def test_sort_y(self):
"""sorting on the Y coordinate in place"""
targs = read_targets("testing_fodder/frame/cam1.", 333)
revs = read_targets("testing_fodder/frame/cam1_reversed.", 333)
revs.sort_y()
for targ, rev in zip(targs, revs):
self.assertTrue(targ.pos(), rev.pos())
def test_sort_y(self):
"""sorting on the Y coordinate in place"""
targs = read_targets("testing_fodder/frame/cam1.", 333)
revs = read_targets("testing_fodder/frame/cam1_reversed.", 333)
revs.sort_y()
for targ, rev in zip(targs, revs):
self.failUnless(targ.pos(), rev.pos())
def test_write_targets(self):
"""Round-trip test of writing targets."""
targs = read_targets("../../liboptv/tests/testing_fodder/sample_", 42)
targs.write(b"testing_fodder/round_trip.", 1)
tback = read_targets("testing_fodder/round_trip.", 1)
self.assertEqual(len(targs), len(tback))
self.assertEqual([targ.tnr() for targ in targs],
[targ.tnr() for targ in tback])
self.assertEqual([targ.pos()[0] for targ in targs],
[targ.pos()[0] for targ in tback])
self.assertEqual([targ.pos()[1] for targ in targs],
[targ.pos()[1] for targ in tback])
def test_write_targets(self):
"""Round-trip test of writing targets."""
targs = read_targets("../../liboptv/tests/testing_fodder/sample_", 42)
targs.write("testing_fodder/round_trip.", 1)
tback = read_targets("testing_fodder/round_trip.", 1)
self.failUnlessEqual(len(targs), len(tback))
self.failUnlessEqual([targ.tnr() for targ in targs],
[targ.tnr() for targ in tback])
self.failUnlessEqual([targ.pos()[0] for targ in targs],
[targ.pos()[0] for targ in tback])
self.failUnlessEqual([targ.pos()[1] for targ in targs],
[targ.pos()[1] for targ in tback])
def detect_part_track(self, info):
""" track detected particles is handled by 2 bindings:
1) tracking_framebuf.read_targets(..)
2) ptv.py_get_mark_track_c(..)
"""
info.object.clear_plots(remove_background=False) #clear everything
info.object.update_plots(info.object.orig_image, is_float=False)
prm = info.object.exp1.active_params.m_params
seq_first = prm.Seq_First #get sequence parameters
seq_last = prm.Seq_Last
base_names = [
prm.Basename_1_Seq, prm.Basename_2_Seq, prm.Basename_3_Seq,
prm.Basename_4_Seq
]
info.object.load_set_seq_image(
seq_first) #load first seq image and set appropriate C array
n_images = len(info.object.camera_list)
print "Starting detect_part_track"
x1_a, x2_a, y1_a, y2_a = [], [], [], []
for i in range(n_images): #initialize result arrays
x1_a.append([])
x2_a.append([])
y1_a.append([])
y2_a.append([])
for i_seq in range(seq_first, seq_last + 1): #loop over sequences
for i_img in range(n_images):
intx_green, inty_green, intx_blue, inty_blue = [], [], [], []
imx, imy, zoomx, zoomy, zoomf = ptv.py_get_mark_track_c(i_img)
targets = read_targets(base_names[i_img], i_seq)
for h in range(len(targets)):
#get data from C
tx, ty = targets[h].pos()
if (targets[h].tnr() > -1):
intx_green.append(int(imx / 2 + zoomf * (tx - zoomx)))
inty_green.append(int(imy / 2 + zoomf * (ty - zoomy)))
# else:
# intx_blue.append(int(imx/2 + zoomf*(tx - zoomx)))
# inty_blue.append(int(imy/2 + zoomf*(ty - zoomy)))
x1_a[i_img] = x1_a[
i_img] + intx_green # add current step to result array
# x2_a[i_img]=x2_a[i_img]+intx_blue
y1_a[i_img] = y1_a[i_img] + inty_green
# y2_a[i_img]=y2_a[i_img]+inty_blue
# info.object.camera_list[i_img].drawcross(str(i_seq)+"x_tr_gr",str(i_seq)+"y_tr_gr",intx_green,inty_green,"green",3)
# info.object.camera_list[i_img].drawcross(str(i_seq)+"x_tr_bl",str(i_seq)+"y_tr_bl",intx_blue,inty_blue,"blue",2)
#plot result arrays
for i_img in range(n_images):
info.object.camera_list[i_img].drawcross("x_tr_gr", "y_tr_gr",
x1_a[i_img], y1_a[i_img],
"green", 3)
# info.object.camera_list[i_img].drawcross("x_tr_bl","y_tr_bl",x2_a[i_img],y2_a[i_img],"blue",2)
info.object.camera_list[i_img]._plot.request_redraw()
print "Finished detect_part_track"
def test_read_targets(self):
"""Reading a targets file from Python."""
targs = read_targets("../../liboptv/tests/testing_fodder/sample_", 42)
self.assertEqual(len(targs), 2)
self.assertEqual([targ.tnr() for targ in targs], [1, 0])
self.assertEqual([targ.pos()[0] for targ in targs], [1127., 796.])
self.assertEqual([targ.pos()[1] for targ in targs], [796., 809.])
def test_read_targets(self):
"""Reading a targets file from Python."""
