视觉任务Jetson专用SDK集成文档

cd demo
mkdir build && cd build
cmake ..
make
# make install 为可选，也可将lib所在路径添加为环境变量
sudo make install
sudo ldconfig
./easyedge_batch_inference {模型RES文件夹}  {测试图片路径或仅包含图片的文件夹路径}

baidu@nano:~/ljay/easydl/sdk/demo/build$ ./easyedge_batch_inference ../../../../RES/ /ljay/images/mix008.jpeg
2020-08-06 20:56:30,665 INFO [EasyEdge] 548125646864 Compiling model for fast inference, this may take a while (Acceleration)
2020-08-06 20:57:58,427 INFO [EasyEdge] 548125646864 Optimized model saved to: /home/baidu/.baidu/easyedge/jetson/mcache/24110044320/m_cache, Don't remove it
Results of image /ljay/images/mix008.jpeg: 
2, kiwi, p:0.997594 loc: 0.352087, 0.56119, 0.625748, 0.868399
2, kiwi, p:0.993221 loc: 0.45789, 0.0730294, 0.73641, 0.399429
2, kiwi, p:0.992884 loc: 0.156876, 0.0598725, 0.3802, 0.394706
1, tomato, p:0.992125 loc: 0.523592, 0.389156, 0.657738, 0.548069
1, tomato, p:0.991821 loc: 0.665461, 0.419503, 0.805282, 0.573558
1, tomato, p:0.989883 loc: 0.297427, 0.439999, 0.432197, 0.59325
1, tomato, p:0.981654 loc: 0.383444, 0.248203, 0.506606, 0.400926
1, tomato, p:0.971682 loc: 0.183775, 0.556587, 0.286996, 0.711361
1, tomato, p:0.968722 loc: 0.379391, 0.0386965, 0.51672, 0.209681
Done

# ./easyedge_serving {res_dir} {serial_key} {host, default 0.0.0.0} {port, default 24401}
 ./easyedge_serving ../../../../RES "1111-1111-1111-1111" 0.0.0.0  24401

HTTP is now serving at 0.0.0.0:24401

// step 0: 设置序列号
global_controller()->set_licence_key("set your license here");

// step 1: 配置模型资源目录
TensorRTConfig config;
config.model_dir = {模型文件目录};
config.max_batch_size = {支持的最大batch size}

// step 2: 创建并初始化Predictor
auto predictor = global_controller()->CreateEdgePredictor<TensorRTConfig>(config);
if (predictor->init() != EDGE_OK) {
    exit(-1);
}

// step 3: 预测图像
auto img = cv::imread({图片路径});
std::vector<EdgeResultData> result;
predictor->infer(img, result);

virtual int set_licence_key(const std::string& license) = 0;

auto predictor = global_controller()->CreateEdgePredictor<TensorRTConfig>(config);
if (predictor->init() != EDGE_OK) {
        exit(-1);
}

/**
 * @brief
 * 单图预测接口
 * @param image: must be BGR , HWC format (opencv default)
 * @param result
 * @return
 */
virtual int infer(
        cv::Mat& image, std::vector<EdgeResultData>& result
) = 0;

/**
 * @brief
 * 批量图片预测接口
 * @param image: must be BGR , HWC format (opencv default)
 * @param result
 * @return
 */
virtual int infer(
        std::vector<cv::Mat>& image,
        std::vector<std::vector<EdgeResultData>>& results
) = 0;

/**
  * @brief
  * 批量图片预测接口，带阈值
  * @related infer(cv::Mat & image, EdgeColorFormat origin_color_format, std::vector<EdgeResultData> &result, float threshold)
  */
virtual int infer(
            std::vector<cv::Mat> &images,
            EdgeColorFormat origin_color_format,
            std::vector<std::vector<EdgeResultData>> &results,
            float threshold
) = 0;

