>Naomark detection

Output

Now that we explained how to get the detection results, we'll now explain how the result variable is organized.

• If no naomarks are detected, the variable is empty. More precisely, it is an array with zero element. (ie, printed as [ ] in python)
• If N naomarks are detected, then the variable structure consists of two fields:

  [ [ TimeStampField ] [ Mark_info_0 , Mark_info_1, . . . , Mark_info_N-1 ] ]

  with

  - TimeStampField = [ TimeStamp_seconds, Timestamp_microseconds ]. This field is the time stamp of the image that was used to perform the detection.

  - Mark_info = [ ShapeInfo, ExtraInfo ]. For each detected mark, we have one Mark_info field.

  ShapeInfo = [ 0, alpha, beta, sizeX, sizeY, heading]. alpha and beta represent the Naomark's location in terms of camera angles - sizeX and sizeY are the mark's size in camera angles - the heading angle describes how the Naomark is oriented about the vertical axis with regards to NAO's head.

  ExtraInfo = [ MarkID ] . Mark ID is the number written on the naomark and which corresponds to its pattern.

  The python example code below demonstrates how to handle the extractor ouput so as to get the interesting information.