How to speed up the retrieval of the corners of a mask
$begingroup$
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 my implementation is 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):
# Read image
image = img
height,width,channels=img.shape
# Detect objects
r = self.model.detect([image], verbose=0)[0]
mask=r['masks']
print(mask)
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
for x in range(0, len(mask)):
for y in range(0, len(mask[x])):
if(mask[x][y]):
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 (ie)
[[[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]]]
python performance algorithm image numpy
asked 2 hours ago
Seth KitchenSeth Kitchen
1937
$endgroup$
add a comment |
$begingroup$
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 my implementation is 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):
# Read image
image = img
height,width,channels=img.shape
# Detect objects
r = self.model.detect([image], verbose=0)[0]
mask=r['masks']
print(mask)
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
for x in range(0, len(mask)):
for y in range(0, len(mask[x])):
if(mask[x][y]):
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 (ie)
[[[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]]]
python performance algorithm image numpy
asked 2 hours ago
Seth KitchenSeth Kitchen
1937
$endgroup$
$begingroup$
Is my understanding correct, that you want to find an axis-aligned bounding box that encompasses all of theTruevalues inmask?
$endgroup$
– 200_success
2 hours ago
$begingroup$
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
$endgroup$
– Seth Kitchen
2 hours ago
1
$begingroup$
My mistake, then. It looks like you're looking for an arbitrary bounding quadrilateral (not necessarily a rectangle, and not axis-aligned).
$endgroup$
– 200_success
2 hours ago
add a comment |
$begingroup$
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 my implementation is 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):
# Read image
image = img
height,width,channels=img.shape
# Detect objects
r = self.model.detect([image], verbose=0)[0]
mask=r['masks']
print(mask)
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
for x in range(0, len(mask)):
for y in range(0, len(mask[x])):
if(mask[x][y]):
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 (ie)
[[[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]]]
python performance algorithm image numpy
asked 2 hours ago
Seth KitchenSeth Kitchen
1937
$endgroup$
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 my implementation is 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):
# Read image
image = img
height,width,channels=img.shape
# Detect objects
r = self.model.detect([image], verbose=0)[0]
mask=r['masks']
print(mask)
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
for x in range(0, len(mask)):
for y in range(0, len(mask[x])):
if(mask[x][y]):
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 (ie)
[[[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]]]
python performance algorithm image numpy
python performance algorithm image numpy
asked 2 hours ago
Seth KitchenSeth Kitchen
1937
asked 2 hours ago
Seth KitchenSeth Kitchen
1937
edited 2 hours ago
Seth Kitchen
asked 2 hours ago
Seth KitchenSeth Kitchen
1937
asked 2 hours ago
Seth KitchenSeth Kitchen
1937
asked 2 hours ago
Seth KitchenSeth Kitchen
1937
1937
$begingroup$
Is my understanding correct, that you want to find an axis-aligned bounding box that encompasses all of theTruevalues inmask?
$endgroup$
– 200_success
2 hours ago
$begingroup$
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
$endgroup$
– Seth Kitchen
2 hours ago
1
$begingroup$
My mistake, then. It looks like you're looking for an arbitrary bounding quadrilateral (not necessarily a rectangle, and not axis-aligned).
$endgroup$
– 200_success
2 hours ago
add a comment |
$begingroup$
Is my understanding correct, that you want to find an axis-aligned bounding box that encompasses all of theTruevalues inmask?
$endgroup$
– 200_success
2 hours ago
$begingroup$
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
$endgroup$
– Seth Kitchen
2 hours ago
1
$begingroup$
My mistake, then. It looks like you're looking for an arbitrary bounding quadrilateral (not necessarily a rectangle, and not axis-aligned).
$endgroup$
– 200_success
2 hours ago
$begingroup$
Is my understanding correct, that you want to find an axis-aligned bounding box that encompasses all of the
True values in mask?$endgroup$
– 200_success
2 hours ago
$begingroup$
Is my understanding correct, that you want to find an axis-aligned bounding box that encompasses all of the
True values in mask?$endgroup$
– 200_success
2 hours ago
$begingroup$
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
$endgroup$
– Seth Kitchen
2 hours ago
$begingroup$
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
$endgroup$
– Seth Kitchen
2 hours ago
1
1
$begingroup$
My mistake, then. It looks like you're looking for an arbitrary bounding quadrilateral (not necessarily a rectangle, and not axis-aligned).
$endgroup$
– 200_success
2 hours ago
$begingroup$
My mistake, then. It looks like you're looking for an arbitrary bounding quadrilateral (not necessarily a rectangle, and not axis-aligned).
$endgroup$
– 200_success
2 hours ago
add a comment |
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$begingroup$
Is my understanding correct, that you want to find an axis-aligned bounding box that encompasses all of the
Truevalues inmask?$endgroup$
– 200_success
2 hours ago
$begingroup$
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
$endgroup$
– Seth Kitchen
2 hours ago
1
$begingroup$
My mistake, then. It looks like you're looking for an arbitrary bounding quadrilateral (not necessarily a rectangle, and not axis-aligned).
$endgroup$
– 200_success
2 hours ago