def test_single_camera_point_positions(self):
"""Point positions for a single camera case"""
num_cams = 1
# prepare MultimediaParams
cpar_file = b'testing_fodder/single_cam/parameters/ptv.par'
vpar_file = b'testing_fodder/single_cam/parameters/criteria.par'
cpar = ControlParams(num_cams)
cpar.read_control_par(cpar_file)
mult_params = cpar.get_multimedia_params()
vpar = VolumeParams()
vpar.read_volume_par(vpar_file)
ori_name = b'testing_fodder/single_cam/calibration/cam_1.tif.ori'
add_name = b'testing_fodder/single_cam/calibration/cam_1.tif.addpar'
calibs = []
# read calibration for each camera from files
new_cal = Calibration()
new_cal.from_file(ori_file=ori_name, add_file=add_name)
calibs.append(new_cal)
# 3d point
points = np.array([[1, 1, 0],
[-1, -1, 0]], dtype=float)
targs_plain = []
targs_jigged = []
jigg_amp = 0.4
new_plain_targ = image_coordinates(
points, calibs[0], mult_params)
targs_plain.append(new_plain_targ)
jigged_points = points - np.r_[0, jigg_amp, 0]
new_jigged_targs = image_coordinates(
jigged_points, calibs[0], mult_params)
targs_jigged.append(new_jigged_targs)
targs_plain = np.array(targs_plain).transpose(1,0,2)
targs_jigged = np.array(targs_jigged).transpose(1,0,2)
skew_dist_plain = point_positions(targs_plain, cpar, calibs, vpar)
skew_dist_jigged = point_positions(targs_jigged, cpar, calibs, vpar)
if np.any(np.linalg.norm(points - skew_dist_plain[0], axis=1) > 1e-6):
self.fail('Rays converge on wrong position.')
if np.any(np.linalg.norm(jigged_points - skew_dist_jigged[0], axis=1) > 1e-6):
self.fail('Rays converge on wrong position after jigging.')
def setUp(self):
self.input_volume_par_file_name = "testing_fodder/volume_parameters/volume.par"
self.temp_output_directory = "testing_fodder/volume_parameters/testing_output"
# create a temporary output directory (will be deleted by the end of test)
if not os.path.exists(self.temp_output_directory):
os.makedirs(self.temp_output_directory)
# create an instance of VolumeParams class
self.vol_obj = VolumeParams()
def test_single_camera_point_positions(self):
"""Point positions for a single camera case"""
num_cams = 1
# prepare MultimediaParams
cpar_file = b'testing_fodder/single_cam/parameters/ptv.par'
vpar_file = b'testing_fodder/single_cam/parameters/criteria.par'
cpar = ControlParams(num_cams)
cpar.read_control_par(cpar_file)
mult_params = cpar.get_multimedia_params()
vpar = VolumeParams()
vpar.read_volume_par(vpar_file)
ori_name = b'testing_fodder/single_cam/calibration/cam_1.tif.ori'
add_name = b'testing_fodder/single_cam/calibration/cam_1.tif.addpar'
calibs = []
# read calibration for each camera from files
new_cal = Calibration()
new_cal.from_file(ori_file=ori_name, add_file=add_name)
calibs.append(new_cal)
# 3d point
points = np.array([[1, 1, 0], [-1, -1, 0]], dtype=float)
targs_plain = []
targs_jigged = []
jigg_amp = 0.4
new_plain_targ = image_coordinates(points, calibs[0], mult_params)
targs_plain.append(new_plain_targ)
jigged_points = points - np.r_[0, jigg_amp, 0]
new_jigged_targs = image_coordinates(jigged_points, calibs[0],
mult_params)
targs_jigged.append(new_jigged_targs)
targs_plain = np.array(targs_plain).transpose(1, 0, 2)
targs_jigged = np.array(targs_jigged).transpose(1, 0, 2)
skew_dist_plain = point_positions(targs_plain, cpar, calibs, vpar)
skew_dist_jigged = point_positions(targs_jigged, cpar, calibs, vpar)
if np.any(np.linalg.norm(points - skew_dist_plain[0], axis=1) > 1e-6):
self.fail('Rays converge on wrong position.')
if np.any(
np.linalg.norm(jigged_points -
skew_dist_jigged[0], axis=1) > 1e-6):
self.fail('Rays converge on wrong position after jigging.')
def setUp(self):
self.input_ori_file_name = b'testing_fodder/calibration/cam1.tif.ori'
self.input_add_file_name = b'testing_fodder/calibration/cam2.tif.addpar'
self.control_file_name = b'testing_fodder/control_parameters/control.par'
self.volume_file_name = b'testing_fodder/corresp/criteria.par'
self.calibration = Calibration()
self.calibration.from_file(self.input_ori_file_name,
self.input_add_file_name)
self.control = ControlParams(4)
self.control.read_control_par(self.control_file_name)
self.vpar = VolumeParams()
self.vpar.read_volume_par(self.volume_file_name)
def test_rich_compare(self):
self.vol_obj2 = VolumeParams()
self.vol_obj2.read_volume_par(self.input_volume_par_file_name)
self.vol_obj3 = VolumeParams()
self.vol_obj3.read_volume_par(self.input_volume_par_file_name)
self.failUnless(self.vol_obj2 == self.vol_obj3)
self.failIf(self.vol_obj2 != self.vol_obj3)
self.vol_obj2.set_cn(-999)
self.failUnless(self.vol_obj2 != self.vol_obj3)
self.failIf(self.vol_obj2 == self.vol_obj3)
with self.assertRaises(TypeError):
var = (self.vol_obj2 < self.vol_obj3)
def test_rich_compare(self):
self.vol_obj2 = VolumeParams()
self.vol_obj2.read_volume_par(self.input_volume_par_file_name)
self.vol_obj3 = VolumeParams()
self.vol_obj3.read_volume_par(self.input_volume_par_file_name)
self.failUnless(self.vol_obj2 == self.vol_obj3)
self.failIf(self.vol_obj2 != self.vol_obj3)
self.vol_obj2.set_cn(-999)
self.failUnless(self.vol_obj2 != self.vol_obj3)
self.failIf(self.vol_obj2 == self.vol_obj3)
with self.assertRaises(TypeError):
var = (self.vol_obj2 < self.vol_obj3)
def test_full_corresp(self):
"""Full scene correspondences"""
print "about to dump core"
cpar = ControlParams(4)
cpar.read_control_par("testing_fodder/corresp/control.par")
vpar = VolumeParams()
vpar.read_volume_par("testing_fodder/corresp/criteria.par")
# Cameras are at so high angles that opposing cameras don't see each
# other in the normal air-glass-water setting.
cpar.get_multimedia_params().set_layers([1.0001], [1.])
