def test_rich_compare(self):
self.cp_obj2 = ControlParams(4)
self.cp_obj2.read_control_par(self.input_control_par_file_name)
self.cp_obj3 = ControlParams(4)
self.cp_obj3.read_control_par(self.input_control_par_file_name)
self.failUnless(self.cp_obj2 == self.cp_obj3)
self.failIf(self.cp_obj2 != self.cp_obj3)
self.cp_obj2.set_hp_flag(False)
self.failUnless(self.cp_obj2 != self.cp_obj3)
self.failIf(self.cp_obj2 == self.cp_obj3)
with self.assertRaises(TypeError):
var = (self.cp_obj2 > self.cp_obj3) # unhandled operator >
def setUp(self):
self.input_img = np.array(
[[0, 0, 0, 0, 0], [0, 255, 255, 255, 0], [0, 255, 255, 255, 0],
[0, 255, 255, 255, 0], [0, 0, 0, 0, 0]],
dtype=np.uint8)
self.filter_hp = 0
self.control = ControlParams(4)
self.control.set_image_size((5, 5))
def setUp(self):
self.input_control_par_file_name = "testing_fodder/control_parameters/control.par"
self.temp_output_directory = "testing_fodder/control_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 ControlParams class
self.cp_obj = ControlParams(4)
def setUp(self):
self.input_ori_file_name = r'testing_fodder/calibration/cam1.tif.ori'
self.input_add_file_name = r'testing_fodder/calibration/cam2.tif.addpar'
self.control_file_name = r'testing_fodder/control_parameters/control.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)
def setUp(self):
control_file_name = r'testing_fodder/corresp/control.par'
self.control = ControlParams(4)
self.control.read_control_par(control_file_name)
self.cal = Calibration()
self.cal.from_file("testing_fodder/calibration/cam1.tif.ori",
"testing_fodder/calibration/cam1.tif.addpar")
self.orig_cal = Calibration()
self.orig_cal.from_file("testing_fodder/calibration/cam1.tif.ori",
"testing_fodder/calibration/cam1.tif.addpar")
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_transforms(self):
"""Transform in well-known setup gives precalculates results."""
cpar = ControlParams(1)
cpar.set_image_size((1280, 1000))
cpar.set_pixel_size((0.1, 0.1))
metric_pos = np.array([[1., 1.], [-10., 15.], [20., -30.]])
pixel_pos = np.array([[650., 490.], [540., 350.], [840., 800.]])
np.testing.assert_array_almost_equal(
pixel_pos, convert_arr_metric_to_pixel(metric_pos, cpar))
np.testing.assert_array_almost_equal(
metric_pos, convert_arr_pixel_to_metric(pixel_pos, cpar))
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_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 test_instantiate_fast(self):
"""ControlParams instantiation through constructor"""
cp = ControlParams(4, ['headers', 'hp', 'allcam'], (1280, 1024),
(15.15,16.16), 18, [19.19], [21.21], 20.20)
self.failUnless(cp.get_num_cams() == 4)
self.failUnless(cp.get_hp_flag())
self.failUnless(cp.get_allCam_flag())
self.failUnless(cp.get_tiff_flag())
self.failUnless(cp.get_image_size(), (1280, 1024))
self.failUnless(cp.get_pixel_size() == (15.15,16.16))
self.failUnless(cp.get_chfield() == 0)
mm = cp.get_multimedia_params()
self.failUnless(mm.get_n1() == 18)
self.failUnless(mm.get_n2()[0] == 19.19)
self.failUnless(mm.get_n3() == 20.20)
self.failUnless(mm.get_d()[0] == 21.21)
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_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 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_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 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_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 setUp(self):
self.control = ControlParams(4)
self.calibration = Calibration()
import numpy as np
from optv.calibration import Calibration
from optv.parameters import ControlParams
from optv.imgcoord import image_coordinates
from optv.transforms import convert_arr_metric_to_pixel
num_cams = 3
num_frames = 5
velocity = 0.01
part_traject = np.zeros((num_frames, 3))
part_traject[:, 0] = np.r_[:num_frames] * velocity
# Find targets on each camera.
cpar = ControlParams(3)
cpar.read_control_par("testing_fodder/track/parameters/control_newpart.par")
targs = []
for cam in xrange(num_cams):
cal = Calibration()
cal.from_file("testing_fodder/cal/sym_cam%d.tif.ori" % (cam + 1),
"testing_fodder/cal/cam1.tif.addpar")
targs.append(
convert_arr_metric_to_pixel(
image_coordinates(part_traject, cal, cpar.get_multimedia_params()),
cpar))
for frame in xrange(num_frames):
# write 3D positions:
with open("testing_fodder/track/res_orig/particles.%d" % (frame + 1),
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()
