Android OpenCV（三十九）：模板匹配

Vaccae

发布于 2021-07-07 19:11:45

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发布于 2021-07-07 19:11:45

什么是模板匹配？

模板匹配是一种用于在较大图像中搜索和查找模板图像位置的方法。OpenCV提供matchTemplate()方法来实现模板匹配功能。模板匹配结果返回的是灰度图像，其中每个像素表示该像素的邻域与模板匹配程度。假设输入图像的大小（W * H），模板图像的大小为（w * h），则输出图像的大小将为（W - w + 1，H - h + 1）。获得结果后，可以使用minMaxLoc()方法查找最大/最小值位置，并将其作为矩形的左上角，以（w，h）作为矩形的宽度和高度来确定模板匹配到的区域。

模板匹配原理

在要检测的图像上，从左到右，从上到下遍历这一幅图像，从上到下计算模板与重叠子图像的像素匹配度，如果匹配的程度越大，这说明相同的可能性越大。只是这个匹配度的计算有讲究。

模板匹配原理

API

public static void matchTemplate(Mat image, Mat templ, Mat result, int method, Mat mask)

参数一：image，待匹配图像。必须是8位或者32位浮点图像。

参数二：templ，模板图像，类型与输入图像一致，并且大小不能大于源图像。

参数三：result，输出结果，必须是单通道32位浮点数，假设源图像W*H,模板图像w*h, 则结果必须为（W-w+1）*（H-h+1）的大小。

参数四：method，匹配方式标志位。若为TM_SQDIFF或者TM_SQDIFF_NORMED，计算值越小，匹配度越高，剩下的几个标志位，计算值越大，匹配度越高。

// C++: enum TemplateMatchModes
public static final int
        TM_SQDIFF = 0,
        TM_SQDIFF_NORMED = 1,
        TM_CCORR = 2,
        TM_CCORR_NORMED = 3,
        TM_CCOEFF = 4,
        TM_CCOEFF_NORMED = 5;

参数五：mask，可选掩码。必须和templ参数大小相同，要么和templ通道数相同，要么单通道。如果数据类型为＃CV_8U，则将掩码解释为二进制掩码，表示仅使用掩码为非零的元素，并且权重与实际掩码值无关（一直等于1）。若数据类型为＃CV_32F，掩码值将作为权重参与计算。

标记位

设

R(x,y)为结果矩阵， T(x^，,y^, )为模板矩阵， I(x,y)为源图像矩阵

TM_SQDIFF

R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2

with mask:

R(x,y)= \sum _{x',y'} \left( (T(x',y')-I(x+x',y+y')) \cdot M(x',y') \right)^2

TM_SQDIFF_NORMED

R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{ x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}

with mask:

R(x,y)= \frac{\sum _{x',y'} \left( (T(x',y')-I(x+x',y+y')) \cdot M(x',y') \right)^2}{\sqrt{\sum_{x',y'} \left( T(x',y') \cdot M(x',y') \right)^2 \cdot \sum_{x',y'} \left( I(x+x',y+y') \cdot M(x',y') \right)^2}}

TM_CCORR

R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))

with mask:

R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y') \cdot M(x',y') ^2)

TM_CCORR_NORMED

R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y'))}{\sqrt{ \sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}

with mask:

R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y') \cdot M(x',y')^2)}{\sqrt{\sum_{x',y'} \left( T(x',y') \cdot M(x',y') \right)^2 \cdot \sum_{x',y'} \left( I(x+x',y+y') \cdot M(x',y') \right)^2}}

TM_CCOEFF

R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I'(x+x',y+y'))

where

\begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{ x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}

with mask:

\begin{array}{l} T'(x',y')=M(x',y') \cdot \left( T(x',y') - \frac{1}{\sum _{x'',y''} M(x'',y'')} \cdot \sum _{x'',y''} (T(x'',y'') \cdot M(x'',y'')) \right) \\ I'(x+x',y+y')=M(x',y') \cdot \left( I(x+x',y+y') - \frac{1}{\sum _{x'',y''} M(x'',y'')} \cdot \sum _{x'',y''} (I(x+x'',y+y'') \cdot M(x'',y'')) \right) \end{array}

TM_CCOEFF_NORMED

R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }

操作

/**
 * 模板匹配
 * author: yidong
 * 2020/10/23
 */
class MatchTemplateActivity : AppCompatActivity() {

    private lateinit var mBinding: ActivityMatchTemplateBinding
    private lateinit var mRgb: Mat
    private lateinit var mTemplate: Mat
    private var method = Imgproc.TM_SQDIFF
        set(value) {
            field = value
            doMatch(field)
        }

    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        mBinding = DataBindingUtil.setContentView(this, R.layout.activity_match_template)
        title = "TM_SQDIFF"
        val bgr = Utils.loadResource(this, R.drawable.kobe)
        mRgb = Mat()
        mTemplate = Mat()
        Imgproc.cvtColor(bgr, mRgb, Imgproc.COLOR_BGR2RGB)
        val templateBgr = Utils.loadResource(this, R.drawable.kobe_template)
        Imgproc.cvtColor(templateBgr, mTemplate, Imgproc.COLOR_BGR2RGB)
        mBinding.ivLena.showMat(mTemplate)
        doMatch(Imgproc.TM_CCOEFF)
    }

    private fun doMatch(method: Int) {
        val tmp = mRgb.clone()
        val result = Mat()
        Imgproc.matchTemplate(mRgb, mTemplate, result, method)
        val minMaxLoc = Core.minMaxLoc(result)
        val topLeft = if (method == Imgproc.TM_SQDIFF || method == Imgproc.TM_SQDIFF_NORMED) {
            minMaxLoc.minLoc;
        } else {
            minMaxLoc.maxLoc;
        }
        val rect = Rect(topLeft, Size(mTemplate.cols().toDouble(), mTemplate.rows().toDouble()))
        Imgproc.rectangle(tmp, rect, Scalar(255.0, 0.0, 0.0), 4, Imgproc.LINE_8)
        mBinding.ivResult.showMat(mRgb)
        tmp.release()
    }

    override fun onCreateOptionsMenu(menu: Menu?): Boolean {
        menuInflater.inflate(R.menu.menu_match_template, menu)
        return true
    }

    override fun onOptionsItemSelected(item: MenuItem): Boolean {
        when (item.itemId) {
            R.id.match_tm_sqdiff -> {
                method = Imgproc.TM_SQDIFF
                title = "TM_SQDIFF"
            }
            R.id.match_tm_sqdiff_normed -> {
                method = Imgproc.TM_SQDIFF_NORMED
                title = "TM_SQDIFF_NORMED"
            }
            R.id.match_tm_ccoeff -> {
                method = Imgproc.TM_CCOEFF
                title = "TM_CCOEFF"
            }

            R.id.match_tm_ccoeff_normed -> {
                method = Imgproc.TM_CCOEFF_NORMED
                title = "TM_CCOEFF_NORMED"
            }
            R.id.match_tm_ccorr -> {
                method = Imgproc.TM_CCORR
                title = "TM_CCORR"
            }
            R.id.match_tm_ccorr_normed -> {
                method = Imgproc.TM_CCORR_NORMED
                title = "TM_CCORR_NORMED"
            }
        }
        return true
    }

    override fun onDestroy() {
        mTemplate.release()
        mRgb.release()
        super.onDestroy()
    }
}

结果

模板匹配结果

源码

https://github.com/onlyloveyd/LearningAndroidOpenCV

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