同步链接:https://yangshun.win/blogs/e48e9a13/
github code: https://github.com/busyboxs/BaiDuAICPP
品牌 logo 识别能识别超过 2 万类商品 logo,支持用户创建属于自己的品牌 logo 图库,可准确识别图片中品牌 logo 的名称,适用于需要快速获取品牌信息的业务场景中
应用场景
- 品牌信息获取:根据拍摄照片,识别图片中商品 logo 名称,加快品牌信息获取速度,给消费者轻松高效的信息获取体验,促进消费者向投资者转化,适用于需要快速获取品牌信息的业务场景中
接口描述
该请求用于检测和识别图片中的台标、品牌商标等 logo 信息。即对于输入的一张图片(可正常解码,且长宽比适宜),输出图片中 logo 的名称、位置和置信度。
使用时,可直接调用 logo 识别-检索接口,支持识别超过 2 万类 logo 名称;当效果欠佳时,可以建立子库(在控制台创建应用并申请建库)并通过调用 logo 入口接口完成自定义 logo 入库,再调用 logo 识别-检索接口,选择在自定义 logo 库内检索,提高识别效果。
请求说明
- HTTP 方法: POST
- 请求 URL: https://aip.baidubce.com/rest/2.0/image-classify/v2/logo
- URL参数: access_token
- Header 参数: Content-Type = application/x-www-form-urlencoded
- Body 参数:见下表
返回说明
返回参数如下表:
返回示例如下:
{
"log_id": 843411868,
"result_num": 1,
"result": [
{
"type": 0,
"name": "科颜氏",
"probability": 0.99998807907104,
"location": {
"width": 296,
"top": 20,
"height": 128,
"left": 23
}
}
]
}
了解更多关于 logo 识别-入库[https://ai.baidu.com/ai-doc/IMAGERECOGNITION/Ok3bcxc59#logo%E8%AF%86%E5%88%AB%E5%85%A5%E5%BA%93] 和 logo 识别-删除[https://ai.baidu.com/ai-doc/IMAGERECOGNITION/Ok3bcxc59#logo%E8%AF%86%E5%88%AB%E5%88%A0%E9%99%A4]
C++ 代码实现调用
这里假设已经将环境配置好了,环境配置的文章可以参考 Windows 下使用 Vcpkg 配置百度 AI 图像识别 C++开发环境(VS2017)[https://yangshun.win/blogs/3b103680/]。
为了方便,首先根据返回参数定义了两个结构体,结构体包括了返回参数中的参数,如下:
struct Location {
int left;
int top;
int width;
int height;
void print() {
std::cout << "\n\t left: " << left << " top: " << top << " width: " << width << " height: " << height << '\n';
}
void draw(cv::Mat &img) {
cv::Rect rect(left, top, width, height);
cv::rectangle(img, rect, cv::Scalar(255, 0, 255), 3);
}
};
struct LogoInfo {
int type;
std::string name;
float probability;
Location location;
void print() {
std::cout << std::setw(30) << std::setfill('-') << '\n';
std::cout << "type: " << type << "\n";
std::cout << "name: " << name << "\n";
std::cout << "probability: " << std::fixed << std::setprecision(4) << probability << "\n";
std::cout << "location: ";
location.print();
}
void draw(cv::Mat &img) {
location.draw(img);
}
};
在 Location 结构体中,定义了一个 print 方法以打印 logo 位置信息。draw 方法用于在图像中画出 logo 的边框。
在 LogoInfo 结构体中,定义了一个 print 方法以打印 logo 结果信息。draw 方法用于在图像中画出 logo 的边框。
然后定义了一个类来调用接口并获取结果
class Logo
{
public:
Logo();
~Logo();
Json::Value request(std::string imgBase64, std::map& options);
uint32_t getResultNum();
// get all return results
void getAllResult(std::vector& results);
// only get first result
void getResult(LogoInfo& result);
private:
Json::Value obj_;
std::string url_;
uint32_t result_num_;
// file to save token key
std::string filename_;
};
类中的私有成员 obj_ 表示返回结果对应的 json 对象。url_ 表示请求的 url,result_num_ 表示返回的结果数,filename_ 表示用于存储 access token 的文件的文件名。
request 函数输入请求图像的 base64 编码以及请求参数,返回一个 json 对象,json 对象中包含请求的结果。
getAllResult 获取请求的结果,总共有 top_num 条结果。
getResult 获取置信度最高的一条结果。
完整代码如下
util.h 和 util.cpp 代码参见 (简单调用篇 01) 通用物体和场景识别高级版 - C++ 简单调用[https://yangshun.win/blogs/cd08a730/]
Logo.h 代码如下:
#pragma once
#include "util.h"
struct Location {
int left;
int top;
int width;
int height;
void print() {
std::cout << "\n\t left: " << left << " top: " << top << " width: " << width << " height: " << height << '\n';
}
void draw(cv::Mat &img) {
cv::Rect rect(left, top, width, height);
cv::rectangle(img, rect, cv::Scalar(255, 0, 255), 3);
}
};
struct LogoInfo {
int type;
std::string name;
float probability;
Location location;
void print() {
std::cout << std::setw(30) << std::setfill('-') << '\n';
std::cout << "type: " << type << "\n";
std::cout << "name: " << name << "\n";
std::cout << "probability: " << std::fixed << std::setprecision(4) << probability << "\n";
std::cout << "location: ";
location.print();
}
void draw(cv::Mat &img) {
