def tiepoint_error_calculation(self,
image_collection_id,
scene_id,
history=None):
from PIL import Image
import numpy as np
import vpgl_adaptor
from voxel_globe.meta import models
import voxel_globe.tools
from voxel_globe.tools.camera import get_krt
from voxel_globe.tools.celery import Popen
from voxel_globe.tools.xml_dict import load_xml
self.update_state(state='INITIALIZE',
meta={
'id': image_collection_id,
'scene': scene_id
})
image_collection = models.ImageCollection.objects.get(
id=image_collection_id).history(history)
control_points = {}
for fr, image in enumerate(image_collection.images.all()):
image = image.history(history)
tiepoint_ids = set([
x
for imagen in models.Image.objects.filter(objectId=image.objectId)
for x in imagen.tiepoint_set.all().values_list('objectId',
flat=True)
])
for tiepoint_id in tiepoint_ids:
tiepoint = models.TiePoint.objects.get(
objectId=tiepoint_id, newerVersion=None).history(history)
#demoware code hack!
if not 'error' in tiepoint.geoPoint.name.lower():
continue
if not tiepoint.deleted:
control_point_id = tiepoint.geoPoint.objectId
if control_point_id not in control_points:
control_points[control_point_id] = {'tiepoints': {}}
control_points[control_point_id]['tiepoints'][fr] = list(
tiepoint.point)
lla_xyz = models.ControlPoint.objects.get(
objectId=control_point_id,
newerVersion=None).history(history).point.coords
control_points[control_point_id]['3d'] = [
lla_xyz[x] for x in [1, 0, 2]
]
#filter only control points with more than 1 tiepoint
control_points = {
k: v
for k, v in control_points.iteritems()
if len(v['tiepoints'].keys()) > 1
}
origin_xyz = list(models.Scene.objects.get(id=scene_id).origin)
lvcs = vpgl_adaptor.create_lvcs(origin_xyz[1], origin_xyz[0],
origin_xyz[2], 'wgs84')
for control_point in control_points:
control_points[control_point][
'lvcs'] = vpgl_adaptor.convert_to_local_coordinates2(
lvcs, *control_points[control_point]['3d'])
images = {}
with voxel_globe.tools.task_dir('visualsfm', cd=True) as processing_dir:
dummy_imagename = os.path.join(processing_dir, 'blank.jpg')
img = Image.fromarray(np.empty([1, 1], dtype=np.uint8))
img.save(dummy_imagename)
#Thank you stupid site file
for fr, image in enumerate(image_collection.images.all()):
(K, R, T, o) = get_krt(image.history(history), history=history)
images[fr] = image.objectId
with open(os.path.join(processing_dir, 'frame_%05d.txt' % fr),
'w') as fid:
print >> fid, (("%0.18f " * 3 + "\n") * 3) % (
K[0, 0], K[0, 1], K[0, 2], K[1, 0], K[1, 1], K[1, 2],
K[2, 0], K[2, 1], K[2, 2])
print >> fid, (("%0.18f " * 3 + "\n") * 3) % (
R[0, 0], R[0, 1], R[0, 2], R[1, 0], R[1, 1], R[1, 2],
R[2, 0], R[2, 1], R[2, 2])
print >> fid, ("%0.18f " * 3 + "\n") % (T[0, 0], T[1, 0], T[2,
0])
site_in_name = os.path.join(processing_dir, 'site.xml')
site_out_name = os.path.join(processing_dir, 'site2.xml')
with open(site_in_name, 'w') as fid:
fid.write('''<BWM_VIDEO_SITE name="Triangulation">
<videoSiteDir path="%s">
</videoSiteDir>
<videoPath path="%s">
</videoPath>
<cameraPath path="%s/*.txt">
</cameraPath>
<Objects>
</Objects>ve
<Correspondences>\n''' % (processing_dir, dummy_imagename, processing_dir))
for control_point_index, control_point_id in enumerate(
control_points):
fid.write('<Correspondence id="%d">\n' % control_point_index)
for fr, tie_point in control_points[control_point_id][
'tiepoints'].iteritems():
fid.write('<CE fr="%d" u="%f" v="%f"/>\n' %
(fr, tie_point[0], tie_point[1]))
fid.write('</Correspondence>\n')
control_points[control_point_id]['id'] = control_point_index
fid.write('''</Correspondences>
</BWM_VIDEO_SITE>\n''')
#triangulate the points
Popen([
'bwm_triangulate_2d_corrs', '-site', site_in_name, '-out',
site_out_name
],
logger=logger).wait()
#Read in the result, and load into points_triangulate structure
