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 runVisualSfm(self, imageCollectionId, sceneId, cleanup=True, history=None):
from voxel_globe.meta import models
from voxel_globe.order.visualsfm.models import Order
from os import environ as env
from os.path import join as path_join
import os
import shutil
from .tools import writeNvm, writeGcpFile, generateMatchPoints, runSparse,\
readNvm
import voxel_globe.tools
from voxel_globe.tools.wget import download as wget
from voxel_globe.tools.camera import get_kto
import voxel_globe.tools.enu as enu
import numpy
import boxm2_adaptor
import boxm2_scene_adaptor
from voxel_globe.tools.xml_dict import load_xml
from django.contrib.gis.geos import Point
from voxel_globe.tools.image import convert_image
from distutils.spawn import find_executable
from glob import glob
self.update_state(state='INITIALIZE', meta={'stage': 0})
#Make main temp dir and cd into it
with voxel_globe.tools.task_dir('visualsfm', cd=True) as processing_dir:
#Because visualsfm is so... bad, I have to copy it locally so I can
#configure it
visualsfm_exe = os.path.join(
processing_dir, os.path.basename(os.environ['VIP_VISUALSFM_EXE']))
shutil.copy(find_executable(os.environ['VIP_VISUALSFM_EXE']),
visualsfm_exe)
with open(os.path.join(processing_dir, 'nv.ini'), 'w') as fid:
fid.write('param_search_multiple_models 0\n')
fid.write('param_use_siftgpu 2\n')
matchFilename = path_join(processing_dir, 'match.nvm')
sparce_filename = path_join(processing_dir, 'sparse.nvm')
#This can NOT be changed in version 0.5.25
gcpFilename = matchFilename + '.gcp'
logger.debug('Task %s is processing in %s' %
(self.request.id, processing_dir))
image_collection = models.ImageCollection.objects.get(
id=imageCollectionId).history(history)
imageList = image_collection.images.all()
#A Little bit of database logging
oid = Order(processingDir=processing_dir,
imageCollection=image_collection)
### if 1:
### try: #Not fully integrated yet
### sift_gpu = siftgpu.SiftGPU()
### except:
### pass
localImageList = []
for x in range(len(imageList)):
#Download the image locally
image = imageList[x].history(history)
self.update_state(state='INITIALIZE',
meta={
'stage': 'image fetch',
'i': x,
'total': len(imageList)
})
imageName = image.originalImageUrl
extension = os.path.splitext(imageName)[1].lower()
localName = path_join(processing_dir,
'frame_%05d%s' % (x + 1, extension))
wget(imageName, localName, secret=True)
#Convert the image if necessary
if extension not in ['.jpg', '.jpeg', '.pgm', '.ppm']:
self.update_state(state='INITIALIZE',
meta={
'stage': 'image convert',
'i': x,
'total': len(imageList)
})
#Add code here to converty to jpg for visual sfm
if extension in ['.png']: #'not implemented':
from PIL import Image
image_temp = Image.open(localName)
if len(image_temp.mode
) > 1: #Stupid visual sfm is picky :(
new_local_name = os.path.splitext(
localName)[0] + '.ppm'
else:
new_local_name = os.path.splitext(
localName)[0] + '.pgm'
new_local_name = os.path.splitext(localName)[0] + '.jpg'
###ingest.convert_image(localName, new_local_name, 'PNM')
convert_image(localName,
new_local_name,
'JPEG',
options=('QUALITY=100', ))
os.remove(localName)
localName = new_local_name
else:
raise Exception('Unsupported file type')
imageInfo = {'localName': localName, 'index': x}
try:
[K, T, llh] = get_kto(image, history=history)
imageInfo['K_intrinsics'] = K
imageInfo['transformation'] = T
imageInfo['enu_origin'] = llh
except:
pass
localImageList.append(imageInfo)
### if 1:
### try: #not fully integrated yet
### sift_gpu.create_sift(localName, os.path.splitext(localName)[0]+'.sift')
### except:
### pass
# filenames = list(imageList.values_list('imageUrl'))
# logger.info('The image list 0is %s' % filenames)
self.update_state(state='PROCESSING',
meta={
'stage': 'generate match points',
'processing_dir': processing_dir,
'total': len(imageList)
})
generateMatchPoints(map(lambda x: x['localName'], localImageList),
matchFilename,
logger=logger,
executable=visualsfm_exe)
# cameras = [];
# for image in imageList:
# if 1:
# #try:
# [K, T, llh] = get_kto(image);
# cameras.append({'image':image.id, 'K':K, 'tranformation':
# T, 'origin':llh})
# #except:
# pass
# origin = numpy.median(origin, axis=0)
# origin = [-92.215197, 37.648858, 268.599]
scene = models.Scene.objects.get(id=sceneId).history(history)
