# Retrieval of the corners of a mask

I have trained a polygon detector neural network to recognize the mask of "quadrilateral" (the mask generates curvy lines so it's not exactly a quadrilateral). I would like to get the corners of the quadrilateral.

I believe the best approach is to get the points in the mask that are closest to the corners of the image. First question is, are these valid assumptions? Second question is, is this the best approach?

• Top-Left is minimum distance between (0,0) and mask.
• Top-Right is minimum distance between (width, 0) and mask.
• Bottom-Left is minimum distance between (0, height) and mask.
• Bottom-Right is minimum distance between (width, height) and mask.

The last question is, is my implementation slow? The Neural network generates the mask in .7 seconds, but it's taking my loop ~2 seconds to find the corners. Can this be sped up?

def predict(self,img):
image = img
height,width,channels=img.shape

# Detect objects
r = self.model.detect([image], verbose=0)[0]
x1=0
x2=0
x3=0
x4=0
y1=0
y2=0
y3=0
y4=0
minDistanceTopLeft=999999
minDistanceTopRight=999999
minDistanceBottomLeft=999999
minDistanceBottomRight=999999
xAverage=0.0
yAverage=0.0
distToTopLeft=(x-0)*(x-0)+(y-0)*(y-0)
if(distToTopLeft<minDistanceTopLeft):
minDistanceTopLeft=distToTopLeft
x1=x
y1=y
distToTopRight=(x-width)*(x-width)+(y-0)*(y-0)
if(distToTopRight<minDistanceTopRight):
minDistanceTopRight=distToTopRight
x2=x
y2=y
distToBottomLeft=(x-0)*(x-0)+(y-height)*(y-height)
if(distToBottomLeft<minDistanceBottomLeft):
minDistanceBottomLeft=distToBottomLeft
x4=x
y4=y
distToBottomRight=(x-width)*(x-width)+(y-height)*(y-height)
if(distToBottomRight<minDistanceBottomRight):
minDistanceBottomRight=distToBottomRight
x3=x
y3=y
toReturn=np.array([x1, y1, x2, y2, x3, y3, x4, y4, 1])
return [toReturn.tolist()]


Mask is a numpy array of booleans:

[[[False]
[False]
[False]
...
[False]
[False]
[False]]

[[False]
[False]
[False]
...
[False]
[False]
[False]]

[[False]
[False]
[False]
...
[False]
[False]
[False]]

...

[[False]
[False]
[False]
...
[False]
[False]
[False]]

[[False]
[False]
[False]
...
[False]
[False]
[False]]

[[False]
[False]
[False]
...
[False]
[False]
[False]]]

• Is my understanding correct, that you want to find an axis-aligned bounding box that encompasses all of the True values in mask? Commented Mar 5, 2019 at 0:00
• Could be, I guess I need to do more research into what axis-aligned mean - I don't know if its considered a bounding box since those are normally rectangles right? Mine can be a parallelogram. Thank you for this comment though, it has given me search terms I can look into. But yes True values in mask Commented Mar 5, 2019 at 0:03
• My mistake, then. It looks like you're looking for an arbitrary bounding quadrilateral (not necessarily a rectangle, and not axis-aligned). Commented Mar 5, 2019 at 0:07
• Please describe mask. Is it an image with all pixel set within a (convex?) quadrangle? Any restrictions on the quadrangle (square, rectangular, ...). Any restriction on the alignment? Please describe what you try to find. The corners of the input quadrangle? Commented Jan 5, 2020 at 15:19

assorted findings

• your code does not document what predict() accomplishes
• I don't even get how the name predict is telling/helpful
• your code documents neither the approach chosen nor alternatives disregarded
• comparing a cheaper monotone function of Euclidean distance: nice
• naming the variables without fussing that it's equivalent Euclidean at the end of the day rather than equal to or sum/Manhattan or max: nice, again
• camelCase is not pythonic
• initial value for minDistances should be height*height+width*width+1
• the approach visits each and every element of mask
• no mask[x].index(True) (careful with mask[x].reverse().index(True))
• the squares get computed time and again
looks especially off with x
• code for the four pairs looks repetitive
naming too, come to think of it
• the mask example is useless for showing one value, only

context provided is lacking: what is get the corners of [not-exactly-]quadrilateral?

alternative approaches to find closest to the [image] corners

• start from the middle
when you find an element set, you still have to inspect all the element closer to the corner, up to the corner itself
just complicates iteration
• proceed in order of increasing distance from the corners
+: you find elements set close to the corner early on
you don't need to look any further for that corner
-: even with at least one element set, there may be more than two visits on average (solitary True in one corner)