def py_detection_proc_c(list_of_images, cpar, tpar, cals):
""" Detection of targets """
detections, corrected = [], []
for i_cam, img in enumerate(list_of_images):
targs = target_recognition(img, tpar, i_cam, cpar)
targs.sort_y()
detections.append(targs)
mc = MatchedCoords(targs, cpar, cals[i_cam])
corrected.append(mc)
return detections, corrected
def test_two_targets(self):
img = np.array([[0, 0, 0, 0, 0], [0, 255, 0, 0, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 251, 0], [0, 0, 0, 0, 0]],
dtype=np.uint8)
cpar = ControlParams(4, image_size=(5, 5))
tpar = TargetParams(gvthresh=[250, 100, 20, 20],
discont=5,
pixel_count_bounds=(1, 10),
min_sum_grey=12,
xsize_bounds=(1, 10),
ysize_bounds=(1, 10))
targs = target_recognition(img, tpar, 0, cpar)
self.assertEqual(len(targs), 2)
self.assertEqual(targs[0].count_pixels(), (1, 1, 1))
# Exclude the first target and try again:
tpar.set_grey_thresholds([252, 100, 20, 20])
targs = target_recognition(img, tpar, 0, cpar)
self.assertEqual(len(targs), 1)
self.assertEqual(targs[0].count_pixels(), (1, 1, 1))
def test_one_targets2(self):
img = np.array(
[[0, 0, 0, 0, 0], [0, 255, 250, 250, 0], [0, 251, 253, 251, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
dtype=np.uint8)
cpar = ControlParams(4, image_size=(5, 5))
tpar = TargetParams(gvthresh=[250, 100, 20, 20],
discont=5,
pixel_count_bounds=(1, 10),
min_sum_grey=12,
xsize_bounds=(1, 10),
ysize_bounds=(1, 10))
targs = target_recognition(img, tpar, 0, cpar)
self.assertEqual(len(targs), 1)
self.assertEqual(targs[0].count_pixels(), (4, 3, 2))
def py_detection_proc_c(list_of_images, cpar, tpar, cals):
""" Detection of targets """
pftVersionParams = par.PftVersionParams(path='./parameters')
pftVersionParams.read()
Existing_Target = np.bool(pftVersionParams.Existing_Target)
detections, corrected = [], []
for i_cam, img in enumerate(list_of_images):
if Existing_Target:
targs = read_targets(cpar.get_img_base_name(i_cam), 0)
else:
targs = target_recognition(img, tpar, i_cam, cpar)
targs.sort_y()
detections.append(targs)
mc = MatchedCoords(targs, cpar, cals[i_cam])
corrected.append(mc)
return detections, corrected
def py_sequence_loop(exp):
""" Runs a sequence of detection, stereo-correspondence, determination and stores
the data in the cam#.XXX_targets (rewritten) and rt_is.XXX files. Basically
it is to run the batch as in pyptv_batch.py without tracking
"""
n_cams, cpar, spar, vpar, tpar, cals = \
exp.n_cams, exp.cpar, exp.spar, exp.vpar, exp.tpar, exp.cals
pftVersionParams = par.PftVersionParams(path='./parameters')
pftVersionParams.read()
Existing_Target = np.bool(pftVersionParams.Existing_Target)
# sequence loop for all frames
for frame in range(spar.get_first(), spar.get_last() + 1):
print("processing frame %d" % frame)
detections = []
corrected = []
for i_cam in range(n_cams):
if Existing_Target:
targs = read_targets(spar.get_img_base_name(i_cam), frame)
else:
imname = spar.get_img_base_name(i_cam) + str(frame).encode()
print(imname)
if not os.path.exists(imname):
print(os.path.abspath(os.path.curdir))
print('{0} does not exist'.format(imname))
img = imread(imname.decode())
# time.sleep(.1) # I'm not sure we need it here
hp = simple_highpass(img, cpar)
targs = target_recognition(hp, tpar, i_cam, cpar)
targs.sort_y()
detections.append(targs)
mc = MatchedCoords(targs, cpar, cals[i_cam])
pos, pnr = mc.as_arrays()
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 range(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 range(len(cals))])
pos, rcm = point_positions(flat.transpose(1, 0, 2), cpar, cals, vpar)
# if len(cals) == 1: # single camera case
# sorted_corresp = np.tile(sorted_corresp,(4,1))
# sorted_corresp[1:,:] = -1
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
print(default_naming['corres'])
rt_is = open(default_naming['corres'] + b'.' + str(frame).encode(),
'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()
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()