我试图将此代码应用于图像以提取图像特征（例如边缘），但是当我运行该代码时，输出没有出来，没有反应，我不知道为什么。我希望您能帮助我解决这个问题。这是PCNN（脉冲耦合神经网络）代码，我在github.com上找到了它，我将用它来检测图像边缘。如果您提供我，我想将PCNN的其他代码用于边缘检测和背景扣除。
感谢所有人。
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
import scipy.ndimage.filters as fi
from scipy.signal import fftconvolve as conv
from scipy.misc import imresize
import cv2
img=cv2.imread('1.jpg')
def gaussian_kernel(size=5, sigma=1.):
if size % 2 == 0:
size += 1
k = np.zeros((size,size))
k[size/2, size/2] = 1
k = fi.gaussian_filter(k, sigma)
return k
def preprocess(img, size):
'''Preprocess the input image. Resize and convert to greyscale.
'''
i = imresize(img, size, 'bicubic')
i = np.dot(i[...,:3], [0.3, 0.6, 0.1])
return i
class PulseNet(object):
'''A simple pulsing net where each each neuron pulses independently of all
others. When a nueron fires, the threshold gets reset to a default value
and then decays over time. In this model, the neurons connected to higher-
intensity inputs will fire more frequently. It's not a particularly useful
model, but serves as a good starting point.
'''
def __init__(self, w, h):
'''
'''
size = (w,h)
self.F = np.zeros(size)
self.Y = np.zeros(size)
self.T = np.zeros(size)
self.v = 500.
self.t = 0.7
def step(self, S):
'''
'''
F = S
Y = np.where(F > self.T, 1, 0)
T = np.where(Y==1, self.v, self.t*self.T)
self.F = F
self.Y = Y
self.T = T
return self.Y
class ICM(PulseNet):
'''Intersecting Cortical Model.
This is a simplified version of Eckhorn's original biologically derived
model, created specifically to be used in image processing tasks. It is
based on the following equations:
1) F_ij[n+1] = f*F_ij[n] + S_ij + W{Y}_ij
2) Y_ij[n+1] = 1 if F_ij[n+1] > T_ij[n] else 0
3) T_ij[n+1] = g*T_ij[n] + h*Y_ij[n+1]
S is the input image
F is the internal neuron state
Y is the neuron outputs
T is the state of the dynamic thresholds
0 < g < f < 1, scalers
h is a large scaler used to increase the dynamic threshold after firing
W{} describes the connections between the neurons
Curvature flow model of W:
4) W{A} = A' = [[F_2a'{M{A'}} + F_1a'{A'}] < 0.5]
5) A' = A + [F_1a{M{A}} > 0.5]
6) [F_1a{X}]_ij = X_ij if A_ij == 0 else 0
7) [F_2a{X}]_ij = X_ij if A_ij == 1 else 0
8) [X > d]_ij = 1 if X_ij >= d else 0
9) [X < d]_ij = 1 if X_ij <= d else 0
'''
def __init__(self, w, h, update='autowave'):
'''
'''
update_methods = {
'autowave': self._centripetal_autowave_update,
'smooth': self._smooth_kernel_update
}
if update not in update_methods:
raise Exception('{} is not a valid update method ({})'.format(
update, ','.join(update_methods)))
self.update = update_methods[update]
self.f = 0.5
self.g = 0.45
self.h = 150.
size = (h,w)
self.F = np.zeros(size)
self.Y = np.zeros(size)
self.T = np.ones(size) * self.h * 5
self.W = gaussian_kernel()
def step(self, S):
'''
'''
F = S + self.f*self.F + self.update()
Y = np.where(F > self.T, 1, 0)
T = self.g*self.T + self.h*Y
self.F = F
self.Y = Y
self.T = T
return self.Y
def _smooth_kernel_update(self):
return conv(self.Y, self.W, mode='same')
def _centripetal_autowave_update(self):
'''Curvature flow model of W:
4) W{A} = A' = [[F_2a'{M{A'}} + F_1a'{A'}] < 0.5]
5) A' = A + [F_1a{M{A}} > 0.5]
6) [F_1a{X}]_ij = X_ij if A_ij == 0 else 0
7) [F_2a{X}]_ij = X_ij if A_ij == 1 else 0
8) [X > d]_ij = 1 if X_ij >= d else 0
9) [X < d]_ij = 1 if X_ij <= d else 0
'''
M_Y = conv(self.Y, self.W, mode='same')
Y_p = self.Y + (np.where(self.Y==0, M_Y, 0) >= 0.5)
M_Yp = conv(Y_p, self.W, mode='same')
W = Y_p + ((np.where(Y_p==1, M_Yp, 0) + np.where(Y_p==0, Y_p, 0)) <= 0.5)
return W
class Simulator(object):
'''Convenience class for simulating models and viewing the results.
'''
def __init__(self):
pass
def simulate(self, input_img, model, steps=8):
'''
'''
input_img= cv2.imread('1.jpg')
for _ in range(steps):
res = model.step(input_img)
self.results.append(res)
def plot_results(self):
'''
'''
plt.subplot2grid((2,6), (0,0), rowspan=2, colspan=2)
plt.imshow(self.img, cmap='gray')
plt.xticks([])
plt.yticks([])
for n,i in enumerate(self.results):
plt.subplot2grid((2,6), (n/4, n%4 + 2))
plt.imshow(i)
plt.xticks([])
plt.yticks([])
plt.show()
cv2.imshow('witti',self.img)