def setUp(self):
self.input_tracking_par_file_name = "testing_fodder/tracking_parameters/track.par"
# create an instance of TrackingParams class
# testing setters that are used in constructor
self.track_obj1 = TrackingParams(
accel_lim=1.1, angle_lim=2.2, add_particle=1,
velocity_lims=[[3.3, 4.4], [5.5, 6.6], [7.7, 8.8]])
def test_comparison(self):
# create two identical objects
self.track_obj2 = TrackingParams(
accel_lim=1.1, angle_lim=2.2, add_particle=1,
velocity_lims=[[3.3, 4.4], [5.5, 6.6], [7.7, 8.8]])
self.failUnless(self.track_obj2 == self.track_obj1)
self.failIf(self.track_obj2 != self.track_obj1)
# change one instance variable of track_obj2 and check that the comparisons results are inverted
# please note that the operands '==' and '!=' must be checked separately
self.track_obj2.set_dvxmin(999.999)
self.failUnless(self.track_obj2 != self.track_obj1)
self.failIf(self.track_obj2 == self.track_obj1)
def setUp(self):
with open("testing_fodder/track/conf.yaml") as f:
yaml_conf = yaml.load(f)
seq_cfg = yaml_conf['sequence']
cals = []
img_base = []
for cix, cam_spec in enumerate(yaml_conf['cameras']):
cam_spec.setdefault('addpar_file', None)
cal = Calibration()
cal.from_file(cam_spec['ori_file'], cam_spec['addpar_file'])
cals.append(cal)
img_base.append(seq_cfg['targets_template'].format(cam=cix + 1))
cpar = ControlParams(len(yaml_conf['cameras']), **yaml_conf['scene'])
vpar = VolumeParams(**yaml_conf['correspondences'])
tpar = TrackingParams(**yaml_conf['tracking'])
spar = SequenceParams(image_base=img_base,
frame_range=(seq_cfg['first'], seq_cfg['last']))
self.tracker = Tracker(cpar, vpar, tpar, spar, cals, framebuf_naming)
def py_start_proc_c(n_cams):
""" Read parameters """
# Control parameters
cpar = ControlParams(n_cams)
cpar.read_control_par(b'parameters/ptv.par')
# Sequence parameters
spar = SequenceParams(num_cams=n_cams)
spar.read_sequence_par(b'parameters/sequence.par', n_cams)
# Volume parameters
vpar = VolumeParams()
vpar.read_volume_par(b'parameters/criteria.par')
# Tracking parameters
track_par = TrackingParams()
track_par.read_track_par(b'parameters/track.par')
# Target parameters
tpar = TargetParams(n_cams)
tpar.read(b'parameters/targ_rec.par')
# Examine parameters, multiplane (single plane vs combined calibration)
epar = par.ExamineParams()
epar.read()
# Calibration parameters
cals = []
for i_cam in range(n_cams):
cal = Calibration()
tmp = cpar.get_cal_img_base_name(i_cam)
cal.from_file(tmp + b'.ori', tmp + b'.addpar')
cals.append(cal)
return cpar, spar, vpar, track_par, tpar, cals, epar
def run_batch(new_seq_first, new_seq_last):
""" this file runs inside exp_path, so the other names are
prescribed by the OpenPTV type of a folder:
/parameters
/img
/cal
/res
"""
# read the number of cameras
with open('parameters/ptv.par', 'r') as f:
n_cams = int(f.readline())
# Control parameters
cpar = ControlParams(n_cams)
cpar.read_control_par(b'parameters/ptv.par')
# Sequence parameters
spar = SequenceParams(num_cams=n_cams)
spar.read_sequence_par(b'parameters/sequence.par', n_cams)
spar.set_first(new_seq_first)
spar.set_last(new_seq_last)
# Volume parameters
vpar = VolumeParams()
vpar.read_volume_par(b'parameters/criteria.par')
# Tracking parameters
track_par = TrackingParams()
track_par.read_track_par(b'parameters/track.par')
# Target parameters
tpar = TargetParams()
tpar.read(b'parameters/targ_rec.par')
#
# Calibration parameters
cals = []
for i_cam in range(n_cams):
cal = Calibration()
tmp = cpar.get_cal_img_base_name(i_cam)
cal.from_file(tmp + b'.ori', tmp + b'.addpar')
cals.append(cal)
# sequence loop for all frames
for frame in range(new_seq_first, new_seq_last + 1):
print("processing frame %d" % frame)
detections = []
corrected = []
for i_cam in range(n_cams):
imname = spar.get_img_base_name(i_cam) + str(frame)
img = imread(imname)
hp = simple_highpass(img, cpar)
targs = target_recognition(hp, tpar, i_cam, cpar)
print(targs)
targs.sort_y()
detections.append(targs)
mc = MatchedCoords(targs, cpar, cals[i_cam])
pos, pnr = mc.as_arrays()
print(i_cam)
corrected.append(mc)
# if any([len(det) == 0 for det in detections]):
# return False
# Corresp. + positions.
sorted_pos, sorted_corresp, num_targs = correspondences(
detections, corrected, cals, vpar, cpar)
# Save targets only after they've been modified:
for i_cam in xrange(n_cams):
detections[i_cam].write(spar.get_img_base_name(i_cam), frame)
print("Frame " + str(frame) + " had " \
+ repr([s.shape[1] for s in sorted_pos]) + " correspondences.")
# Distinction between quad/trip irrelevant here.
sorted_pos = np.concatenate(sorted_pos, axis=1)
sorted_corresp = np.concatenate(sorted_corresp, axis=1)
flat = np.array([corrected[i].get_by_pnrs(sorted_corresp[i]) \
for i in xrange(len(cals))])
pos, rcm = point_positions(flat.transpose(1, 0, 2), cpar, cals, vpar)
if len(cals) < 4:
print_corresp = -1 * np.ones((4, sorted_corresp.shape[1]))
print_corresp[:len(cals), :] = sorted_corresp
else:
print_corresp = sorted_corresp
# Save rt_is
rt_is = open(default_naming['corres'] + '.' + str(frame), 'w')
rt_is.write(str(pos.shape[0]) + '\n')
for pix, pt in enumerate(pos):
pt_args = (pix + 1, ) + tuple(pt) + tuple(print_corresp[:, pix])
rt_is.write("%4d %9.3f %9.3f %9.3f %4d %4d %4d %4d\n" % pt_args)
rt_is.close()
# end of a sequence loop
tracker = Tracker(cpar, vpar, track_par, spar, cals, default_naming)
tracker.full_forward()