origin = list(scene.origin)
if scene.geolocated:
self.update_state(state='PROCESSING',
meta={'stage': 'writing gcp points'})
#find the middle origin, and make it THE origin
data = [] #.name .llh_xyz
for imageInfo in localImageList:
try:
r = imageInfo['transformation'][0:3, 0:3]
t = imageInfo['transformation'][0:3, 3:]
enu_point = -r.transpose().dot(t)
if not numpy.array_equal(imageInfo['enu_origin'], origin):
ecef = enu.enu2xyz(
refLong=imageInfo['enu_origin'][0],
refLat=imageInfo['enu_origin'][1],
refH=imageInfo['enu_origin'][2],
#e=imageInfo['transformation'][0, 3],
#n=imageInfo['transformation'][1, 3],
#u=imageInfo['transformation'][2, 3])
e=enu_point[0],
n=enu_point[1],
u=enu_point[2])
enu_point = enu.xyz2enu(refLong=origin[0],
refLat=origin[1],
refH=origin[2],
X=ecef[0],
Y=ecef[1],
Z=ecef[2])
# else:
# enu_point = imageInfo['transformation'][0:3, 3];
dataBit = {
'filename': imageInfo['localName'],
'xyz': enu_point
}
data.append(dataBit)
#Make this a separate ingest process, making CAMERAS linked to the
#images
#data = arducopter.loadAdjTaggedMetadata(
# r'd:\visualsfm\2014-03-20 13-22-44_adj_tagged_images.txt');
#Make this read the cameras from the DB instead
writeGcpFile(data, gcpFilename)
except: #some images may have no camera
pass
oid.lvcsOrigin = str(origin)
oid.save()
self.update_state(state='PROCESSING', meta={'stage': 'sparse SFM'})
runSparse(matchFilename,
sparce_filename,
gcp=scene.geolocated,
shared=True,
logger=logger,
executable=visualsfm_exe)
self.update_state(state='FINALIZE',
meta={'stage': 'loading resulting cameras'})
#prevent bundle2scene from getting confused and crashing
sift_data = os.path.join(processing_dir, 'sift_data')
os.mkdir(sift_data)
for filename in glob(os.path.join(processing_dir, '*.mat')) +\
glob(os.path.join(processing_dir, '*.sift')):
shutil.move(filename, sift_data)
if scene.geolocated:
#Create a uscene.xml for the geolocated case. All I want out of this is
#the bounding box and gsd calculation.
boxm2_adaptor.bundle2scene(sparce_filename,
processing_dir,
isalign=False,
out_dir="")
cams = readNvm(path_join(processing_dir, 'sparse.nvm'))
#cams.sort(key=lambda x:x.name)
#Since the file names are frame_00001, etc... and you KNOW this order is
#identical to localImageList, with some missing
for cam in cams:
frameName = cam.name
#frame_00001, etc....
imageInfo = filter(
lambda x: x['localName'].endswith(frameName),
localImageList)[0]
#I have to use endswith instead of == because visual sfm APPARENTLY
#decides to take some liberty and make absolute paths relative
image = imageList[imageInfo['index']].history(history)
(k, r, t) = cam.krt(width=image.imageWidth,
height=image.imageHeight)
logger.info('Origin is %s' % str(origin))
llh_xyz = enu.enu2llh(lon_origin=origin[0],
lat_origin=origin[1],
h_origin=origin[2],
east=cam.translation_xyz[0],
north=cam.translation_xyz[1],
up=cam.translation_xyz[2])
grcs = models.GeoreferenceCoordinateSystem.create(
name='%s 0' % image.name,
xUnit='d',
yUnit='d',
zUnit='m',
location='SRID=4326;POINT(%0.15f %0.15f %0.15f)' %
(origin[0], origin[1], origin[2]),
service_id=self.request.id)
grcs.save()
cs = models.CartesianCoordinateSystem.create(
name='%s 1' % (image.name),
service_id=self.request.id,
xUnit='m',
yUnit='m',
zUnit='m')
cs.save()
transform = models.CartesianTransform.create(
name='%s 1_0' % (image.name),
service_id=self.request.id,
rodriguezX=Point(*r[0, :]),
rodriguezY=Point(*r[1, :]),
rodriguezZ=Point(*r[2, :]),
translation=Point(t[0][0], t[1][0], t[2][0]),
coordinateSystem_from_id=grcs.id,
coordinateSystem_to_id=cs.id)
transform.save()
camera = image.camera
try:
camera.update(service_id=self.request.id,
focalLengthU=k[0, 0],
focalLengthV=k[1, 1],
principalPointU=k[0, 2],
principalPointV=k[1, 2],
coordinateSystem=cs)
except:
camera = models.Camera.create(name=image.name,
service_id=self.request.id,
focalLengthU=k[0, 0],
focalLengthV=k[1, 1],
principalPointU=k[0, 2],
principalPointV=k[1, 2],
coordinateSystem=cs)
camera.save()
image.update(camera=camera)
logger.info(str(cams[0]))
else:
from vsi.tools.natural_sort import natural_sorted
from glob import glob
from vsi.io.krt import Krt
from voxel_globe.tools.camera import save_krt
boxm2_adaptor.bundle2scene(sparce_filename,