cpar.get_multimedia_params().set_n3(1.0001)
cals = []
img_pts = []
corrected = []
for c in xrange(4):
cal = Calibration()
cal.from_file(
"testing_fodder/calibration/sym_cam%d.tif.ori" % (c + 1),
"testing_fodder/calibration/cam1.tif.addpar")
cals.append(cal)
# Generate test targets.
targs = TargetArray(16)
for row, col in np.ndindex(4, 4):
targ_ix = row*4 + col
# Avoid symmetric case:
if (c % 2):
targ_ix = 15 - targ_ix
targ = targs[targ_ix]
pos3d = 10*np.array([[col, row, 0]], dtype=np.float64)
pos2d = image_coordinates(
pos3d, cal, cpar.get_multimedia_params())
targ.set_pos(convert_arr_metric_to_pixel(pos2d, cpar)[0])
targ.set_pnr(targ_ix)
targ.set_pixel_counts(25, 5, 5)
targ.set_sum_grey_value(10)
img_pts.append(targs)
mc = MatchedCoords(targs, cpar, cal)
corrected.append(mc)
sorted_pos, sorted_corresp, num_targs = correspondences(
img_pts, corrected, cals, vpar, cpar)
self.failUnlessEqual(num_targs, 16)
def test_full_corresp(self):
"""Full scene correspondences"""
cpar = ControlParams(4)
cpar.read_control_par(r"testing_fodder/corresp/control.par")
vpar = VolumeParams()
vpar.read_volume_par(r"testing_fodder/corresp/criteria.par")
# Cameras are at so high angles that opposing cameras don't see each
# other in the normal air-glass-water setting.
cpar.get_multimedia_params().set_layers([1.0001], [1.])
cpar.get_multimedia_params().set_n3(1.0001)
cals = []
img_pts = []
corrected = []
for c in xrange(4):
cal = Calibration()
cal.from_file(
"testing_fodder/calibration/sym_cam%d.tif.ori" % (c + 1),
"testing_fodder/calibration/cam1.tif.addpar")
cals.append(cal)
# Generate test targets.
targs = TargetArray(16)
for row, col in np.ndindex(4, 4):
targ_ix = row * 4 + col
# Avoid symmetric case:
if (c % 2):
targ_ix = 15 - targ_ix
targ = targs[targ_ix]
pos3d = 10 * np.array([[col, row, 0]], dtype=np.float64)
pos2d = image_coordinates(pos3d, cal,
cpar.get_multimedia_params())
targ.set_pos(convert_arr_metric_to_pixel(pos2d, cpar)[0])
targ.set_pnr(targ_ix)
targ.set_pixel_counts(25, 5, 5)
targ.set_sum_grey_value(10)
img_pts.append(targs)
mc = MatchedCoords(targs, cpar, cal)
corrected.append(mc)
sorted_pos, sorted_corresp, num_targs = correspondences(
img_pts, corrected, cals, vpar, cpar)
self.failUnlessEqual(num_targs, 16)
def test_single_cam_corresp(self):
"""Single camera correspondence"""
cpar = ControlParams(1)
cpar.read_control_par("testing_fodder/single_cam/parameters/ptv.par")
vpar = VolumeParams()
vpar.read_volume_par(
"testing_fodder/single_cam/parameters/criteria.par")
# Cameras are at so high angles that opposing cameras don't see each
# other in the normal air-glass-water setting.
cpar.get_multimedia_params().set_layers([1.], [1.])
cpar.get_multimedia_params().set_n3(1.)
cals = []
img_pts = []
corrected = []
cal = Calibration()
cal.from_file(
"testing_fodder/single_cam/calibration/cam_1.tif.ori",
"testing_fodder/single_cam/calibration/cam_1.tif.addpar")
cals.append(cal)
# Generate test targets.
targs = TargetArray(9)
for row, col in np.ndindex(3, 3):
targ_ix = row * 3 + col
targ = targs[targ_ix]
pos3d = 10 * np.array([[col, row, 0]], dtype=np.float64)
pos2d = image_coordinates(pos3d, cal, cpar.get_multimedia_params())
targ.set_pos(convert_arr_metric_to_pixel(pos2d, cpar)[0])
targ.set_pnr(targ_ix)
targ.set_pixel_counts(25, 5, 5)
targ.set_sum_grey_value(10)
img_pts.append(targs)
mc = MatchedCoords(targs, cpar, cal)
corrected.append(mc)
sorted_pos, sorted_corresp, num_targs = correspondences(
img_pts, corrected, cals, vpar, cpar)
self.failUnlessEqual(num_targs, 9)
def test_single_cam_corresp(self):
"""Single camera correspondence"""
cpar = ControlParams(1)
cpar.read_control_par(b"testing_fodder/single_cam/parameters/ptv.par")
vpar = VolumeParams()
vpar.read_volume_par(b"testing_fodder/single_cam/parameters/criteria.par")
# Cameras are at so high angles that opposing cameras don't see each
# other in the normal air-glass-water setting.
cpar.get_multimedia_params().set_layers([1.], [1.])
cpar.get_multimedia_params().set_n3(1.)
