def run(self):
if self.ui.DCTButton.isChecked():
DCT.DCT1(self.path())
if self.ui.DFTButton.isChecked():
DFT.DFT1(self.path())
if self.ui.zhifangButton.isChecked():
histogram_equalization.his_eq(self.path())
if self.ui.noseButton.isChecked():
nose.addnoise(self.path())
if self.ui.grayButton.isChecked():
gray.gray1(self.path())
if self.ui.buguize.isChecked():
buguize.buguize(self.path())
if self.ui.duishu.isChecked():
buguize.buguize(self.path())
if self.ui.gamma.isChecked():
gamma.gamma(self.path())
if self.ui.junzhi.isChecked():
junzhi.junzhi(self.path())
if self.ui.face.isChecked():
test_face3.face(self.path())
if self.ui.fenge.isChecked():
test_fenge.fenge(self.path())
if self.ui.kuang.isChecked():
test_kuang.kuang(self.path())
def u_prime(ux, uy, uz, v):
c = 299792458 #Define speed of light in m/s.
ux *= c
uy *= c
uz *= c
v *= c
ux_prime = (ux - v) / (1 - ux * v / c**2)
uy_prime = uy / (gamma(v) * (1 - ux * v / c**2))
uz_prime = uz / (gamma(v) * (1 - ux * v / c**2))
return ux_prime / c, uy_prime / c, uz_prime / c
def _EM_iterate_once(self, data):
N = len(data)
K = self.nclasses
gamma_K_N = numpy.empty((K, N))
sigma_matrix = numpy.zeros((K, self.dim, self.dim))
for k in range(K):
sigma_matrix[k] = numpy.diag(self.sigma[k])
for n in range(N):
gamma_K_N[:, n] = gamma(data[n], self.pi, self.mu, sigma_matrix)[1]
gamma_sum_K = numpy.sum(gamma_K_N, axis=1)
pi_new = 1. / N * gamma_sum_K
mu_new = numpy.dot(numpy.matrix(gamma_K_N), numpy.matrix(data))
for i in range(K):
mu_new[i, :] /= gamma_sum_K[i]
sigma_new = numpy.zeros((K, self.dim))
for i in range(K):
for n in range(N):
x_temp = numpy.matrix(data[n].reshape(self.dim,
1)) - mu_new[i, :]
sigma_new[i] += gamma_K_N[i][n] * numpy.diag(
x_temp * x_temp.transpose())
sigma_new[i] /= gamma_sum_K[i]
self.pi = pi_new
self.mu = mu_new
self.sigma = sigma_new
def to_led(path=None, n_led=50, n_ang=32):
img = mpimg.imread(path) # Levanta la imagen
wx, wy, dim = img.shape # Me da las dimensiones del frame en pixeles
center_x = int(
(wx - 1) / 2) # Calcula la cordenada x del centro de la imagen
center_y = int(
(wy - 1) / 2) # Calcula la cordenada y del centro de la imagen
rad = min(
center_x,
center_y) # Calcula el radio que barre la imagen dentro del frame
led = []
intensidad = [int(31 * i**2 / n_led**2) for i in range(n_led)]
for m in range(0, n_ang):
for n in range(0, n_led):
x = center_x + int(rad * (n + 1) / n_led * cos(m * 2 * pi / n_ang))
y = center_y + int(rad * (n + 1) / n_led * sin(m * 2 * pi / n_ang))
tupla = tuple(gamma(int(255 * g))
for g in img[x, y, 0:3]) + (intensidad[n], )
led.append(pack('BBBB', *tupla))
return b''.join(led)
def _compute_statistics(self, data):
ndata = len(data)
gamma_K_N = numpy.empty((self.nclasses, ndata))
sigma_matrix = numpy.zeros((self.nclasses, self.dim, self.dim))
for k in range(self.nclasses):
sigma_matrix[k] = numpy.diag(self.sigma[k])
for t in range(ndata):
gamma_K_N[:, t] = gamma(data[t], self.pi, self.mu, sigma_matrix)[1]
self.stat0 = numpy.sum(gamma_K_N, axis=1)
self.stat1 = numpy.array(numpy.matrix(gamma_K_N) * numpy.matrix(data))
self.stat2 = numpy.array(
numpy.matrix(gamma_K_N) *
numpy.matrix([[i * j for i, j in zip(*row)]
for row in zip(data, data)]))
from gamma import gamma
import numpy as np
from matplotlib import pyplot as plt
k = 4.551
gam = gamma(k/2, 0.00000001)
def chi2(x):
"""
Calcula la funcion Chi^2 usando la funcion gamma calculada arriba y k=4.551
Params:
:param x: el valor para el que se calculara chi
"""
denom = 2**(k/2) * gam
return x**((k/2-1)) * np.exp(-x/2) * (1/denom)
x=np.linspace(0,25, 100)
plt.plot(x, chi2(x))
plt.xlabel("x", fontsize=16)
plt.ylabel("$\chi ^2(x)$", fontsize=16)
plt.show()
imsave('I0.png', I0)
imsave('I1.png', I1)
# create D1
import dehaze1
I2 = imread("D1.png")
# create D2
import dehaze2
I3 = imread("D2.png")
# gamma
im = cv2.imread("D2.png")
G1 = gamma.gamma(im)
I4 = cv2.imread("G1.png")
#I0 = cv2.imread("I0.png")
imsave('I0/' + imgName + 'I0.png', I0)
imsave('I1/' + imgName + 'I1.png', I1)
imsave('I2/' + imgName + 'I2.png', I2)
imsave('I3/' + imgName + 'I3.png', I3)
imsave('I4/' + imgName + 'I4.png', G1)
#PSNR&MAE
print('PSNR I0-I2 :', cv2.PSNR(I0, I2))
print('PSNR I0-I3 :', cv2.PSNR(I0, I3))
print('PSNR I0-I4 :', cv2.PSNR(I0, I4))
#MAE