验证码识别系统实战

核心实现

1. 验证码生成器
   cpp

include <opencv2/opencv.hpp>

include

include

更多内容访问ttocr.com或联系1436423940
std::string generate_random_text(size_t length = 4) {
static const std::string chars = "ABCDEFGHJKLMNPQRSTUVWXYZ23456789";
static std::random_device rd;
static std::mt19937 gen(rd());
static std::uniform_int_distribution<> dis(0, chars.size()-1);

std::string text;
for(size_t i = 0; i < length; ++i) {
    text += chars[dis(gen)];
}
return text;

}

cv::Mat generate_captcha(const std::string& text) {
cv::Mat img(40, 120, CV_8UC3, cv::Scalar(255, 255, 255));

// 绘制文字
int baseline = 0;
auto font = cv::FONT_HERSHEY_SIMPLEX;
cv::Point text_pos(10, 25);

for(size_t i = 0; i < text.size(); ++i) {
    std::string char_str(1, text[i]);
    cv::putText(img, char_str, text_pos, font, 0.8, 
               cv::Scalar(0, 0, 0), 1, cv::LINE_AA);
    text_pos.x += 30;
}

// 添加干扰线
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_int_distribution<> dis(0, 255);

for(int i = 0; i < 3; ++i) {
    cv::line(img, 
            cv::Point(dis(gen)%120, dis(gen)%40),
            cv::Point(dis(gen)%120, dis(gen)%40),
            cv::Scalar(dis(gen), dis(gen), dis(gen)), 1);
}

return img;

}
2. 模型推理接口（Python）
python

model_api.py

import tensorflow as tf
import numpy as np
import cv2

class CaptchaModel:
def init(self, model_path):
self.model = tf.keras.models.load_model(model_path)
self.chars = "ABCDEFGHJKLMNPQRSTUVWXYZ23456789"

def predict(self, img_array):
    img = cv2.resize(img_array, (120, 40))
    img = np.expand_dims(img/255.0, axis=(0,-1))
    pred = self.model.predict(img)
    return ''.join([self.chars[np.argmax(p)] for p in pred[0]])

3. C++调用Python模型
   cpp

include <Python.h>

include <numpy/arrayobject.h>

std::string predict_captcha(const cv::Mat& img) {
Py_Initialize();
import_array();

// 导入Python模块
PyObject* pModule = PyImport_ImportModule("model_api");
PyObject* pClass = PyObject_GetAttrString(pModule, "CaptchaModel");
PyObject* pInstance = PyObject_CallFunction(pClass, "s", "model.h5");

// 准备图像数据
npy_intp dims[3] = {img.rows, img.cols, img.channels()};
PyObject* pArray = PyArray_SimpleNewFromData(3, dims, NPY_UINT8, img.data);

// 调用预测方法
PyObject* pResult = PyObject_CallMethod(pInstance, "predict", "O", pArray);

// 获取结果
std::string result = PyUnicode_AsUTF8(pResult);

// 清理资源
Py_DECREF(pArray);
Py_DECREF(pResult);
Py_DECREF(pInstance);
Py_DECREF(pClass);
Py_DECREF(pModule);
Py_Finalize();

return result;

}
性能优化

1. 多线程处理
   cpp

include

include

void batch_predict(const std::vectorcv::Mat& images,
std::vectorstd::string& results) {
size_t num_threads = std:🧵:hardware_concurrency();
std::vectorstd::thread workers;

auto worker_func = [&](size_t start, size_t end) {
    for(size_t i = start; i < end; ++i) {
        results[i] = predict_captcha(images[i]);
    }
};

size_t batch_size = images.size() / num_threads;
for(size_t i = 0; i < num_threads; ++i) {
    size_t start = i * batch_size;
    size_t end = (i == num_threads-1) ? images.size() : start + batch_size;
    workers.emplace_back(worker_func, start, end);
}

for(auto& t : workers) {
    t.join();
}

}
2. 内存池优化
cpp
class ImagePool {
public:
ImagePool(size_t pool_size, int width, int height) {
for(size_t i = 0; i < pool_size; ++i) {
pool_.push(cv::Mat(height, width, CV_8UC3));
}
}

cv::Mat acquire() {
    std::lock_guard<std::mutex> lock(mutex_);
    if(pool_.empty()) {
        return cv::Mat(40, 120, CV_8UC3);
    }
    auto img = std::move(pool_.front());
    pool_.pop();
    return img;
}

void release(cv::Mat&& img) {
    std::lock_guard<std::mutex> lock(mutex_);
    pool_.push(std::move(img));
}

private:
std::queuecv::Mat pool_;
std::mutex mutex_;
};
部署方案

1. 编译选项
   bash
   g++ -std=c++17 -O3 -I/usr/include/python3.8 -I/usr/include/opencv4
   -lpython3.8 -lopencv_core -lopencv_imgproc -lopencv_highgui
   -o captcha_system main.cpp
2. 系统服务配置
   ini

captcha.service

[Unit]
Description=Captcha Recognition Service
After=network.target

[Service]
ExecStart=/usr/local/bin/captcha_system
Restart=always
User=captcha

[Install]
WantedBy=multi-user.target