验证码破解系统开发实战（Go语言版）

核心功能实现

1. 验证码生成器
   go
   package main

import (
"image"
"image/color"
"image/draw"
"image/png"
"math/rand"
"os"
"time"
)
更多内容访问ttocr.com或联系1436423940
func generateCaptcha(text string) *image.RGBA {
width, height := 120, 40
img := image.NewRGBA(image.Rect(0, 0, width, height))

// 白色背景
draw.Draw(img, img.Bounds(), &image.Uniform{color.White}, image.Point{}, draw.Src)

// 绘制文字
rand.Seed(time.Now().UnixNano())
for i, c := range text {
	x := 10 + i*30 + rand.Intn(5)
	y := 10 + rand.Intn(10)
	drawChar(img, x, y, string(c))
}

// 添加干扰线
addNoise(img)

return img

}

func drawChar(img *image.RGBA, x, y int, char string) {
// 实现字符绘制
// ...
}
2. 深度学习模型（Python接口）
python

model.py

import tensorflow as tf

def create_model():
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(40, 120, 1)),
tf.keras.layers.MaxPooling2D((2,2)),
tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(36*4, activation='softmax'),
tf.keras.layers.Reshape((4, 36))
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
return model
3. Go调用Python模型
go
// predict.go
package main

/*

cgo pkg-config: python3

include <Python.h>

*/
import "C"
import (
"fmt"
"unsafe"
)

func predictCaptcha(imgPath string) string {
pyCode := fmt.Sprintf(`
import model
import cv2
import numpy as np

model = model.create_model()
model.load_weights('model.h5')

img = cv2.imread('%s', 0)
img = cv2.resize(img, (120, 40))
img = np.expand_dims(img/255.0, axis=(0,-1))

pred = model.predict(img)
chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"
result = ''.join([chars[np.argmax(p)] for p in pred[0]])
print(result)
`, imgPath)

cstr := C.CString(pyCode)
defer C.free(unsafe.Pointer(cstr))

pyResult := C.PyRun_SimpleString(cstr)
if pyResult != 0 {
	return "预测失败"
}

return "预测结果"

}
关键优化技术
性能优化

go
// 并发生成验证码
func generateBatch(count int) []image.RGBA {
results := make([]image.RGBA, count)
var wg sync.WaitGroup

for i := 0; i < count; i++ {
	wg.Add(1)
	go func(idx int) {
		defer wg.Done()
		text := randomString(4)
		results[idx] = generateCaptcha(text)
	}(i)
}

wg.Wait()
return results

}
模型加速

bash

转换TensorFlow模型为TensorRT引擎

trtexec --onnx=model.onnx --saveEngine=model.engine --fp16
部署方案
编译为可执行文件

bash
go build -o captcha_system main.go
生产环境部署

bash

使用Supervisor管理进程

[program:captcha]
command=/path/to/captcha_system
autostart=true
autorestart=true