def roomTemperature():
exp = Experiment()
plotting = Plotter()
plotting.initialize(filt = "median, movingAverage", skip = 500)
dimension = 20
exp.run_experiments_multiple(RoomTemperature(), {},\
[VAR(),\
ECVARMAOGD(),\
OnlineWaveFilteringParameterFree(),\
OnlineWaveFilteringParameterFree(),\
OnlineWaveFilteringParameterFree()
],\
[{'p' : 8, 'dim': dimension},\
{'p' : 8, 'dim': dimension, 'lr': 1},\
{'max_k' : 10, 'action_dim': dimension, 'out_dim': dimension, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'max_k' : 30, 'action_dim': dimension, 'out_dim': dimension, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'max_k' : 50, 'action_dim': dimension, 'out_dim': dimension, 'opt': Hedge(), 'optForSubPredictors': FTRL()}
],\
['VAR_8',\
'ECVARMA_OGD16',\
'OnlineWaveFilteringParameterFree10',\
'OnlineWaveFilteringParameterFree30',\
'OnlineWaveFilteringParameterFree50'
],\
n_runs = 20,\
plotter = plotting,\
verbose = True,
action_generator = ProblemBasedAction(RoomTemperature()))
def sp500():
exp = Experiment()
plotting = Plotter()
plotting.initialize(filt = "movingAverage", skip = 100)
exp.run_experiments_multiple(SP500(), {},\
[ARMA(),\
ARIMA(),\
OnlineWaveFilteringParameterFree(),\
OnlineWaveFilteringParameterFree(),\
OnlineWaveFilteringParameterFree()
],\
[{'p' : 64, 'optimizer': RealOGD(hyperparameters={'lr':10.0})},\
{'p' : 16, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':10.0})},\
{'max_k' : 20, 'action_dim': 1, 'out_dim': 1, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'max_k' : 50, 'action_dim': 1, 'out_dim': 1, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'max_k' : 100, 'action_dim': 1, 'out_dim': 1, 'opt': Hedge(), 'optForSubPredictors': FTRL()}
],\
['ARMA_OGD',\
'ARIMA_OGD',\
'OnlineWaveFilteringParameterFree20',\
'OnlineWaveFilteringParameterFree50',\
'OnlineWaveFilteringParameterFree100'
],\
n_runs = 20,\
plotter = plotting,\
verbose = True,
action_generator = ProblemBasedAction(SP500()))
def artif_exp_4():
exp = Experiment()
plotting = Plotter()
plotting.initialize(yscale='log',
filt="median, movingAverage",
skip=100,
ylabel="Average Squared Error",
col=['c', 'r', 'g', 'orange', 'k'])
T = 1000
ag = RandomAction(mu=0, sigma=0.3)
exp.run_experiments_multiple(Setting4(), {"timesteps" : T},\
[OnlineWaveFilteringParameterFree(),\
OnlineWaveFiltering(),\
EMKalmanFilter(),\
SSIDKalmanFilter(),\
Consistency()],\
[{'timesteps': T, 'max_k' : 30, 'action_dim': 1, 'out_dim': 1, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'timesteps': T, 'k' : 10, 'lr': 1e-2, 'action_dim': 1, 'out_dim': 1, 'R_m': 3.0},\
{'timesteps': T, 'order': 2, 'data': 100, 'iter': 500},\
{'timesteps': T, 'order': 2, 'data': 100},\
{'timesteps': T, 'out_dim': 1}],\
['OnlineWaveFilteringParameterFree',\
'OnlineWaveFiltering',\
'EM',\
'4SID',\
'Consistency'],\
n_runs = 20,\
plotter = plotting,\
action_generator = ag,\
verbose = True)
def artif_exp_2():
exp = Experiment()
plotting = Plotter()
plotting.initialize(yscale='log',
filt="median, movingAverage",
skip=100,
ylabel="Average Squared Error",
col=['c', 'r', 'g', 'orange', 'k'])
A = np.array([[0.999, 0], [0, 0.5]])
B = np.array([[1.0], [1.0]])
C = np.array([[1.0, 1.0]])
D = np.array([[0.0]])
T = 1000
ag = RandomAction(mu=0, sigma=0.3)
exp.run_experiments_multiple(LDS(), {"timesteps" : T, 'action_dim': 1, 'hidden_dim': 2, 'out_dim': 1, 'partially_observable': True,\
'system_params': {'A': A, 'B' : B, 'C': C, 'D': D, 'noise_distribution': 'normal'}},\
[OnlineWaveFilteringParameterFree(),\
OnlineWaveFiltering(),\
