【EdgeBoard FZ5 边缘AI计算盒】paddle lite 的c++

nemon

试验一下baidu的例子。
登录盒子后，下载paddle lite 1.5.1的例子：

1. wget https://platform.bj.bcebos.com/edgeboard/1.5.1/PaddleLiteSample.zip

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解压

1. unzip PaddleLiteSample.zip

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进去

1. cd PaddleLiteSample/detection

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如果没有build目录，创建一个

1. mkdir build

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清理build目录

1. cd build
   
2. rm -rf *

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调用cmake 创建 Makefile

1. cmake ..

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控制台输出一大堆

1. -- The C compiler identification is GNU 8.2.0
   
2. -- The CXX compiler identification is GNU 8.2.0
   
3. -- Check for working C compiler: /usr/bin/cc
   
4. -- Check for working C compiler: /usr/bin/cc -- works
   
5. -- Detecting C compiler ABI info
   
6. -- Detecting C compiler ABI info - done
   
7. -- Detecting C compile features
   
8. -- Detecting C compile features - done
   
9. -- Check for working CXX compiler: /usr/bin/c++
   
10. -- Check for working CXX compiler: /usr/bin/c++ -- works
    
11. -- Detecting CXX compiler ABI info
    
12. -- Detecting CXX compiler ABI info - done
    
13. -- Detecting CXX compile features
    
14. -- Detecting CXX compile features - done
    
15. -- OpenCV found (/usr/local/share/OpenCV)，opencv_core;opencv_videoio;opencv_highgui;opencv_imgproc;opencv_imgcodecs;opencv_ml;opencv_video
    
16. PADDLELITE_FOUND
    
17. -- Configuring done
    
18. -- Generating done
    
19. -- Build files have been written to: /home/root/workspace/PaddleLiteSample/detection/build

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编译工程

1. make

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控制台又输出一堆

1. Scanning dependencies of target image_detection
   
2. [ 16%] Building CXX object CMakeFiles/image_detection.dir/src/camera.cpp.o
   
3. [ 33%] Building CXX object CMakeFiles/image_detection.dir/src/image_detection.cpp.o
   
4. [ 50%] Linking CXX executable image_detection
   
5. [ 50%] Built target image_detection
   
6. Scanning dependencies of target video_detection
   
7. [ 66%] Building CXX object CMakeFiles/video_detection.dir/src/camera.cpp.o
   
8. [ 83%] Building CXX object CMakeFiles/video_detection.dir/src/video_detection.cpp.o
   
9. [100%] Linking CXX executable video_detection
   
10. [100%] Built target video_detection

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调用一个执行试试看

1. ./image_detection ../configs/yolov3/screw.json
   

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控制台输出结果

1. driver_version: 1.5.1
   
2. paddle_lite_version: 1.5.1
   
3. label:0,score:0.980071 loc:823,352,224,159
   
4. label:0,score:0.978424 loc:1076,516,206,177
   
5. label:0,score:0.975051 loc:932,135,217,136
   
6. label:0,score:0.973695 loc:628,680,196,165
   
7. label:0,score:0.880789 loc:996,568,123,188
   
8. label:0,score:0.845378 loc:817,651,118,187
   
9. label:1,score:0.95993 loc:651,225,139,128
   
10. label:1,score:0.958707 loc:661,519,146,147
    
11. label:1,score:0.958172 loc:657,363,150,144
    
12. label:1,score:0.957937 loc:828,512,142,135
    
13. label:1,score:0.957928 loc:780,146,138,130
    
14. label:1,score:0.957118 loc:1106,260,153,146
    
15. label:1,score:0.955453 loc:1142,611,152,160
    
16. label:1,score:0.953481 loc:1082,419,145,133
    
17. label:1,score:0.94732 loc:967,275,148,136
    
18. label:1,score:0.932704 loc:918,699,141,129
    

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看看输出的图片

最后看一下src里面的主程序：

1. #include <iostream>
   
2. #include <fstream>
   
3. #include <vector>
   
4. #include <thread>
   
5. #include <unistd.h>
   
6. #include <mutex>
   
7. #include <csignal>
   
8. #include <stdio.h>
   
9. 
   
10. #include <opencv2/opencv.hpp>
    
11. #include "opencv2/core/core.hpp"
    
12. #include "opencv2/highgui/highgui.hpp"
    
13. 
    
14. #include "paddle_api.h"
    
15. #include "json.hpp"
    
16. 
    
17. using namespace std;
    
18. using namespace cv;
    
19. using namespace paddle::lite_api;
    
20. using json = nlohmann::json;
    
21. 
    
22. std::shared_ptr<paddle::lite_api::PaddlePredictor> g_predictor;
    
23. static float THRESHOLD = 0.3;
    
24. 
    
25. void init(json& j) {
    
26.     std::string model_dir = j["model"];
    
27.     std::vector<Place> valid_places({
    
28.         Place{TARGET(kFPGA), PRECISION(kFP16), DATALAYOUT(kNHWC)},
    
29.         Place{TARGET(kHost), PRECISION(kFloat)},
    
30.         Place{TARGET(kARM), PRECISION(kFloat)},
    
31.     });
    
32. 
    
33.     paddle::lite_api::CxxConfig config;
    
34.     bool combined = true;
    
35. 
    
36.     if (combined) {
    
37.         config.set_model_file(model_dir + "/model");
    
38.         config.set_param_file(model_dir + "/params");
    
39.     } else {
    
40.         config.set_model_dir(model_dir);
    
41.     }
    
42. 
    