processing_dir,
isalign=True,
out_dir=processing_dir)
#While the output dir is used for the b2s folders, uscene.xml is cwd
#They are both set to processing_dir, so everything works out well
aligned_cams = glob(os.path.join(processing_dir, 'cams_krt', '*'))
#sort them naturally in case there are more then 99,999 files
aligned_cams = natural_sorted(aligned_cams)
if len(aligned_cams) != len(imageList):
#Create a new image collection
new_image_collection = models.ImageCollection.create(
name="SFM Result Subset (%s)" % image_collection.name,
service_id=self.request.id)
# for image in image_collection.images.all():
# new_image_collection.images.add(image)
new_image_collection.save()
frames_keep = set(
map(
lambda x: int(os.path.splitext(x.split('_')[-2])[0]) -
1, aligned_cams))
for frame_index in frames_keep:
new_image_collection.images.add(imageList[frame_index])
# frames_remove = set(xrange(len(imageList))) - frames_keep
#
# for remove_index in list(frames_remove):
# #The frame number refers to the nth image in the image collection,
# #so frame_00100.tif is the 100th image, starting the index at one
# #See local_name above
#
# #remove the images sfm threw away
# new_image_collection.remove(imageList[remove_index])
image_collection = new_image_collection
frames_keep = list(frames_keep)
else:
frames_keep = xrange(len(aligned_cams))
#---Update the camera models in the database.---
for camera_index, frame_index in enumerate(frames_keep):
krt = Krt.load(aligned_cams[camera_index])
image = imageList[frame_index].history(history)
save_krt(self.request.id,
image,
krt.k,
krt.r,
krt.t, [0, 0, 0],
srid=4326)
#---Update scene information important for the no-metadata case ---
scene_filename = os.path.join(processing_dir, 'model', 'uscene.xml')
boxm_scene = boxm2_scene_adaptor.boxm2_scene_adaptor(scene_filename)
scene.bbox_min = 'POINT(%0.15f %0.15f %0.15f)' % boxm_scene.bbox[0]
scene.bbox_max = 'POINT(%0.15f %0.15f %0.15f)' % boxm_scene.bbox[1]
#This is not a complete or good function really... but it will get me the
#information I need.
scene_dict = load_xml(scene_filename)
block = scene_dict['block']
scene.default_voxel_size='POINT(%f %f %f)' % \
(float(block.at['dim_x']), float(block.at['dim_y']),
float(block.at['dim_z']))
scene.save()
return oid.id
def runVisualSfm(self, imageCollectionId, sceneId, cleanup=True, history=None):
from voxel_globe.meta import models
from voxel_globe.order.visualsfm.models import Order
from os import environ as env
from os.path import join as path_join
import os
import shutil
from .tools import writeNvm, writeGcpFile, generateMatchPoints, runSparse,\
readNvm
import voxel_globe.tools
from voxel_globe.tools.wget import download as wget
from voxel_globe.tools.camera import get_kto
import voxel_globe.tools.enu as enu
import numpy
import boxm2_adaptor
import boxm2_scene_adaptor
from voxel_globe.tools.xml_dict import load_xml
from django.contrib.gis.geos import Point
from voxel_globe.tools.image import convert_image
from distutils.spawn import find_executable
from glob import glob
self.update_state(state='INITIALIZE', meta={'stage':0})
#Make main temp dir and cd into it
with voxel_globe.tools.task_dir('visualsfm', cd=True) as processing_dir:
#Because visualsfm is so... bad, I have to copy it locally so I can
#configure it
visualsfm_exe = os.path.join(processing_dir,
os.path.basename(os.environ['VIP_VISUALSFM_EXE']))
shutil.copy(find_executable(os.environ['VIP_VISUALSFM_EXE']),
visualsfm_exe)
with open(os.path.join(processing_dir, 'nv.ini'), 'w') as fid:
fid.write('param_search_multiple_models 0\n')
fid.write('param_use_siftgpu 2\n')
matchFilename = path_join(processing_dir, 'match.nvm');
sparce_filename = path_join(processing_dir, 'sparse.nvm');
#This can NOT be changed in version 0.5.25
gcpFilename = matchFilename + '.gcp'
logger.debug('Task %s is processing in %s' % (self.request.id,
processing_dir))
image_collection = models.ImageCollection.objects.get(
id=imageCollectionId).history(history);
imageList = image_collection.images.all();
#A Little bit of database logging
oid = Order(processingDir=processing_dir, imageCollection=image_collection)
### if 1:
### try: #Not fully integrated yet
### sift_gpu = siftgpu.SiftGPU()
### except:
### pass
localImageList = [];
