#Output
img_out = './synthetictests/' + name + '/' + ff + '_' + notes + '_pred/'
if not os.path.isdir(img_out):
os.makedirs(img_out)
gridsize = 18
threshold = 8
#Create KF
print 'Loading synthetic data streams'
capture = VideoStream(v_in, threshold)
frame = capture.current_frame()
mask, ctrs, fd = capture.backsub()
distmesh = DistMesh(frame, h0 = gridsize)
distmesh.load(dm_in)
#Load true data
f_mesh = open(m_in, 'r')
lines = f_mesh.readlines()
nF = len(lines)-1
nX = int(lines[0].split(',')[1])
truestates = np.zeros((nF, nX*4), dtype = np.float32)
predstates = np.zeros((nF, nX*4), dtype = np.float32)
for i in range(1,nF+1):
line = lines[i]
truestates[i-1,:] = [float(x) for x in line.split(',')[1:]]
predstates[0,:] = truestates[0,:]
rms_vel = np.zeros(nF)
refframe = frame
vid[:, :, idx] = grayframe
masks[:, :, idx] = mask
tracking_mask = cv2.imread(mask_in)
tracking_mask = cv2.cvtColor(tracking_mask, cv2.COLOR_BGR2GRAY)
(mask, ctrs, fd) = findObjectThreshold(tracking_mask, threshold=threshold)
#distmesh = DistMesh(refframe, h0 = gridsize)
#distmesh.createMesh(ctrs, fd, refframe, plot = True)
#Save this distmesh and reload it for quicker testing
#distmesh.save(mfsf_in + dm_out)
distmesh = DistMesh(refframe, h0=gridsize)
distmesh.load(mfsf_in + dm_out)
refpositions = distmesh.p
#Create dummy input for flow frame
flowframe = np.zeros((nx, nx, 2))
#Create Kalman Filter object to store mesh and make use of plotting functions
kf = IteratedMSKalmanFilter(distmesh, refframe, flowframe, cuda=cuda)
#Perturb mesh points according to MFSF flow field and save screenshot output
nF = u.shape[2]
N = kf.size() / 4
#Load estimated neuron tracks
truepositions = np.zeros((nF, nC, 2))
ret, frame, mask = capture.read() if idx == nref: refframe = frame.copy() vid[:,:,idx] = frame masks[:,:,idx] = mask #Generate ctrs and fd (mask, ctrs, fd) = findObjectThreshold(tracking_mask, threshold = threshold) #distmesh = DistMesh(refframe, h0 = gridsize) #distmesh.createMesh(ctrs, fd, refframe, plot = True) #Save this distmesh and reload it for quicker testing #distmesh.save(dm_out) distmesh = DistMesh(refframe, h0 = gridsize) distmesh.load(dm_out) refpositions = distmesh.p #Create dummy input for flow frame flowframe = np.zeros((nx, nx, 2)) #Create Kalman Filter object to store mesh and make use of plotting functions kf = IteratedMSKalmanFilter(distmesh, refframe, flowframe, cuda = cuda) N = kf.size()/4 totalframes = 0 for vidx in range(nV): #Load MFSF data a = loadmat(mfsf_matfiles[vidx]) params = a['parmsOF']
def runIEKF(eps_Z, eps_J, eps_M, eps_F):
print 'Running with:'
print '- eps_Z:', eps_Z
print '- eps_J:', eps_J
print '- eps_M:', eps_M
print '- eps_F:', eps_F
if mask_flow:
notes = 'masked_iekf'
else:
notes = 'iekf'
notes += '_eps_Z_%f'%eps_Z
notes += '_eps_J_%f'%eps_J
notes += '_eps_M_%f'%eps_M
notes += '_eps_F_%f'%eps_F
img_out = './synthetictests/' + name + '/' + ff + '_' + notes + '_pred/'
if not os.path.isdir(img_out):
os.makedirs(img_out)
print 'Loading synthetic data streams'
capture = VideoStream(v_in, threshold)
frame = capture.current_frame()
mask, ctrs, fd = capture.backsub()
distmesh = DistMesh(frame, h0 = gridsize)
distmesh.load(dm_in)
#Load true data
f_mesh = open(m_in, 'r')
lines = f_mesh.readlines()
nF = len(lines)-1
nX = int(lines[0].split(',')[1])
truestates = np.zeros((nF, nX*4), dtype = np.float32)
predstates = np.zeros((nF, nX*4), dtype = np.float32)
for i in range(1,nF+1):
line = lines[i]
truestates[i-1,:] = [float(x) for x in line.split(',')[1:]]
predstates[0,:] = truestates[0,:]
rms_vel = np.zeros(nF)
rms_pos = np.zeros(nF)
flowstream = FlowStream(flow_in)
ret_flow, flowframe = flowstream.read()
kf = IteratedKalmanFilter(distmesh, frame, flowframe, cuda = cuda, sparse = sparse,\
eps_F = eps_F, eps_Z = eps_Z, eps_J = eps_J, eps_M = eps_M, nI = nI)
count = 0
print 'Tracking with Kalman filter'
while(capture.isOpened() and count < max_frames):
#for idx in range(1):
count += 1
ret, frame, grayframe, mask = capture.read()
ret_flow, flowframe = flowstream.read()
if ret is False or ret_flow is False:
break
print 'Frame %d' % count
kf.compute(grayframe, flowframe, mask, maskflow = mask_flow, imageoutput = img_out+'solution_frame_%03d'%count)
#kf.compute(grayframe, flowframe, mask, maskflow = mask_flow)
predstates[count,:] = np.squeeze(kf.state.X)
r_pos = truestates[count,0:(2*nX)]-predstates[count,0:(2*nX)]
r_vel = truestates[count,(2*nX):]-predstates[count,(2*nX):]
rms_pos[count] = np.sqrt(np.mean(np.multiply(r_pos, r_pos)))
rms_vel[count] = np.sqrt(np.mean(np.multiply(r_vel, r_vel)))
print 'RMS_pos:', rms_pos[count], 'RMS_vel:', rms_vel[count]
print 'Saving'
np.savez('./synthetictests/' + name + '/' + ff + '_' + notes + '_pred.npz', predstates, truestates, rms_pos, rms_vel)
print 'Done... how\'d we do?'
#Make plot of tracking error
plt.plot(range(nF), rms_pos, label='RMS position')
plt.plot(range(nF), rms_vel, label='RMS velocity')
plt.legend(loc='upper left')
plt.ylabel('RMS')
plt.xlabel('Frame')
plt.savefig('./synthetictests/' + name + '/' + ff + '_' + notes + '_pred_rms.eps')
