大家好，
今天本魔要繼續和大家分享如何自己寫一個圖像識別程式，
上一篇我們已經用官方的範例做出分類辨識的效果了，
今天要來做一個更完整的辨識程式，
首先打開 Terminal 視窗並輸入：
$ mkdir ~/my-recognition
$ cd ~/my-recognition
$ touch my-recognition.cpp
$ touch CMakeLists.txt
完成後你會看到家目錄下多了一個 my-recognition 資料夾，
並且裡面有 my-recognition.cpp 與 CMakeLists.txt，
如下圖：
接著我們下載一些官方提供的範例圖片：
$ wget https://github.com/dusty-nv/jetson-inference/raw/master/data/images/black_bear.jpg
$ wget https://github.com/dusty-nv/jetson-inference/raw/master/data/images/brown_bear.jpg
$ wget https://github.com/dusty-nv/jetson-inference/raw/master/data/images/polar_bear.jpg
完成後會多出三張圖：
接著可以來寫程式囉！
用 gedit 來開啟 my-recognition.cpp 編輯：
$ gedit my-recognition.cpp
接著在 貼上下面的程式：
// include imageNet header for image recognition
#include
// include loadImage header for loading images
#include
// main entry point
int main( int argc, char** argv )
{
// a command line argument containing the image filename is expected,
// so make sure we have at least 2 args (the first arg is the program)
if( argc < 2 )
{
printf("my-recognition: expected image filename as argument\n");
printf("example usage: ./my-recognition my_image.jpg\n");
return 0;
}
// retrieve the image filename from the array of command line args
const char* imgFilename = argv[1];
// these variables will be used to store the image data and dimensions
// the image data will be stored in shared CPU/GPU memory, so there are
// pointers for the CPU and GPU (both reference the same physical memory)
float* imgCPU = NULL; // CPU pointer to floating-point RGBA image data
float* imgCUDA = NULL; // GPU pointer to floating-point RGBA image data
int imgWidth = 0; // width of the image (in pixels)
int imgHeight = 0; // height of the image (in pixels)
// load the image from disk as float4 RGBA (32 bits per channel, 128 bits per pixel)
if( !loadImageRGBA(imgFilename, (float4**)&imgCPU, (float4**)&imgCUDA, &imgWidth, &imgHeight) )
{
printf("failed to load image '%s'\n", imgFilename);
return 0;
}
// load the GoogleNet image recognition network with TensorRT
// you can use imageNet::ALEXNET to load AlexNet model instead
imageNet* net = imageNet::Create(imageNet::GOOGLENET);
// check to make sure that the network model loaded properly
if( !net )
{
printf("failed to load image recognition network\n");
return 0;
}
// this variable will store the confidence of the classification (between 0 and 1)
float confidence = 0.0;
// classify the image with TensorRT on the GPU (hence we use the CUDA pointer)
// this will return the index of the object class that the image was recognized as (or -1 on error)
const int classIndex = net->Classify(imgCUDA, imgWidth, imgHeight, &confidence);
// make sure a valid classification result was returned
if( classIndex >= 0 )
{
// retrieve the name/description of the object class index
const char* classDescription = net->GetClassDesc(classIndex);
// print out the classification results
printf("image is recognized as '%s' (class #%i) with %f%% confidence\n",
classDescription, classIndex, confidence * 100.0f);
}
else
{
// if Classify() returned < 0, an error occurred
printf("failed to classify image\n");
}
// free the network's resources before shutting down
delete net;
// this is the end of the example!
return 0;
}
簡單的說明一下這段程式在做些什麼，
首先用第二個引數來讀取圖片，然後載入圖像辨識網路 (GOOGLENET)，
接著會將載入的圖片做分類與看相似度，
最後將結果輸出至銀幕上。
接著我們要寫 CMakeLists.txt，一樣用 gedit 來開啟 CMakeLists.txt 編輯：
$ gedit CMakeLists.txt
接著貼上：
# require CMake 2.8 or greater
cmake_minimum_required(VERSION 2.8)
# declare my-recognition project
project(my-recognition)
# import jetson-inference and jetson-utils packages.
# note that if you didn't do "sudo make install"
# while building jetson-inference, this will error.
find_package(jetson-utils)
find_package(jetson-inference)
# CUDA and Qt4 are required
find_package(CUDA)
find_package(Qt4)
# setup Qt4 for build
include(${QT_USE_FILE})
add_definitions(${QT_DEFINITIONS})
# compile the my-recognition program
cuda_add_executable(my-recognition my-recognition.cpp)
# link my-recognition to jetson-inference library
target_link_libraries(my-recognition jetson-inference)
完成後就可以編譯囉！輸入：
$ cd ~/my-recognition
$ cmake .
$ make
完成後就可以執行程式來進行辨識啦！
輸入：
$ ./my-recognition polar_bear.jpg
輸出結果 ice bear、polar bear 是北極熊無誤！
輸入：
$ ./my-recognition brown_bear.jpg
結果是 brown bear 棕熊無誤。
輸入：
$ ./my-recognition black_bear.jpg
結果是 American black bear 美國黑熊，「真的假的？連國家都辨識的出來阿？」
玩到這裡同事 Vic 忽然說，該不會它只能辨識這些官方圖片吧？
所以本魔就上網找了一隻猴子，畢竟也是有毛髮的動物，到底有沒有辦法辨別呢？
結果是 guenon monkey 正確，本魔找的時候不知道牠的品種是長尾猴呢！
結果 Vic 不信邪說了：「該不會檔名會影響它的辨識吧？」
所以本魔又找了一張金剛的圖片，並將檔名存為 aaa.jpg，
結果是 gorilla 大猩猩，本魔還以為會辨識出 King kong 呢！
上網查了一下，原來金剛就是大猩猩，只是金剛是通俗的講法，
所以還是算辨識正確！
本篇的教學就到此為止啦～，
各位一樣可以嘗試用其它圖片來測試看看哦！
喜歡這個系列的話可以追蹤我的blogger哦，
本魔近期都會不定時更新此系列教學，
如果有疑問或任何指教也歡迎在下方留言提出哦！
