def __init__(self, parser=None):
print('Training directory: ', self.train_dir)
print('Test directory: ', self.test_dir)
print('Transfer learning directory: ', self.transfer_dir)
print('Data save directory: ', self.save_dir)
print()
init_params(self.d, parser=parser)
import numpy as np
import cv2
import initialization as init
tc = init.init_params()
def find_nearest(self, a):
A = np.asarray(self)
idx = (np.abs(A - a)).argmin()
return A[idx], idx
#SENSOR-PLANE DESIGN based on SENSOR REGION WIDTH, CLASS/LABEL-SIZE and NUMBER of DETECTORS
def sensorplane_geometry():
fl_num_det_row = int(np.floor(tc.NUM_CLASS**(0.5)))
ce_num_det_row = int(np.ceil(tc.NUM_CLASS**(0.5)))
choices = np.asarray([fl_num_det_row, ce_num_det_row])
square_arrgmnt = choices**2
remain_det = np.abs(tc.NUM_CLASS - square_arrgmnt)
slct = np.argmin(remain_det)
slct_ = np.mod(slct + 1, 2)
rows_label = choices[slct]
col_distribution = np.ones((rows_label, 1)) * rows_label
col_distribution[rows_label // 2 - remain_det[slct] // 2:rows_label // 2 -
remain_det[slct] // 2 + remain_det[slct]] = choices[slct_]
nDet = int(np.amin(col_distribution))
NDet = int(np.amax(col_distribution))