EMKalmanFilter(),\
SSIDKalmanFilter(),\
Consistency()],\
[{'timesteps': T, 'max_k' : 30, 'action_dim': 1, 'out_dim': 1, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'timesteps': T, 'k' : 10, 'lr': 1e-3, 'action_dim': 1, 'out_dim': 1, 'R_m': 1.0},\
{'timesteps': T, 'order': 2, 'data': 100, 'iter': 500},\
{'timesteps': T, 'order': 2, 'data': 100},\
{'timesteps': T, 'out_dim': 1}],\
['OnlineWaveFilteringParameterFree',\
'OnlineWaveFiltering',\
'EM',\
'4SID',\
'Consistency'],\
n_runs = 20,\
plotter = plotting,\
action_generator = ag,\
verbose = True)
def artif_exp_5():
exp = Experiment()
plotting = Plotter()
plotting.initialize(yscale='log',
filt="median, movingAverage",
skip=100,
ylabel="Average Squared Error",
col=['c', 'r', 'g', 'k'])
A = np.diag([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
B = np.eye(10)
C = np.random.normal(size=(10, 10)) * 0.3
D = np.zeros((10, 10))
T = 1000
ag = BlockAction(prob_repeat=0.8, sigma=0.3)
exp.run_experiments_multiple(LDS(), {'action_dim': 10, 'hidden_dim': 10, 'out_dim': 10, 'partially_observable': True,\
'system_params': {'A': A, 'B' : B, 'C': C, 'D': D}},\
[OnlineWaveFilteringParameterFree(),\
OnlineWaveFiltering(),\
EMKalmanFilter(),\
Consistency()],\
[{'timesteps': T, 'max_k' : 30, 'action_dim': 10, 'out_dim': 10, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'timesteps': T, 'k' : 30, 'lr': 1e-4, 'action_dim': 10, 'out_dim': 10, 'R_m': 5},\
{'timesteps': T, 'order': 10, 'data': 100, 'iter': 500},\
{'timesteps': T, 'out_dim': 10}],\
['OnlineWaveFilteringParameterFree',\
'OnlineWaveFiltering',\
'EM',\
'Consistency'],\
action_generator = ag,\
n_runs = 20,\
plotter = plotting,\
verbose = True)
def trackBoth():
#with open('vid2/position_file.csv', mode='w') as file:
# writer = csv.writer(file, delimiter=',')
# writer.writerow(["count", "detectedX", "detectedY", "myX", "myY", "kalX", "kalY"])
# Camera frame
frame = None
count = 0
# >>>> Kalman Filter
stateSize = 4
measSize = 4
contrSize = 0
kf = OnlineWaveFilteringParameterFree()
kf.initialize({
'timesteps': 3000,
'max_k': 30,
'action_dim': 1 * measSize,
'out_dim': stateSize,
'opt': FTRL(),
'optForSubPredictors': FTRL()
})
state = np.zeros(stateSize) # [x,y,v_x,v_y,w,h]
meas = np.zeros(measSize) # [z_x,z_y,z_w,z_h]
lastMeas = meas
lastLastMeas = lastMeas
kfOld = KalmanFilter()
state2 = np.zeros(6)
H = np.eye(6)[[0, 1, 4, 5]]
# Process Noise Covariance Matrix Q
# [ Ex 0 0 0 0 0 ]
# [ 0 Ey 0 0 0 0 ]
# [ 0 0 Ev_x 0 0 0 ]
# [ 0 0 0 Ev_y 0 0 ]
# [ 0 0 0 0 Ew 0 ]
# [ 0 0 0 0 0 Eh ]
#cv::setIdentity(kf.processNoiseCov, cv::Scalar(1e-2))
processNoiseCov = np.zeros((6, 6))
processNoiseCov[0, 0] = 1e-2
processNoiseCov[1, 1] = 1e-2
processNoiseCov[2, 2] = 5.0
processNoiseCov[3, 3] = 5.0
processNoiseCov[4, 4] = 1e-2
processNoiseCov[5, 5] = 1e-2
# Measures Noise Covariance Matrix R
#cv2.setIdentity(kf.measurementNoiseCov, 1e-1) #TODO
# <<<< Kalman Filter
#kf.initialize({'h': state, 'A' : np.eye(6), 'B': np.zeros(6), 'C': np.eye(6), 'P': np.eye(6), 'Q': np.zeros(6), 'R': processNoiseCov})
kfOld.initialize({
'h': state2,
'A': np.eye(6),
'B': np.zeros(6),
'C': np.eye(6),
'D': np.zeros(6),
'P': np.eye(6),
'Q': processNoiseCov,
'R': np.eye(6)
})
# Camera Index
idx = -1
# Camera Capture
cap = cv2.VideoCapture(idx)
#succ = cap.open(idx)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1024)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 768)
print("\nHit 'q' to exit...\n")
ch = 0
ticks = 0
found = False
notFoundCount = 0
detectedX = 0
detectedY = 0
myX = 0
