def height_map_error(self, image_id, history=None):
import numpy as np
import vpgl_adaptor
from vsi.io.image import imread, GdalReader
from voxel_globe.meta import models
import voxel_globe.tools
from voxel_globe.tools.celery import Popen
from voxel_globe.tools.wget import download as wget
tie_points_yxz = []
control_points_yxz = []
image = models.Image.objects.get(id=image_id).history(history)
with voxel_globe.tools.task_dir('height_map_error_calculation', cd=True) as processing_dir:
wget(image.originalImageUrl, image.original_filename, secret=True)
height_reader = GdalReader(image.original_filename, autoload=True)
transform = height_reader.object.GetGeoTransform()
height = height_reader.raster()
tie_point_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 tie_point_id in tie_point_ids:
tie_point = models.TiePoint.objects.get(objectId=tie_point_id, newerVersion=None).history(history)
if not tie_point.deleted:
lla_xyz = models.ControlPoint.objects.get(objectId = tie_point.geoPoint.objectId, newerVersion=None).history(history).point.coords
control_points_yxz.append([lla_xyz[x] for x in [1,0,2]])
tie_points_yxz.append([transform[4]*(tie_point.point.coords[0]+0.5) + transform[5]*(tie_point.point.coords[1]+0.5) + transform[3],
transform[1]*(tie_point.point.coords[0]+0.5) + transform[2]*(tie_point.point.coords[1]+0.5) + transform[0],
height[tie_point.point.coords[1], tie_point.point.coords[0]]])
origin_yxz = np.mean(np.array(control_points_yxz), axis=0)
tie_points_local = []
control_points_local = []
lvcs = vpgl_adaptor.create_lvcs(origin_yxz[0], origin_yxz[1], origin_yxz[2], 'wgs84')
for tie_point in tie_points_yxz:
tie_points_local.append(vpgl_adaptor.convert_to_local_coordinates2(lvcs, *tie_point))
for control_point in control_points_yxz:
control_points_local.append(vpgl_adaptor.convert_to_local_coordinates2(lvcs, *control_point))
error = np.linalg.norm(np.array(tie_points_local)-np.array(control_points_local), axis=0)/(len(tie_points_local)**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 make_order_3(request, image_collection_id, scene_id):
import voxel_globe.tools.enu as enu
from voxel_globe.tools.camera import get_llh
import numpy as np
image_collection = models.ImageCollection.objects.get(id=image_collection_id)
image_list = image_collection.images.all()
scene = models.Scene.objects.get(id=scene_id);
geolocated = scene.geolocated
# #if min==max, they are probably all zeros and the bounding box isn't set yet
# if scene.bbox_min == scene.bbox_max:
# #TODO: Replace this with bundle2scene in visualsfm task
# llhs = []
# for image in image_list:
# llhs.append(get_llh(image.history()))
# llhs = np.array(llhs)
# bbox_min = llhs.min(axis=0)
# bbox_max = llhs.max(axis=0)
# #The above code calculated the bounding box of the cameras. Set the
# #bounding box of the scene to be from the bottom of the lowest camera to
# #down to sea level. Hard to do anything else intelligently
# bbox_max[2] = bbox_min[2]
# bbox_min[2] = 0
# voxel_size = 1
# else:
bbox_min = scene.bbox_min
bbox_max = scene.bbox_max
voxel_size = sum(scene.default_voxel_size.coords)/3.0
#if geolocated, convert lvcs to lla
if geolocated:
from vpgl_adaptor import create_lvcs, convert_local_to_global_coordinates
origin = scene.origin.coords
lvcs = create_lvcs(origin[1], origin[0], origin[2], "wgs84");
(bbox_min[1], bbox_min[0], bbox_min[2]) = convert_local_to_global_coordinates(lvcs,bbox_min[1], bbox_min[0], bbox_min[2])
(bbox_max[1], bbox_max[0], bbox_max[2]) = convert_local_to_global_coordinates(lvcs,bbox_max[1], bbox_max[0], bbox_max[2])
bbox = {'x_min':bbox_min[0],
'x_max':bbox_max[0],
'y_min':bbox_min[1],
'y_max':bbox_max[1],
'z_min':bbox_min[2],
'z_max':bbox_max[2]}
return render(request, 'order/build_voxel_world/html/make_order_3.html',
{'scene_id':scene_id, 'bbox':bbox, 'geolocated':geolocated,
'voxel_size': voxel_size,
'image_collection_id':image_collection_id})
def make_order_3(request, image_collection_id, scene_id):
import voxel_globe.tools.enu as enu
from voxel_globe.tools.camera import get_llh
import numpy as np
image_collection = models.ImageCollection.objects.get(id=image_collection_id)
image_list = image_collection.images.all()
scene = models.Scene.objects.get(id=scene_id);
geolocated = scene.geolocated
