浅谈halcon图像拼接

图像拼接方法

1直接拼接,去两张图,直接拼接,适用于没有变形的,分割的图像复原整个图像.
read_image (Image, ‘1.bmp’)
dev_close_window ()
dev_open_window_fit_image (Image, 0, 0, -1, -1, WindowHandle)
dev_display (Image)
read_image (Image1, ‘2.bmp’)
dev_close_window ()
dev_open_window_fit_image (Image1, 0, 0, -1, -1, WindowHandle1)
dev_display (Image1)
concat_obj (Image, Image1, ObjectsConcat)
tile_images (ObjectsConcat, TiledImage, 1, ‘vertical’)
dev_close_window ()
dev_open_window_fit_image (TiledImage, 0, 0, -1, -1, WindowHandle2)
dev_display (TiledImage)
stop ()
get_image_size (Image1, Width, Height)
concat_obj (Image, Image1, ObjectsConcat1)//拼接宽度Rowoffset，高度，其它参数 默认
tile_images_offset (ObjectsConcat1, TiledImage1, [0,Height], [0,0], [-1,-1], [-1,-1], [-1,-1], [-1,-1], Width, Height*2)
dev_close_window ()
dev_open_window_fit_image (TiledImage1, 0, 0, -1, -1, WindowHandle2)
dev_display (TiledImage1)
stop ()

2第二种模版匹配的拼接,需要找到两张图的共同区域,然后进行拼接.,感兴趣的自己找两张同样大小的图片测试看看.
dev_update_window (‘off’)
dev_close_window ()
dev_open_window (0, 0, 640, 480, ‘white’, WindowHandle)
dev_set_color (‘green’)
set_display_font (WindowHandle, 14, ‘mono’, ‘true’, ‘false’)
gen_empty_obj (Images)
width:=[]
height:=[]
*设置图片的路径(需要修改)
list_files (‘…//pi’, [‘files’,‘follow_links’], ImageFiles)
tuple_regexp_select (ImageFiles, [‘\.(tif|tiff|gif|bmp|jpg|jpeg|jp2|png|pcx|pgm|ppm|pbm|xwd|ima|hobj)${}^{\prime }{,}^{\prime }ignor{e}_{c}as{e}^{\prime }],ImageFiles)forJ:=0to|ImageFiles|-1by1rea{d}_{i}mage(Image,ImageFiles[J])ge{t}_{i}mag{e}_{s}ize(Image,Width,Height)width:=[width,Width]height:=[height,Height]conca{t}_{o}bj(Images,Image,Images)de{v}_{d}isplay(Image)dis{p}_{m}essage(WindowHandle{,}^{\prime }Imag{e}^{\prime }+J$’d’, ‘image’, -1, -1, ‘black’, ‘true’)
wait_seconds (1)
endfor
disp_continue_message (WindowHandle, ‘black’, ‘true’)
stop ()
maxHeight:=sum(height)
*设置合成列坐标(需要修改) 三张图用这个

• height:= [0,height[0],sum([height[0],height[1]])]
  height:= [0,height[0]]
  *图片拼接
  tile_images_offset (Images, TiledImage,height, [0,0], [-1,-1], [-1,-1], [-1,-1], [-1,-1],width[0],maxHeight)
  *创建缩小区域的地区
  dev_close_window ()
  dev_open_window_fit_image (TiledImage, 0, 0, -1, -1, WindowHandle1)
  dev_display (TiledImage)
  wait_seconds (1)
  *这里是设置图片对比联系的顺序(需要修改)
  *例如:From[0] — To[0] 1 — 2, 1和2进行对比
  *设置迭代图片的次序
  From := [1]
  To := [2]
  Num := |From|
  ProjMatrices := []
  Rows1 := []
  Cols1 := []
  Rows2 := []
  Cols2 := []
  NumMatches := []

set_display_font (WindowHandle1, 16, ‘mono’, ‘true’, ‘false’)
for J := 0 to Num-1 by 1
F := From[J]
T := To[J]
select_obj (Images, ImageF, F)
get_image_size (ImageF, Width1, Height1)
dev_set_window_extents (-1, -1, Width1, Height1)
dev_display (ImageF)
disp_message (WindowHandle1, ‘绘制出第’+F+‘张图片与第’+T+‘张图片的重叠区域’, ‘window’, 0, 0, ‘black’, ‘true’)
*绘制要搜索的区域ImageF
draw_rectangle1 (WindowHandle1, Row1, Column1, Row2, Column2)
gen_rectangle1 (RectangleF, Row1, Column1, Row2, Column2)