location.draw(img);
}
};
class Logo
{
public:
Logo();
~Logo();
Json::Value request(std::string imgBase64, std::map& options);
uint32_t getResultNum();
// get all return results
void getAllResult(std::vector& results);
// only get first result
void getResult(LogoInfo& result);
private:
Json::Value obj_;
std::string url_;
uint32_t result_num_;
// file to save token key
std::string filename_;
};
void logoTest();
Logo.cpp 代码如下:
#include "Logo.h"
Logo::Logo()
{
filename_ = "tokenKey";
url_ = "https://aip.baidubce.com/rest/2.0/image-classify/v2/logo";
}
Logo::~Logo()
{
}
Json::Value Logo::request(std::string imgBase64, std::map& options)
{
std::string response;
Json::Value obj;
std::string token;
// 1. get HTTP post body
std::string body;
mergeHttpPostBody(body, imgBase64, options);
// 2. get HTTP url with access token
std::string url = url_;
getHttpPostUrl(url, filename_, token);
// 3. post request, response store the result
int status_code = httpPostRequest(url, body, response);
if (status_code != CURLcode::CURLE_OK) {
obj["curl_error_code"] = status_code;
obj_ = obj;
return obj; // TODO: maybe should exit
}
// 4. make string to json object
generateJson(response, obj);
// if access token is invalid or expired, we will get a new one
if (obj["error_code"].asInt() == 110 || obj["error_code"].asInt() == 111) {
token = getTokenKey();
writeFile(filename_, token);
return request(imgBase64, options);
}
obj_ = obj;
// check for other error code
checkErrorWithExit(obj);
return obj;
}
uint32_t Logo::getResultNum()
{
return obj_["result_num"].asInt();
}
void Logo::getAllResult(std::vector& results)
{
result_num_ = getResultNum();
results.reserve(result_num_);
LogoInfo tmp;
for (uint32_t i = 0; i < result_num_; ++i) {
tmp.type = obj_["result"][i]["type"].asInt();
tmp.name = UTF8ToGB(obj_["result"][i]["name"].asString().c_str());
tmp.probability = obj_["result"][i]["probability"].asFloat();
tmp.location.left = obj_["result"][i]["location"]["left"].asInt();
tmp.location.top = obj_["result"][i]["location"]["top"].asInt();
tmp.location.width = obj_["result"][i]["location"]["width"].asInt();
tmp.location.height = obj_["result"][i]["location"]["height"].asInt();
results.push_back(tmp);
}
}
void Logo::getResult(LogoInfo & result)
{
result.type = obj_["result"][0]["type"].asInt();
result.name = UTF8ToGB(obj_["result"][0]["name"].asString().c_str());
result.probability = obj_["result"][0]["probability"].asFloat();
result.location.left = obj_["result"][0]["location"]["left"].asInt();
result.location.top = obj_["result"][0]["location"]["top"].asInt();
result.location.width = obj_["result"][0]["location"]["width"].asInt();
result.location.height = obj_["result"][0]["location"]["height"].asInt();
}
void logoTest()
{
std::cout << "size: " << sizeof(LogoInfo) << "\n";
// read image and encode to base64
std::string imgFile = "./images/logo_test.jpg";
std::string imgBase64;
imgToBase64(imgFile, imgBase64);
// set options
std::map options;
// options["custom_lib"] = true;
Json::Value obj;
Logo logoObj;
obj = logoObj.request(imgBase64, options);
LogoInfo result;
logoObj.getResult(result);
result.print();
cv::Mat img = cv::imread(imgFile);
result.draw(img);
cv::namedWindow("Logo Test", cv::WINDOW_NORMAL);
cv::imshow("Logo Test", img);
std::vector results;
logoObj.getAllResult(results);
cv::Mat img1 = cv::imread(imgFile);
cv::namedWindow("Logo Tests", cv::WINDOW_NORMAL);
for (auto & vec : results) {
vec.print();
vec.draw(img1);
}
cv::imshow("Logo Tests", img1);
cv::waitKey();
}
main.cpp 代码如下:
#include "util.h"
#include "Logo.h"
#include
int main() {
logoTest();
system("pause");
return EXIT_SUCCESS;
}
运行结果
测试图像