def test_read(self): inp_filename = "testing_fodder/target_parameters/targ_rec.par" tp = TargetParams() tp.read(inp_filename) self.assertEqual(tp.get_max_discontinuity(), 5) self.assertEqual(tp.get_pixel_count_bounds(), (3, 100)) self.assertEqual(tp.get_xsize_bounds(), (1, 20)) self.assertEqual(tp.get_ysize_bounds(), (1, 20)) self.assertEqual(tp.get_min_sum_grey(), 3) numpy.testing.assert_array_equal( tp.get_grey_thresholds(), [3, 2, 2, 3])
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()