Add pcv.create_labels function

[HaleySchuhl]

Describe your changes
Adding the pcv.create_labels function will serve a similar purpose to multi ROI use cases of pcv.roi_objects. Separates multiple entities and creates a mask with unique pixel intensity values for each of the unique entities. Can also handle seed scatter examples (assume perfect input mask where num(contours) = num(actual seeds) where you'd provide no ROIs.

Type of update
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• Bug fix
• New feature or feature enhancement
• Update to documentation
• Work in progress

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• PR functionality reviewed in a Jupyter Notebook
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[HaleySchuhl]

In order to call pcv.create_labels(mask=cleaned_mask) we'll have to set a default for rois=None . This case would assume one entity (e.g. seed) per contour and would be used in a seed scatter flatbed scan type design. Technically, would be no different functionality-wise to run pcv.create_labels(mask=cleaned_mask, rois=None, roi_type="partial) since we propose to have default values. Output is a grayscale mask with num + 1 unique pixel values.

If you have multiple rois, then call pcv.create_labels(mask=cleaned_mask, rois=grid_rois, roi_type="partial") . Again output is a grayscale mask with num + 1 unique pixel values.

Finally, in the case where you have just one entity/object of interest per image, utilize pcv.roi.filter. Output is a binary mask a.k.a. still num + 1 unique values. Theoretically, pcv.create_labels(mask=mask, rois=singular_roi, roi_type="partial") and pcv.roi.filter(mask=mask, roi=singular_roi, roi_type="partial") would produce equivalent results.