flight_path = '/media/sean/D2F2E7B2F2E798CD/Users/student/neo_data/'
flight = tb.open_file('/media/sean/D2F2E7B2F2E798CD/Users/student/neo_data/fc2_f5.hdf', 'r')
pva = flight.root.nov_span.pva
pva_times = flight.root.nov_span.pva.cols.t_valid
img_times = flight.root.camera.image_raw.compressed.metadata.cols.t_valid
img_array = flight.root.camera.image_raw.compressed.images
# Find the Lat Lon of the center point of the image
img0 = img_times[0] - 1.0
imgN = img_times[-1] + 1.0
good_pva = pva.read_where('(t_valid >= img0) & (t_valid <= imgN)')
lon_lat_h = np.array(
[good_pva[:]['lon'], good_pva[:]['lat'], good_pva[:]['height']]).T
rpy = np.array([nu.DcmToRpy(dcm.reshape(3, 3)) for dcm in good_pva['c_nav_veh']])
pva_interp = interp1d(good_pva['t_valid'], np.hstack((lon_lat_h, rpy)).T)
srtm_path = '/media/sean/D2F2E7B2F2E798CD/Users/student/neo_data/srtm'
cam_path = '/home/sean/PFVisNavLibBranches/navfeatdb/data/fc2_cam_model.yaml'
uvan_frames = '/home/sean/PFVisNavLibBranches/navfeatdb/data/fc2_pod_frames.yaml'
geoid_file = '/media/sean/D2F2E7B2F2E798CD/Users/student/neo_data/egm96-15.tif'
tf = llproj.LocalLevelProjector(srtm_path, geoid_file)
tf.load_cam_and_vehicle_frames(uvan_frames)
tf.load_camera_cal(cam_path)
idx1 = 343
num_feat = 4500
img1 = img_array[idx1]
out_path = args.output
if (not args.append) and (os.path.exists(out_path)):
shutil.rmtree(out_path)
if not os.path.exists(out_path):
os.makedirs(os.path.join(out_path, 'feat/df'))
os.makedirs(os.path.join(out_path, 'feat/desc'))
img0 = img_times[0] - 1.0
imgN = img_times[-1] + 1.0
good_pva = pva.read_where('(t_valid >= img0) & (t_valid <= imgN)')
lon_lat_h = np.array(
[good_pva[:]['lon'], good_pva[:]['lat'], good_pva[:]['height']]).T
rpy = np.array(
[nu.DcmToRpy(dcm.reshape(3, 3)) for dcm in good_pva['c_nav_veh']])
pva_interp = interp1d(good_pva['t_valid'], np.hstack((lon_lat_h, rpy)).T)
srtm_path = args.srtm
cam_path = os.path.join(args.meta, 'fc2_cam_model.yaml')
uvan_frames = os.path.join(args.meta, 'fc2_pod_frames.yaml')
geoid_file = args.geoid
tf = llproj.LocalLevelProjector(srtm_path, geoid_file)
tf.load_cam_and_vehicle_frames(uvan_frames)
tf.load_camera_cal(cam_path)
# det_f = os.path.join(args.meta, 'default_detector_SIFT.yaml')
# des_f = os.path.join(args.meta, 'default_descriptor_SIFT.yaml')
# detector, descriptor = f2d.load_feature_operators(det_f, des_f)
# detector = cv2.BRISK_create()
def extract_kp_from_frame(flight_dict):
"""
This function extracts Keypoints
:param flight_dict:
:param img_num:
:param o_path:
:param kp_det_func:
:param kp_desc_func:
:param p_meta:
:return:
"""
import navfeatdb.db.features as fdb
import bcolz
import os
import numpy as np
import tables as tb
from scipy.interpolate import interp1d
import navfeatdb.utils.nav as nu
import navfeatdb.projection.localprojection as llproj
flight_hdf = flight_dict['flight_hdf']
img_num = flight_dict['img_num']
o_path = flight_dict['o_path']
kp_det_func = flight_dict['kp_det_func']
kp_desc_func = flight_dict['kp_desc_func']
p_meta = flight_dict['p_meta']
flight = tb.open_file(flight_hdf, 'r')
pva = flight.root.nov_span.pva
pva_times = flight.root.nov_span.pva.cols.t_valid
img_times = flight.root.camera.image_raw.compressed.metadata.cols.t_valid
images = flight.root.camera.image_raw.compressed.images
img0 = img_times[0] - 1.0
imgN = img_times[-1] + 1.0
good_pva = pva.read_where('(t_valid >= img0) & (t_valid <= imgN)')
lon_lat_h = np.array(
[good_pva[:]['lon'], good_pva[:]['lat'], good_pva[:]['height']]).T
rpy = np.array(
[nu.DcmToRpy(dcm.reshape(3, 3)) for dcm in good_pva['c_nav_veh']])
pva_interp = interp1d(good_pva['t_valid'], np.hstack((lon_lat_h, rpy)).T)
llh_rpy = pva_interp(img_times[img_num])
lon_lat_h = llh_rpy[0:3]
c_n_v = nu.rpy_to_cnb(*llh_rpy[3:])
proj = llproj.LocalLevelProjector(p_meta['srtm_path'],
p_meta['geoid_file'])
proj.load_cam_and_vehicle_frames(p_meta['uvan_frames'])
proj.load_camera_cal(p_meta['cam_path'])
aie = fdb.AirborneImageExtractor(kp_det_func(), kp_desc_func(), proj)
feat_df, desc = aie.extract_features(images[img_num], lon_lat_h, c_n_v)
center_wgs = proj.project_center(lon_lat_h, c_n_v).flatten()
df_path = os.path.join(o_path, 'feat/df/feat_%d.hdf' % img_num)
desc_path = os.path.join(o_path, 'feat/desc/desc_%d' % img_num)
if feat_df is None:
kp_meta = [
0, center_wgs[0], center_wgs[1], df_path, desc_path, flight_hdf,
img_num
]
else:
kp_meta = [
desc.shape[0], center_wgs[0], center_wgs[1], df_path, desc_path,
flight_hdf, img_num
]
feat_df.to_hdf(os.path.join(o_path, df_path),
'feat_df',
mode='w',
format='table',
complib='zlib',
complevel=7)
bcolz.carray(desc, rootdir=os.path.join(o_path, desc_path),
mode='w').flush()
flight.close()
return kp_meta