cals = []
img_pts = []
corrected = []
cal = Calibration()
cal.from_file(
b"testing_fodder/single_cam/calibration/cam_1.tif.ori",
b"testing_fodder/single_cam/calibration/cam_1.tif.addpar")
cals.append(cal)
# Generate test targets.
targs = TargetArray(9)
for row, col in np.ndindex(3, 3):
targ_ix = row*3 + col
targ = targs[targ_ix]
pos3d = 10*np.array([[col, row, 0]], dtype=np.float64)
pos2d = image_coordinates(
pos3d, cal, cpar.get_multimedia_params())
targ.set_pos(convert_arr_metric_to_pixel(pos2d, cpar)[0])
targ.set_pnr(targ_ix)
targ.set_pixel_counts(25, 5, 5)
targ.set_sum_grey_value(10)
img_pts.append(targs)
mc = MatchedCoords(targs, cpar, cal)
corrected.append(mc)
_, _, num_targs = correspondences(
img_pts, corrected, cals, vpar, cpar)
self.failUnlessEqual(num_targs, 9)
def setUp(self):
self.input_volume_par_file_name = "testing_fodder/volume_parameters/volume.par"
self.temp_output_directory = "testing_fodder/volume_parameters/testing_output"
# create a temporary output directory (will be deleted by the end of test)
if not os.path.exists(self.temp_output_directory):
os.makedirs(self.temp_output_directory)
# create an instance of VolumeParams class
self.vol_obj = VolumeParams()
def py_determination_proc_c(n_cams, sorted_pos, sorted_corresp, corrected):
""" Returns 3d positions """
# Control parameters
cpar = ControlParams(n_cams)
cpar.read_control_par(b'parameters/ptv.par')
# Volume parameters
vpar = VolumeParams()
vpar.read_volume_par(b'parameters/criteria.par')
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)
# 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) < 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 in a temporary file
fname = b"".join([default_naming['corres'],b'.123456789']) # hard-coded frame number
with open(fname, 'w') as rt_is:
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)
def test_init_kwargs(self):
"""Initialize volume parameters with keyword arguments"""
xlay = numpy.array([111.1, 222.2])
zlay = [r_[333.3, 555.5], r_[444.4, 666.6]]
zmin, zmax = zip(*zlay)
vol_obj = VolumeParams(x_span=xlay, z_spans=zlay,
pixels_tot=1, pixels_x=2, pixels_y=3,
ref_gray=4, epipolar_band=5, min_correlation=6)
numpy.testing.assert_array_equal(xlay, vol_obj.get_X_lay())
numpy.testing.assert_array_equal(zmin, vol_obj.get_Zmin_lay())
numpy.testing.assert_array_equal(zmax, vol_obj.get_Zmax_lay())
self.failUnless(vol_obj.get_cn() == 1)
self.failUnless(vol_obj.get_cnx() == 2)
self.failUnless(vol_obj.get_cny() == 3)
self.failUnless(vol_obj.get_csumg() == 4)
self.failUnless(vol_obj.get_eps0() == 5)
self.failUnless(vol_obj.get_corrmin() == 6)
def setUp(self):
self.input_ori_file_name = b'testing_fodder/calibration/cam1.tif.ori'
self.input_add_file_name = b'testing_fodder/calibration/cam2.tif.addpar'
self.control_file_name = b'testing_fodder/control_parameters/control.par'
self.volume_file_name = b'testing_fodder/corresp/criteria.par'
self.calibration = Calibration()
self.calibration.from_file(
self.input_ori_file_name, self.input_add_file_name)
self.control = ControlParams(4)
self.control.read_control_par(self.control_file_name)
self.vpar = VolumeParams()
self.vpar.read_volume_par(self.volume_file_name)
def py_determination_proc_c(n_cams, sorted_pos, sorted_corresp, corrected):
""" Returns 3d positions """
# Control parameters
cpar = ControlParams(n_cams)
cpar.read_control_par('parameters/ptv.par')
# Volume parameters
vpar = VolumeParams()
vpar.read_volume_par('parameters/criteria.par')
cals = []
for i_cam in xrange(n_cams):
cal = Calibration()
tmp = cpar.get_cal_img_base_name(i_cam)
cal.from_file(tmp + '.ori', tmp + '.addpar')
cals.append(cal)
# 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) == 1: # single camera case
sorted_corresp = np.tile(sorted_corresp, (4, 1))
sorted_corresp[1:, :] = -1
# Save rt_is in a temporary file
frame = 123456789 # just a temporary workaround. todo: think how to write
with open(default_naming['corres'] + '.' + str(frame), 'w') as rt_is:
rt_is.write(str(pos.shape[0]) + '\n')
for pix, pt in enumerate(pos):
pt_args = (pix + 1, ) + tuple(pt) + tuple(sorted_corresp[:, pix])
rt_is.write("%4d %9.3f %9.3f %9.3f %4d %4d %4d %4d\n" % pt_args)
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 test_two_cameras(self):
ori_tmpl = "testing_fodder/calibration/sym_cam{cam_num}.tif.ori"
add_file = "testing_fodder/calibration/cam1.tif.addpar"
orig_cal = Calibration()
orig_cal.from_file(
ori_tmpl.format(cam_num=1).encode(), add_file.encode())
proj_cal = Calibration()
proj_cal.from_file(
ori_tmpl.format(cam_num=3).encode(), add_file.encode())
# reorient cams:
orig_cal.set_angles(np.r_[0., -np.pi / 4., 0.])
proj_cal.set_angles(np.r_[0., 3 * np.pi / 4., 0.])
cpar = ControlParams(4)
cpar.read_control_par(b"testing_fodder/corresp/control.par")
sens_size = cpar.get_image_size()
vpar = VolumeParams()
vpar.read_volume_par(b"testing_fodder/corresp/criteria.par")
vpar.set_Zmin_lay([-10, -10])
vpar.set_Zmax_lay([10, 10])
mult_params = cpar.get_multimedia_params()
mult_params.set_n1(1.)
mult_params.set_layers(np.array([1.]), np.array([1.]))
mult_params.set_n3(1.)