xml = load_xml(site_out_name)
points_triangulate_id = []
points_triangulate = []
for correspondence in xml['Correspondences']['Correspondence']:
points_triangulate_id.append(int(correspondence.at['id']))
points_triangulate.append(
(float(correspondence['corr_world_point'].at['X']),
float(correspondence['corr_world_point'].at['Y']),
float(correspondence['corr_world_point'].at['Z'])))
#Read the points out of the control points structure, but make sure they are
#in the same order (check id == point_id
points_orig = []
for point_id in points_triangulate_id:
point = [
v['lvcs'] for k, v in control_points.iteritems()
if v['id'] == point_id
]
points_orig.append(point[0])
points_orig = np.array(points_orig)
points_triangulate = np.array(points_triangulate)
error = np.linalg.norm((points_orig - points_triangulate),
axis=0) / (points_orig.shape[0]**0.5)
result = {}
result['error'] = list(error)
result['horizontal_accuracy'] = 2.4477 * 0.5 * (error[0] + error[1])
result['vertical_accuracy'] = 1.96 * error[2]
return result
def tiepoint_registration(self, image_collection_id, history=None):
from PIL import Image
import numpy as np
from django.contrib.gis import geos
import vpgl_adaptor
from vsi.io.krt import Krt
from voxel_globe.meta import models
import voxel_globe.tools
from voxel_globe.tools.camera import get_krt, save_krt
from voxel_globe.tools.celery import Popen
from voxel_globe.tools.xml_dict import load_xml
self.update_state(state='INITIALIZE', meta={'id': image_collection_id})
image_collection = models.ImageCollection.objects.get(
id=image_collection_id).history(history)
control_points = {}
for fr, image in enumerate(image_collection.images.all()):
image = image.history(history)
tiepoint_ids = set([
x
for imagen in models.Image.objects.filter(objectId=image.objectId)
for x in imagen.tiepoint_set.all().values_list('objectId',
flat=True)
])
for tiepoint_id in tiepoint_ids:
tiepoint = models.TiePoint.objects.get(
objectId=tiepoint_id, newerVersion=None).history(history)
#demoware code hack!
if 'error' in tiepoint.geoPoint.name.lower():
continue
if not tiepoint.deleted:
control_point_id = tiepoint.geoPoint.objectId
if control_point_id not in control_points:
control_points[control_point_id] = {'tiepoints': {}}
control_points[control_point_id]['tiepoints'][fr] = list(
tiepoint.point)
lla_xyz = models.ControlPoint.objects.get(
objectId=control_point_id,
newerVersion=None).history(history).point.coords
control_points[control_point_id]['3d'] = [
lla_xyz[x] for x in [1, 0, 2]
]
#filter only control points with more than 1 tiepoint
control_points = {
k: v
for k, v in control_points.iteritems()
if len(v['tiepoints'].keys()) > 1
}
origin_yxz = np.mean([v['3d'] for k, v in control_points.iteritems()],
axis=0)
lvcs = vpgl_adaptor.create_lvcs(origin_yxz[0], origin_yxz[1],
origin_yxz[2], 'wgs84')
for control_point in control_points:
control_points[control_point][
'lvcs'] = vpgl_adaptor.convert_to_local_coordinates2(
lvcs, *control_points[control_point]['3d'])
images = {}
with voxel_globe.tools.task_dir('visualsfm', cd=True) as processing_dir:
dummy_imagename = os.path.join(processing_dir, 'blank.jpg')
img = Image.fromarray(np.empty([1, 1], dtype=np.uint8))
img.save(dummy_imagename)
#Thank you stupid site file
for fr, image in enumerate(image_collection.images.all()):
(K, R, T, o) = get_krt(image.history(history), history=history)
images[fr] = image.objectId
with open(os.path.join(processing_dir, 'frame_%05d.txt' % fr),
'w') as fid:
print >> fid, (("%0.18f " * 3 + "\n") * 3) % (
K[0, 0], K[0, 1], K[0, 2], K[1, 0], K[1, 1], K[1, 2],
K[2, 0], K[2, 1], K[2, 2])
print >> fid, (("%0.18f " * 3 + "\n") * 3) % (
R[0, 0], R[0, 1], R[0, 2], R[1, 0], R[1, 1], R[1, 2],
R[2, 0], R[2, 1], R[2, 2])
