Creates attributes used to adjust hand tracking resolution


## Description
no description
## Parameters

<table>
<thead>
	<tr>
		<th>Name</th>
		<th>Type</th>
		<th>Description</th>
	</tr>
</thead>
<tr>
	<td>selfieMode</td>
	<td><div class='bg-emerald-800 px-2 py-px text-white rounded-sm'>boolean</div></td>
	<td>selfieMode</td>
</tr>
<tr>
	<td>maxNumHands</td>
	<td><div class='bg-orange-800 px-2 py-px text-white rounded-sm'>integer</div></td>
	<td>Maximum number of hands to detect</td>
</tr>
<tr>
	<td>modelComplexity</td>
	<td><div class='bg-emerald-800 px-2 py-px text-white rounded-sm'>boolean</div></td>
	<td>Complexity of the hand landmark model: 0 or 1. Landmark accuracy as well as inference latency generally go up with the model complexity</td>
</tr>
<tr>
	<td>minDetectionConfidence</td>
	<td><div class='bg-yellow-800 px-2 py-px text-white rounded-sm'>float</div></td>
	<td>Minimum confidence value ([0.0, 1.0]) from the hand detection model for the detection to be considered successful</td>
</tr>
<tr>
	<td>minTrackingConfidence</td>
	<td><div class='bg-yellow-800 px-2 py-px text-white rounded-sm'>float</div></td>
	<td>Minimum confidence value ([0.0, 1.0]) from the landmark-tracking model for the hand landmarks to be considered tracked successfully, or otherwise hand detection will be invoked automatically on the next input image. Setting it to a higher value can increase robustness of the solution, at the expense of a higher latency. Ignored if static_image_mode is true, where hand detection simply runs on every image</td>
</tr>
</table>