from __future__ import division
import os, sys, cv2, random, copy
import numpy as np
from math import *
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)),
"../"))
from ParticleFilter import particles
from dataRead import readMat, normalize
from config import data_config
from utils import motionFCN, gradient_1, gradient_2, resample2, resample1, gradient_1_bk, gradient_2_bk, average_gradient
from matplotlib import pyplot as plt
from Propagation import propagation
# prepare data & choose arbitary dimension
data_path = os.path.join(data_config['root_dir'], data_config['data_name'])
data = readMat(data_path)
# data = normalize(data)
# if data is canoe but not standard
data = data.transpose()
Dims, Length = data.shape
print(Dims, Length)
'''
use first several frames to generate the particles
assume does not know the future frames
'''
training_stage = [0, 50]
# dim = 7
dim = int(random.random() * Dims)
data_mean = np.mean(data[dim, :])
import os, sys, cv2, random, copy
import numpy as np
from math import *
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)),
"../"))
from ParticleFilter import particles
from dataRead import readMat, normalize
from config import data_config
from utils import motionFCN, gradient_1, gradient_2, resample2, resample1, gradient_1_bk, gradient_2_bk, average_gradient
from matplotlib import pyplot as plt
from Propagation import propagation
# prepare data & choose arbitary dimension
data_path = os.path.join(data_config['root_dir'], data_config['data_name'])
data = normalize(readMat(data_path))
# print(data.shape)
Dims, Length = data.shape
# dim = 7
dim = int(random.random() * Dims)
data_mean = np.mean(data[dim, :])
# highlight
pred_range = [64, 74]
# init 100 particles
N = 100
P = []
Estimation = []
from math import *
import random, os, sys
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)),
"../"))
from dataRead import readMat, normalize
from config import data_config
from utils import motionFCN, gradient_1, gradient_2, resample2, resample1
import numpy as np
import matplotlib.pyplot as plt
from ParticleFilter import particles
if __name__ == '__main__':
# prepare data & choose arbitary dimension
data_path = os.path.join(data_config['root_dir'], data_config['data_name'])
measurements = normalize(readMat(data_path))
print(measurements.shape)
Dims, Length = measurements.shape
# dim = 7
dim = int(random.random() * Dims)
data_mean = np.mean(measurements[dim, :])
# init 100 particles
N = 50
P = []
Estimation = []
for i in range(N):
x = particles()
x.set(random.gauss(data_mean, 0.2), 0.005, 5.0, 1)
