def plikelihood(self):
(x, y), (h, w), a = self.det
det = np.floor(np.array([[x, y, h, w]]))
yrep = np.ones((self.num_p, 4)) * det
R2 = np.sum(np.power(self.p - yrep, 2), 1)
width = 2 * (np.amax(np.sqrt(R2)) - np.amin(np.sqrt(R2)))
prob = np.exp(-R2 / width)
prob = prob / np.sum(prob)
self.prob = prob
self.sortprob()
def plikelihood(self):
if self.update:
(x, y), (h, w), a = self.rb
det = np.floor(np.array([[x, y, h, w, x - self.p_star[0], y - self.p_star[1]]]))
yrep = np.ones((self.num_p, 6)) * det
R2 = np.sum(np.power(self.p[:, 0:6] - yrep, 2), 1)
width = 2 * (np.amax(np.sqrt(R2)) - np.amin(np.sqrt(R2)))
prob = np.exp(- R2 / width)
a = np.sum(prob)
if a != 0.0:
prob = prob / np.sum(prob)
self.prob = prob
self.sortprob()
self.calculatepstar()
def plikelihood(self):
if self.update:
(x, y), (h, w), a = self.rb
det = np.floor(
np.array([[x, y, h, w, x - self.p_star[0],
y - self.p_star[1]]]))
yrep = np.ones((self.num_p, 6)) * det
R2 = np.sum(np.power(self.p[:, 0:6] - yrep, 2), 1)
width = 2 * (np.amax(np.sqrt(R2)) - np.amin(np.sqrt(R2)))
prob = np.exp(-R2 / width)
a = np.sum(prob)
if a != 0.0:
prob = prob / np.sum(prob)
self.prob = prob
self.sortprob()
self.calculatepstar()
def plikelihood_new(self):
if self.update:
(x, y), (h, w), a = self.rb
detectionExtended = np.floor(np.array([[x, y, h, w, x - self.p_star[0], y - self.p_star[1]]]))
yrep = np.ones((self.num_p, 6)) * detectionExtended
prob = gaussMixModelPDF(oneParticle,detectionExtended,p_star)
R2 = np.sum(np.power(self.p[:, 0:6] - yrep, 2), 1)
width = 2 * (np.amax(np.sqrt(R2)) - np.amin(np.sqrt(R2)))
prob = np.exp(- R2 / width)
a = np.sum(prob)
if a != 0.0:
prob = prob / np.sum(prob)
self.prob = prob
self.sortprob()
self.calculatepstar()
def plikelihood_new(self):
if self.update:
(x, y), (h, w), a = self.rb
detectionExtended = np.floor(
np.array([[x, y, h, w, x - self.p_star[0],
y - self.p_star[1]]]))
yrep = np.ones((self.num_p, 6)) * detectionExtended
prob = gaussMixModelPDF(oneParticle, detectionExtended, p_star)
R2 = np.sum(np.power(self.p[:, 0:6] - yrep, 2), 1)
width = 2 * (np.amax(np.sqrt(R2)) - np.amin(np.sqrt(R2)))
prob = np.exp(-R2 / width)
a = np.sum(prob)
if a != 0.0:
prob = prob / np.sum(prob)
self.prob = prob
self.sortprob()
self.calculatepstar()
def normpdf(x, m, v):
return (1 / (np.sqrt(2 * np.pi) * v)) * np.exp(-(1./2) * np.power((x - m) / v, 2))
def normpdf(x, m, v):
return (1 / (np.sqrt(2 * np.pi) * v)) * np.exp(-(1. / 2) * np.power(
(x - m) / v, 2))
