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How to speed up the retrieval of the corners of a mask
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$begingroup$
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]]
python performance
asked 7 mins 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]]
python performance
asked 7 mins ago
Seth KitchenSeth Kitchen
1937
$endgroup$
$begingroup$
Oversight on my part- thanks @200_success have updated
$endgroup$
– Seth Kitchen
49 secs 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]]
python performance
asked 7 mins 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]]
python performance
python performance
asked 7 mins ago
Seth KitchenSeth Kitchen
1937
asked 7 mins ago
Seth KitchenSeth Kitchen
1937
edited 1 min ago
Seth Kitchen
asked 7 mins ago
Seth KitchenSeth Kitchen
1937
asked 7 mins ago
Seth KitchenSeth Kitchen
1937
asked 7 mins ago
Seth KitchenSeth Kitchen
1937
1937
$begingroup$
Oversight on my part- thanks @200_success have updated
$endgroup$
– Seth Kitchen
49 secs ago
add a comment |
$begingroup$
Oversight on my part- thanks @200_success have updated
$endgroup$
– Seth Kitchen
49 secs ago
$begingroup$
Oversight on my part- thanks @200_success have updated
$endgroup$
– Seth Kitchen
49 secs ago
$begingroup$
Oversight on my part- thanks @200_success have updated
$endgroup$
– Seth Kitchen
49 secs ago
add a comment |
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$begingroup$
Oversight on my part- thanks @200_success have updated
$endgroup$
– Seth Kitchen
49 secs ago