OpenCV - 滑动拼图验证码自动识别与匹配

OpenCV - 滑动拼图验证码自动识别与匹配

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目录

• OpenCV - 滑动拼图验证码自动识别与匹配
  • • 在线体验
    • 技术分析
    • 实现源码

在线体验

体验地址：https://www.fengkongcloud.com/trial/captcha.html

技术分析

1、抓包获取前景图和背景图

前景

背景

2、计算前景图非透明区域的边界

计算方法：Python 图像处理：计算 PNG 非透明区域最小外接矩形

3、裁切前景图和背景图

背景图中两个相似块，以前景图的非透明边界去横切背景图，能排除干扰项

4、高斯滤波与边缘检测

为了提升图像匹配的准确性，使用高斯滤波与边缘检测算法，对图像预处理

5、模板匹配

使用 OpenCV 的 matchTemplate 匹配相似位置

实现源码

import cv2
import numpy as np


def template_match(image_bg, image_fg):
    res = cv2.matchTemplate(image_bg, image_fg, cv2.TM_CCOEFF)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    top_left = max_loc
    return top_left[0]


def sobel_edge(image):
    image_x = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=3)
    abs_x = cv2.convertScaleAbs(image_x)
    image_y = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=3)
    abs_y = cv2.convertScaleAbs(image_y)
    dst = cv2.addWeighted(abs_x, 0.5, abs_y, 0.5, 0)
    return np.asarray(dst, dtype=np.uint8)


def edge(img):
    np_data = np.array(img)
    rr = np.where(np_data != 0)
    xmin = min(rr[1])
    ymin = min(rr[0])
    xmax = max(rr[1])
    ymax = max(rr[0])
    return xmin, ymin, xmax, ymax


def read(path):
    image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
    image = cv2.GaussianBlur(image, (1, 1), 0)
    return sobel_edge(image)


if __name__ == '__main__':
    fg_image = read('f93a164e974e18fb3d9f35fc67d7c11d_bg.jpg')
    bg_image = read('f93a164e974e18fb3d9f35fc67d7c11d_fg.png')
    xmin, ymin, xmax, ymax = edge(fg_image)
    fg_image = fg_image[ymin:ymax, :]
    bg_image = bg_image[ymin:ymax, :]
    print(template_match(bg_image, fg_image))

Java版本

package com.p6spy.p6spy.controller.opencv;

import org.opencv.core.Core;
import org.opencv.core.Core.MinMaxLocResult;
import org.opencv.highgui.HighGui;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Point;
import org.opencv.core.Size;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
/***
 * @ClassName: Good-Good-Good-Good-Good-Good
 * @Description: 
 * @Author liuren
 * @DateTime 2022年8月17日 下午3:35:50
 */
public class Good {

	private static String bigFilePath = "D:\\imageimage\\example\\003_big.png";
	private static String littleFilePath = "D:\\imageimage\\example\\003_little.png";
	public static String dllPath = "C://Windows//System32//opencv_java460.dll";
	
	public static void main(String[] args) {
		System.load(dllPath);
		Mat big_mat  = read(bigFilePath);
		Mat little_mat  = read(littleFilePath);
		double dist = template_match(big_mat, little_mat);
		System.out.println(dist);
	} 
	
	public static Mat read(String path) {
		Mat img = Imgcodecs.imread(path);
		Mat grayImg = new Mat();
		/**
		 * 将图像img由BGR转为灰度图，结果保存到tempImg
		 */
		Imgproc.cvtColor(img, grayImg, Imgproc.COLOR_BGR2GRAY);
		
		/**
		 * 高斯滤波降噪
		 */
		Mat blurImg = new Mat();
		Imgproc.GaussianBlur(grayImg, blurImg, new Size(1,1), 0);
		return sobel_edge(blurImg);
	}
	
	public static Mat sobel_edge(Mat fileMat) {
		Mat dst = new Mat();
		Imgproc.Sobel(fileMat, dst, CvType.CV_64F, 1, 0, 3);
		Mat dst_new = new Mat();
		Core.convertScaleAbs(dst, dst_new);

		Mat dst_t = new Mat();
		Imgproc.Sobel(fileMat, dst_t, CvType.CV_64F, 1, 0, 3);
		Mat dst_f = new Mat();
		Core.convertScaleAbs(dst_t, dst_f);

		Mat dst_s = new Mat();
		Core.addWeighted(dst_new, 0.5, dst_f, 0.5, 0, dst_s);
		// 调用方法
		edge(dst_s);
		HighGui.imshow("title-1", dst_s);
		HighGui.waitKey(10);
		return dst_s;
	}
	
	public static void edge(Mat fileMat) {

	}
	
	public static double template_match(Mat image_bg, Mat image_fg) {
		Mat result = new Mat();
		Imgproc.matchTemplate(image_bg, image_fg, result, Imgproc.TM_CCOEFF); // 归一化平方差匹配法
		MinMaxLocResult  minMaxLocResult = Core.minMaxLoc(result);
		double min_val = minMaxLocResult.minVal;
		double max_val = minMaxLocResult.maxVal;
		Point min_loc = minMaxLocResult.minLoc;
		Point max_loc = minMaxLocResult.maxLoc;
		return max_loc.x;
	}

	
}

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