about ML

[meghalithic]

Contours can be defined as the curves or outlines that represent the boundaries of objects or shapes within an image. These curves join all the continuous points (along the boundary) having the same color or intensity, highlighting the structural properties of objects and providing a way to extract and represent shape information from images.

The contours are used to define the mask

element is to state the condition of a pixel (i.e., 1 or 0; black or white) (see here). Morphological operations “probe” an image with a structuring element. This structuring element defines the neighborhood to be examined around each pixel. And based on the given operation and the size of the structuring element we are able to adjust our output image (from here). That is, we can ask for pixels that are similar to the one we are looking at.

The bbox is a rectangle around the mask d1 and d2 are automatically extracted using cv2 by creating an rectangle, or bbox xc, yc are centroids of the rectangle drawn using mask_util.toBbox(), where the bbox is around the mask

cX and cY are centroid of the mask (not the rectangle around the mask)

all outputs are floats

bbox always comes out as (top, left) and (bottom, right)

[meghalithic]

Getting start and end points of major axis in cv2 from mask: maybe something here

[meghalithic]

really, need to figure out how to eliminate images that we don't want.

1) any disconnected avicularia or operculum are removed 2) any autozooids with the wrong number of avicularia or operculum are removed 3) play around with boundaries of image to exclude zooids on the edge and therefore incomplete

[meghalithic]

Arthur said that the area of the autozooid includes the avicularia and operculum.