lsf pc update

Author: lsf

apps/app_ptq.cpp

@@ -18,7 +18,7 @@
 
 using namespace std;
 
-class Logger : public nvinfer1::ILogger{
+class LLogger : public nvinfer1::ILogger{
 public:
     void log(Severity severity, const char* msg) noexcept override {
         if (severity == Severity::kINTERNAL_ERROR) {
@@ -38,7 +38,7 @@ class Logger : public nvinfer1::ILogger{
         }
     }
 };
-static Logger gLogger;
+static LLogger gLogger;
 
 template<typename _T>
 static shared_ptr<_T> make_nvshared(_T* ptr){

apps/app_rtdetr.cpp

@@ -71,7 +71,7 @@ void performance(const string& engine_file, int gpuid){
     }
 
     int batch = 8;
-    std::vector<cv::Mat> images{cv::imread("imgs/car.jpg"), cv::imread("imgs/gril.jpg"),
+    std::vector<cv::Mat> images{cv::imread("imgs/bus.jpg"), cv::imread("imgs/girl.jpg"),
                                 cv::imread("imgs/group.jpg"), cv::imread("imgs/yq.jpg")};
     for (int i = images.size(); i < batch; ++i)
         images.push_back(images[i % 4]);
@@ -122,7 +122,7 @@ void batch_inference(const string& engine_file, int gpuid){
     // 等待全部推理结束
     boxes_array.back().get();
 
-    string root_res = "infer_res";
+    string root_res = "infer_res/rtdetr";
     for(int i = 0; i < boxes_array.size(); ++i){
         cv::Mat image = images[i];
         auto boxes = boxes_array[i].get();

apps/app_seg.cpp

@@ -41,7 +41,7 @@ void inference_seg(const string& engine_file, int gpuid){
     auto image = cv::imread("imgs/frame_3.jpg");
     auto res = predictor->seg(image);
 
-    cv::Mat color_img(res.size(), CV_8UC3, cv::Scalar(0, 0, 0));
+    cv::Mat color_img(image.size(), CV_8UC3, cv::Scalar(0, 0, 0));
     // 遍历每个像素点，根据类别索引应用颜色映射
     for (int i = 0; i < res.rows; ++i) {
         for (int j = 0; j < res.cols; ++j) {
@@ -51,11 +51,9 @@ void inference_seg(const string& engine_file, int gpuid){
                                                           PPSeg::color_map[pixel_value][0]);
         }
     }
-    cv::Mat out_color_img;
-    cv::resize(color_img, out_color_img, image.size());
-    out_color_img.convertTo(out_color_img, CV_8UC3);
+    cv::Mat out_color_img(image.size(), CV_8UC3, cv::Scalar(0, 0, 0));
     float alpha = 0.7;
-    out_color_img = (1 - alpha) * image + alpha * out_color_img;
+    out_color_img = (1 - alpha) * image + alpha * color_img;
     cv::imwrite("infer_res/seg_frame_3.jpg", out_color_img);
 }
 

