Does Python or any of its modules have an equivalent of MATLAB's conv2 function? More specifically, I'm interested in something that does the same computation as conv2(A, B, 'same')
in MATLAB.
While the other answers already mention scipy.signal.convolve2d
as an equivalent, i found that the results do differ when using mode='same'
.
While Matlab's conv2
results in artifacts on the bottom and right of an image, scipy.signal.convolve2d
has the same artifacts on the top and left of an image.
See these links for plots showing the behaviour (not enough reputation to post the images directly):
Upper left corner of convoluted Barbara
Lower right corner of convoluted Barbara
The following wrapper might not be very efficient, but solved the problem in my case by rotating both input arrays and the output array, each by 180 degrees:
import numpy as np
from scipy.signal import convolve2d
def conv2(x, y, mode='same'):
return np.rot90(convolve2d(np.rot90(x, 2), np.rot90(y, 2), mode=mode), 2)
看起来scipy.signal.convolve2d就是你要找的。
You must provide an offset for each non-singleton dimension to reproduce the results of Matlab's conv2. A simple implementation supporting the 'same' option, only, could be made like this
import numpy as np
from scipy.ndimage.filters import convolve
def conv2(x,y,mode='same'):
"""
Emulate the function conv2 from Mathworks.
Usage:
z = conv2(x,y,mode='same')
TODO:
- Support other modes than 'same' (see conv2.m)
"""
if not(mode == 'same'):
raise Exception("Mode not supported")
# Add singleton dimensions
if (len(x.shape) < len(y.shape)):
dim = x.shape
for i in range(len(x.shape),len(y.shape)):
dim = (1,) + dim
x = x.reshape(dim)
elif (len(y.shape) < len(x.shape)):
dim = y.shape
for i in range(len(y.shape),len(x.shape)):
dim = (1,) + dim
y = y.reshape(dim)
origin = ()
# Apparently, the origin must be set in a special way to reproduce
# the results of scipy.signal.convolve and Matlab
for i in range(len(x.shape)):
if ( (x.shape[i] - y.shape[i]) % 2 == 0 and
x.shape[i] > 1 and
y.shape[i] > 1):
origin = origin + (-1,)
else:
origin = origin + (0,)
z = convolve(x,y, mode='constant', origin=origin)
return z
scipy.ndimage.convolve
在 n 维中进行。
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