# Central point translates to central point because cameras point
# directly at each other.
mid = np.r_[sens_size] / 2.
line = epipolar_curve(mid, orig_cal, proj_cal, 5, cpar, vpar)
self.failUnless(np.all(abs(line - mid) < 1e-6))
# An equatorial point draws a latitude.
line = epipolar_curve(mid - np.r_[100., 0.], orig_cal, proj_cal, 5,
cpar, vpar)
np.testing.assert_array_equal(np.argsort(line[:, 0]),
np.arange(5)[::-1])
self.failUnless(np.all(abs(line[:, 1] - mid[1]) < 1e-6))
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 test_two_cameras(self):
ori_tmpl = "testing_fodder/calibration/sym_cam{cam_num}.tif.ori"
add_file = "testing_fodder/calibration/cam1.tif.addpar"
orig_cal = Calibration()
orig_cal.from_file(ori_tmpl.format(cam_num=1).encode(), add_file.encode())
proj_cal = Calibration()
proj_cal.from_file(ori_tmpl.format(cam_num=3).encode(), add_file.encode())
# reorient cams:
orig_cal.set_angles(np.r_[0., -np.pi/4., 0.])
proj_cal.set_angles(np.r_[0., 3*np.pi/4., 0.])
cpar = ControlParams(4)
cpar.read_control_par(b"testing_fodder/corresp/control.par")
sens_size = cpar.get_image_size()
vpar = VolumeParams()
vpar.read_volume_par(b"testing_fodder/corresp/criteria.par")
vpar.set_Zmin_lay([-10, -10])
vpar.set_Zmax_lay([10, 10])
mult_params = cpar.get_multimedia_params()
mult_params.set_n1(1.)
mult_params.set_layers(np.array([1.]), np.array([1.]))
mult_params.set_n3(1.)
# Central point translates to central point because cameras point
# directly at each other.
mid = np.r_[sens_size]/2.
line = epipolar_curve(mid, orig_cal, proj_cal, 5, cpar, vpar)
self.failUnless(np.all(abs(line - mid) < 1e-6))
# An equatorial point draws a latitude.
line = epipolar_curve(
mid - np.r_[100., 0.], orig_cal, proj_cal, 5, cpar, vpar)
np.testing.assert_array_equal(np.argsort(line[:,0]), np.arange(5)[::-1])
self.failUnless(np.all(abs(line[:,1] - mid[1]) < 1e-6))
class Test_VolumeParams(unittest.TestCase):
def setUp(self):
self.input_volume_par_file_name = "testing_fodder/volume_parameters/volume.par"
self.temp_output_directory = "testing_fodder/volume_parameters/testing_output"
# create a temporary output directory (will be deleted by the end of test)
if not os.path.exists(self.temp_output_directory):
os.makedirs(self.temp_output_directory)
# create an instance of VolumeParams class
self.vol_obj = VolumeParams()
def test_read_volume(self):
# Fill the VolumeParams object with parameters from test file
self.vol_obj.read_volume_par(self.input_volume_par_file_name)
# check that all parameters are equal to the contents of test file
numpy.testing.assert_array_equal(numpy.array([111.111, 222.222]), self.vol_obj.get_X_lay())
numpy.testing.assert_array_equal(numpy.array([333.333, 444.444]), self.vol_obj.get_Zmin_lay())
numpy.testing.assert_array_equal(numpy.array([555.555, 666.666]), self.vol_obj.get_Zmax_lay())
self.failUnless(self.vol_obj.get_cnx() == 777.777)
self.failUnless(self.vol_obj.get_cny() == 888.888)
self.failUnless(self.vol_obj.get_cn() == 999.999)
self.failUnless(self.vol_obj.get_csumg() == 1010.1010)
self.failUnless(self.vol_obj.get_corrmin() == 1111.1111)
self.failUnless(self.vol_obj.get_eps0() == 1212.1212)
def test_setters(self):
xlay = numpy.array([111.1, 222.2])
self.vol_obj.set_X_lay(xlay)
numpy.testing.assert_array_equal(xlay, self.vol_obj.get_X_lay())
zmin = numpy.array([333.3, 444.4])
self.vol_obj.set_Zmin_lay(zmin)
numpy.testing.assert_array_equal(zmin, self.vol_obj.get_Zmin_lay())
zmax = numpy.array([555.5, 666.6])
self.vol_obj.set_Zmax_lay(zmax)
numpy.testing.assert_array_equal(zmax, self.vol_obj.get_Zmax_lay())
self.vol_obj.set_cn(1)
self.failUnless(self.vol_obj.get_cn() == 1)
self.vol_obj.set_cnx(2)
self.failUnless(self.vol_obj.get_cnx() == 2)
self.vol_obj.set_cny(3)
self.failUnless(self.vol_obj.get_cny() == 3)
self.vol_obj.set_csumg(4)
self.failUnless(self.vol_obj.get_csumg() == 4)
self.vol_obj.set_eps0(5)
self.failUnless(self.vol_obj.get_eps0() == 5)
self.vol_obj.set_corrmin(6)
self.failUnless(self.vol_obj.get_corrmin() == 6)
def test_init_kwargs(self):
"""Initialize volume parameters with keyword arguments"""
xlay = numpy.array([111.1, 222.2])
zlay = [r_[333.3, 555.5], r_[444.4, 666.6]]
zmin, zmax = zip(*zlay)
vol_obj = VolumeParams(x_span=xlay, z_spans=zlay,
pixels_tot=1, pixels_x=2, pixels_y=3,
ref_gray=4, epipolar_band=5, min_correlation=6)
numpy.testing.assert_array_equal(xlay, vol_obj.get_X_lay())
numpy.testing.assert_array_equal(zmin, vol_obj.get_Zmin_lay())
numpy.testing.assert_array_equal(zmax, vol_obj.get_Zmax_lay())
self.failUnless(vol_obj.get_cn() == 1)
self.failUnless(vol_obj.get_cnx() == 2)
self.failUnless(vol_obj.get_cny() == 3)
self.failUnless(vol_obj.get_csumg() == 4)