apps/app_yolov10.cpp

@@ -0,0 +1,171 @@
+
+#include <fstream>
+#include <opencv2/opencv.hpp>
+#include "yolov10/yolov10.hpp"
+
+using namespace std;
+
+inline vector<string> cocolabels = {
+        "person", "bicycle", "car", "motorcycle", "airplane",
+        "bus", "train", "truck", "boat", "traffic light", "fire hydrant",
+        "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse",
+        "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack",
+        "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis",
+        "snowboard", "sports ball", "kite", "baseball bat", "baseball glove",
+        "skateboard", "surfboard", "tennis racket", "bottle", "wine glass",
+        "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich",
+        "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
+        "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv",
+        "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave",
+        "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
+        "scissors", "teddy bear", "hair drier", "toothbrush"
+};
+
+inline std::tuple<uint8_t, uint8_t, uint8_t> hsv2bgr(float h, float s, float v){
+    const int h_i = static_cast<int>(h * 6);
+    const float f = h * 6 - h_i;
+    const float p = v * (1 - s);
+    const float q = v * (1 - f*s);
+    const float t = v * (1 - (1 - f) * s);
+    float r, g, b;
+    switch (h_i) {
+        case 0:r = v; g = t; b = p;break;
+        case 1:r = q; g = v; b = p;break;
+        case 2:r = p; g = v; b = t;break;
+        case 3:r = p; g = q; b = v;break;
+        case 4:r = t; g = p; b = v;break;
+        case 5:r = v; g = p; b = q;break;
+        default:r = 1; g = 1; b = 1;break;}
+    return make_tuple(static_cast<uint8_t>(b * 255), static_cast<uint8_t>(g * 255), static_cast<uint8_t>(r * 255));
+}
+
+inline std::tuple<uint8_t, uint8_t, uint8_t> random_color(int id){
+    float h_plane = ((((unsigned int)id << 2) ^ 0x937151) % 100) / 100.0f;;
+    float s_plane = ((((unsigned int)id << 3) ^ 0x315793) % 100) / 100.0f;
+    return hsv2bgr(h_plane, s_plane, 1);
+}
+
+inline string get_file_name(const string& path, bool include_suffix){
+    if (path.empty()) return "";
+    int p = path.rfind('/');
+    int e = path.rfind('\\');
+    p = std::max(p, e);
+    p += 1;
+    //include suffix
+    if (include_suffix)
+        return path.substr(p);
+    int u = path.rfind('.');
+    if (u == -1)
+        return path.substr(p);
+
+    if (u <= p) u = path.size();
+    return path.substr(p, u - p);
+}
+
+void performance_v10(const string& engine_file, int gpuid){
+    auto infer = YOLOV10::create_infer(engine_file, gpuid, 0.5);
+    if(infer == nullptr){
+        printf("infer is nullptr.\n");
+        return;
+    }
+
+    int batch = 1;
+    std::vector<cv::Mat> images{cv::imread("imgs/bus.jpg"), cv::imread("imgs/girl.jpg"),
+                                cv::imread("imgs/group.jpg"), cv::imread("imgs/yq.jpg")};
+    for (int i = images.size(); i < batch; ++i)
+        images.push_back(images[i % 4]);
+
+    // warmup
+    vector<shared_future<YOLOV10::BoxArray>> boxes_array;
+    for(int i = 0; i < 10; ++i)
+        boxes_array = infer->commits(images);
+    boxes_array.back().get();
+    boxes_array.clear();
+
+    // 测试 100 轮
+    const int ntest = 100;
+    auto start = std::chrono::steady_clock::now();
+    for(int i  = 0; i < ntest; ++i)
+        boxes_array = infer->commits(images);
+    // 等待全部推理结束
+    boxes_array.back().get();
+
+    std::chrono::duration<double> during = std::chrono::steady_clock::now() - start;
+    double all_time = 1000.0 * during.count();
+    float avg_time = all_time / ntest / images.size();
+    printf("Average time: %.2f ms, FPS: %.2f\n", engine_file.c_str(), avg_time, 1000 / avg_time);
+}
+
