGE_SOURCE_DIR)
+        if os.path.splitext(f.lower())[1] in IMG_EXTENSIONS
+    ]
+
+    if not image_files:
+        print("❌ 未找到图像文件")
+        exit(1)
+
+    total_errors = []
+    skipped = 0
+    colors_gt = [(0, 255, 0), (0, 200, 0), (0, 150, 0), (0, 100, 0)]
+    colors_pred = [(0, 0, 255)] * 4
+
+    for img_filename in image_files:
+        img_path = os.path.join(IMAGE_SOURCE_DIR, img_filename)
+        label_path = os.path.join(LABEL_DIR, os.path.splitext(img_filename)[0] + '.txt')
+
+        img = cv2.imread(img_path)
+        if img is None:
+            print(f"❌ 无法读取图像: {img_path}")
+            skipped += 1
+            continue
+
+        gt_kpts = load_keypoints_from_label(label_path, img.shape)
+        if gt_kpts is None or len(gt_kpts) < 4:
+            print(f"⚠️ 跳过 {img_filename}：标签点不足")
+            skipped += 1
+            continue
+
+        results = model(img, verbose=False)
+        if not results or results[0].keypoints is None or len(results[0].keypoints) == 0:
+            print(f"⚠️ {img_filename}: 无检测结果，跳过")
+            skipped += 1
+            continue
+
+        pred_kpts = results[0].keypoints.xy[0].cpu().numpy()
+        if pred_kpts.shape[0] != gt_kpts.shape[0]:
+            print(f"⚠️ {img_filename}: 点数不匹配 GT={len(gt_kpts)}, Pred={len(pred_kpts)}，跳过")
+            skipped += 1
+            continue
+
+        # 计算误差
+        errors = np.linalg.norm(pred_kpts - gt_kpts, axis=1)
+        mean_error = np.mean(errors)
+        total_errors.append(mean_error)
+
+        print(f"📸 {img_filename}: 每点误差={np.round(errors, 2)} 像素, 平均误差={mean_error:.2f}px")
+
+        # 可视化
+        vis_img = img.copy()
+        vis_img = draw_keypoints_on_image(vis_img, gt_kpts, colors_gt, label_prefix='GT')
+        vis_img = draw_keypoints_on_image(vis_img, pred_kpts, colors_pred, label_prefix='P')
+
+        save_path = os.path.join(OUTPUT_DIR, f"compare_{img_filename}")
+        cv2.imwrite(save_path, vis_img)
+
+    # ====================== 结果统计 ======================
+    print("\n======================")
+    if total_errors:
+        print(f"🎯 有效样本数: {len(total_errors)} 张")
+        print(f"🚫 跳过样本数: {skipped} 张")
+        print(f"📈 平均关键点误差: {np.mean(total_errors):.2f} 像素")
+    else:
+        print(f"⚠️ 所有样本均被跳过（跳过 {skipped} 张）")
+    print("======================")

ailai_pc/jianzhi/jz.py

@@ -0,0 +1,95 @@
+import torch
+import torch.nn as nn
+from ultralytics import YOLO
+
+# ------------------- 核心剪枝函数 -------------------
+def prune_conv_bn(conv_bn, keep_idx):
+    """剪枝 ConvBNAct 模块的 Conv + BN"""
+    conv = conv_bn.conv
+    bn = conv_bn.bn
+
+    # 跳过 depthwise
+    if conv.groups != 1:
+        return conv_bn
+
+    # 剪枝 conv
+    new_conv = nn.Conv2d(
+        in_channels=conv.in_channels,
+        out_channels=len(keep_idx),
+        kernel_size=conv.kernel_size,
+        stride=conv.stride,
+        padding=conv.padding,
+        dilation=conv.dilation,
+        groups=conv.groups,
+        bias=(conv.bias is not None)
+    ).to(conv.weight.device)
+    new_conv.weight.data = conv.weight.data[keep_idx].clone()
+    if conv.bias is not None:
+        new_conv.bias.data = conv.bias.data[keep_idx].clone()
+
+    # 剪枝 BN
+    if bn is not None:
+        new_bn = nn.BatchNorm2d(len(keep_idx)).to(bn.weight.device)
+        new_bn.weight.data = bn.weight.data[keep_idx].clone()
+        new_bn.bias.data   = bn.bias.data[keep_idx].clone()
+        new_bn.running_mean = bn.running_mean[keep_idx].clone()
+        new_bn.running_var  = bn.running_var[keep_idx].clone()
+    else:
+        new_bn = None
+
+    # 替换模块
+    conv_bn.conv = new_conv
+    conv_bn.bn = new_bn
+    return conv_bn
+
+def get_prune_idx(conv_bn, prune_ratio=0.3):
+    """根据 BN gamma 或 L2 norm 计算要保留的通道索引"""
+    conv = conv_bn.conv
+    bn = conv_bn.bn
+    if bn is not None:
+        gamma = bn.weight.data.abs()
+    else:
+        gamma = conv.weight.data.view(conv.out_channels, -1).norm(p=2, dim=1)
+    keep_num = max(int(conv.out_channels * (1 - prune_ratio)), 1)
+    _, idxs = torch.topk(gamma, keep_num)
+    return idxs
+
+def prune_yolov11_model(model, prune_ratio=0.3):
+    """遍历 YOLO 模型，剪枝所有 ConvBNAct"""
+    for name, m in model.named_modules():
+        if m.__class__.__name__ == "ConvBNAct":
+            keep_idx = get_prune_idx(m, prune_ratio)
+            prune_conv_bn(m, keep_idx)
+    return model
+
+# ------------------- 主流程 -------------------
+def main(model_path="best.pt", save_path="yolov11_pruned_ts.pt",
+         prune_ratio=0.3, device="cuda"):
+
+    # 加载 YOLO 模型
+    model = YOLO(model_path).model
+    model.eval().to(device)
+
+    # 剪枝
+    print(f"✅ 开始剪枝，比例: {prune_ratio}")
+    model = prune_yolov11_model(model, prune_ratio)
+    print("✅ 剪枝完成")
+
+    # 构造 dummy 输入
+    example_inputs = torch.randn(1, 3, 640, 640).to(device)
+
+    # TorchScript 跟踪
+    print("🔹 开始 TorchScript 跟踪...")
+    traced_model = torch.jit.trace(model, example_inputs)
+    traced_model = torch.jit.optimize_for_inference(traced_model)
+
+    # 保存 TorchScript 模型
+    traced_model.save(save_path)
+    print(f"✅ TorchScript 剪枝模型已保存: {save_path}")
+
+if __name__ == "__main__":
+    main(
+        model_path="best.pt",
+        save_path="yolov11_pruned_ts.pt",
+        prune_ratio=0.3
+    )

