行人检测(人体检测)3：Android实现人体检测(含源码，可实时人体检测)

# 转换yolov5s05模型
python export.py --weights "runs/yolov5s05_320/weights/best.pt" --img-size 320 320

# 转换yolov5s模型
python export.py --weights "runs/yolov5s_640/weights/best.pt" --img-size 640 640

package com.cv.tnn.model;

import android.graphics.Bitmap;

public class Detector {

    static {
        System.loadLibrary("tnn_wrapper");
    }


    /***
     * 初始化模型
     * @param model： TNN *.tnnmodel文件文件名（含后缀名）
     * @param root：模型文件的根目录，放在assets文件夹下
     * @param model_type：模型类型
     * @param num_thread：开启线程数
     * @param useGPU：关键点的置信度，小于值的坐标会置-1
     */
    public static native void init(String model, String root, int model_type, int num_thread, boolean useGPU);

    /***
     * 检测
     * @param bitmap 图像（bitmap），ARGB_8888格式
     * @param score_thresh：置信度阈值
     * @param iou_thresh：  IOU阈值
     * @return
     */
    public static native FrameInfo[] detect(Bitmap bitmap, float score_thresh, float iou_thresh);
}

.....
    # Exports
    if 'torchscript' in include:
        export_torchscript(model, img, file, optimize)
    if 'onnx' in include:
        model.model[-1].export = True  # TNN不支持5个维度,修改输出格式
        export_onnx(model, img, file, opset, train, dynamic, simplify=simplify)
    if 'coreml' in include:
        export_coreml(model, img, file)

    # Finish
    print(f'\nExport complete ({time.time() - t:.2f}s)'
          f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
          f'\nVisualize with https://netron.app')

.....

struct YOLOv5Param {
    ModelType model_type;                  // 模型类型，MODEL_TYPE_TNN，MODEL_TYPE_NCNN等
    int input_width;                       // 模型输入宽度，单位：像素
    int input_height;                      // 模型输入高度，单位：像素
    bool use_rgb;                          // 是否使用RGB作为模型输入（PS：接口固定输入BGR，use_rgb=ture时，预处理将BGR转换为RGB）
    bool padding;
    int num_landmarks;                     // 关键点个数
    NetNodes InputNodes;                   // 输入节点名称
    NetNodes OutputNodes;                  // 输出节点名称
    vector<YOLOAnchor> anchors;
    vector<string> class_names;            // 类别集合
};

anchors:
  - [ 10,13, 16,30, 33,23 ]  # P3/8
  - [ 30,61, 62,45, 59,119 ]  # P4/16
  - [ 116,90, 156,198, 373,326 ]  # P5/32

anchors:
  - [ 5, 6, 8, 15, 16, 12 ]  # P3/8
  - [ 15, 30, 31, 22, 30, 60 ]  # P4/16
  - [ 58, 45, 78, 99, 186, 163 ]  # P5/32

//YOLOv5s模型参数
static YOLOv5Param YOLOv5s_640 = {MODEL_TYPE_TNN,
                                  640,
                                  640,
                                  true,
                                  true,
                                  0,
                                  {{{"images", nullptr}}}, //InputNodes
                                  {{{"boxes", nullptr},   //OutputNodes
                                    {"scores", nullptr}}},
                                  {
                                          {"434", 32, {{116, 90}, {156, 198}, {373, 326}}},
                                          {"415", 16, {{30, 61}, {62, 45}, {59, 119}}},
                                          {"output", 8, {{10, 13}, {16, 30}, {33, 23}}},
                                  },
                                  CLASS_NAME
};

//YOLOv5s05模型参数
static YOLOv5Param YOLOv5s05_ANCHOR_416 = {MODEL_TYPE_TNN,
                                           416,
                                           416,
                                           true,
                                           true,
                                           0,
                                           {{{"images", nullptr}}}, //InputNodes
                                           {{{"boxes", nullptr},   //OutputNodes
                                             {"scores", nullptr}}},
                                           {
                                                   {"434", 32,{{75, 58}, {101, 129}, {242, 212}}},
                                                   {"415", 16, {{20, 40}, {40, 29}, {38, 77}}},
                                                   {"output", 8, {{6, 8}, {10, 20}, {21, 15}}}, //
                                           },
                                           CLASS_NAME
};
//YOLOv5s05模型参数
static YOLOv5Param YOLOv5s05_ANCHOR_320 = {MODEL_TYPE_TNN,
                                           320,
                                           320,
                                           true,
                                           true,
                                           0,
                                           {{{"images", nullptr}}}, //InputNodes
                                           {{{"boxes", nullptr},   //OutputNodes
                                             {"scores", nullptr}}},
                                           {
                                                   {"434", 32, {{58, 45}, {78, 99}, {186, 163}}},
                                                   {"415", 16, {{15, 30}, {31, 22}, {30, 60}}},
                                                   {"output", 8, {{5, 6}, {8, 15}, {16, 12}}}, //
                                           },
                                           CLASS_NAME
};