self.failUnless(vol_obj.get_eps0() == 5)
self.failUnless(vol_obj.get_corrmin() == 6)
# testing __richcmp__ comparison method of VolumeParams class
def test_rich_compare(self):
self.vol_obj2 = VolumeParams()
self.vol_obj2.read_volume_par(self.input_volume_par_file_name)
self.vol_obj3 = VolumeParams()
self.vol_obj3.read_volume_par(self.input_volume_par_file_name)
self.failUnless(self.vol_obj2 == self.vol_obj3)
self.failIf(self.vol_obj2 != self.vol_obj3)
self.vol_obj2.set_cn(-999)
self.failUnless(self.vol_obj2 != self.vol_obj3)
self.failIf(self.vol_obj2 == self.vol_obj3)
with self.assertRaises(TypeError):
var = (self.vol_obj2 < self.vol_obj3)
def tearDown(self):
# remove the testing output directory and its files
shutil.rmtree(self.temp_output_directory)
def test_init_kwargs(self):
"""Initialize volume parameters with keyword arguments"""
xlay = numpy.array([111.1, 222.2])
zlay = [r_[333.3, 555.5], r_[444.4, 666.6]]
zmin, zmax = zip(*zlay)
vol_obj = VolumeParams(x_span=xlay, z_spans=zlay,
pixels_tot=1, pixels_x=2, pixels_y=3,
ref_gray=4, epipolar_band=5, min_correlation=6)
numpy.testing.assert_array_equal(xlay, vol_obj.get_X_lay())
numpy.testing.assert_array_equal(zmin, vol_obj.get_Zmin_lay())
numpy.testing.assert_array_equal(zmax, vol_obj.get_Zmax_lay())
self.failUnless(vol_obj.get_cn() == 1)
self.failUnless(vol_obj.get_cnx() == 2)
self.failUnless(vol_obj.get_cny() == 3)
self.failUnless(vol_obj.get_csumg() == 4)
self.failUnless(vol_obj.get_eps0() == 5)
self.failUnless(vol_obj.get_corrmin() == 6)
class Test_VolumeParams(unittest.TestCase):
def setUp(self):
self.input_volume_par_file_name = "testing_fodder/volume_parameters/volume.par"
self.temp_output_directory = "testing_fodder/volume_parameters/testing_output"
# create a temporary output directory (will be deleted by the end of test)
if not os.path.exists(self.temp_output_directory):
os.makedirs(self.temp_output_directory)
# create an instance of VolumeParams class
self.vol_obj = VolumeParams()
def test_read_volume(self):
# Fill the VolumeParams object with parameters from test file
self.vol_obj.read_volume_par(self.input_volume_par_file_name)
# check that all parameters are equal to the contents of test file
numpy.testing.assert_array_equal(numpy.array([111.111, 222.222]), self.vol_obj.get_X_lay())
numpy.testing.assert_array_equal(numpy.array([333.333, 444.444]), self.vol_obj.get_Zmin_lay())
numpy.testing.assert_array_equal(numpy.array([555.555, 666.666]), self.vol_obj.get_Zmax_lay())
self.failUnless(self.vol_obj.get_cnx() == 777.777)
self.failUnless(self.vol_obj.get_cny() == 888.888)
self.failUnless(self.vol_obj.get_cn() == 999.999)
self.failUnless(self.vol_obj.get_csumg() == 1010.1010)
self.failUnless(self.vol_obj.get_corrmin() == 1111.1111)
self.failUnless(self.vol_obj.get_eps0() == 1212.1212)
def test_setters(self):
xlay = numpy.array([111.1, 222.2])
self.vol_obj.set_X_lay(xlay)
numpy.testing.assert_array_equal(xlay, self.vol_obj.get_X_lay())
zmin = numpy.array([333.3, 444.4])
self.vol_obj.set_Zmin_lay(zmin)
numpy.testing.assert_array_equal(zmin, self.vol_obj.get_Zmin_lay())
zmax = numpy.array([555.5, 666.6])
self.vol_obj.set_Zmax_lay(zmax)
numpy.testing.assert_array_equal(zmax, self.vol_obj.get_Zmax_lay())
self.vol_obj.set_cn(1)
self.failUnless(self.vol_obj.get_cn() == 1)
self.vol_obj.set_cnx(2)
self.failUnless(self.vol_obj.get_cnx() == 2)
self.vol_obj.set_cny(3)
self.failUnless(self.vol_obj.get_cny() == 3)
self.vol_obj.set_csumg(4)
self.failUnless(self.vol_obj.get_csumg() == 4)
self.vol_obj.set_eps0(5)
self.failUnless(self.vol_obj.get_eps0() == 5)
self.vol_obj.set_corrmin(6)
self.failUnless(self.vol_obj.get_corrmin() == 6)
def test_init_kwargs(self):
"""Initialize volume parameters with keyword arguments"""
xlay = numpy.array([111.1, 222.2])
zlay = [r_[333.3, 555.5], r_[444.4, 666.6]]
zmin, zmax = zip(*zlay)
vol_obj = VolumeParams(x_span=xlay, z_spans=zlay,
pixels_tot=1, pixels_x=2, pixels_y=3,
ref_gray=4, epipolar_band=5, min_correlation=6)
numpy.testing.assert_array_equal(xlay, vol_obj.get_X_lay())
numpy.testing.assert_array_equal(zmin, vol_obj.get_Zmin_lay())
numpy.testing.assert_array_equal(zmax, vol_obj.get_Zmax_lay())
self.failUnless(vol_obj.get_cn() == 1)
self.failUnless(vol_obj.get_cnx() == 2)
self.failUnless(vol_obj.get_cny() == 3)
self.failUnless(vol_obj.get_csumg() == 4)
self.failUnless(vol_obj.get_eps0() == 5)
self.failUnless(vol_obj.get_corrmin() == 6)
# testing __richcmp__ comparison method of VolumeParams class
def test_rich_compare(self):
self.vol_obj2 = VolumeParams()
self.vol_obj2.read_volume_par(self.input_volume_par_file_name)
self.vol_obj3 = VolumeParams()
self.vol_obj3.read_volume_par(self.input_volume_par_file_name)
