电子商务网站建设好么,自动设计logo的网站,手工网站做蛋糕盒子,产品外观设计公司第一#xff0c;Brenner梯度法、
第二#xff0c;Tenegrad梯度法、
第三#xff0c;laplace梯度法、
第四#xff0c;方差法、
第五#xff0c;能量梯度法。
此实例通过使用Halcon实现5种清晰度算法函数#xff1a; 1. 方差算法函数#xff1b; 2. 拉普拉斯能量函数…第一Brenner梯度法、
第二Tenegrad梯度法、
第三laplace梯度法、
第四方差法、
第五能量梯度法。
此实例通过使用Halcon实现5种清晰度算法函数 1. 方差算法函数 2. 拉普拉斯能量函数 3. 能量梯度函数 4. Brenner函数 5. Tenegrad函数 测试效果如下图片找到峰值对应的那张图确实是最清晰的那张使用直方图显示清晰度结果如果有更好的方法那就跟帖回复吧。 此实例有HalconBBS群友提供 *evaluate_definition的使用例子 *使用halcon自带的图片 *实现了五种评价函数 *选择算子的Method值可以观察不同评价函数的效果。 read_image (Image, pcb_focus/pcb_focus_telecentric_106) dev_update_off () dev_close_window () dev_open_window_fit_image (Image, 0, 0, 752, 480, WindowHandle) set_display_font (WindowHandle, 16, mono, true, false) dev_set_color (lime green) dev_set_line_width (3) Ret:[] get_image_size(Image, Width, Height) for Index : 1 to 121 by 1 read_image (Image, pcb_focus/pcb_focus_telecentric_Index$03d) evaluate_definition (Image, Tenegrad, Value) dev_display (Image) Ret:[Ret,Value] endfor *使用直方图显示清晰度结果如果有更好的方法那就跟帖回复吧 VMax:max(Ret) VMin:min(Ret) GRet : 100*(Ret-VMin)/(VMax-VMin) gen_region_histo(Region, Ret, 255, 255, 1) *找到峰值对应的那张图确实是最清晰的那张。 qxd:find(Ret, max(Ret)) read_image (GoodImage, pcb_focus/pcb_focus_telecentric_qxd$03d) dev_display (GoodImage) dev_display (Region) evaluate_definition函数代码如下
scale_image_max(Image, Image)
get_image_size(Image, Width, Height)if(Method Deviation)
*方差法region_to_mean (Image, Image, ImageMean) convert_image_type (ImageMean, ImageMean, real)convert_image_type (Image, Image, real) sub_image(Image, ImageMean, ImageSub, 1, 0)mult_image(ImageSub, ImageSub, ImageResult, 1, 0)intensity(ImageResult, ImageResult, Value, Deviation) elseif(Method laplace)
*拉普拉斯能量函数laplace (Image, ImageLaplace4, signed, 3, n_4)laplace (Image, ImageLaplace8, signed, 3, n_8)add_image(ImageLaplace4,ImageLaplace4,ImageResult1, 1, 0)add_image(ImageLaplace4,ImageResult1,ImageResult1, 1, 0)add_image(ImageLaplace8,ImageResult1,ImageResult1, 1, 0)mult_image(ImageResult1, ImageResult1, ImageResult, 1, 0)intensity(ImageResult, ImageResult, Value, Deviation)elseif(Method energy)
*能量梯度函数crop_part(Image, ImagePart00, 0, 0, Width-1, Height-1)crop_part(Image, ImagePart01, 0, 1, Width-1, Height-1)crop_part(Image, ImagePart10, 1, 0, Width-1, Height-1)convert_image_type (ImagePart00, ImagePart00, real)convert_image_type (ImagePart10, ImagePart10, real)convert_image_type (ImagePart01, ImagePart01, real)sub_image(ImagePart10, ImagePart00, ImageSub1, 1, 0)mult_image(ImageSub1, ImageSub1, ImageResult1, 1, 0)sub_image(ImagePart01, ImagePart00, ImageSub2, 1, 0)mult_image(ImageSub2, ImageSub2, ImageResult2, 1, 0)add_image(ImageResult1, ImageResult2, ImageResult, 1, 0) intensity(ImageResult, ImageResult, Value, Deviation)
elseif(Method Brenner)
*Brenner函数法crop_part(Image, ImagePart00, 0, 0, Width, Height-2)convert_image_type (ImagePart00, ImagePart00, real)crop_part(Image, ImagePart20, 2, 0, Width, Height-2)convert_image_type (ImagePart20, ImagePart20, real)sub_image(ImagePart20, ImagePart00, ImageSub, 1, 0)mult_image(ImageSub, ImageSub, ImageResult, 1, 0)intensity(ImageResult, ImageResult, Value, Deviation)