for x in range(len(imageList)):
#Download the image locally
image = imageList[x].history(history);
self.update_state(state='INITIALIZE', meta={'stage':'image fetch', 'i':x,
'total':len(imageList)})
imageName = image.originalImageUrl;
extension = os.path.splitext(imageName)[1].lower()
localName = path_join(processing_dir, 'frame_%05d%s' % (x+1, extension));
wget(imageName, localName, secret=True)
#Convert the image if necessary
if extension not in ['.jpg', '.jpeg', '.pgm', '.ppm']:
self.update_state(state='INITIALIZE',
meta={'stage':'image convert', 'i':x, 'total':len(imageList)})
#Add code here to converty to jpg for visual sfm
if extension in ['.png']:#'not implemented':
from PIL import Image
image_temp = Image.open(localName)
if len(image_temp.mode) > 1: #Stupid visual sfm is picky :(
new_local_name = os.path.splitext(localName)[0] + '.ppm';
else:
new_local_name = os.path.splitext(localName)[0] + '.pgm';
new_local_name = os.path.splitext(localName)[0] + '.jpg';
###ingest.convert_image(localName, new_local_name, 'PNM')
convert_image(localName, new_local_name, 'JPEG',
options=('QUALITY=100',))
os.remove(localName)
localName = new_local_name;
else:
raise Exception('Unsupported file type');
imageInfo = {'localName':localName, 'index':x}
try:
[K, T, llh] = get_kto(image, history=history);
imageInfo['K_intrinsics'] = K;
imageInfo['transformation'] = T;
imageInfo['enu_origin'] = llh;
except:
pass
localImageList.append(imageInfo);
### if 1:
### try: #not fully integrated yet
### sift_gpu.create_sift(localName, os.path.splitext(localName)[0]+'.sift')
### except:
### pass
# filenames = list(imageList.values_list('imageUrl'))
# logger.info('The image list 0is %s' % filenames)
self.update_state(state='PROCESSING',
meta={'stage':'generate match points',
'processing_dir':processing_dir,
'total':len(imageList)})
generateMatchPoints(map(lambda x:x['localName'], localImageList),
matchFilename, logger=logger, executable=visualsfm_exe)
# cameras = [];
# for image in imageList:
# if 1:
# #try:
# [K, T, llh] = get_kto(image);
# cameras.append({'image':image.id, 'K':K, 'tranformation':
# T, 'origin':llh})
# #except:
# pass
# origin = numpy.median(origin, axis=0)
# origin = [-92.215197, 37.648858, 268.599]
scene = models.Scene.objects.get(id=sceneId).history(history)
origin = list(scene.origin)
if scene.geolocated:
self.update_state(state='PROCESSING',
meta={'stage':'writing gcp points'})
#find the middle origin, and make it THE origin
data = []#.name .llh_xyz
for imageInfo in localImageList:
try:
r = imageInfo['transformation'][0:3, 0:3]
t = imageInfo['transformation'][0:3, 3:]
enu_point = -r.transpose().dot(t);
if not numpy.array_equal(imageInfo['enu_origin'], origin):
ecef = enu.enu2xyz(refLong=imageInfo['enu_origin'][0],
refLat=imageInfo['enu_origin'][1],
refH=imageInfo['enu_origin'][2],
#e=imageInfo['transformation'][0, 3],
#n=imageInfo['transformation'][1, 3],
#u=imageInfo['transformation'][2, 3])
e=enu_point[0],
n=enu_point[1],
u=enu_point[2])
enu_point = enu.xyz2enu(refLong=origin[0],
refLat=origin[1],
refH=origin[2],
X=ecef[0],
Y=ecef[1],
Z=ecef[2])
# else:
# enu_point = imageInfo['transformation'][0:3, 3];
dataBit = {'filename':imageInfo['localName'], 'xyz':enu_point}
data.append(dataBit);
#Make this a separate ingest process, making CAMERAS linked to the
#images
#data = arducopter.loadAdjTaggedMetadata(
# r'd:\visualsfm\2014-03-20 13-22-44_adj_tagged_images.txt');
#Make this read the cameras from the DB instead
writeGcpFile(data, gcpFilename)
except: #some images may have no camera
pass
oid.lvcsOrigin = str(origin)
oid.save()
self.update_state(state='PROCESSING', meta={'stage':'sparse SFM'})
runSparse(matchFilename, sparce_filename, gcp=scene.geolocated,
shared=True, logger=logger, executable=visualsfm_exe)
self.update_state(state='FINALIZE',
meta={'stage':'loading resulting cameras'})
#prevent bundle2scene from getting confused and crashing
sift_data = os.path.join(processing_dir, 'sift_data')
os.mkdir(sift_data)
for filename in glob(os.path.join(processing_dir, '*.mat')) +\
glob(os.path.join(processing_dir, '*.sift')):
shutil.move(filename, sift_data)
if scene.geolocated:
#Create a uscene.xml for the geolocated case. All I want out of this is
#the bounding box and gsd calculation.
