秒杀乐玩极速找图最新opencv4.11模板匹配 GO开发通用调用API

callme大大 · 发表于 2025-5-27 13:59:48

本帖最后由 callme大大于 2025-5-27 14:08 编辑

运行效果图：

调用C++编译的DLL 原版 GO多线程加持  带来无与伦比的速度
可以手动测试：http://127.0.0.1:8080/

估计大多数人是无法完成这个opencv的原版编译和调用，众所周知WIN环境下编译gocv的环境搭建非常麻烦，坑巨TM的多。
我这里给出了编译好的包，直接使用即可，不带任何广告。最后附上了源码，想改可以自己改一下。

附上GO源码（用这个源码编译坑多别问懒得回，直接用我编译好的成品）：
package main

import (
"encoding/base64"
"encoding/json"
"fmt"
"gocv.io/x/gocv"
"html/template"
"io"
"log"
"net/http"
"strconv"
)

// MatchRequest 请求结构体
type MatchRequest struct {
BigImage string  `json:"big_image"`
SmallImage string  `json:"small_image"`
MinSim    float64 `json:"min_similarity"`
}

// MatchResponse 响应结构体
type MatchResponse struct {
Found    bool `json:"found"`
X       int    `json:"x"`
Y       int    `json:"y"`
Similarity float32 `json:"similarity"`
Message string  `json:"message,omitempty"`
}

// decodeBase64Image 将Base64字符串解码为gocv.Mat
func decodeBase64Image(data string) (gocv.Mat, error) {
imgBytes, err := base64.StdEncoding.DecodeString(data)
if err != nil {
   return gocv.NewMat(), fmt.Errorf("Base64解码错误: %v", err)
}
mat, err := gocv.IMDecode(imgBytes, gocv.IMReadColor)
if err != nil {
   return gocv.NewMat(), fmt.Errorf("IMDecode错误: %v", err)
}
if mat.Empty() {
   return gocv.NewMat(), fmt.Errorf("解码后的Mat为空")
}
return mat, nil
}

// matchHandler 处理匹配请求的HTTP处理函数
func matchHandler(w http.ResponseWriter, r *http.Request) {
if r.Method != http.MethodPost {
   http.Error(w, "仅支持POST方法", http.StatusMethodNotAllowed)
   return
}

var req MatchRequest
body, err := io.ReadAll(r.Body)
if err != nil {
   respond(w, false, 0, 0, 0, "读取请求体失败: "+err.Error())
   return
}
defer r.Body.Close()

if err := json.Unmarshal(body, &req); err != nil {
   respond(w, false, 0, 0, 0, "JSON解析失败: "+err.Error())
   return
}

minSim := 0.8
if req.MinSim > 0 && req.MinSim < 1.0 {
   minSim = req.MinSim
}

// 解码大图
bigMat, err := decodeBase64Image(req.BigImage)
if err != nil {
   respond(w, false, 0, 0, 0, "大图解码失败: "+err.Error())
   return
}
defer bigMat.Close()

// 解码小图
smallMat, err := decodeBase64Image(req.SmallImage)
if err != nil {
   respond(w, false, 0, 0, 0, "小图解码失败: "+err.Error())
   return
}
defer smallMat.Close()

// 检查尺寸
if bigMat.Cols() < smallMat.Cols() || bigMat.Rows() < smallMat.Rows() {
   respond(w, false, 0, 0, 0, "小图尺寸大于大图")
   return
}

// 模板匹配
result := gocv.NewMat()
defer result.Close()

gocv.MatchTemplate(bigMat, smallMat, &result, gocv.TmCcoeffNormed, gocv.NewMat())
_, maxVal, _, maxLoc := gocv.MinMaxLoc(result)

// 判断是否匹配成功
if float64(maxVal) >= minSim {
   respond(w, true, maxLoc.X, maxLoc.Y, maxVal, "")
} else {
   respond(w, false, 0, 0, maxVal, "未找到符合相似度要求的匹配")
}
}