+void batch_inference_v10(const string& engine_file, int gpuid){
+    auto infer = YOLOV10::create_infer(engine_file, gpuid, 0.5);
+    if(infer == nullptr){
+        printf("infer is nullptr.\n");
+        return;
+    }
+
+    vector<cv::String> files_;
+    files_.reserve(100);
+    cv::glob("imgs/*.jpg", files_, true);
+    vector<string> files(files_.begin(), files_.end());
+
+    vector<cv::Mat> images;
+    for(const auto& file : files){
+        auto image = cv::imread(file);
+        images.emplace_back(image);
+    }
+
+    vector<shared_future<YOLOV10::BoxArray>> boxes_array;
+    boxes_array = infer->commits(images);
+
+    // 等待全部推理结束
+    boxes_array.back().get();
+
+    string root_res = "infer_res/yolov10";
+    for(int i = 0; i < boxes_array.size(); ++i){
+        cv::Mat image = images[i];
+        auto boxes = boxes_array[i].get();
+        for(auto & ibox : boxes){
+            cv::Scalar color;
+            std::tie(color[0], color[1], color[2]) = random_color(ibox.label);
+            cv::rectangle(image, cv::Point(ibox.left, ibox.top), cv::Point(ibox.right, ibox.bottom), color, 2);
+
+            auto name = cocolabels[ibox.label];
+            auto caption = cv::format("%s %.2f", name.c_str(), ibox.confidence);
+            int text_width = cv::getTextSize(caption, 0, 1, 2, nullptr).width + 10;
+            cv::rectangle(image, cv::Point(ibox.left-2, ibox.top-32), cv::Point(ibox.left + text_width, ibox.top), color, -1);
+            cv::putText(image, caption, cv::Point(ibox.left, ibox.top-5), 0, 1, cv::Scalar::all(0), 2, 16);
+        }
+        string file_name = get_file_name(files[i], false);
+        string save_path = cv::format("%s/%s.jpg", root_res.c_str(), file_name.c_str());
+        cv::imwrite(save_path, image);
+        printf("Save to %s, %d object\n", save_path.c_str(), boxes.size());
+    }
+}
+
+
+void single_inference_v10(const string& engine_file, int gpuid){
+    auto infer = YOLOV10::create_infer(engine_file, gpuid, 0.6);
+    if(infer == nullptr){
+        printf("infer is nullptr.\n");
+        return;
+    }
+
+    auto image = cv::imread("imgs/bus.jpg");
+    auto boxes = infer->commit(image).get();
+
+    for(auto& ibox : boxes){
+        cv::Scalar color;
+        std::tie(color[0], color[1], color[2]) = random_color(ibox.label);
+        cv::rectangle(image, cv::Point(ibox.left, ibox.top), cv::Point(ibox.right, ibox.bottom), color, 2);
+
+        auto name = cocolabels[ibox.label];
+        auto caption = cv::format("%s %.2f", name.c_str(), ibox.confidence);
+        int text_width = cv::getTextSize(caption, 0, 1, 2, nullptr).width + 10;
+        cv::rectangle(image, cv::Point(ibox.left-2, ibox.top-32), cv::Point(ibox.left + text_width, ibox.top), color, -1);
+        cv::putText(image, caption, cv::Point(ibox.left, ibox.top-5), 0, 1, cv::Scalar::all(0), 2, 16);
+    }
+    cv::imwrite("infer_res/result.jpg", image);
+}
+
+
+

apps/ppseg/ppseg.cpp

@@ -1,6 +1,3 @@
-//
-// Created by lsf on 24-5-20.
-//
 
 #include "ppseg.hpp"
 
@@ -91,6 +88,7 @@ namespace PPSeg{
                             parray, input_width_ * sizeof(int32_t));
             cv::Mat out;
             out_img.convertTo(out, CV_8UC1);
+            cv::resize(out, out, image.size(), 0, 0, cv::INTER_NEAREST);
 
             return out;
         }

apps/ppseg/ppseg.hpp

@@ -1,6 +1,3 @@
-//
-// Created by lsf on 24-5-20.
-//
 
 #ifndef PPSEG_HPP
 #define PPSEG_HPP

apps/rtdetr/README.md

@@ -156,7 +156,7 @@ namespace Yolo{
             // output_array_device 是输出 output 经过 decode 的结果
             TRT::Tensor output_array_device{};
 
-            // load success：设置好了输入，宽高，batch，allocator等，返回true
+            /// load success：设置好了输入，宽高，batch，allocator等，返回true
             result.set_value(true);
 
             // 先调整 shape 再 分配 input Tensor GPU 内存