def motionmodel(self):
p = self.p
y, x = p.shape
a0 = np.floor(np.random.uniform(-15, 15, [y, 1]))
a1 = np.floor(np.random.uniform(-15, 15, [y, 1]))
a2 = np.floor(np.random.uniform(-3, 3, [y, 1]))
a3 = np.floor(np.random.uniform(-3, 3, [y, 1]))
p[:, 0] = p[:, 0] + a0[:, 0]
p[:, 1] = p[:, 1] + a1[:, 0]
p[:, 2] = p[:, 2] + a2[:, 0]
p[:, 3] = p[:, 3] + a3[:, 0]
self.p = p
def init_p(self):
(x, y), (h, w), a = self.rb
self.p = np.floor(
np.random.multivariate_normal([x, y, h, w, 0, 0], self.cov,
self.num_p))
self.p_star = [x, y, h, w, 0, 0]
"""
def init_p(self):
(x, y), (h, w), a = self.det
self.p = np.floor(
np.random.multivariate_normal(
[x, y, h, w],
self.cov,
self.num_p
)
)
def init_p(self):
(x, y), (h, w), a = self.rb
self.p = np.floor(
np.random.multivariate_normal(
[x, y, h, w, 0, 0],
self.cov,
self.num_p
)
)
self.p_star = [x, y, h, w, 0, 0]
"""
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()