struct TensorRTConfig : public EdgePredictorConfig {
    std::string cache_name{"m_cache"};
    /**
     * @brief GPU工作空间大小设置
     * workspace_size = workspace_prefix * (1 << workspace_offset)
     * workspace_offset: 10 = KB, 20 = MB, 30 = GB
     */
    int workspace_prefix{16};
    int workspace_offset{20};
    /**
     * @brief 生成最大 batch_size 为 max_batch_size 的优化模型，单次预测图片数量可以小于或等于此值
     */
    int max_batch_size{1};
    /**
     * @brief 设置使用哪张 GPU 卡
     */
    int device{0};
    /**
     * @brief 模型编译等级
     * 0：无论当前设置的max_batch_size是多少，仅使用历史编译产出（如果存在）
     * 1：如果当前max_batch_size与历史编译产出的max_batch_size不相等时，则重新编译模型（推荐）
     * 2：无论历史编译产出的max_batch_size为多少，均根据当前max_batch_size重新编译模型
     */
    int compile_level{1};
    /**
     * @brief 设置device对应的卡可以支持的最大并发量
     * 实际预测的时候对应卡的最大并发量不超过这里设置的范围
     */
    int max_concurrency{1};
    /**
     * @brief 是否开启 fp16 模式预测，需要硬件支持
     */
    bool fp16{false};
};

EdgeLogConfig log_config;
log_config.enable_debug = true;
global_controller()->set_log_config(log_config);

struct EdgeResultData {
    int index;  // 分类结果的index
    std::string label;  // 分类结果的label
    float prob;  // 置信度

    // 物体检测或图像分割时才有
    float x1, y1, x2, y2;  // (x1, y1): 左上角， （x2, y2): 右下角； 均为0~1的长宽比例值。

    // 图像分割时才有
    cv::Mat mask;  // 0, 1 的mask
    std::string mask_rle;  // Run Length Encoding，游程编码的mask
};

 /**
     * @brief 开启一个简单的demo http服务。
     * 该方法会block直到收到sigint/sigterm。
     * http服务里，图片的解码运行在cpu之上，可能会降低推理速度。
     * @tparam ConfigT
     * @param config
     * @param host
     * @param port
     * @param service_id service_id  user parameter, uri '/get/service_id' will respond this value with 'text/plain'
     * @param instance_num 实例数量，根据内存/显存/时延要求调整
     * @return
     */
    template<typename ConfigT>
    int start_http_server(
            const ConfigT &config,
            const std::string &host,
            int port,
            const std::string &service_id,
            int instance_num = 1);

import requests

with open('./1.jpg', 'rb') as f:
    img = f.read()
    result = requests.post(
	    'http://127.0.0.1:24401/',
	    params={'threshold': 0.1},
	    data=img).json()

{
    "cost_ms": 52,
    "error_code": 0,
    "results": [
        {
            "confidence": 0.94482421875,
            "index": 1,
            "label": "IronMan",
            "x1": 0.059185408055782318,
            "x2": 0.18795496225357056,
            "y1": 0.14762254059314728,
            "y2": 0.52510076761245728
        },
        {
            "confidence": 0.94091796875,
            "index": 1,
            "label": "IronMan",
            "x1": 0.79151463508605957,
            "x2": 0.92310667037963867,
            "y1": 0.045728668570518494,
            "y2": 0.42920106649398804
        }
      ]
}

# 在终端输入如下命令并回车
$ head -n 1 /etc/nv_tegra_release
# 就会输出类似如下结果
$ # # R32 (release), REVISION: 4.3, GCID: 21589087, BOARD: t210ref, EABI: aarch64, DATE: Fri Jun 26 04:38:25 UTC 2020

L4T R32.4.3  -->  JetPack4.4
L4T R32.4.2  -->  JetPack4.4DP
L4T R32.2.1  -->  JetPack4.2.2
L4T R32.2.0  -->  JetPack4.2.1

# 1. 运行下面这条命令可以查询开发板当前的运行功率模式
$ sudo nvpmodel -q verbose
# $ NV Power Mode: MAXN
# $ 0
# 如果输出为MAXN代表是最大功率模式

# 2. 若需要把功率调到最大，运行下面这条命令
$ sudo nvpmodel -m 0

# 如果你进入了桌面系统，也可以在桌面右上角有个按钮可以切换模式

# 3. 查询资源利用率
$ sudo tegrastats

export HTTP_PROXY="http://192.168.1.100:8888"
./easyedge_demo ...

headers = curl_slist_append(headers, "Expect:");

export LD_LIBRARY_PATH=/home/work/baidu/cpp/lib:${LD_LIBRARY_PATH}

sudo make install