Blob分析之Board_Segmentation_Uncalib懒树懒

Blob分析之Board_Segmentation_Uncalib

* **************************************************************************************************
*  电路板投影矫正
*Projective rectification of a circuit board
* **************************************************************************************************
* 关闭窗体
dev_close_window ()
*设置窗体更新为off
dev_update_window (\'off\')
*读取左右窗体图片
read_image (Image1, \'/stereo/uncalib/board_l\')
read_image (Image2, \'/stereo/uncalib/board_r\')
*计算像素的最大和最小值，按照最大值比例化各个像素
scale_image_max (Image1, Image1)
scale_image_max (Image2, Image2)
*获取图像指针
get_image_pointer1 (Image1, Pointer, Type, Width, Height)
ScaleImage := 0.4
*打开窗体
dev_open_window (0, 0, Width * ScaleImage, Height * ScaleImage, \'black\', Window1)
*设置显示字体
set_display_font (Window1, 14, \'mono\', \'true\', \'false\')
*显示图像1
dev_display (Image1)
*打开窗体
dev_open_window (0, Width * ScaleImage + 8, Width * ScaleImage, Height * ScaleImage, \'black\', Window2)
*显示图像2
dev_display (Image2)
*聚焦窗体1
dev_set_window (Window1)
*显示信息
disp_message (Window1, \'Uncalibrated Stereo Image Pair\', \'window\', 12, 12, \'black\', \'true\')
* Compute Fundamental Matrix
* 用harris算子计算兴趣点也就是基础矩阵
points_harris (Image1, 3, 1, 0.2, 1e5, Row1, Col1)
points_harris (Image2, 3, 1, 0.2, 1e5, Row2, Col2)
*通过两个图像之间的对应的点对来自动计算两个图像之间的基本转换矩阵，
match_fundamental_matrix_ransac (Image1, Image2, Row1, Col1, Row2, Col2, \'ncc\', 21, 0, 200, 20, 50, 0, 0.9, \'gold_standard\', 0.3, 1, FMatrix, CovFMat, Error, Points1, Points2)
Row1 := subset(Row1,Points1)
Col1 := subset(Col1,Points1)
Row2 := subset(Row2,Points2)
Col2 := subset(Col2,Points2)
* 投影矫正开始
* Projective Rectification
* 执行立体投影矫正
gen_binocular_proj_rectification (Map1, Map2, FMatrix, [], Width, Height, Width, Height, 1, \'bilinear_map\', CovFMatRect, H1, H2)
*投影图像
map_image (Image1, Map1, Image1)
map_image (Image2, Map2, Image2)
*投影变换
projective_trans_pixel (H1, Row1, Col1, Row1, Col1)
projective_trans_pixel (H2, Row2, Col2, Row2, Col2)
*
*
* Find the dominant plane in the images by computing the 2D homography.
* Then, the left image is transformed such that points lying in this dominant plane do coincide in both images.
* *通过两个图像之间的对应的点对来自动计算两个图像之间的基本投影变换
proj_match_points_ransac (Image1, Image2, Row1, Col1, Row2, Col2, \'ncc\', 11, 0, 200, 100, 100, 0, 0.6, \'normalized_dlt\', 2, 1, HomMat, Points1, Points2)
*投影变换图像
projective_trans_image (Image1, Image1, HomMat, \'bilinear\', \'false\', \'true\')
* 1. image (cyan) and the 2. image (red) overlaid:
* Regions close to the dominant plane are gray, whereas regions distant to this plane are red or cyan.
  *右下角显示 \'Press Run (F5) to continue\' 这个信息
disp_continue_message (Window1, \'black\', \'true\')
stop ()
*把三个图像转换为一个三通道图像
compose3 (Image1, Image2, Image2, Image)
*湖区图像指针
get_image_pointer1 (Image, Pointer, Type, Width, Height)
*聚焦到窗体2
dev_set_window (Window2)
*关闭窗体
dev_close_window ()
*聚焦窗体1
dev_set_window (Window1)
*清楚窗体
dev_clear_window ()
ScaleImage := 0.4
*放大窗体
dev_set_window_extents (-1, -1, Width * ScaleImage, Height * ScaleImage)
dev_display (Image)
*显示信息
disp_message (Window1, \'Images rectified relative to the dominant plane\', \'window\', 12, 12, \'black\', \'true\')
* The difference between corresponding points, also called parallax, is reciprocal to the 3D distance
* from the space point the reference plane.
* 利用关联技术来计算矫正图像对之间的差别
binocular_disparity (Image1, Image2, Disparity, Score, \'ncc\', 15, 15, 5, -5, 5, 1, 0.8, \'left_right_check\', \'interpolation\')
  *右下角显示 \'Press Run (F5) to continue\' 这个信息
disp_continue_message (Window1, \'black\', \'true\')
stop ()
dev_clear_window ()
dev_display (Disparity)
disp_message (Window1, \'Disparity map\', \'window\', 12, 12, \'black\', \'true\')
*
disp_continue_message (Window1, \'black\', \'true\')
stop ()
*清楚窗体
dev_clear_window ()
*阈值分割
threshold (Disparity, Region, 0.5, 3)
*生成一个圆
gen_circle (StructElem, 10, 10, 3)
*开运算:开运算的目的是先腐蚀后膨胀；目的是把细微连在一起的两块目标分开了，而闭运算则相反
opening (Region, StructElem, Region)
*设置显示颜色
dev_set_color (\'slate blue\')
*吓死你好i图像
dev_display (Image1)
*显示区域
dev_display (Region)
*显示信息
disp_message (Window1, \'Segmentation of components with large height\', \'window\', 12, 12, \'black\', \'true\')

posted on

2020-07-30 15:08 

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