// respond 统一响应函数
func respond(w http.ResponseWriter, found bool, x, y int, sim float32, msg string) {
resp := MatchResponse{
   Found:    found,
   X:       x,
   Y:       y,
   Similarity: sim,
   Message: msg,
}
data, err := json.MarshalIndent(resp, "", "  ")
if err != nil {
   http.Error(w, "响应编码失败: "+err.Error(), http.StatusInternalServerError)
   return
}
w.Header().Set("Content-Type", "application/json")
w.Write(data)
}

// HTML主页模板
const indexHTML = `
<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<title>图片模板匹配测试</title>
<style>
body { font-family: Arial, sans-serif; margin: 40px; }
input[type="file"] { margin-bottom: 15px; }
#resp { width: 100%; height: 180px; }
.img-preview { max-width: 300px; max-height: 200px; margin: 10px 0; }
</style>
</head>
<body>
<h2>图片模板匹配手动测试</h2>
<form id="form">
<label>大图: <input type="file" id="big" accept="image/*" required></label><br>
<img id="bigPreview" class="img-preview"><br>
<label>小图: <input type="file" id="small" accept="image/*" required></label><br>
<img id="smallPreview" class="img-preview"><br>
<label>最小相似度(0~1): <input type="number" id="minSim" step="0.01" value="0.8" min="0" max="1"></label><br><br>
<button type="submit">查找小图位置</button>
</form>
<br>
<label>接口响应信息:</label><br>
<textarea id="resp" readonly></textarea>

<script>
function fileToBase64(file) {
return new Promise((resolve, reject) => {
      const reader = new FileReader();
      reader.onload = e => resolve(e.target.result.split(',')[1]);
      reader.onerror = e => reject(e);
      reader.readAsDataURL(file);
});
}

function showPreview(input, imgId) {
input.addEventListener('change', function() {
      const file = input.files[0];
      if (file) {
         const url = URL.createObjectURL(file);
         document.getElementById(imgId).src = url;
      }
});
}
showPreview(document.getElementById('big'), 'bigPreview');
showPreview(document.getElementById('small'), 'smallPreview');

document.getElementById('form').onsubmit = async function(e) {
e.preventDefault();
const bigFile = document.getElementById('big').files[0];
const smallFile = document.getElementById('small').files[0];
const minSim = parseFloat(document.getElementById('minSim').value);

if (!bigFile || !smallFile) {
      alert("请上传两张图片");
      return;
}

try {
      document.getElementById('resp').value = "图片编码中...";
      const [bigB64, smallB64] = await Promise.all([
         fileToBase64(bigFile),
         fileToBase64(smallFile)
      ]);
      document.getElementById('resp').value = "请求中...";
      const resp = await fetch('/match', {
         method: 'POST',
         headers: {'Content-Type': 'application/json'},
         body: JSON.stringify({
            big_image: bigB64,
            small_image: smallB64,
            min_similarity: minSim
         })
      });
      const text = await resp.text();
      document.getElementById('resp').value = text;
} catch (err) {
      document.getElementById('resp').value = "请求失败: " + err;
}
};
</script>
</body>
</html>
`

// indexHandler 返回主页
func indexHandler(w http.ResponseWriter, r *http.Request) {
w.Header().Set("Content-Type", "text/html; charset=utf-8")
tmpl := template.Must(template.New("index").Parse(indexHTML))
tmpl.Execute(w, nil)
}

func main() {
http.HandleFunc("/", indexHandler)
http.HandleFunc("/match", matchHandler)
port := 8080
log.Printf("服务启动，监听端口: %d", port)
log.Fatal(http.ListenAndServe(":"+strconv.Itoa(port), nil))
}

这里下载完整的编译好的：

通过网盘分享的文件：GO_opencv找图.rar
链接: https://pan.baidu.com/s/19Dm3r7eidWu5XxulQoTAKw 提取码: schm
--来自百度网盘超级会员v7的分享
使用方法：使用post直接本地调用api 传json数据。

地址：http://127.0.0.1:8080/match

图片要base64编码传输。注意自己传数据，简易用e2ee
{
  "big_image": "（大图的Base64字符串）",
  "small_image": "（小图的Base64字符串）",
  "min_similarity": 0.8
}