def height_map_error(self, image_id):
import numpy as np
import vpgl_adaptor_boxm2_batch as 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 vsi.tools.file_util import lncp
tie_points_yxz = []
control_points_yxz = []
image = models.Image.objects.get(id=image_id)
height_reader = GdalReader(image.filename_path, autoload=True)
transform = height_reader.object.GetGeoTransform()
height = height_reader.raster()
del height_reader
tie_points = image.tiepoint_set.all()
for tie_point in tie_points:
lla_xyz = tie_point.control_point.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 get_point_cloud(point_cloud_id, number_points=None):
from voxel_globe.meta import models
from vpgl_adaptor_boxm2_batch 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)
lvcs = create_lvcs(point_cloud.origin[1], point_cloud.origin[0], point_cloud.origin[2], 'wgs84')
ply = PlyData.read(str(point_cloud.filename_path))
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 create_scene_xml(gpu_device, refinements, gsd,
lla1=None, lla2=None, lvcs1=None, lvcs2=None,
origin=None, output_file=sys.stdout, model_dir = ".",
appearance_models=None, number_bins=1,
n_bytes_gpu=None):
''' Create a scene xml file based off of input parameters
Required arguments:
gpu_device - The GPU device used for the block size calculation. The
scene will be made to fit specifically on that gpu device
refinements - Number of refinement passes. Max should be 3, even if you
refine more times than 3 times. gsd - The desired voxel size. This is in
meters when using lla notation
Mutually exclusive arguments:
lla1,lla2 - The min and max (in order) longitude, latitude, altitude
coordinates for bounding box of the scene. Will be expanded to the next
sublock
lvcs1, lvcs2 - The min and max (in order) x, y, z coordinates for
bounding box of the scene. Will be expanded to the next sublock
Optional arguments:
origin - Origin of scene. Default: lla1 or (0,0,0) if lvcs1 is used
output_file - Output file object. Must support .write.
Default: sys.stdout
model_dir - The model dir used. is_model_local="true" is always used
Default: "."
appearance_models - Tuple of appearance models to be used.
Default: ('boxm2_mog3_grey','boxm2_num_obs')
number_bins - Sets the num_illumination_bins. Default: 1
n_bytes_gpu - Optional override gpu_device memory size. Useful for CPU
OpenCL
'''
#impose mutually exclusive constraint
assert(lla1 is None or lvcs1 is None)
assert(lla2 is None or lvcs2 is None)
if origin is None:
if lla1 is None:
origin = (0,0,0)
else:
origin = lla1
if lvcs1 is None or lvcs2 is None:
lvcs = vpgl_adaptor.create_lvcs(lat=origin[1], lon=origin[0], el=origin[2],
csname="wgs84")
# transform the coordinate system from lat/lon/height to a lvcs (meters) with
# the origin at one corner of the scene
if lvcs1 is None:
lvcs1 = vpgl_adaptor.convert_to_local_coordinates2(lvcs,
lla1[1], lla1[0], lla1[2])
if lvcs2 is None:
lvcs2 = vpgl_adaptor.convert_to_local_coordinates2(lvcs,
lla2[1], lla2[0], lla2[2])
lvcs_size = map(lambda x,y:y-x, lvcs1, lvcs2)
if n_bytes_gpu==None:
n_bytes_gpu = gpu_memory(gpu_device)
(n_blocks, n_subblocks, subblock_len) = calculate_block_parameters(
n_bytes_gpu, refinements, gsd, lvcs_size)
generate_scene_xml(output_file, model_dir,
num_blocks=n_blocks, num_subblocks=n_subblocks,
subblock_size=subblock_len, appearance_models=appearance_models,
num_bins=number_bins, max_level=refinements+1, lvcs_og=origin,
local_og=lvcs1)
def tiepoint_registration(self, image_set_id, camera_set_id):
from PIL import Image
import numpy as np
from django.contrib.gis.geos import Point
import vpgl_adaptor_boxm2_batch as 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_set_id})
image_set = models.ImageSet.objects.get(id=image_set_id)
control_points = {}
for fr,image in enumerate(image_set.images.all()):
tiepoints = image.tiepoint_set.all()
for tiepoint in tiepoints:
#demoware code hack!
