detect_indent_fft.hdev相关例程学习（检测凹痕）

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Index:.../Applications/Surface-Inspection/detect_indent_fft.hdev
主要步骤：

* Optimize the fft speed for the specific image size

optimize_rft_speed (Width, Height, 'standard')

* 选取了最优rft变化速度的图像大小

* Construct a suitable filter by combining two gaussian

* filters

Sigma1 := 10.0

Sigma2 := 3.0

gen_gauss_filter (GaussFilter1, Sigma1, Sigma1, 0.0, 'none', 'rft', Width, Height)

gen_gauss_filter (GaussFilter2, Sigma2, Sigma2, 0.0, 'none', 'rft', Width, Height)

sub_image (GaussFilter1, GaussFilter2, Filter, 1, 0)

* 建立两个高斯滤波器然后又结合起来
*要搞明白究竟何为高斯滤波器

* Process the images iteratively

NumImages := 11

for Index := 1 to NumImages by 1

* Read an image and convert it to gray values

read_image (Image, 'plastics/plastics_' + Index$'02')

rgb1_to_gray (Image, Image)

* Perform the convolution in the frequency domain

rft_generic (Image, ImageFFT, 'to_freq', 'none', 'complex', Width)

convol_fft (ImageFFT, Filter, ImageConvol)

rft_generic (ImageConvol, ImageFiltered, 'from_freq', 'n', 'real', Width)

* 各种rft变化加卷积在rft变化

* Process the filtered image

gray_range_rect (ImageFiltered, ImageResult, 10, 10)
*通过此步骤就找到了凹痕所在，看了这个算子的帮助文档，我就明白关键在于计算了每个mask中灰度的差，然后给一个总返回值，没有参考文献，看来是halcon自己的算法。

min_max_gray (ImageResult, ImageResult, 0, Min, Max, Range)

threshold (ImageResult, RegionDynThresh, max([5.55,Max * 0.8]), 255)

connection (RegionDynThresh, ConnectedRegions)

select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 4, 99999)

union1 (SelectedRegions, RegionUnion)

closing_circle (RegionUnion, RegionClosing, 10)

connection (RegionClosing, ConnectedRegions1)

select_shape (ConnectedRegions1, SelectedRegions1, 'area', 'and', 10, 99999)

area_center (SelectedRegions1, Area, Row, Column)

* 一系列提取算法，没有需要关注的point

学习了这个例程，明白如何有效构建高斯滤波器和利用fft变化，halcon里有rft和fft不知道内在区别，但核心都是FFT。

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