如何在图像提取条码区域

以快递上的一维码为例，由于快递标签处有很多的文字，符号和边框，增大了定位的难度。（如下图）而且有很多噪声的影响。

 halcon实现：

 1，读入图像，提取轮廓

 有两种方法：

基于边缘提取

blob分析

read_image (Image, 'C:/Users/86175/Desktop/4.png')

get_image_size (Image, Width, Height)

dev_open_window (0, 0, Width/2, Height/2, 'black', WindowHandle)

dev_display (Image)

rgb1_to_gray (Image, GrayImage)

*第一种边缘提取初步提取轮廓

laplace_of_gauss(GrayImage, ImageLaplace, 2)//拉布拉斯算子

threshold(ImageLaplace, Region3, 5, 127)

skeleton(Region3, Skeleton3)

connection (Skeleton3, ConnectedRegions2)

select_shape (ConnectedRegions2, SelectedRegions, ['area','width','height'], 'and', [0,0,0], [220,80,200])

*第二种Blob分析法初步提取条码

threshold (GrayImage, Regions, 7, 107)

connection (Regions, ConnectedRegions)

二者提取效果差不多，但是考虑到Blob分析法可能对相机像素有较大的要求，否则二值化后可能提取不出条码线，个人觉得用边缘提取的方式较稳定。

 2，筛选条码区域

*通过面积和矩形度筛选

select_shape (ConnectedRegions, SelectedRegions1, 'area', 'and', 60, 1500)

select_shape_std (SelectedRegions1, SelectedRegions, 'rectangle2', 60)

*提取轮廓

union1 (SelectedRegions, RegionUnion)

skeleton (RegionUnion, Skeleton4)

gen_contours_skeleton_xld (Skeleton4, Contours1, 1, 'filter')

*通过轮廓长度过滤小噪声

select_contours_xld (Contours1, SelectedContours, 'contour_length', 30, 200, -0.5, 0.5)

 3，拟合直线，进一步筛选

*因为条码都是直线，可以通过角度相同的直线分割条码

*拟合直线

fit_line_contour_xld (SelectedContours, 'tukey', -1, 0, 5, 2, RowBegin, ColBegin, RowEnd, ColEnd, Nr, Nc, Dist1)

*画拟合线

gen_region_line (RegionLines1, RowBegin, ColBegin, RowEnd, ColEnd)

*在0~-180°里每-30°查找一次条码

for i:=-1 to -150 by -30

    select_shape (RegionLines1, SelectedRegions3, 'phi', 'and', rad(i-30),  rad(i))

    count_obj (SelectedRegions3, Number)

    if(Number>12)

        union1 (SelectedRegions3, RegionUnion1)

        area_center (RegionUnion1, Area1, Row, Column)

        orientation_region (RegionUnion1, Phi)

        if(Phi>rad(90))

            Phi:=Phi-rad(180)

        endif

        if(Phi<-rad(90))

            Phi:=rad(180)+Phi

        endif

        vector_angle_to_rigid (Row, Column, Phi, Row, Column,rad(0), HomMat2D)

        affine_trans_region (RegionUnion1, RegionAffineTrans1, HomMat2D, 'nearest_neighbor')

        affine_trans_image (GrayImage, ImageAffineTrans, HomMat2D, 'constant', 'false')   

        closing_rectangle1 (RegionAffineTrans1, RegionClosing, 80, 2)

        connection (RegionClosing, ConnectedRegions1)

        select_shape (ConnectedRegions1, SelectedRegions4, 'area', 'and', 2954.26, 50000)

        shape_trans (SelectedRegions4, RegionTrans, 'rectangle2')

        region_features (RegionTrans, 'width', w)

        region_features (RegionTrans, 'height', h)

        if(w/h<2.5 or w/h>10)

            continue

        endif

        reduce_domain (ImageAffineTrans, RegionTrans, ImageReduced1)

        threshold (ImageReduced1, Regions1, 160, 254)

        area_center (Regions1, Area, Row1, Column1)

        threshold (ImageReduced1, Regions2, 0, 120)

        area_center (Regions2, Area2, Row2, Column2)

        if(Area>Area2)

            if(Area/Area2>2)

                continue

            endif

        endif

        if(Area2>Area)

            if(Area2/Area>2)

                continue

            endif

        endif

        dilation_circle (RegionTrans, RegionDilation, 6)

        reduce_domain (ImageAffineTrans, RegionDilation, ImageReduced)

    endif

endfor