I have a 3-d Numpy array flow
as follows:
flow = np.random.uniform(low=-1.0, high=1.0, size=(720,1280,2))
# Suppose flow[0] are x-coordinates. flow[1] are y-coordinates.
Need to calculate the angle for each x,y point. Here is how I have implemented it:
def calcAngle(a):
assert(len(a) == 2)
(x, y) = a
# angle_deg = 0
angle_deg = np.angle(x + y * 1j, deg=True)
return angle_deg
fangle = np.apply_along_axis(calcAngle, axis=2, arr=flow)
# The above statement takes 14.0389318466 to execute
The calculation of angle at each point takes 14.0389318466 seconds
to execute on my Macbook Pro.
Is there a way I could speed this up, probably by using some matrix operation, rather than processing each pixel one at a time.
numpy.angle
supports vectorized operation. So, just feed in the first and second column slices to it for the final output, like so -
fangle = np.angle(flow[...,0] + flow[...,1] * 1j, deg=True)
Verification -
In [9]: flow = np.random.uniform(low=-1.0, high=1.0, size=(720,1280,2))
In [17]: out1 = np.apply_along_axis(calcAngle, axis=2, arr=flow)
In [18]: out2 = np.angle(flow[...,0] + flow[...,1] * 1j, deg=True)
In [19]: np.allclose(out1, out2)
Out[19]: True
Runtime test -
In [10]: %timeit np.apply_along_axis(calcAngle, axis=2, arr=flow)
1 loop, best of 3: 8.27 s per loop
In [11]: %timeit np.angle(flow[...,0] + flow[...,1] * 1j, deg=True)
10 loops, best of 3: 47.6 ms per loop
In [12]: 8270/47.6
Out[12]: 173.73949579831933
173x+
speedup!
You can use numpy.arctan2()
to get the angle in radians, and then convert to degrees with numpy.rad2deg()
:
fangle = np.rad2deg(np.arctan2(flow[:,:,1], flow[:,:,0]))
On my computer, this is a little faster than Divakar's version:
In [17]: %timeit np.angle(flow[...,0] + flow[...,1] * 1j, deg=True)
10 loops, best of 3: 44.5 ms per loop
In [18]: %timeit np.rad2deg(np.arctan2(flow[:,:,1], flow[:,:,0]))
10 loops, best of 3: 35.4 ms per loop
A more efficient way to use np.angle()
is to create a complex view of flow
. If flow
is an array of type np.float64
with shape (m, n, 2)
, then flow.view(np.complex128)[:,:,0]
will be an array of type np.complex128
with shape (m, n)
:
fangle = np.angle(flow.view(np.complex128)[:,:,0], deg=True)
This appears to be a smidge faster than using arctan2
followed by rad2deg
(but the difference is not far above the measurement noise of timeit
):
In [47]: %timeit np.angle(flow.view(np.complex128)[:,:,0], deg=True)
10 loops, best of 3: 35 ms per loop
Note that this might not work if flow
was creating as the tranpose of some other array, or as a slice of another array using steps bigger than 1.
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.