def export(self, reconstruction, graph, data):
exporter = csfm.OpenMVSExporter()
for camera in reconstruction.cameras.values():
if camera.projection_type == 'perspective':
w, h = camera.width, camera.height
K = np.array([
[camera.focal, 0, (w - 1.0) / 2 / max(w, h)],
[0, camera.focal, (h - 1.0) / 2 / max(w, h)],
[0, 0, 1],
])
exporter.add_camera(str(camera.id), K)
for shot in reconstruction.shots.values():
if shot.camera.projection_type == 'perspective':
image_path = data._undistorted_image_file(shot.id)
exporter.add_shot(str(os.path.abspath(image_path)),
str(shot.id), str(shot.camera.id),
shot.pose.get_rotation_matrix(),
shot.pose.get_origin())
for point in reconstruction.points.values():
shots = graph[point.id].keys()
coordinates = np.array(point.coordinates, dtype=np.float64)
exporter.add_point(coordinates, shots)
opensfm_io.mkdir_p(data.data_path + '/openmvs')
exporter.export(data.data_path + '/openmvs/scene.mvs')
def import_video_with_gpx(video_file, gpx_file, output_path, dx, dt=None, start_time=None, visual=False, image_description=None):
points = geotag_from_gpx.get_lat_lon_time(gpx_file)
orientation = video_orientation(video_file)
if start_time:
video_start_time = dateutil.parser.parse(start_time)
else:
try:
exifdate = Popen(['exiftool', '-CreateDate', '-b', video_file], stdout=PIPE).stdout.read()
video_start_time = datetime.datetime.strptime(exifdate,'%Y:%m:%d %H:%M:%S')
except:
print 'Video recording timestamp not found. Using first GPS point time.'
video_start_time = points[0][0]
try:
duration = Popen(['exiftool', '-MediaDuration', '-b', video_file], stdout=PIPE).stdout.read()
video_duration = float(duration)
video_end_time = video_start_time + datetime.timedelta(seconds=video_duration)
except:
print 'Video end time not found. Using last GPS point time.'
video_end_time = points[-1][0]
print 'GPS track starts at:', points[0][0]
print 'Video starts at:', video_start_time
# Extract video frames.
opensfm_io.mkdir_p(output_path)
key_points = geotag_from_gpx.sample_gpx(points, dx, dt)
cap = cv2.VideoCapture(video_file)
image_files = []
for p in key_points:
dt = (p[0] - video_start_time).total_seconds()
if dt > 0:
CAP_PROP_POS_MSEC = cv2.CAP_PROP_POS_MSEC if context.OPENCV3 else cv2.cv.CV_CAP_PROP_POS_MSEC
cap.set(CAP_PROP_POS_MSEC, int(dt * 1000))
ret, frame = cap.read()
if ret:
print 'Grabbing frame for time', p[0]
filepath = os.path.join(output_path, p[0].strftime("%Y_%m_%d_%H_%M_%S_%f")[:-3] + '.jpg')
cv2.imwrite(filepath, frame)
geotag_from_gpx.add_exif_using_timestamp(filepath, points, timestamp=p[0], orientation=orientation)
# Display the resulting frame
if visual:
# Display the resulting frame
max_display_size = 800
resize_ratio = float(max_display_size) / max(frame.shape[0], frame.shape[1])
frame = cv2.resize(frame, dsize=(0, 0), fx=resize_ratio, fy=resize_ratio)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == 27:
break
image_files.append(filepath)
# When everything done, release the capture
cap.release()
if visual:
cv2.destroyAllWindows()
return image_files
def __save_features(self, filepath, image, points, descriptors, colors=None):
opensfm_io.mkdir_p(self.__feature_path())
feature_type = self.config.get('feature_type')
if ((feature_type == 'AKAZE' and self.config.get('akaze_descriptor') in ['MLDB_UPRIGHT', 'MLDB']) or
(feature_type == 'HAHOG' and self.config.get('hahog_normalize_to_uchar', False))):
feature_data_type = np.uint8
else:
feature_data_type = np.float32
np.savez_compressed(filepath,
points=points.astype(np.float32),
descriptors=descriptors.astype(feature_data_type),
colors=colors)
def save_raw_depthmap(self, image, depth, plane, score):
opensfm_io.mkdir_p(self._depthmap_path())
filepath = self._depthmap_file(image, 'raw.npz')
np.savez_compressed(filepath, depth=depth, plane=plane, score=score)
def save_undistorted_image(self, image, array):
opensfm_io.mkdir_p(self._undistorted_image_path())
cv2.imwrite(self._undistorted_image_file(image), array[:, :, ::-1])
def save_matches(self, image, matches):
opensfm_io.mkdir_p(self.__matches_path())
with gzip.open(self.__matches_file(image), 'wb') as fout:
pickle.dump(matches, fout)
def save_exif(self, image, data):
opensfm_io.mkdir_p(self.__exif_path())
with open(self.__exif_file(image), 'w') as fout:
opensfm_io.json_dump(data, fout)