43.     config.set_valid_places(valid_places);
    
44.     auto predictor = paddle::lite_api::CreatePaddlePredictor(config);
    
45.     g_predictor = predictor;
    
46. 
    
47.     THRESHOLD = j["threshold"];
    
48. }
    
49. 
    
50. Mat read_image(json& value, float* data) {
    
51. 
    
52.     auto image = value["image"];
    
53.     Mat img = imread(image);
    
54.     std::string format = value["format"];
    
55.     std::transform(format.begin(), format.end(),format.begin(), ::toupper);
    
56. 
    
57.     int width = value["input_width"];
    
58.     int height = value["input_height"];
    
59.     std::vector<float> mean = value["mean"];
    
60.     std::vector<float> scale = value["scale"];
    
61. 
    
62.     Mat img2;
    
63.     resize(img, img2, Size(width, height));
    
64.    
    
65.     Mat sample_float;
    
66.     img2.convertTo(sample_float, CV_32FC3);
    
67. 
    
68.     int index = 0;
    
69.     for (int row = 0; row < sample_float.rows; ++row) {
    
70.         float* ptr = (float*)sample_float.ptr(row);
    
71.         for (int col = 0; col < sample_float.cols; col++) {
    
72.             float* uc_pixel = ptr;
    
73.             float b = uc_pixel[0];
    
74.             float g = uc_pixel[1];
    
75.             float r = uc_pixel[2];
    
76. 
    
77.             if (format == "RGB") {
    
78.                 data[index] = (r - mean[0]) * scale[0];
    
79.                 data[index + 1] = (g - mean[1]) * scale[1];
    
80.                 data[index + 2] = (b - mean[2]) * scale[2];
    
81.             } else {
    
82.                 data[index] = (b - mean[0]) * scale[0];
    
83.                 data[index + 1] = (g - mean[1]) * scale[1];
    
84.                 data[index + 2] = (r - mean[2]) * scale[2];
    
85.             }
    
86.             ptr += 3;
    
87.             index += 3;
    
88.         }
    
89.     }
    
90.     return img;
    
91. }
    
92. 
    
93. void drawRect(const Mat &mat, float *data, int len, bool yolo) {
    
94.   for (int i = 0; i < len; i++) {
    
95.     float index = data[0];
    
96.     float score = data[1];
    
97.     if (score > THRESHOLD) {
    
98.         int x1 = 0;
    
99.         int y1 = 0;
    
100.         int x2 = 0;
     
101.         int y2 = 0;
     
102.         if (yolo) {
     
103.             x1 = static_cast<int>(data[2]);
     
104.             y1 = static_cast<int>(data[3]);
     
105.             x2 = static_cast<int>(data[4]);
     
106.             y2 = static_cast<int>(data[5]);
     
107.         } else {
     
108.             x1 = static_cast<int>(data[2] * mat.cols);
     
109.             y1 = static_cast<int>(data[3] * mat.rows);
     
110.             x2 = static_cast<int>(data[4] * mat.cols);
     
111.             y2 = static_cast<int>(data[5] * mat.rows);
     
112.         }
     
113.         int width = x2 - x1;
     
114.         int height = y2 - y1;
     
115. 
     
116.         cv::Point pt1(x1, y1);
     
117.         cv::Point pt2(x2, y2);
     
118.         cv::rectangle(mat, pt1, pt2, cv::Scalar(102, 0, 255), 3);
     
119.         std::cout << "label:" << index << ",score:" << score << " loc:";
     
120.         std::cout << x1 << "," << y1 << "," << width << "," << height
     
121.                 << std::endl;
     
122.     }
     
123.     data += 6;
     
124.   }
     
125.   imwrite("result.jpg", mat);
     
126. }
     
127. 
     
128. void predict(json& value) {
     
129.     int width = value["input_width"];
     
130.     int height = value["input_height"];
     
131. 
     
132.     auto input = g_predictor->GetInput(0);
     
133.     input->Resize({1, 3, height, width});
     
134.     auto* in_data = input->mutable_data<float>();
     
135. 
     
136.     Mat img = read_image(value, in_data);
     
137.     bool is_yolo = false;
     
138.     auto network_type = value["network_type"];
     
139.     if (network_type != nullptr && network_type == "YOLOV3") {
     
140.         is_yolo = true;
     
141.         auto img_shape = g_predictor->GetInput(1);
     
142.         img_shape->Resize({1, 2});
     
143.         auto* img_shape_data = img_shape->mutable_data<int32_t>();
     
144.         img_shape_data[0] = img.rows;
     
145.         img_shape_data[1] = img.cols;
     
146.     }
     
147.     g_predictor->Run();
     
148. 
     
149.     auto output = g_predictor->GetOutput(0);
     
150.     float *data = output->mutable_data<float>();
     
151.     int size = output->shape()[0];
     
152. 
     
153.     auto image = value["image"];
     
154.     drawRect(img, data, size, is_yolo);
     
155. }
     
156. 
     
157. int main(int argc, char* argv[]){
     
158.     std::string path;
     
159.     if (argc > 1) {
     
160.         path = argv[1];
     
161.     } else {
     
162.         path = "../configs/config.json";
     
163.     }
     
164.    
     
165.     json j;
     
166.     std::ifstream is(path);
     
167.     is >> j;
     
168.     init(j);
     
169.     predict(j);
     
170.     return 0;
     
171. }
     
172. <font size="2">
     
173. </font>

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代码中，43-45行创建预测器，第128行函数predict进行预测，drawRect函数负责打印结果输出图片。

沈浩

船长，你那个AVR XMEGA-A3BU Xplained开发板还在吗，能否福利在下啊