if 'error' in tiepoint.control_point.name.lower():
continue
if tiepoint.control_point.id not in control_points:
control_points[tiepoint.control_point.id] = {'tiepoints':{}}
control_points[tiepoint.control_point.id]['tiepoints'][fr] = list(tiepoint.point)
lla_xyz = tiepoint.control_point.point.coords
control_points[tiepoint.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('tiepoint_registration', 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_set.images.all()):
(K,R,T,o) = get_krt(image, camera_set_id)
images[fr] = image.id
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_set.scene.bbox_min), list(image_set.scene.bbox_max), transform)
#calculate the new voxel size
default_voxel_size=Point(*(x*scale for x in image_set.scene.default_voxel_size))
scene = models.Scene(name="Tie Point Registered %s" % (image_set.scene.name,),
service_id=self.request.id,
origin=Point(origin_yxz[1], origin_yxz[0], origin_yxz[2]),
bbox_min=Point(*bbox_min),
bbox_max=Point(*bbox_max),
default_voxel_size=default_voxel_size,
geolocated=True)
scene.save()
image_set.scene=scene
image_set.save()
camera_set=voxel_globe.meta.models.CameraSet(\
name="%s" % (image_set.scene.name,),
images=image_set, service_id=self.request.id)
camera_set.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(id=image_id)
camera = save_krt(self.request.id, image, krt.k, krt.r, krt.t, [origin_yxz[x] for x in [1,0,2]], srid=4326)
camera_set.cameras.add(camera)
def tiepoint_error_calculation(self, image_set_id, camera_set_id, scene_id):
from PIL import Image
import numpy as np
import vpgl_adaptor_boxm2_batch as 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, XmlListConfig, XmlList
self.update_state(state='INITIALIZE', meta={'id':image_set_id, 'scene':scene_id})
image_set = models.ImageSet.objects.get(id=image_set_id)
control_points = {}
for fr,image in enumerate(image_set.images.all()):
tiepoints = image.tiepoint_set.all()
for tiepoint in tiepoints:
#demoware code hack!
if tiepoint.control_point.id not in control_points:
control_points[tiepoint.control_point.id] = {'tiepoints':{}}
control_points[tiepoint.control_point.id]['tiepoints'][fr] = list(tiepoint.point)
lla_xyz = tiepoint.control_point.point.coords
control_points[tiepoint.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'])
with voxel_globe.tools.task_dir('tiepoint_error_calculation', 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_set.images.all()):
(K,R,T,o) = get_krt(image, camera_set_id)
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>
<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=[]
correspondences = xml['Correspondences']['Correspondence']
if not isinstance(correspondences, XmlListConfig) and not isinstance(correspondences, XmlList):
correspondences = [correspondences]
for correspondence in correspondences:
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):
from voxel_globe.meta import models
from vpgl_adaptor_boxm2_batch 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)
lvcs = create_lvcs(point_cloud.origin[1], point_cloud.origin[0],
point_cloud.origin[2], 'wgs84')
ply = PlyData.read(str(point_cloud.filename_path))
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 update_geometry_polygon(request):
import voxel_globe.meta.models as models
from django.contrib.gis.geos import Point, Polygon, GEOSGeometry
import numpy as np
import brl_init
import vpgl_adaptor_boxm2_batch as vpgl_adaptor
image_id = int(request.POST['image_id'])
points = GEOSGeometry(request.POST['points'])
sattelgeometryobject_id = int(request.POST['sattelgeometryobject_id'])
site_id = int(request.POST['site_id'])
projection_mode = request.POST['projection_mode']
site = models.SattelSite.objects.get(id=site_id)
image = models.Image.objects.get(id=image_id)
camera = image.camera_set.filter(
cameraset=site.camera_set).select_subclasses('rpccamera')[0]
sattelgeometryobject = models.SattelGeometryObject.objects.get(
id=sattelgeometryobject_id)
vpgl_camera = vpgl_adaptor.load_rational_camera_from_txt(camera.rpc_path)