ailai_pc/lingshi.py

@@ -0,0 +1,8 @@
+from ultralytics import YOLO
+import torch
+model = YOLO("/home/hx/开发/ailai_image_obb/ailai_pc/detect.pt")
+
+for name, module in model.model.named_modules():
+    if isinstance(module, torch.nn.Conv2d):
+        w = module.weight
+        print(f"{name} -> min: {w.min().item():.3f}, max: {w.max().item():.3f}")

ailai_pc/point_test.py

@@ -5,7 +5,7 @@ import os
 
 # ====================== 用户配置 ======================
 MODEL_PATH = 'best.pt'
-IMAGE_SOURCE_DIR = './train'  # 👈 修改为你的图像文件夹路径
+IMAGE_SOURCE_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2/val'  # 👈 修改为你的图像文件夹路径
 OUTPUT_DIR = './output_images'  # 保存结果的文件夹
 
 # 支持的图像扩展名

ailai_pc/rename_file.py

@@ -2,10 +2,10 @@ import os
 import shutil
 
 # ================= 用户配置 =================
-FOLDER_PATH = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2'  # 图片和 txt 所在文件夹
+FOLDER_PATH = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/20251020'  # 图片和 txt 所在文件夹
 IMG_EXT = '.jpg'
 TXT_EXT = '.txt'
-START_NUM = 1            # 从 1 开始编号
+START_NUM = 571            # 从 1 开始编号
 
