def gnrt_user(train_data, one_img, gnrt_num, user, bldshps, all_data):
for ci in range(gnrt_num):
train_data.append(base.TrainOnePoint(one_img, user))
train_data[-1].dis = one_img.dis.copy()
first = random.randrange(len(all_data))
train_data[-1].user = all_data[first].user.copy()
util.recal_dis(train_data[-1], bldshps)
util.get_slt_land_cor_init(train_data[-1], bldshps, user)
def gnrt_rot(train_data, one_img, gnrt_num, user, bldshps):
for ci in range(gnrt_num):
train_data.append(base.TrainOnePoint(one_img, user))
train_data[-1].dis = one_img.dis.copy()
#here, after rand the dif of angle, we should update slt
for i in range(3):
train_data[-1].init_angle[i] \
+=2*rand_angle_range[i]*np.random.random((1,))-rand_angle_range[i]
util.get_slt_land_cor_init(train_data[-1], bldshps, user)
def gnrt_tslt(train_data, one_img, gnrt_num, user):
print('gnrt_tslt')
for ci in range(gnrt_num):
train_data.append(base.TrainOnePoint(one_img, user))
for i in range(2):
train_data[-1].init_tslt[i]\
+=2*rand_tslt_range[i]*np.random.random((1,))-rand_tslt_range[i]
train_data[-1].init_tslt[2]\
+=2*rand_tslt_range[2]*train_data[-1].tslt[2]*np.random.random((1,))-rand_tslt_range[2]*train_data[-1].tslt[2]
def gnrt_fcs(train_data, one_img, gnrt_num, user, bldshps):
for ci in range(gnrt_num):
train_data.append(base.TrainOnePoint(one_img, user))
# =============================================================================
# print('one_img land cor------------------one_img:\n',one_img.land_cor)
# print('land cor------------------',train_data[-1].land_cor)
# =============================================================================
train_data[-1].dis = one_img.dis.copy()
train_data[-1].fcs += (np.random.random(
(1, ))[0]) * range_rand_fcs * 2 - range_rand_fcs
util.recal_dis(train_data[-1], bldshps)
def gnrt_exp(train_data, one_img, gnrt_num, user, all_data, bldshps):
for ci in range(gnrt_num):
train_data.append(base.TrainOnePoint(one_img, user))
if (rand_pick_exp == 1):
first = random.randrange(len(all_data))
second = random.randrange(len(all_data[first].data))
train_data[-1].init_exp = all_data[first].data[second].exp.copy()
else:
for i in range(1, train_data[-1].init_exp.shape[0]):
train_data[-1].init_exp[i]\
+=2*rand_exp_range*np.random.random((1,))-rand_exp_range
util.get_slt_land_cor_init(train_data[-1], bldshps, user)