self.failUnless(self.vol_obj2 == self.vol_obj3)
self.failIf(self.vol_obj2 != self.vol_obj3)
self.vol_obj2.set_cn(-999)
self.failUnless(self.vol_obj2 != self.vol_obj3)
self.failIf(self.vol_obj2 == self.vol_obj3)
with self.assertRaises(TypeError):
var = (self.vol_obj2 < self.vol_obj3)
def tearDown(self):
# remove the testing output directory and its files
shutil.rmtree(self.temp_output_directory)
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()
class Test_Orientation(unittest.TestCase):
def setUp(self):
self.input_ori_file_name = b'testing_fodder/calibration/cam1.tif.ori'
self.input_add_file_name = b'testing_fodder/calibration/cam2.tif.addpar'
self.control_file_name = b'testing_fodder/control_parameters/control.par'
self.volume_file_name = b'testing_fodder/corresp/criteria.par'
self.calibration = Calibration()
self.calibration.from_file(self.input_ori_file_name,
self.input_add_file_name)
self.control = ControlParams(4)
self.control.read_control_par(self.control_file_name)
self.vpar = VolumeParams()
self.vpar.read_volume_par(self.volume_file_name)
def test_match_detection_to_ref(self):
"""Match detection to reference (sortgrid)"""
xyz_input = np.array([(10, 10, 10), (200, 200, 200), (600, 800, 100),
(20, 10, 2000), (30, 30, 30)],
dtype=float)
coords_count = len(xyz_input)
xy_img_pts_metric = image_coordinates(
xyz_input, self.calibration, self.control.get_multimedia_params())
xy_img_pts_pixel = convert_arr_metric_to_pixel(xy_img_pts_metric,
control=self.control)
# convert to TargetArray object
target_array = TargetArray(coords_count)
for i in range(coords_count):
target_array[i].set_pnr(i)
target_array[i].set_pos(
(xy_img_pts_pixel[i][0], xy_img_pts_pixel[i][1]))
# create randomized target array
indices = range(coords_count)
shuffled_indices = list(range(coords_count))
while indices == shuffled_indices:
random.shuffle(shuffled_indices)
rand_targ_array = TargetArray(coords_count)
for i in range(coords_count):
rand_targ_array[shuffled_indices[i]].set_pos(target_array[i].pos())
rand_targ_array[shuffled_indices[i]].set_pnr(target_array[i].pnr())
# match detection to reference
matched_target_array = match_detection_to_ref(cal=self.calibration,
ref_pts=xyz_input,
img_pts=rand_targ_array,
cparam=self.control)
# assert target array is as before
for i in range(coords_count):
if matched_target_array[i].pos() != target_array[i].pos() \
or matched_target_array[i].pnr() != target_array[i].pnr():
self.fail()
# # pass ref_pts and img_pts with non-equal lengths
# with self.assertRaises(ValueError):
# match_detection_to_ref(cal=self.calibration,
# ref_pts=xyz_input,
# img_pts=TargetArray(coords_count - 1),
# cparam=self.control)
def test_point_positions(self):
"""Point positions"""
# prepare MultimediaParams
mult_params = self.control.get_multimedia_params()
mult_params.set_n1(1.)
mult_params.set_layers(np.array([1.]), np.array([1.]))
mult_params.set_n3(1.)
# 3d point
points = np.array([[17, 42, 0], [17, 42, 0]], dtype=float)
num_cams = 4
ori_tmpl = 'testing_fodder/calibration/sym_cam{cam_num}.tif.ori'
add_file = b'testing_fodder/calibration/cam1.tif.addpar'
calibs = []
targs_plain = []
targs_jigged = []
jigg_amp = 0.5
# read calibration for each camera from files
for cam in range(num_cams):
ori_name = ori_tmpl.format(cam_num=cam + 1).encode()
new_cal = Calibration()
new_cal.from_file(ori_file=ori_name, add_file=add_file)
calibs.append(new_cal)
for cam_num, cam_cal in enumerate(calibs):
new_plain_targ = image_coordinates(
points, cam_cal, self.control.get_multimedia_params())
targs_plain.append(new_plain_targ)
if (cam_num % 2) == 0:
jigged_points = points - np.r_[0, jigg_amp, 0]
else:
jigged_points = points + np.r_[0, jigg_amp, 0]
new_jigged_targs = image_coordinates(
jigged_points, cam_cal, self.control.get_multimedia_params())
targs_jigged.append(new_jigged_targs)
targs_plain = np.array(targs_plain).transpose(1, 0, 2)
targs_jigged = np.array(targs_jigged).transpose(1, 0, 2)
skew_dist_plain = point_positions(targs_plain, self.control, calibs,
self.vpar)
skew_dist_jigged = point_positions(targs_jigged, self.control, calibs,
self.vpar)
if np.any(skew_dist_plain[1] > 1e-10):
self.fail(('skew distance of target#{targ_num} ' \
+ 'is more than allowed').format(
targ_num=np.nonzero(skew_dist_plain[1] > 1e-10)[0][0]))
if np.any(np.linalg.norm(points - skew_dist_plain[0], axis=1) > 1e-6):
self.fail('Rays converge on wrong position.')
if np.any(skew_dist_jigged[1] > 0.7):
self.fail(('skew distance of target#{targ_num} ' \
+ 'is more than allowed').format(
targ_num=np.nonzero(skew_dist_jigged[1] > 1e-10)[0][0]))
if np.any(np.linalg.norm(points - skew_dist_jigged[0], axis=1) > 0.1):
self.fail('Rays converge on wrong position after jigging.')
