h013_05_halcon_dff

1 © 2003-2009 MVTec Software GmbH Depth from Focus Depth From Focus: Idea n Within a scene object points have different distances to the camera n The camera has a limited depth of field n Depending on the distance and the focus different object points are displayed sharply in the image n Taking images with various object distances each object point can be displayed sharply in at least one image n By determining in which image an object point is projected sharply the distance can be determined Concept: Basic Process Steps Depth from focus Focus Series Depth image Sharp Image Operator: depth_from_focus Apply the depth from focus method Determine n Depth map, i.e., the distance for each pixel n Confidence of the distance value Usage n Use the focus series as input n Select the appropriate filter method n Get the depth information as an index for each pixel Parameters n MultiFocusImage: Focus series as multi channel image n Depth: Index image where the value for each pixel corresponds to the index of the image with the maximum sharpness n Confidence: Reliability of the depth measurement n Filter: Filter type to determine the sharpness n Selection: Handling of points with low confidence select_grayvalues_from_channels Reconstruction of a sharp image Determine n Select the sharpest gray value for each coordinate n Use the depth image as index table Usage n Use the focus series and the depth image as input n Reconstruct the focused image Parameters n MultiChannelImage: Focus series as multi channel image n IndexImage: Depth image used as index table n Selected: Reconstructed sharp image Rules to Take Images n Avoid overexposure n Use camera with a wide dynamic range n Use a camera with low noise n Use diffusive illumination n Avoid reflections n Cover the whole distance range n Use at least 10 images n The maximum number of images is limited by the camera noise n Do not move the camera or the object n The distance between the light source and the object should remain constant 2 Handling of Image Sequences Read a multichannel image in one step Names := [] for i := 1 to 10 by 1 Names := [Names,'dff/focus_'+(i$'.2')] endfor read_image (Image, Names) Handling of Image Sequences Combine all images grabbed to a multi channel image Handle the data as a sequence of images in an image tuple grab_image(MultiChannelImage , FGHandle) for i := 1 to 10 by 1 grab_image(Image, FGHandle) append_channel(MultiChannelImage, Image, TmpImage) copy_obj(TmpImage, MultiChannelImage, 1, 1) endfor grab_image(ImageTuple , FGHandle) for i := 1 to 10 by 1 grab_image(Image, FGHandle) concat_obj(ImageTuple, Image, TmpImage) copy_obj(TmpImage, ImageTuple, 1, -1) endfor channels_to_image(ImageTuple, MultiChannelImage) Handling of Image Sequences Select and display all single channels of a multi channel image count_channels(MultiChannelImage, Number) for i := 1 to Number by 1 access_channel (MultiChannelImage, Image, i) dev_display(Image,WindowHandle) endfor Aberration n When looking (perpendicular) on a planar surface one would expect that all pixels can be in focus at the same time n But this is not correct: Either the center or the outer part of the image will be completely in focus n This effect is called aberration n When measuring height information with depth from focus this kind of error causes defects n It is not difficult to determine the aberration n Based on this “calibration” a correct measurement can be applied Aberration Calibration Setup n A planar surface with a reasonable texture is used as the reference plane n The camera is mounted perpendicular to this surface n If needed, the angles can be determined by using the HALCON camera calibration n It is important that the same distance and camera setting are being used that will be applied during the application n Using depth from focus the “curvature of the surface”, i.e., the aberration are determined n The extracted reference surface is used to correct the later measurements n For further use it is recommended to store the reference image to file Aberration: Distance Reference n The aberration can approximated by a paraboloidal function n The approximation has the advantage of suppressing incorrect measurements n The parameters of the paraboloidal function can be used to generate a reference image n But subtracting the reference image from the depth measurement the error caused by the aberration can be corrected depth_from_focus (Image, Depth, Confidence, 'highpass', 'local') threshold (Confidence, HighConf, 30, 255) fit_surface_second_order (HighConf, Depth, 'tukey', 10, 2, Alpha, Beta, Gamma, Delta, Epsilon, Zeta) gen_image_surface_second_order (Aberration, 'real', Alpha, Beta, Gamma, Delta, Epsilon, Zeta, Height/2, Width/2, Width, Height) 3 Example: Aberration Volume Measurement n In contrast to stereo the height information is not calibrated n The values in the height image are the indexes of the input images n To measure a real world height or a volume, the distance in between these images must be known n In the most easy case all images are taken with the same movement z n So if two coordinates differ by an index values of n the real world distance will be z x n, with the unit of z n Besides this, the unit in x and y, i.e., the size of a pixel must be known n Make sure that all dimensions are given in the same unit n The volume can be determined with the operator area_center_gray n The resulting value must be multiplied by x, y, and z Volume Measurement Example: Tilted Metal Surface Outlier points in raw data Example: SMD Component Example: Defect on a Metal Trace