targs = read_targets("../../liboptv/tests/testing_fodder/sample_", 42)
self.failUnlessEqual(len(targs), 2)
self.failUnlessEqual([targ.tnr() for targ in targs], [1, 0])
self.failUnlessEqual([targ.pos()[0] for targ in targs], [1127., 796.])
self.failUnlessEqual([targ.pos()[1] for targ in targs], [796., 809.])
def image_targets(self, frame, cam):
tmpl = re.sub('%1', '%d', self._tmpl)
tmpl = re.sub('%2', '', tmpl)
targets = read_targets(tmpl % (cam + 1), frame)
return TargetListModel(self,
[dict(zip(['x', 'y'], targets[t].pos())) \
for t in xrange(len(targets))] )
def image_targets(self, frame, cam):
tmpl = re.sub('%1', '%d', self._tmpl)
tmpl = re.sub('%2', '', tmpl)
targets = read_targets(tmpl % (cam + 1), frame)
return TargetListModel(self,
[dict(zip(['x', 'y'], targets[t].pos())) \
for t in xrange(len(targets))] )
def detect_part_track(self, info):
""" track detected particles is handled by 2 bindings:
1) tracking_framebuf.read_targets(..)
2) ptv.py_get_mark_track_c(..)
"""
info.object.clear_plots(remove_background=False) #clear everything
info.object.update_plots(info.object.orig_image,is_float=False)
prm = info.object.exp1.active_params.m_params
seq_first = prm.Seq_First #get sequence parameters
seq_last = prm.Seq_Last
base_names = [prm.Basename_1_Seq, prm.Basename_2_Seq,
prm.Basename_3_Seq, prm.Basename_4_Seq]
info.object.load_set_seq_image(seq_first) #load first seq image and set appropriate C array
n_images=len(info.object.camera_list)
print "Starting detect_part_track"
x1_a,x2_a,y1_a,y2_a=[],[],[],[]
for i in range (n_images): #initialize result arrays
x1_a.append([])
x2_a.append([])
y1_a.append([])
y2_a.append([])
for i_seq in range(seq_first, seq_last+1): #loop over sequences
for i_img in range(n_images):
intx_green,inty_green,intx_blue,inty_blue=[],[],[],[]
imx, imy, zoomx, zoomy, zoomf = ptv.py_get_mark_track_c(i_img)
targets = read_targets(base_names[i_img], i_seq)
for h in range(len(targets)):
#get data from C
tx, ty = targets[h].pos()
if (targets[h].tnr() > -1):
intx_green.append(int(imx/2 + zoomf*(tx - zoomx)))
inty_green.append(int(imy/2 + zoomf*(ty - zoomy)))
else:
intx_blue.append(int(imx/2 + zoomf*(tx - zoomx)))
inty_blue.append(int(imy/2 + zoomf*(ty - zoomy)))
x1_a[i_img]=x1_a[i_img]+intx_green # add current step to result array
x2_a[i_img]=x2_a[i_img]+intx_blue
y1_a[i_img]=y1_a[i_img]+inty_green
y2_a[i_img]=y2_a[i_img]+inty_blue
# info.object.camera_list[i_img].drawcross(str(i_seq)+"x_tr_gr",str(i_seq)+"y_tr_gr",intx_green,inty_green,"green",3)
# info.object.camera_list[i_img].drawcross(str(i_seq)+"x_tr_bl",str(i_seq)+"y_tr_bl",intx_blue,inty_blue,"blue",2)
#plot result arrays
for i_img in range(n_images):
info.object.camera_list[i_img].drawcross("x_tr_gr","y_tr_gr",x1_a[i_img],y1_a[i_img],"green",3)
info.object.camera_list[i_img].drawcross("x_tr_bl","y_tr_bl",x2_a[i_img],y2_a[i_img],"blue",2)
info.object.camera_list[i_img]._plot.request_redraw()
print "Finished detect_part_track"
def test_instantiate(self):
"""Creating a MatchedCoords object"""
cal = Calibration()
cpar = ControlParams(4)
cal.from_file("testing_fodder/calibration/cam1.tif.ori",
"testing_fodder/calibration/cam2.tif.addpar")
cpar.read_control_par("testing_fodder/corresp/control.par")
targs = read_targets("testing_fodder/frame/cam1.", 333)
mc = MatchedCoords(targs, cpar, cal)
pos, pnr = mc.as_arrays()
# x sorted?
self.failUnless(np.all(pos[1:, 0] > pos[:-1, 0]))
# Manually verified order for the loaded data:
np.testing.assert_array_equal(
pnr, np.r_[6, 11, 10, 8, 1, 4, 7, 0, 2, 9, 5, 3, 12])
def test_instantiate(self):
"""Creating a MatchedCoords object"""
cal = Calibration()
cpar = ControlParams(4)
cal.from_file(
b"testing_fodder/calibration/cam1.tif.ori",
b"testing_fodder/calibration/cam2.tif.addpar")
cpar.read_control_par(b"testing_fodder/corresp/control.par")
targs = read_targets("testing_fodder/frame/cam1.", 333)
mc = MatchedCoords(targs, cpar, cal)
pos, pnr = mc.as_arrays()
# x sorted?
self.failUnless(np.all(pos[1:,0] > pos[:-1,0]))
# Manually verified order for the loaded data:
np.testing.assert_array_equal(
pnr, np.r_[6, 11, 10, 8, 1, 4, 7, 0, 2, 9, 5, 3, 12])
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()
