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 pmfrnd(self):
x = np.arange(self.num_p)
dist = np.cumsum(self.prob)
rndnum = np.random.rand()
k = np.sum(rndnum > dist)
return x[k]
def pmfrnd(self):
x = np.arange(self.num_p)
dist = np.cumsum(self.prob)
rndnum = np.random.rand()
k = np.sum(rndnum > dist)
return x[k]
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 resample(self):
if np.sum(self.prob) > 0:
p = self.p
y, x = p.shape
new_p = np.zeros((y, x))
for ii in range(len(self.p)):
idx = self.pmfrnd()
new_p[ii, :] = p[idx, :]
self.p = new_p
def resample(self):
if np.sum(self.prob) > 0:
p = self.p
y, x = p.shape
new_p = np.zeros((y, x))
for ii in range(len(self.p)):
idx = self.pmfrnd()
new_p[ii, :] = p[idx, :]
self.p = new_p
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 calculatepstar(self):
if np.sum(self.prob) != 0:
self.p_star = np.average(self.p, axis = 0, weights = self.prob)
else:
self.p_star = np.average(self.p, axis = 0)
def calculatepstar(self):
if np.sum(self.prob) != 0:
self.p_star = np.average(self.p, axis=0, weights=self.prob)
else:
self.p_star = np.average(self.p, axis=0)