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 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 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 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
