depth_from_focusdepth_from_focusdepth_from_focusDepthFromFocusDepthFromFocus (Operator)

Name

depth_from_focusdepth_from_focusdepth_from_focusDepthFromFocusDepthFromFocus — Extract depth using mutiple focus levels.

Signature

Description

The operator depth_from_focusdepth_from_focusdepth_from_focusDepthFromFocusDepthFromFocus extracts the depth using a focus sequence. The images of the focus sequence have to passed as a multi channel image (MultiFocusImageMultiFocusImageMultiFocusImageMultiFocusImagemultiFocusImage). The depth for each pixel will be returned in DepthDepthDepthDepthdepth as the channel number. The parameter ConfidenceConfidenceConfidenceConfidenceconfidence returns a confidence value for each depth estimation: The larger this value, the higher the confidence of the depth estimation is.

depth_from_focusdepth_from_focusdepth_from_focusDepthFromFocusDepthFromFocus selects the pixels with the best focus of all focus levels. The method used to extract these pixels is specified by the parameters FilterFilterFilterFilterfilter and SelectionSelectionSelectionSelectionselection.

For the parameter FilterFilterFilterFilterfilter, you can choose between the values 'highpass'"highpass""highpass""highpass""highpass" and 'bandpass'"bandpass""bandpass""bandpass""bandpass". To determine the focus within the image a high- or a bandpass filter can be applied. The larger the filter response, the more in focus is the image at this location. Compared to the highpass filter, the bandpass filter suppresses high frequencies. This is useful in particular in images containing strong noise.

Optionally, you can smooth the filtered image using the mean filter by passing two additional integer values for the mask size in the parameter FilterFilterFilterFilterfilter (e.g. 'highpass, 7, 7'"highpass, 7, 7""highpass, 7, 7""highpass, 7, 7""highpass, 7, 7"). This blurs the in-focus region with neighboring pixels and thus allows to bridge small areas with no texture within the image. Note, however, that this smoothing does not suppress noise in the original image, since it is applied only after high- or bandpass filtering.

The parameter SelectionSelectionSelectionSelectionselection determines how the optimum focus level is selected. If you pass the value 'next_maximum'"next_maximum""next_maximum""next_maximum""next_maximum", the closest focus maximum in the neighborhood is used. In contrast, if you pass the value 'local'"local""local""local""local", the focus level is determined based on the focus values of all focus levels of the pixel. With 'next_maximum'"next_maximum""next_maximum""next_maximum""next_maximum", you typically achieve a slightly smoothed and more robust result.

This additional smoothing is useful if no telecentric lenses are used to take the input images. In this case, the position of an object will slightly shift within the sequence. By adding appropriate smoothing, this effect can be partially compensated.

Attention

If the filter mask for FilterFilterFilterFilterfilter is specified with even values, the routine uses the next larger odd values instead (this way the center of the filter mask is always explicitly determined).

Parallelization

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Automatically parallelized on tuple level.
• Automatically parallelized on internal data level.

Parameters

Example (C++)

compose3(Focus0,Focus1,Focus2,&MultiFocus);
depth_from_focus(MultiFocus,&Depth,&Confidence,'highpass','next_maximum');
mean_image(Depth,&Smooth,15,15);
select_grayvalues_from_channels(MultiChannel,Smooth,SharpImage);
threshold(Confidence,HighConfidence,10,255);
reduce_domain(SharpImage,HighConfidence,ConfidentSharp);

Possible Predecessors

compose2compose2compose2Compose2Compose2, compose3compose3compose3Compose3Compose3, compose4compose4compose4Compose4Compose4, add_channelsadd_channelsadd_channelsAddChannelsAddChannels, read_imageread_imageread_imageReadImageReadImage, read_sequenceread_sequenceread_sequenceReadSequenceReadSequence

Possible Successors

select_grayvalues_from_channelsselect_grayvalues_from_channelsselect_grayvalues_from_channelsSelectGrayvaluesFromChannelsSelectGrayvaluesFromChannels, mean_imagemean_imagemean_imageMeanImageMeanImage, binomial_filterbinomial_filterbinomial_filterBinomialFilterBinomialFilter, gauss_imagegauss_imagegauss_imageGaussImageGaussImage, thresholdthresholdthresholdThresholdThreshold

See also

count_channelscount_channelscount_channelsCountChannelsCountChannels

Module

3D Metrology