 # ================= 获取文件列表 =================
 files = os.listdir(FOLDER_PATH)

ailai_pc/yolo_obb_dataset/11111.py

@@ -0,0 +1,138 @@
+import os
+import cv2
+import numpy as np
+from rknnlite.api import RKNNLite
+
+# ====================== 配置 ======================
+MODEL_PATH = "yolo11.rknn"      # RKNN 模型路径
+IMG_PATH = "11.jpg"             # 待检测图片
+IMG_SIZE = (640, 640)           # 模型输入尺寸 (w,h)
+OBJ_THRESH = 0.001              # 目标置信度阈值
+NMS_THRESH = 0.45               # NMS 阈值
+CLASS_NAME = ["bag"]            # 单类别
+OUTPUT_DIR = "./result"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# ====================== 工具函数 ======================
+def letterbox_resize(image, size, bg_color=114):
+    target_w, target_h = size
+    h, w = image.shape[:2]
+    scale = min(target_w / w, target_h / h)
+    new_w, new_h = int(w * scale), int(h * scale)
+    resized = cv2.resize(image, (new_w, new_h))
+    canvas = np.full((target_h, target_w, 3), bg_color, dtype=np.uint8)
+    dx, dy = (target_w - new_w) // 2, (target_h - new_h) // 2
+    canvas[dy:dy + new_h, dx:dx + new_w] = resized
+    return canvas, scale, dx, dy
+
+def dfl_numpy(position):
+    """Distribution Focal Loss 解析，纯 NumPy 版本"""
+    n, c, h, w = position.shape
+    p_num = 4
+    mc = c // p_num
+    y = position.reshape(n, p_num, mc, h, w)
+    y = np.exp(y) / np.sum(np.exp(y), axis=2, keepdims=True)
+    acc = np.arange(mc).reshape(1,1,mc,1,1)
+    y = np.sum(y * acc, axis=2)
+    return y
+
+def box_process(position):
+    """解析网络输出的框坐标"""
+    grid_h, grid_w = position.shape[2:4]
+    col, row = np.meshgrid(np.arange(grid_w), np.arange(grid_h))
+    col = col.reshape(1,1,grid_h,grid_w)
+    row = row.reshape(1,1,grid_h,grid_w)
+    grid = np.concatenate((col,row), axis=1)
+    stride = np.array([IMG_SIZE[1] // grid_h, IMG_SIZE[0] // grid_w]).reshape(1,2,1,1)
+
+    position = dfl_numpy(position)
+    box_xy = grid + 0.5 - position[:,0:2,:,:]
+    box_xy2 = grid + 0.5 + position[:,2:4,:,:]
+    xyxy = np.concatenate((box_xy*stride, box_xy2*stride), axis=1)
+    return xyxy
+
+def filter_boxes(boxes, box_confidences, box_class_probs):
+    # sigmoid objectness
+    box_confidences = 1 / (1 + np.exp(-box_confidences))
+    # softmax class probs
+    box_class_probs = np.exp(box_class_probs)
+    box_class_probs /= np.sum(box_class_probs, axis=-1, keepdims=True)
+
+    box_confidences = box_confidences.reshape(-1)
+    class_max_score = np.max(box_class_probs, axis=-1)
+    classes = np.argmax(box_class_probs, axis=-1)
+    _pos = np.where(class_max_score * box_confidences >= OBJ_THRESH)
+    boxes = boxes[_pos]
+    classes = classes[_pos]
+    scores = (class_max_score * box_confidences)[_pos]
+    return boxes, classes, scores
+
+def post_process(outputs, scale, dx, dy):
+    boxes, classes_conf, scores = [], [], []
+    branch_num = 3
+    for i in range(branch_num):
+        boxes.append(box_process(outputs[i*3]))
+        classes_conf.append(outputs[i*3+1])
+        scores.append(outputs[i*3+2])  # 使用真实 class 输出
+
+    def sp_flatten(x):
+        ch = x.shape[1]
+        x = x.transpose(0,2,3,1)
+        return x.reshape(-1,ch)
+
+    boxes = np.concatenate([sp_flatten(b) for b in boxes])
+    classes_conf = np.concatenate([sp_flatten(c) for c in classes_conf])
+    scores = np.concatenate([sp_flatten(s) for s in scores])
+
+    boxes, classes, scores = filter_boxes(boxes, scores, classes_conf)
+
+    if boxes.shape[0] == 0:
+        return None, None, None
+
+    # 只保留置信度最高的框
+    max_idx = np.argmax(scores)
+    boxes = boxes[max_idx:max_idx+1]
+    classes = classes[max_idx:max_idx+1]
+    scores = scores[max_idx:max_idx+1]
+
+    # 映射回原图
+    boxes[:, [0,2]] -= dx
+    boxes[:, [1,3]] -= dy
+    boxes /= scale
+    boxes = boxes.clip(min=0)
+
+    return boxes, classes, scores
+
+def draw(image, boxes, scores, classes):
+    for box, score, cl in zip(boxes, scores, classes):
+        x1, y1, x2, y2 = [int(b) for b in box]
+        cv2.rectangle(image, (x1, y1), (x2, y2), (255,0,0), 2)
+        cv2.putText(image, f"{CLASS_NAME[cl]} {score:.3f}", (x1, y1-5),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,0,255), 2)
+
+# ====================== 主流程 ======================
+img = cv2.imread(IMG_PATH)
+if img is None:
+    raise ValueError(f"Image {IMG_PATH} not found!")
+
+img_resized, scale, dx, dy = letterbox_resize(img, IMG_SIZE)
+input_data = np.expand_dims(img_resized, 0)  # 4 维输入
+
+rknn = RKNNLite(verbose=False)
+rknn.load_rknn(MODEL_PATH)
+rknn.init_runtime()
+outputs = rknn.inference([input_data])
+rknn.release()
+
+print("Outputs len:", len(outputs))
+for i, out in enumerate(outputs):
+    print(f"outputs[{i}].shape = {out.shape}, min={out.min()}, max={out.max()}, mean={out.mean():.4f}")
+
+boxes, classes, scores = post_process(outputs, scale, dx, dy)
+if boxes is None:
+    print("Detected 0 boxes")
+else:
+    draw(img, boxes, scores, classes)
+    result_path = os.path.join(OUTPUT_DIR, os.path.basename(IMG_PATH))
+    cv2.imwrite(result_path, img)
+    print(f"Detection result saved to {result_path}")