initial_guess = np.mean(image.attributes['planet_rest_response']
['geometry']['coordinates'][0][0:4],
axis=0)
initial_guess = np.hstack((initial_guess, 0)) #elevation guess is 0
lvcs_points = []
gps_points = []
if projection_mode == "z-plane":
projection_height = float(request.POST['height'])
for point in points.coords[0]:
point = vpgl_adaptor.get_rpc_backprojected_ray(
vpgl_camera, point[0], point[1], projection_height,
initial_guess[0], initial_guess[1], initial_guess[2])
gps_points.append(point)
origin = np.array(gps_points).mean(axis=0)
lvcs = vpgl_adaptor.create_lvcs(origin[1], origin[0], origin[2], "wgs84")
origin = Point(*origin)
for point in gps_points:
point = vpgl_adaptor.convert_to_local_coordinates2(
lvcs, point[1], point[0], point[2])
lvcs_points.append(point)
print sattelgeometryobject.geometry_path
if sattelgeometryobject.geometry_path and os.path.exists(
sattelgeometryobject.geometry_path):
os.remove(sattelgeometryobject.geometry_path)
geometry_filepath = write_ply_file(lvcs_points)
sattelgeometryobject.geometry_path = geometry_filepath
sattelgeometryobject.origin = origin
sattelgeometryobject.geometry = Polygon(gps_points + gps_points[0:1])
sattelgeometryobject.save()
from django.shortcuts import HttpResponse
return HttpResponse('{}', content_type="application/json")
def height_map_error(self, image_id):
import numpy as np
import vpgl_adaptor_boxm2_batch as 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 vsi.tools.file_util import lncp
tie_points_yxz = []
control_points_yxz = []
image = models.Image.objects.get(id=image_id)
height_reader = GdalReader(image.filename_path, autoload=True)
transform = height_reader.object.GetGeoTransform()
height = height_reader.raster()
del height_reader
tie_points = image.tiepoint_set.all()
for tie_point in tie_points:
lla_xyz = tie_point.control_point.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 update_geometry_polygon(request):
import voxel_globe.meta.models as models
from django.contrib.gis.geos import Point, Polygon, GEOSGeometry
import numpy as np
import brl_init
import vpgl_adaptor_boxm2_batch as vpgl_adaptor
image_id = int(request.POST['image_id'])
points = GEOSGeometry(request.POST['points'])
sattelgeometryobject_id = int(request.POST['sattelgeometryobject_id'])
site_id = int(request.POST['site_id'])
projection_mode = request.POST['projection_mode']
site = models.SattelSite.objects.get(id=site_id)
image = models.Image.objects.get(id=image_id)
camera = image.camera_set.filter(cameraset=site.camera_set).select_subclasses('rpccamera')[0]
sattelgeometryobject = models.SattelGeometryObject.objects.get(id=sattelgeometryobject_id)
vpgl_camera = vpgl_adaptor.load_rational_camera_from_txt(camera.rpc_path)
initial_guess = np.mean(image.attributes['planet_rest_response']['geometry']['coordinates'][0][0:4], axis=0)
initial_guess = np.hstack((initial_guess, 0)) #elevation guess is 0
lvcs_points = []
gps_points = []
if projection_mode == "z-plane":
projection_height = float(request.POST['height'])
for point in points.coords[0]:
point = vpgl_adaptor.get_rpc_backprojected_ray(vpgl_camera,
point[0], point[1],
projection_height,
initial_guess[0],
initial_guess[1],
initial_guess[2])
gps_points.append(point)
origin = np.array(gps_points).mean(axis=0)
lvcs = vpgl_adaptor.create_lvcs(origin[1], origin[0], origin[2], "wgs84")
origin = Point(*origin)
for point in gps_points:
point = vpgl_adaptor.convert_to_local_coordinates2(lvcs, point[1], point[0], point[2])
lvcs_points.append(point)
print sattelgeometryobject.geometry_path
if sattelgeometryobject.geometry_path and os.path.exists(sattelgeometryobject.geometry_path):
os.remove(sattelgeometryobject.geometry_path)
geometry_filepath = write_ply_file(lvcs_points)
sattelgeometryobject.geometry_path=geometry_filepath
sattelgeometryobject.origin = origin
sattelgeometryobject.geometry = Polygon(gps_points+gps_points[0:1])
sattelgeometryobject.save()
from django.shortcuts import HttpResponse
return HttpResponse('{}', content_type="application/json")
