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Full Discussion: Duplication | awk | result
Top Forums UNIX for Beginners Questions & Answers Duplication | awk | result Post 303035781 by RudiC on Monday 3rd of June 2019 05:14:22 AM
Old 06-03-2019
Hmmm - I don't see any "ALA" in your recent sample data file - what be the desired result from it? I see one each of the 4 / ARG, 5 / SER, and 6 / ASP combinations...


Please be aware that everybody in here can only see (and work with) what you explicitly (!) write / post, don't assume ANY knowledge of the topic - genetics / biology assumed, in this case - that allows inference of non- given background info from allusions in the text. Describe the (data) problem as profoundly as possible, backed by representative, consistent, and as broad as possible input and desired output samples.
Don't "change horses" between posts - does your sample in post #9 lead to the "false" output in post #7? Or, where should the

Code:
8
8
8
9 ...

result come from?

Last edited by RudiC; 06-03-2019 at 06:27 AM..
 

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OPENCV_PERFORMANCE(1)						   User Commands					     OPENCV_PERFORMANCE(1)

NAME
opencv_performance - evaluate the performance of the classifier SYNOPSIS
opencv_performance [options] DESCRIPTION
opencv_performance evaluates the performance of the classifier. It takes a collection of marked up test images, applies the classifier and outputs the performance, i.e. number of found objects, number of missed objects, number of false alarms and other information. When there is no such collection available test samples may be created from single object image by the opencv_createsamples(1) utility. The scheme of test samples creation in this case is similar to training samples In the output, the table should be read: 'Hits' shows the number of correctly found objects 'Missed' shows the number of missed objects (must exist but are not found, also known as false negatives) 'False' shows the number of false alarms (must not exist but are found, also known as false positives) OPTIONS
opencv_performance supports the following options: -data classifier_directory_name The directory, in which the classifier can be found. -info collection_file_name File with test samples description. -maxSizeDiff max_size_difference Determine the size criterion of reference and detected coincidence. The default is 1.500000. -maxPosDiff max_position_difference Determine the position criterion of reference and detected coincidence. The default is 0.300000. -sf scale_factor Scale the detection window in each iteration. The default is 1.200000. -ni Don't save detection result to an image. This could be useful, if collection_file_name contains paths. -nos number_of_stages Number of stages to use. The default is -1 (all stages are used). -rs roc_size The default is 40. -h sample_height The sample height (must have the same value as used during creation). The default is 24. -w sample_width The sample width (must have the same value as used during creation). The default is 24. The same information is shown, if opencv_performance is called without any arguments/options. EXAMPLES
To create training samples from one image applying distortions and show the results: opencv_performance -data trainout -info tests.dat SEE ALSO
opencv_createsamples(1), opencv_haartraing(1) More information and examples can be found in the OpenCV documentation. AUTHORS
This manual page was written by Daniel Leidert <daniel.leidert@wgdd.de> and Nobuhiro Iwamatsu <iwamatsu@debian.org> for the Debian project (but may be used by others). OpenCV May 2010 OPENCV_PERFORMANCE(1)
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