convolve2d#
- scipy.signal.convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0)[source]#
- Convolve two 2-dimensional arrays. - Convolve in1 and in2 with output size determined by mode, and boundary conditions determined by boundary and fillvalue. - Parameters:
- in1array_like
- First input. 
- in2array_like
- Second input. Should have the same number of dimensions as in1. 
- modestr {‘full’, ‘valid’, ‘same’}, optional
- A string indicating the size of the output: - full
- The output is the full discrete linear convolution of the inputs. (Default) 
- valid
- The output consists only of those elements that do not rely on the zero-padding. In ‘valid’ mode, either in1 or in2 must be at least as large as the other in every dimension. 
- same
- The output is the same size as in1, centered with respect to the ‘full’ output. 
 
- boundarystr {‘fill’, ‘wrap’, ‘symm’}, optional
- A flag indicating how to handle boundaries: - fill
- pad input arrays with fillvalue. (default) 
- wrap
- circular boundary conditions. 
- symm
- symmetrical boundary conditions. 
 
- fillvaluescalar, optional
- Value to fill pad input arrays with. Default is 0. 
 
- Returns:
- outndarray
- A 2-dimensional array containing a subset of the discrete linear convolution of in1 with in2. 
 
 - Examples - Compute the gradient of an image by 2D convolution with a complex Scharr operator. (Horizontal operator is real, vertical is imaginary.) Use symmetric boundary condition to avoid creating edges at the image boundaries. - >>> import numpy as np >>> from scipy import signal >>> from scipy import datasets >>> ascent = datasets.ascent() >>> scharr = np.array([[ -3-3j, 0-10j, +3 -3j], ... [-10+0j, 0+ 0j, +10 +0j], ... [ -3+3j, 0+10j, +3 +3j]]) # Gx + j*Gy >>> grad = signal.convolve2d(ascent, scharr, boundary='symm', mode='same') - >>> import matplotlib.pyplot as plt >>> fig, (ax_orig, ax_mag, ax_ang) = plt.subplots(3, 1, figsize=(6, 15)) >>> ax_orig.imshow(ascent, cmap='gray') >>> ax_orig.set_title('Original') >>> ax_orig.set_axis_off() >>> ax_mag.imshow(np.absolute(grad), cmap='gray') >>> ax_mag.set_title('Gradient magnitude') >>> ax_mag.set_axis_off() >>> ax_ang.imshow(np.angle(grad), cmap='hsv') # hsv is cyclic, like angles >>> ax_ang.set_title('Gradient orientation') >>> ax_ang.set_axis_off() >>> fig.show() 