print >> fid, ("%0.18f " * 3 + "\n") % (T[0, 0], T[1, 0], T[2,
0])
site_in_name = os.path.join(processing_dir, 'site.xml')
site_out_name = os.path.join(processing_dir, 'site2.xml')
with open(site_in_name, 'w') as fid:
fid.write('''<BWM_VIDEO_SITE name="Triangulation">
<videoSiteDir path="%s">
</videoSiteDir>
<videoPath path="%s">
</videoPath>
<cameraPath path="%s/*.txt">
</cameraPath>
<Objects>
</Objects>ve
<Correspondences>\n''' % (processing_dir, dummy_imagename, processing_dir))
for control_point_index, control_point_id in enumerate(
control_points):
fid.write('<Correspondence id="%d">\n' % control_point_index)
for fr, tie_point in control_points[control_point_id][
'tiepoints'].iteritems():
fid.write('<CE fr="%d" u="%f" v="%f"/>\n' %
(fr, tie_point[0], tie_point[1]))
fid.write('</Correspondence>\n')
control_points[control_point_id]['id'] = control_point_index
fid.write('''</Correspondences>
</BWM_VIDEO_SITE>\n''')
#triangulate the points
Popen([
'bwm_triangulate_2d_corrs', '-site', site_in_name, '-out',
site_out_name
],
logger=logger).wait()
#Read in the result, and load into points_triangulate structure
xml = load_xml(site_out_name)
points_triangulate = {'id': [], 'x': [], 'y': [], 'z': []}
for correspondence in xml['Correspondences']['Correspondence']:
points_triangulate['id'].append(int(correspondence.at['id']))
points_triangulate['x'].append(
float(correspondence['corr_world_point'].at['X']))
points_triangulate['y'].append(
float(correspondence['corr_world_point'].at['Y']))
points_triangulate['z'].append(
float(correspondence['corr_world_point'].at['Z']))
#Read the points out of the control points structure, but make sure they are
#in the same order (check id == point_id
points_orig = {'x': [], 'y': [], 'z': []}
for point_id in points_triangulate['id']:
point = [
v['lvcs'] for k, v in control_points.iteritems()
if v['id'] == point_id
]
points_orig['x'].append(point[0][0])
points_orig['y'].append(point[0][1])
points_orig['z'].append(point[0][2])
new_cameras = os.path.join(processing_dir, 'new_cameras')
os.mkdir(new_cameras)
#Make transformation
transform, scale = vpgl_adaptor.compute_transformation(
points_triangulate['x'], points_triangulate['y'],
points_triangulate['z'], points_orig['x'], points_orig['y'],
points_orig['z'], processing_dir, new_cameras)
#calculate the new bounding box
bbox_min, bbox_max = vpgl_adaptor.compute_transformed_box(
list(image_collection.scene.bbox_min),
list(image_collection.scene.bbox_max), transform)
#calculate the new voxel size
default_voxel_size = geos.Point(
*(x * scale for x in image_collection.scene.default_voxel_size))
scene = models.Scene.create(
name=image_collection.scene.name + ' tiepoint registered',
service_id=self.request.id,
origin=geos.Point(origin_yxz[1], origin_yxz[0], origin_yxz[2]),
bbox_min=geos.Point(*bbox_min),
bbox_max=geos.Point(*bbox_max),
default_voxel_size=default_voxel_size,
geolocated=True)
scene.save()
image_collection.scene = scene
image_collection.save()
for fr, image_id in images.iteritems():
krt = Krt.load(os.path.join(new_cameras, 'frame_%05d.txt' % fr))
image = models.Image.objects.get(objectId=image_id,
newerVersion=None)
save_krt(self.request.id,
image,
krt.k,
krt.r,
krt.t, [origin_yxz[x] for x in [1, 0, 2]],
srid=4326)
def runVisualSfm(sfmArg='sfm', args=[], logger=None, executable=None):
if executable == None:
executable = 'visualsfm'
return Popen([executable, sfmArg] + args, logger=logger, shell=False)
def runVisualSfm(sfmArg='sfm', args=[], logger=None):
return Popen([runVisualSfm.program, sfmArg] + args,
logger=logger,
shell=False)
def runVisualSfm(sfmArg='sfm', args=[], logger=None, executable=None):
if executable == None:
executable = os.environ['VIP_VISUALSFM_EXE']
return Popen([executable, sfmArg] + args, logger=logger, shell=False)