elseif(Method Tenegrad)
*Tenegrad函数法sobel_amp (Image, EdgeAmplitude, sum_sqrt, 3)min_max_gray(EdgeAmplitude, EdgeAmplitude, 0, Min, Max, Range)threshold(EdgeAmplitude, Region1, 11.8, 255)region_to_bin(Region1, BinImage, 1, 0, Width, Height)mult_image(EdgeAmplitude, BinImage, ImageResult4, 1, 0)mult_image(ImageResult4, ImageResult4, ImageResult, 1, 0)intensity(ImageResult, ImageResult, Value, Deviation)elseif(Method 2)elseif(Method 3)endifreturn ()
scale_image_max(Image, Image) get_image_size(Image, Width, Height)
if(Method Deviation) *方差法 region_to_mean (Image, Image, ImageMean) convert_image_type (ImageMean, ImageMean, real) convert_image_type (Image, Image, real) sub_image(Image, ImageMean, ImageSub, 1, 0) mult_image(ImageSub, ImageSub, ImageResult, 1, 0) intensity(ImageResult, ImageResult, Value, Deviation) elseif(Method laplace) *拉普拉斯能量函数 laplace (Image, ImageLaplace4, signed, 3, n_4) laplace (Image, ImageLaplace8, signed, 3, n_8) add_image(ImageLaplace4,ImageLaplace4,ImageResult1, 1, 0) add_image(ImageLaplace4,ImageResult1,ImageResult1, 1, 0) add_image(ImageLaplace8,ImageResult1,ImageResult1, 1, 0) mult_image(ImageResult1, ImageResult1, ImageResult, 1, 0) intensity(ImageResult, ImageResult, Value, Deviation)
elseif(Method energy) *能量梯度函数 crop_part(Image, ImagePart00, 0, 0, Width-1, Height-1) crop_part(Image, ImagePart01, 0, 1, Width-1, Height-1) crop_part(Image, ImagePart10, 1, 0, Width-1, Height-1) convert_image_type (ImagePart00, ImagePart00, real) convert_image_type (ImagePart10, ImagePart10, real) convert_image_type (ImagePart01, ImagePart01, real) sub_image(ImagePart10, ImagePart00, ImageSub1, 1, 0) mult_image(ImageSub1, ImageSub1, ImageResult1, 1, 0) sub_image(ImagePart01, ImagePart00, ImageSub2, 1, 0) mult_image(ImageSub2, ImageSub2, ImageResult2, 1, 0) add_image(ImageResult1, ImageResult2, ImageResult, 1, 0) intensity(ImageResult, ImageResult, Value, Deviation) elseif(Method Brenner) *Brenner函数法 crop_part(Image, ImagePart00, 0, 0, Width, Height-2) convert_image_type (ImagePart00, ImagePart00, real) crop_part(Image, ImagePart20, 2, 0, Width, Height-2) convert_image_type (ImagePart20, ImagePart20, real) sub_image(ImagePart20, ImagePart00, ImageSub, 1, 0) mult_image(ImageSub, ImageSub, ImageResult, 1, 0) intensity(ImageResult, ImageResult, Value, Deviation) elseif(Method Tenegrad) *Tenegrad函数法 sobel_amp (Image, EdgeAmplitude, sum_sqrt, 3) min_max_gray(EdgeAmplitude, EdgeAmplitude, 0, Min, Max, Range) threshold(EdgeAmplitude, Region1, 11.8, 255) region_to_bin(Region1, BinImage, 1, 0, Width, Height) mult_image(EdgeAmplitude, BinImage, ImageResult4, 1, 0) mult_image(ImageResult4, ImageResult4, ImageResult, 1, 0) intensity(ImageResult, ImageResult, Value, Deviation) elseif(Method 2)
elseif(Method 3) endif return ()