def test_single_camera_point_positions(self):
"""Point positions for a single camera case"""
num_cams = 1
# prepare MultimediaParams
cpar_file = b'testing_fodder/single_cam/parameters/ptv.par'
vpar_file = b'testing_fodder/single_cam/parameters/criteria.par'
cpar = ControlParams(num_cams)
cpar.read_control_par(cpar_file)
mult_params = cpar.get_multimedia_params()
vpar = VolumeParams()
vpar.read_volume_par(vpar_file)
ori_name = b'testing_fodder/single_cam/calibration/cam_1.tif.ori'
add_name = b'testing_fodder/single_cam/calibration/cam_1.tif.addpar'
calibs = []
# read calibration for each camera from files
new_cal = Calibration()
new_cal.from_file(ori_file=ori_name, add_file=add_name)
calibs.append(new_cal)
# 3d point
points = np.array([[1, 1, 0], [-1, -1, 0]], dtype=float)
targs_plain = []
targs_jigged = []
jigg_amp = 0.4
new_plain_targ = image_coordinates(points, calibs[0], mult_params)
targs_plain.append(new_plain_targ)
jigged_points = points - np.r_[0, jigg_amp, 0]
new_jigged_targs = image_coordinates(jigged_points, calibs[0],
mult_params)
targs_jigged.append(new_jigged_targs)
targs_plain = np.array(targs_plain).transpose(1, 0, 2)
targs_jigged = np.array(targs_jigged).transpose(1, 0, 2)
skew_dist_plain = point_positions(targs_plain, cpar, calibs, vpar)
skew_dist_jigged = point_positions(targs_jigged, cpar, calibs, vpar)
if np.any(np.linalg.norm(points - skew_dist_plain[0], axis=1) > 1e-6):
self.fail('Rays converge on wrong position.')
if np.any(
np.linalg.norm(jigged_points -
skew_dist_jigged[0], axis=1) > 1e-6):
self.fail('Rays converge on wrong position after jigging.')
def test_dumbbell(self):
# prepare MultimediaParams
mult_params = self.control.get_multimedia_params()
mult_params.set_n1(1.)
mult_params.set_layers(np.array([1.]), np.array([1.]))
mult_params.set_n3(1.)
# 3d point
points = np.array([[17.5, 42, 0], [-17.5, 42, 0]], dtype=float)
num_cams = 4
ori_tmpl = 'testing_fodder/dumbbell/cam{cam_num}.tif.ori'
add_file = 'testing_fodder/calibration/cam1.tif.addpar'
calibs = []
targs_plain = []
# read calibration for each camera from files
for cam in range(num_cams):
ori_name = ori_tmpl.format(cam_num=cam + 1)
new_cal = Calibration()
new_cal.from_file(ori_file=ori_name.encode(),
add_file=add_file.encode())
calibs.append(new_cal)
for cam_cal in calibs:
new_plain_targ = flat_image_coordinates(
points, cam_cal, self.control.get_multimedia_params())
targs_plain.append(new_plain_targ)
targs_plain = np.array(targs_plain).transpose(1, 0, 2)
# The cameras are not actually fully calibrated, so the result is not
# an exact 0. The test is that changing the expected distance changes
# the measure.
tf = dumbbell_target_func(targs_plain, self.control, calibs, 35., 0.)
self.assertAlmostEqual(tf, 7.14860, 5) # just a regression test
# As we check the db length, the measure increases...
tf_len = dumbbell_target_func(targs_plain, self.control, calibs, 35.,
1.)
self.assertTrue(tf_len > tf)
# ...but not as much as when giving the wrong length.
tf_too_long = dumbbell_target_func(targs_plain, self.control, calibs,
25., 1.)
self.assertTrue(tf_too_long > tf_len > tf)
class Test_Orientation(unittest.TestCase):
def setUp(self):
self.input_ori_file_name = b'testing_fodder/calibration/cam1.tif.ori'
self.input_add_file_name = b'testing_fodder/calibration/cam2.tif.addpar'
self.control_file_name = b'testing_fodder/control_parameters/control.par'
self.volume_file_name = b'testing_fodder/corresp/criteria.par'
self.calibration = Calibration()
self.calibration.from_file(
self.input_ori_file_name, self.input_add_file_name)
self.control = ControlParams(4)
self.control.read_control_par(self.control_file_name)
self.vpar = VolumeParams()
self.vpar.read_volume_par(self.volume_file_name)
def test_match_detection_to_ref(self):
"""Match detection to reference (sortgrid)"""
xyz_input = np.array([(10, 10, 10),
(200, 200, 200),
(600, 800, 100),
(20, 10, 2000),
(30, 30, 30)], dtype=float)
coords_count = len(xyz_input)
xy_img_pts_metric = image_coordinates(
xyz_input, self.calibration, self.control.get_multimedia_params())
xy_img_pts_pixel = convert_arr_metric_to_pixel(
xy_img_pts_metric, control=self.control)
# convert to TargetArray object
target_array = TargetArray(coords_count)
for i in range(coords_count):
target_array[i].set_pnr(i)
target_array[i].set_pos(
(xy_img_pts_pixel[i][0], xy_img_pts_pixel[i][1]))
# create randomized target array
indices = range(coords_count)
shuffled_indices = list(range(coords_count))
while indices == shuffled_indices:
random.shuffle(shuffled_indices)
rand_targ_array = TargetArray(coords_count)
for i in range(coords_count):
rand_targ_array[shuffled_indices[i]].set_pos(target_array[i].pos())
rand_targ_array[shuffled_indices[i]].set_pnr(target_array[i].pnr())
# match detection to reference
matched_target_array = match_detection_to_ref(cal=self.calibration,
ref_pts=xyz_input,
img_pts=rand_targ_array,
cparam=self.control)
# assert target array is as before
for i in range(coords_count):
if matched_target_array[i].pos() != target_array[i].pos() \
or matched_target_array[i].pnr() != target_array[i].pnr():
self.fail()
# # pass ref_pts and img_pts with non-equal lengths
# with self.assertRaises(ValueError):
# match_detection_to_ref(cal=self.calibration,
# ref_pts=xyz_input,
# img_pts=TargetArray(coords_count - 1),
# cparam=self.control)
def test_point_positions(self):
"""Point positions"""
# prepare MultimediaParams
mult_params = self.control.get_multimedia_params()
mult_params.set_n1(1.)
mult_params.set_layers(np.array([1.]), np.array([1.]))
mult_params.set_n3(1.)