# #if min==max, they are probably all zeros and the bounding box isn't set yet
# if scene.bbox_min == scene.bbox_max:
# #TODO: Replace this with bundle2scene in visualsfm task
# llhs = []
# for image in image_list:
# llhs.append(get_llh(image.history()))
# llhs = np.array(llhs)
# bbox_min = llhs.min(axis=0)
# bbox_max = llhs.max(axis=0)
# #The above code calculated the bounding box of the cameras. Set the
# #bounding box of the scene to be from the bottom of the lowest camera to
# #down to sea level. Hard to do anything else intelligently
# bbox_max[2] = bbox_min[2]
# bbox_min[2] = 0
# voxel_size = 1
# else:
bbox_min = scene.bbox_min
bbox_max = scene.bbox_max
voxel_size = sum(scene.default_voxel_size.coords)/3.0
#if geolocated, convert lvcs to lla
if geolocated:
from vpgl_adaptor import create_lvcs, convert_local_to_global_coordinates
origin = scene.origin.coords
lvcs = create_lvcs(origin[1], origin[0], origin[2], "wgs84");
(bbox_min[1], bbox_min[0], bbox_min[2]) = convert_local_to_global_coordinates(lvcs,bbox_min[1], bbox_min[0], bbox_min[2])
(bbox_max[1], bbox_max[0], bbox_max[2]) = convert_local_to_global_coordinates(lvcs,bbox_max[1], bbox_max[0], bbox_max[2])
bbox = {'x_min':bbox_min[0],
'x_max':bbox_max[0],
'y_min':bbox_min[1],
'y_max':bbox_max[1],
'z_min':bbox_min[2],
'z_max':bbox_max[2]}
return render(request, 'order/build_voxel_world/html/make_order_3.html',
{'scene_id':scene_id, 'bbox':bbox, 'geolocated':geolocated,
'voxel_size': voxel_size,
'image_collection_id':image_collection_id})
def get_point_cloud(point_cloud_id, number_points=None, history=None):
from voxel_globe.meta import models
from vpgl_adaptor import convert_local_to_global_coordinates_array, create_lvcs
import os
import numpy as np
from plyfile import PlyData
point_cloud = models.PointCloud.objects.get(id=point_cloud_id).history(history)
lvcs = create_lvcs(point_cloud.origin[1], point_cloud.origin[0], point_cloud.origin[2], 'wgs84')
ply = PlyData.read(str(os.path.join(point_cloud.directory, 'error.ply')))
data = ply.elements[0].data
if number_points:
try:
import heapq
data = np.array(heapq.nlargest(number_points, ply.elements[0].data,
key=lambda x:x['prob']))
except IndexError: #not a correctly formated ply file. HACK A CODE!
#This is a hack-a-code for Tom's ply file
data = ply.elements[0].data.astype([('x', '<f4'), ('y', '<f4'),
('z', '<f4'), ('red', 'u1'), ('green', 'u1'), ('blue', 'u1'),
('prob', '<f4')])
import copy
blah = copy.deepcopy(data['y'])
data['y'] = data['z']
data['z'] = -blah
blah = copy.deepcopy(data['blue'])
data['blue'] = data['green']
data['green'] = blah
data['prob'] = abs(data['x'] - 10 - sum(data['x'])/len(data['x'])) \
+ abs(data['y'] + 30 - sum(data['y'])/len(data['y'])) \
+ abs(data['z'] - sum(data['z'])/len(data['z']))
data['prob'] = max(data['prob']) - data['prob']
data = np.array(heapq.nlargest(number_points, data,
key=lambda x:x['prob']))
print data['prob']
lla = convert_local_to_global_coordinates_array(lvcs, data['x'].tolist(), data['y'].tolist(), data['z'].tolist());
latitude = np.array(lla[0])
longitude = np.array(lla[1])
altitude = np.array(lla[2])
color = map(lambda r,b,g:'#%02x%02x%02x' % (r, g, b), data['red'], data['green'], data['blue'])
return_data = {"latitude": latitude, "longitude": longitude,
"altitude": altitude, "color": color}
try:
return_data['le'] = data['le']
except ValueError:
return_data['le'] = (-np.ones(len(latitude))).tolist()
try:
return_data['ce'] = data['ce']
except ValueError:
return_data['ce'] = (-np.ones(len(latitude))).tolist()
return return_data
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 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 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 get_point_cloud(point_cloud_id, number_points=None, history=None):
from voxel_globe.meta import models
from vpgl_adaptor import convert_local_to_global_coordinates_array, create_lvcs
import os
import numpy as np
from plyfile import PlyData
point_cloud = models.PointCloud.objects.get(
id=point_cloud_id).history(history)
lvcs = create_lvcs(point_cloud.origin[1], point_cloud.origin[0],
point_cloud.origin[2], 'wgs84')
ply = PlyData.read(str(point_cloud.filename))
data = ply.elements[0].data
if number_points:
try:
import heapq
data = np.array(
heapq.nlargest(number_points,
ply.elements[0].data,
key=lambda x: x['prob']))
except IndexError: #not a correctly formated ply file. HACK A CODE!