myY = 0
kalX = 0
kalY = 0
# >>>>> Main loop
while (ch != 'q' and ch != 'Q'):
precTick = ticks
ticks = cv2.getTickCount()
dT = (1.0 * (ticks - precTick)) / cv2.getTickFrequency()
# Frame acquisition
_, frame = cap.read()
#cv2.imshow("Original", frame)
res = frame.copy()
if (found):
#print(np.concatenate((meas, lastMeas)).shape)
#state = kf.predict(np.concatenate((meas, lastMeas, lastLastMeas)))
#state = kf.predict(np.concatenate((meas, lastMeas)))
state = kf.predict(meas)
print("State post: " + str(state))
rectWidth = int(state[2])
rectHeight = int(state[3])
rectX = int(state[0] - rectWidth / 2.0)
rectY = int(state[1] - rectHeight / 2.0)
centerX = int(state[0])
centerY = int(state[1])
cv2.circle(res, (centerX, centerY), 2, (0, 0, 255))
upper = (rectX, rectY)
lower = (rectX + rectWidth, rectY + rectHeight)
myX = centerX
myY = centerY
try:
cv2.rectangle(res, upper, lower, (0, 0, 255), 2)
cv2.putText(res,
"(" + str(centerX) + ", " + str(centerY) + ")",
(centerX, centerY), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 255))
except TypeError as e:
print(str(e))
#Kalman
# >>>> Matrix A
kfOld.A[0, 2] = dT
kfOld.A[1, 3] = dT
# <<<< Matrix A
print("dT: " + str(dT))
state2 = kfOld.predict(0)
print("State post: " + str(state2))
rectWidth = int(state2[4])
rectHeight = int(state2[5])
rectX = int(state2[0] - rectWidth // 2.0)
rectY = int(state2[1] - rectHeight // 2.0)
centerX = int(state2[0])
centerY = int(state2[1])
kalX = centerX
kalY = centerY
cv2.circle(res, (centerX, centerY), 2, (255, 0, 0))
cv2.rectangle(res, (rectX, rectY),
(rectX + rectWidth, rectY + rectHeight), (255, 0, 0),
2)
cv2.putText(res, "(" + str(centerX) + ", " + str(centerY) + ")",
(centerX, centerY), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 0, 0))
else:
#print(np.concatenate((meas, lastMeas)).shape)
#state = kf.predict(np.concatenate((meas, lastMeas, lastLastMeas)))
#state = kf.predict(np.concatenate((meas, lastMeas)))
state = kf.predict(meas)
lastLastMeas = lastMeas
lastMeas = meas
meas = state
print("State post: " + str(state))
rectWidth = int(state[2])
rectHeight = int(state[3])
rectX = int(state[0] - rectWidth / 2.0)
rectY = int(state[1] - rectHeight / 2.0)
centerX = int(state[0])
centerY = int(state[1])
cv2.circle(res, (centerX, centerY), 2, (0, 0, 255))
myX = centerX
myY = centerY
upper = (rectX, rectY)
lower = (rectX + rectWidth, rectY + rectHeight)
try:
cv2.rectangle(res, upper, lower, (0, 0, 255), 2)
cv2.putText(res,
"(" + str(centerX) + ", " + str(centerY) + ")",
(centerX, centerY), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 255))
except TypeError as e:
print(str(e))
# >>>>> Noise smoothing
blur = cv2.medianBlur(frame,
5) #cv2.GaussianBlur(frame, (5, 5), 3.0, 3.0)
# <<<<< Noise smoothing
# >>>>> HSV conversion
frmHsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)
# <<<<< HSV conversion
# >>>>> Color Thresholding
# Note: change parameters for different colors
rangeRes = cv2.inRange(frmHsv, (MIN_H_BLUE / 2, 100, 80),
(MAX_H_BLUE / 2, 255, 255))
# <<<<< Color Thresholding
# >>>>> Improving the result
#TODO I removed something here
rangeRes = cv2.erode(rangeRes, np.eye(1), iterations=5)
rangeRes = cv2.dilate(rangeRes, np.eye(1), iterations=5)
# <<<<< Improving the result
# Thresholding viewing
cv2.imshow("Threshold", rangeRes)
# >>>>> Contours detection
contours, hierarchy = cv2.findContours(rangeRes, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
# <<<<< Contours detection
# >>>>> Filtering
balls = []
ballsBox = []
for i in range(len(contours)):
