adjust tail-tracking for sparseness and loss of tracking
adjust tail-tracking for sparseness and loss of tracking

Several adjustments to take into account loss of tracking, meaning no
pixels were set in mask for some Y columns:

- Adjust binning/averaging denominators by count of non-zero values
- Don't draw zero values in debug map
- Add tail-length measure for number of tracked Y columns
- Add tail-blur condition when tracked length drops too far below initial average length at idle

These hacks take advantage of numpy's behavior for array divide-by-zero
to return zero rather than NaN nor exceptions.

The end result is better continuous detection of dramatic movements
where the tail tracking is temporarily lost due to motion blur and/or
extremely high curve angles. At these times, the calculated average
position is not meaningful.

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