def _calc_mlc_positions(self, leaf_centers):
"""Calculate the positions of all the MLC pairs."""
diff = np.diff(leaf_centers)
sample_width = np.round(np.median(diff*2/5)/2).astype(int)
for mlc_num, mlc_peak_loc in enumerate(np.round(leaf_centers).astype(int)):
mlc_rows = np.arange(mlc_peak_loc-sample_width, mlc_peak_loc+sample_width+1)
if self.orientation == orientations['UD']:
pix_vals = np.median(self._analysis_array[mlc_rows, :], axis=0)
else:
pix_vals = np.median(self._analysis_array[:, mlc_rows], axis=1)
prof = Profile(pix_vals)
prof.find_FWXM_peaks(fwxm=80, min_peak_distance=0.01, min_peak_height=0.5, interpolate=True)
for idx, peak in enumerate(prof.peaks):
if self.orientation == orientations['UD']:
meas = MLC_Meas((peak.idx, mlc_rows[0]), (peak.idx, mlc_rows[-1]))
else:
meas = MLC_Meas((mlc_rows[0], peak.idx), (mlc_rows[-1], peak.idx))
self.pickets[idx].mlc_meas.append(meas)
def _find_leaf_centers(self, hdmlc):
"""Return the leaf centers perpendicular to the leaf motion."""
# generate some settings
sm_lf_wdth = 5 * self.image.dpmm
bg_lf_wdth = sm_lf_wdth * 2
if hdmlc:
sm_lf_wdth /= 2
bg_lf_wdth /= 2
self._sm_lf_meas_wdth = slmw = int(round(sm_lf_wdth*3/4))
self._bg_lf_meas_wdth = blmw = int(round(bg_lf_wdth*3/4))
bl_ex = int(bg_lf_wdth/4)
sm_ex = int(sm_lf_wdth/4)
# generate leaf profile
if self.orientation == orientations['UD']:
leaf_prof = np.mean(self._analysis_array, 1)
center = self.image.center.y
else:
leaf_prof = np.mean(self._analysis_array, 0)
center = self.image.center.x
leaf_prof = Profile(leaf_prof)
# ground profile to reasonable level
_, peak_idxs = leaf_prof.find_peaks(min_peak_distance=self._sm_lf_meas_wdth, exclude_lt_edge=sm_ex,
exclude_rt_edge=sm_ex)
min_val = leaf_prof.y_values[peak_idxs[0]:peak_idxs[-1]].min()
leaf_prof.y_values[leaf_prof.y_values < min_val] = min_val
# remove unevenness in signal
leaf_prof.y_values = signal.detrend(leaf_prof.y_values, bp=[int(len(leaf_prof.y_values)/3), int(len(leaf_prof.y_values)*2/3)])
_, peak_idxs = leaf_prof.find_peaks(min_peak_distance=self._sm_lf_meas_wdth, exclude_lt_edge=sm_ex, exclude_rt_edge=sm_ex)
leaf_range = (peak_idxs[-1] - peak_idxs[0]) / self.image.dpmm # mm
sm_lf_range = 220 # mm
# find leaf peaks
if leaf_range > sm_lf_range:
lt_biglittle_lf_bndry = int(round(center - 100 * self.image.dpmm))
rt_biglittle_lf_bndry = int(round(center + 100 * self.image.dpmm))
pp = leaf_prof.subdivide([lt_biglittle_lf_bndry, rt_biglittle_lf_bndry], slmw)
if len(pp) != 3:
raise ValueError("3 Profiles weren't found but should have been")
# Left Big MLC region
_, peak_idxs = pp[0].find_peaks(min_peak_distance=blmw, exclude_lt_edge=bl_ex)
peak_diff = np.diff(peak_idxs).mean()
lt_v_idx = np.array(peak_idxs[:-1]) + peak_diff/2
# Middle, small MLC region
_, peak_idxs = pp[1].find_peaks(min_peak_distance=slmw)
peak_diff = np.diff(peak_idxs).mean()
mid_v_idx = np.array(peak_idxs[:-1]) + peak_diff / 2
# Right Big MLC region
_, peak_idxs = pp[2].find_peaks(min_peak_distance=blmw,
exclude_rt_edge=bl_ex)
peak_diff = np.diff(peak_idxs).mean()
rt_v_idx = np.array(peak_idxs[:-1]) + peak_diff / 2
leaf_center_idxs = np.concatenate((lt_v_idx, mid_v_idx, rt_v_idx))
else:
_, peak_idxs = leaf_prof.find_peaks(min_peak_distance=slmw, exclude_lt_edge=sm_ex,
exclude_rt_edge=sm_ex)
_, peak_idxs = leaf_prof.find_FWXM_peaks(min_peak_distance=slmw, interpolate=True)
peak_diff = np.diff(peak_idxs).mean()
leaf_center_idxs = np.array(peak_idxs[:-1]) + peak_diff / 2
return leaf_center_idxs