bBox = cv2.boundingRect(contours[i])
ratio = (1.0 * bBox[2]) / bBox[3]
if (ratio > 1.0):
ratio = 1.0 / ratio
# Searching for a bBox almost square
if (ratio > 0.75 and bBox[2] * bBox[3] >= 400):
balls.append(contours[i])
ballsBox.append(bBox)
# <<<<< Filtering
print("Balls found: " + str(len(ballsBox)))
#assume: (x, y, width, height)
# >>>>> Detection result
for i in range(len(balls)):
cv2.drawContours(res, balls, i, (20, 150, 20), 1)
cv2.rectangle(res, ballsBox[i], (0, 255, 0), 2)
centerX = int(ballsBox[i][0] + ballsBox[i][2] // 2)
centerY = int(ballsBox[i][1] + ballsBox[i][3] // 2)
cv2.circle(res, (centerX, centerY), 2, (20, 150, 20))
cv2.putText(res, "(" + str(centerX) + ", " + str(centerY) + ")",
(centerX, centerY), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 255, 0))
detectedX = centerX
detectedY = centerY
#stringstream sstr
#sstr << "(" << center.x << "," << center.y << ")"
#cv::putText(res, sstr.str(),
# cv::Point(center.x + 3, center.y - 3),
# cv::FONT_HERSHEY_SIMPLEX, 0.5, (20,150,20), 2)
# <<<<< Detection result
# >>>>> Kalman Update
if (len(balls) == 0):
notFoundCount += 1
print("notFoundCount: " + str(notFoundCount))
if (notFoundCount >= 100):
found = False
else:
notFoundCount = 0
lastLastMeas = lastMeas
lastMeas = meas
meas[0] = ballsBox[0][0] + ballsBox[0][2] / 2
meas[1] = ballsBox[0][1] + ballsBox[0][3] / 2
meas[2] = ballsBox[0][2]
meas[3] = ballsBox[0][3]
if (not found): # First detection!
# >>>> Initialization
state2[0] = meas[0]
state2[1] = meas[1]
state2[2] = 0
state2[3] = 0
state2[4] = meas[2]
state2[5] = meas[3]
# <<<< Initialization
kfOld.h = state2
kfOld.P = np.eye(6)
state = meas
found = True
else:
up = np.array([meas[0], meas[1], 0.0, 0.0, meas[2], meas[3]])
#kf.update_parameters(meas) # Correct
kfOld.update_parameters(up)
kf.update_parameters(meas) # Correct
print("Measure matrix: " + str(meas))
# Final result
cv2.imshow("Tracking", res)
#cv2.imwrite("vid2/frame%d.jpg" % count, res)
#with open('vid2/position_file.csv', mode='a') as file:
# writer = csv.writer(file, delimiter=',')
# writer.writerow([count, detectedX, detectedY, myX, myY, kalX, kalY])
count += 1
# User key
ch = cv2.waitKey(1)
def track():
# Camera frame
frame = None
# >>>> Kalman Filter
stateSize = 4
measSize = 4
contrSize = 0
kf = OnlineWaveFilteringParameterFree()
kf.initialize({
'timesteps': 20000,
'max_k': 30,
'action_dim': 2 * measSize,
'out_dim': stateSize,
'opt': FTRL(),
'optForSubPredictors': FTRL()
})
state = np.zeros(stateSize) # [x,y,v_x,v_y,w,h]
meas = np.zeros(measSize) # [z_x,z_y,z_w,z_h]
lastMeas = meas
lastLastMeas = lastMeas
# Camera Index
idx = -1
# Camera Capture
cap = cv2.VideoCapture(idx)
#succ = cap.open(idx)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1024)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 768)
print("\nHit 'q' to exit...\n")
ch = 0
ticks = 0
found = False
notFoundCount = 0
# >>>>> Main loop
while (ch != 'q' and ch != 'Q'):
precTick = ticks
ticks = cv2.getTickCount()
dT = (1.0 * (ticks - precTick)) / cv2.getTickFrequency()
# Frame acquisition
_, frame = cap.read()
#cv2.imshow("Original", frame)
res = frame.copy()
if (found):
#print(np.concatenate((meas, lastMeas)).shape)
#state = kf.predict(np.concatenate((meas, lastMeas, lastLastMeas)))
state = kf.predict(np.concatenate((meas, lastMeas)))
#state = kf.predict(meas)
print("State post: " + str(state))
rectWidth = int(state[2])
rectHeight = int(state[3])
rectX = int(state[0] - rectWidth / 2.0)
rectY = int(state[1] - rectHeight / 2.0)
centerX = int(state[0])
centerY = int(state[1])
