def gup(im):
F = gskernel(3, 1)
# for i in F:
# print(i)
t = conv.Convolve(im, F) #高斯滤波
t = gdown(t) #下采样
return t[0:int(im.shape[0] / 2), 0:int(im.shape[1] / 2)]
def yanzheng(g, l):
huanyuan = []
for i in range(len(l)):
t = l[i] + conv.Convolve(imresize.imresize(g[i + 1], g[i].shape),
gskernel(3, 1), 3)
huanyuan.append(t)
listImShow(huanyuan)
return huanyuan
def lpyr(gsim):
lplcim = []
for i in range(len(gsim) - 1):
# t=gsim[i]-imresize.imresize(gsim[i+1],gsim[i].shape)
t = gsim[i] - conv.Convolve(
imresize.imresize(gsim[i + 1], gsim[i].shape), gskernel(3, 1), 3)
# tmin=t.min()
# t=t+abs(tmin)
# tmax=t.max()
# t=t/tmax*255.0
lplcim.append(t)
return lplcim
def set_muti_gpyr(self):
self.make_list_sigmond()
self.muti.append([])
self.muti[0].append(self.im)
for i in range(self.ceng):
if i != 0:
self.muti.append([])
self.muti[i].append(cv2.pyrDown(self.muti[i - 1][-3]))
for j in range(self.s):
self.muti[i].append(
conv.Convolve(
self.muti[i][0],
pyr.gskernel(15,
self.list_sigmond[self.ceng * i + j])))
def Gsup(im):
F = gskernel(3, 1)
t = conv.Convolve(im, F) #高斯滤波
t = gdown(t) #下采样
return t[0:int(im.shape[0] / 2), 0:int(im.shape[1] / 2)]
c = Theta * ((bins[index] < Theta) & (Theta < bins[index + 1]))
avg = c.sum() / hist[index]
return avg
t = np.zeros((16, 16))
for i in range(t.shape[0]):
for j in range(t.shape[1]):
t[i][j] = (8 - i)**2 + (8 - j)**2
imageA = cv2.imread("1.jpg").astype(np.uint8)
#imageA = cv2.imread("left_01.png")
imageA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)
t = imageA
conv.show(t)
t = conv.Convolve(t, pyr.gskernel(5, 10))
print(pyr.gskernel(5, 10))
print(cv2.getGaussianKernel(5, 10))
conv.show(t)
sigma = 1.6
m, theta = describe_M_Theta(t, sigma)
print(m.shape, theta.shape)
print(m)
print(theta)
avg = main_direction(theta)
print(avg)