select_obj (Images, ImageT, T) 
 get_image_size (ImageT, Width1, Height1) 
dev_set_window_extents (-1, -1,  Width1, Height1) 
dev_display (ImageT) 
disp_message (WindowHandle1, '绘制出第'+T+'张图片与第'+F+'张图片的重叠区域', 'window', 0, 0, 'black', 'true') 
*绘制要搜索的区域ImageT 
draw_rectangle1 (WindowHandle1, Row1, Column1, Row2, Column2) 
gen_rectangle1 (RectangleT, Row1, Column1, Row2, Column2) 
 
reduce_domain (ImageF, RectangleF, ImageFF) 
reduce_domain (ImageT, RectangleT, ImageTT) 
*获取图片信息 
points_foerstner (ImageFF, 1, 2, 3, 200, 0.1, 'gauss', 'false', RowJunctionsF, ColJunctionsF, CoRRJunctionsF, CoRCJunctionsF, CoCCJunctionsF, RowAreaF, ColAreaF, CoRRAreaF, CoRCAreaF, CoCCAreaF) 
points_foerstner (ImageTT, 1, 2, 3, 200, 0.1, 'gauss', 'false', RowJunctionsT, ColJunctionsT, CoRRJunctionsT, CoRCJunctionsT, CoCCJunctionsT, RowAreaT, ColAreaT, CoRRAreaT, CoRCAreaT, CoCCAreaT) 
*对比图片信息 
*通过查找点之间的对应关系来计算两个图像之间的投影变换矩阵 
 *参数一 输入图像1 
 *参数二 输入图像2 
 *参数三 输入的图像1中特征点的行坐标 
 *参数四 输入的图像1中特征点的列坐标 
 *参数五 输入的图想2中特征点的行坐标 
 *参数六 输入的图像2中特征点的列坐标 
 *参数七 输入的灰度值比较指标 
 *参数八 输入的灰度值蒙版的大小 
 *参数九 输入的平均行坐标偏移 
 *参数十 输入的平均列坐标偏移 
 *参数十一 输入的匹配搜索窗口的一半高度 
 *参数十二 输入的匹配搜索窗口的一半宽度 
 *参数十三 输入的旋转角度范围 
 *参数十四  输入的灰度值匹配的阈值 
 *参数十五  输入的变换矩阵估计算法 
 *参数十六 输入的转换一致性检查的阈值 
 *参数十七 输入的随机数生成器的种子 
 *参数十八 输出的均匀投影变换矩阵 
 *参数十九 输出的图像1中匹配的输入点的索引 
 *参数二十 输出的图像2中匹配的输入点的索引 
 *                           1      2           3              4             5              6            7   8   9  10  11    12  13   14        15        16    17         18        19        20 
proj_match_points_ransac (ImageF, ImageT, RowJunctionsF, ColJunctionsF, RowJunctionsT, ColJunctionsT, 'ncc', 15, 0, 0, width[0],width[0], 0, 0.5, 'gold_standard', 2, 45, ProjMatrix, Points1, Points2) 

 
ProjMatrices := [ProjMatrices,ProjMatrix] 
Rows1 := [Rows1,subset(RowJunctionsF,Points1)] 
Cols1 := [Cols1,subset(ColJunctionsF,Points1)] 
Rows2 := [Rows2,subset(RowJunctionsT,Points2)] 
Cols2 := [Cols2,subset(ColJunctionsT,Points2)] 
NumMatches := [NumMatches,|Points1|] 
 
gen_cross_contour_xld (PointsF, RowJunctionsF + height[(F - 1)], ColJunctionsF, 6, rad(45)) 
gen_cross_contour_xld (PointsT, RowJunctionsT + height[(T - 1)], ColJunctionsT, 6, rad(45)) 
 
RowF := subset(RowJunctionsF,Points1) + height[(F - 1)] 
ColF := subset(ColJunctionsF,Points1) 
RowT := subset(RowJunctionsT,Points2) + height[(T - 1)] 
ColT := subset(ColJunctionsT,Points2) 
 
gen_empty_obj (Matches) 
for K := 0 to |RowF| - 1 by 1 
    gen_contour_polygon_xld (Match, [RowF[K],RowT[K]], [ColF[K],ColT[K]]) 
    concat_obj (Matches, Match, Matches) 
endfor 
 
dev_set_color ('blue') 
dev_display (Matches) 
dev_set_color ('green') 
dev_display (PointsF) 
dev_display (PointsT) 
wait_seconds (1) 

endfor
gen_projective_mosaic (Images, MosaicImage, 2, From, To, ProjMatrices, ‘default’, ‘false’, MosaicMatrices2D)
dev_close_window ()
dev_open_window_fit_image (MosaicImage, 0, 0, -1, -1, WindowHandle2)
dev_display (MosaicImage)
get_image_size (MosaicImage, Width2, Height2)