#This is a hack-a-code for Tom's ply file
data = ply.elements[0].data.astype([('x', '<f4'), ('y', '<f4'),
('z', '<f4'), ('red', 'u1'),
('green', 'u1'),
('blue', 'u1'),
('prob', '<f4')])
import copy
blah = copy.deepcopy(data['y'])
data['y'] = data['z']
data['z'] = -blah
data['prob'] = abs(data['x'] - 10 - sum(data['x'])/len(data['x'])) \
+ abs(data['y'] + 30 - sum(data['y'])/len(data['y'])) \
+ abs(data['z'] - sum(data['z'])/len(data['z']))
data['prob'] = max(data['prob']) - data['prob']
data = np.array(
heapq.nlargest(number_points, data, key=lambda x: x['prob']))
print data['prob']
lla = convert_local_to_global_coordinates_array(lvcs, data['x'].tolist(),
data['y'].tolist(),
data['z'].tolist())
latitude = np.array(lla[0])
longitude = np.array(lla[1])
altitude = np.array(lla[2])
color = map(lambda r, g, b: '#%02x%02x%02x' % (r, g, b), data['red'],
data['green'], data['blue'])
return_data = {
"latitude": latitude,
"longitude": longitude,
"altitude": altitude,
"color": color
}
try:
return_data['le'] = data['le']
except ValueError:
return_data['le'] = (-np.ones(len(latitude))).tolist()
try:
return_data['ce'] = data['ce']
except ValueError:
return_data['ce'] = (-np.ones(len(latitude))).tolist()
return return_data
def height_map_error(self, image_id, history=None):
import numpy as np
import vpgl_adaptor
from vsi.io.image import imread, GdalReader
from voxel_globe.meta import models
import voxel_globe.tools
from voxel_globe.tools.celery import Popen
from voxel_globe.tools.wget import download as wget
tie_points_yxz = []
control_points_yxz = []
image = models.Image.objects.get(id=image_id).history(history)
with voxel_globe.tools.task_dir('height_map_error_calculation',
cd=True) as processing_dir:
wget(image.originalImageUrl, image.original_filename, secret=True)
height_reader = GdalReader(image.original_filename, autoload=True)
transform = height_reader.object.GetGeoTransform()
height = height_reader.raster()
tie_point_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 tie_point_id in tie_point_ids:
tie_point = models.TiePoint.objects.get(
objectId=tie_point_id, newerVersion=None).history(history)
if not tie_point.deleted:
lla_xyz = models.ControlPoint.objects.get(
objectId=tie_point.geoPoint.objectId,
newerVersion=None).history(history).point.coords
control_points_yxz.append([lla_xyz[x] for x in [1, 0, 2]])
tie_points_yxz.append([
transform[4] * (tie_point.point.coords[0] + 0.5) +
transform[5] * (tie_point.point.coords[1] + 0.5) +
transform[3], transform[1] *
(tie_point.point.coords[0] + 0.5) + transform[2] *
(tie_point.point.coords[1] + 0.5) + transform[0],
height[tie_point.point.coords[1], tie_point.point.coords[0]]
])
origin_yxz = np.mean(np.array(control_points_yxz), axis=0)
tie_points_local = []
control_points_local = []
lvcs = vpgl_adaptor.create_lvcs(origin_yxz[0], origin_yxz[1],
origin_yxz[2], 'wgs84')
for tie_point in tie_points_yxz:
tie_points_local.append(
vpgl_adaptor.convert_to_local_coordinates2(lvcs, *tie_point))
for control_point in control_points_yxz:
control_points_local.append(
vpgl_adaptor.convert_to_local_coordinates2(lvcs, *control_point))
error = np.linalg.norm(
np.array(tie_points_local) - np.array(control_points_local),
axis=0) / (len(tie_points_local)**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