cv2.circle(res, (centerX, centerY), 2, (0, 0, 255))
upper = (rectX, rectY)
lower = (rectX + rectWidth, rectY + rectHeight)
try:
cv2.rectangle(res, upper, lower, (0, 0, 255), 2)
cv2.putText(res,
"(" + str(centerX) + ", " + str(centerY) + ")",
(centerX, centerY), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 255))
except TypeError as e:
print(str(e))
else:
#print(np.concatenate((meas, lastMeas)).shape)
#state = kf.predict(np.concatenate((meas, lastMeas, lastLastMeas)))
state = kf.predict(np.concatenate((meas, lastMeas)))
#state = kf.predict(meas)
lastLastMeas = lastMeas
lastMeas = meas
meas = state
print("State post: " + str(state))
rectWidth = int(state[2])
rectHeight = int(state[3])
rectX = int(state[0] - rectWidth / 2.0)
rectY = int(state[1] - rectHeight / 2.0)
centerX = int(state[0])
centerY = int(state[1])
cv2.circle(res, (centerX, centerY), 2, (0, 0, 255))
upper = (rectX, rectY)
lower = (rectX + rectWidth, rectY + rectHeight)
try:
cv2.rectangle(res, upper, lower, (0, 0, 255), 2)
cv2.putText(res,
"(" + str(centerX) + ", " + str(centerY) + ")",
(centerX, centerY), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 255))
except TypeError as e:
print(str(e))
# >>>>> Noise smoothing
blur = cv2.medianBlur(frame,
5) #cv2.GaussianBlur(frame, (5, 5), 3.0, 3.0)
# <<<<< Noise smoothing
# >>>>> HSV conversion
frmHsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)
# <<<<< HSV conversion
# >>>>> Color Thresholding
# Note: change parameters for different colors
rangeRes = cv2.inRange(frmHsv, (MIN_H_BLUE / 2, 100, 80),
(MAX_H_BLUE / 2, 255, 255))
# <<<<< Color Thresholding
# >>>>> Improving the result
#TODO I removed something here
rangeRes = cv2.erode(rangeRes, np.eye(1), iterations=5)
rangeRes = cv2.dilate(rangeRes, np.eye(1), iterations=5)
# <<<<< Improving the result
# Thresholding viewing
cv2.imshow("Threshold", rangeRes)
# >>>>> Contours detection
contours, hierarchy = cv2.findContours(rangeRes, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
# <<<<< Contours detection
# >>>>> Filtering
balls = []
ballsBox = []
for i in range(len(contours)):
bBox = cv2.boundingRect(contours[i])
ratio = (1.0 * bBox[2]) / bBox[3]
if (ratio > 1.0):
ratio = 1.0 / ratio
# Searching for a bBox almost square
if (ratio > 0.75 and bBox[2] * bBox[3] >= 400):
balls.append(contours[i])
ballsBox.append(bBox)
# <<<<< Filtering
print("Balls found: " + str(len(ballsBox)))
#assume: (x, y, width, height)
# >>>>> Detection result
for i in range(len(balls)):
cv2.drawContours(res, balls, i, (20, 150, 20), 1)
cv2.rectangle(res, ballsBox[i], (0, 255, 0), 2)
centerX = int(ballsBox[i][0] + ballsBox[i][2] // 2)
centerY = int(ballsBox[i][1] + ballsBox[i][3] // 2)
cv2.circle(res, (centerX, centerY), 2, (20, 150, 20))
#stringstream sstr
#sstr << "(" << center.x << "," << center.y << ")"
#cv::putText(res, sstr.str(),
# cv::Point(center.x + 3, center.y - 3),
# cv::FONT_HERSHEY_SIMPLEX, 0.5, (20,150,20), 2)
# <<<<< Detection result
# >>>>> Kalman Update
if (len(balls) == 0):
notFoundCount += 1
print("notFoundCount: " + str(notFoundCount))
if (notFoundCount >= 100):
found = False
else:
notFoundCount = 0
lastLastMeas = lastMeas
lastMeas = meas
meas[0] = ballsBox[0][0] + ballsBox[0][2] / 2
meas[1] = ballsBox[0][1] + ballsBox[0][3] / 2
meas[2] = ballsBox[0][2]
meas[3] = ballsBox[0][3]
if (not found): # First detection!
state = meas
found = True
else:
kf.update_parameters(meas) # Correct
print("Measure matrix: " + str(meas))
# Final result
cv2.imshow("Tracking", res)
# User key
ch = cv2.waitKey(1)