# 3d point
points = np.array([[17, 42, 0],
[17, 42, 0]], dtype=float)
num_cams = 4
ori_tmpl = 'testing_fodder/calibration/sym_cam{cam_num}.tif.ori'
add_file = b'testing_fodder/calibration/cam1.tif.addpar'
calibs = []
targs_plain = []
targs_jigged = []
jigg_amp = 0.5
# read calibration for each camera from files
for cam in range(num_cams):
ori_name = ori_tmpl.format(cam_num=cam + 1).encode()
new_cal = Calibration()
new_cal.from_file(ori_file=ori_name, add_file=add_file)
calibs.append(new_cal)
for cam_num, cam_cal in enumerate(calibs):
new_plain_targ = image_coordinates(
points, cam_cal, self.control.get_multimedia_params())
targs_plain.append(new_plain_targ)
if (cam_num % 2) == 0:
jigged_points = points - np.r_[0, jigg_amp, 0]
else:
jigged_points = points + np.r_[0, jigg_amp, 0]
new_jigged_targs = image_coordinates(
jigged_points, cam_cal, self.control.get_multimedia_params())
targs_jigged.append(new_jigged_targs)
targs_plain = np.array(targs_plain).transpose(1,0,2)
targs_jigged = np.array(targs_jigged).transpose(1,0,2)
skew_dist_plain = point_positions(targs_plain, self.control, calibs, self.vpar)
skew_dist_jigged = point_positions(targs_jigged, self.control, calibs, self.vpar)
if np.any(skew_dist_plain[1] > 1e-10):
self.fail(('skew distance of target#{targ_num} ' \
+ 'is more than allowed').format(
targ_num=np.nonzero(skew_dist_plain[1] > 1e-10)[0][0]))
if np.any(np.linalg.norm(points - skew_dist_plain[0], axis=1) > 1e-6):
self.fail('Rays converge on wrong position.')
if np.any(skew_dist_jigged[1] > 0.7):
self.fail(('skew distance of target#{targ_num} ' \
+ 'is more than allowed').format(
targ_num=np.nonzero(skew_dist_jigged[1] > 1e-10)[0][0]))
if np.any(np.linalg.norm(points - skew_dist_jigged[0], axis=1) > 0.1):
self.fail('Rays converge on wrong position after jigging.')
def test_single_camera_point_positions(self):
"""Point positions for a single camera case"""
num_cams = 1
# prepare MultimediaParams
cpar_file = b'testing_fodder/single_cam/parameters/ptv.par'
vpar_file = b'testing_fodder/single_cam/parameters/criteria.par'
cpar = ControlParams(num_cams)
cpar.read_control_par(cpar_file)
mult_params = cpar.get_multimedia_params()
vpar = VolumeParams()
vpar.read_volume_par(vpar_file)
ori_name = b'testing_fodder/single_cam/calibration/cam_1.tif.ori'
add_name = b'testing_fodder/single_cam/calibration/cam_1.tif.addpar'
calibs = []
# read calibration for each camera from files
new_cal = Calibration()
new_cal.from_file(ori_file=ori_name, add_file=add_name)
calibs.append(new_cal)
# 3d point
points = np.array([[1, 1, 0],
[-1, -1, 0]], dtype=float)
targs_plain = []
targs_jigged = []
jigg_amp = 0.4
new_plain_targ = image_coordinates(
points, calibs[0], mult_params)
targs_plain.append(new_plain_targ)
jigged_points = points - np.r_[0, jigg_amp, 0]
new_jigged_targs = image_coordinates(
jigged_points, calibs[0], mult_params)
targs_jigged.append(new_jigged_targs)
targs_plain = np.array(targs_plain).transpose(1,0,2)
targs_jigged = np.array(targs_jigged).transpose(1,0,2)
skew_dist_plain = point_positions(targs_plain, cpar, calibs, vpar)
skew_dist_jigged = point_positions(targs_jigged, cpar, calibs, vpar)
if np.any(np.linalg.norm(points - skew_dist_plain[0], axis=1) > 1e-6):
self.fail('Rays converge on wrong position.')
if np.any(np.linalg.norm(jigged_points - skew_dist_jigged[0], axis=1) > 1e-6):
self.fail('Rays converge on wrong position after jigging.')
def test_dumbbell(self):
# prepare MultimediaParams
mult_params = self.control.get_multimedia_params()
mult_params.set_n1(1.)
mult_params.set_layers(np.array([1.]), np.array([1.]))
mult_params.set_n3(1.)
# 3d point
points = np.array([[17.5, 42, 0],
[-17.5, 42, 0]], dtype=float)
num_cams = 4
ori_tmpl = 'testing_fodder/dumbbell/cam{cam_num}.tif.ori'
add_file = 'testing_fodder/calibration/cam1.tif.addpar'
calibs = []
targs_plain = []
# read calibration for each camera from files
for cam in range(num_cams):
ori_name = ori_tmpl.format(cam_num=cam + 1)
new_cal = Calibration()
new_cal.from_file(ori_file=ori_name.encode(), add_file=add_file.encode())
calibs.append(new_cal)
for cam_cal in calibs:
new_plain_targ = flat_image_coordinates(
points, cam_cal, self.control.get_multimedia_params())
targs_plain.append(new_plain_targ)
targs_plain = np.array(targs_plain).transpose(1,0,2)
# The cameras are not actually fully calibrated, so the result is not
# an exact 0. The test is that changing the expected distance changes
# the measure.
tf = dumbbell_target_func(targs_plain, self.control, calibs, 35., 0.)
self.assertAlmostEqual(tf, 7.14860, 5) # just a regression test
# As we check the db length, the measure increases...
tf_len = dumbbell_target_func(
targs_plain, self.control, calibs, 35., 1.)
self.assertTrue(tf_len > tf)
# ...but not as much as when giving the wrong length.
tf_too_long = dumbbell_target_func(
targs_plain, self.control, calibs, 25., 1.)
self.assertTrue(tf_too_long > tf_len > tf)