boxm2_adaptor.bundle2scene(sparce_filename, processing_dir, isalign=False,
out_dir="")
cams = readNvm(path_join(processing_dir, 'sparse.nvm'))
#cams.sort(key=lambda x:x.name)
#Since the file names are frame_00001, etc... and you KNOW this order is
#identical to localImageList, with some missing
for cam in cams:
frameName = cam.name; #frame_00001, etc....
imageInfo = filter(lambda x: x['localName'].endswith(frameName),
localImageList)[0]
#I have to use endswith instead of == because visual sfm APPARENTLY
#decides to take some liberty and make absolute paths relative
image = imageList[imageInfo['index']].history(history)
(k,r,t) = cam.krt(width=image.imageWidth, height=image.imageHeight);
logger.info('Origin is %s' % str(origin))
llh_xyz = enu.enu2llh(lon_origin=origin[0],
lat_origin=origin[1],
h_origin=origin[2],
east=cam.translation_xyz[0],
north=cam.translation_xyz[1],
up=cam.translation_xyz[2])
grcs = models.GeoreferenceCoordinateSystem.create(
name='%s 0' % image.name,
xUnit='d', yUnit='d', zUnit='m',
location='SRID=4326;POINT(%0.15f %0.15f %0.15f)'
% (origin[0], origin[1], origin[2]),
service_id = self.request.id)
grcs.save()
cs = models.CartesianCoordinateSystem.create(
name='%s 1' % (image.name),
service_id = self.request.id,
xUnit='m', yUnit='m', zUnit='m');
cs.save()
transform = models.CartesianTransform.create(
name='%s 1_0' % (image.name),
service_id = self.request.id,
rodriguezX=Point(*r[0,:]),
rodriguezY=Point(*r[1,:]),
rodriguezZ=Point(*r[2,:]),
translation=Point(t[0][0], t[1][0], t[2][0]),
coordinateSystem_from_id=grcs.id,
coordinateSystem_to_id=cs.id)
transform.save()
camera = image.camera;
try:
camera.update(service_id = self.request.id,
focalLengthU=k[0,0], focalLengthV=k[1,1],
principalPointU=k[0,2], principalPointV=k[1,2],
coordinateSystem=cs);
except:
camera = models.Camera.create(name=image.name,
service_id = self.request.id,
focalLengthU=k[0,0], focalLengthV=k[1,1],
principalPointU=k[0,2], principalPointV=k[1,2],
coordinateSystem=cs);
camera.save();
image.update(camera = camera);
logger.info(str(cams[0]))
else:
from vsi.tools.natural_sort import natural_sorted
from glob import glob
from vsi.io.krt import Krt
from voxel_globe.tools.camera import save_krt
boxm2_adaptor.bundle2scene(sparce_filename, processing_dir, isalign=True,
out_dir=processing_dir)
#While the output dir is used for the b2s folders, uscene.xml is cwd
#They are both set to processing_dir, so everything works out well
aligned_cams = glob(os.path.join(processing_dir, 'cams_krt', '*'))
#sort them naturally in case there are more then 99,999 files
aligned_cams = natural_sorted(aligned_cams)
if len(aligned_cams) != len(imageList):
#Create a new image collection
new_image_collection = models.ImageCollection.create(
name="SFM Result Subset (%s)" % image_collection.name,
service_id = self.request.id);
# for image in image_collection.images.all():
# new_image_collection.images.add(image)
new_image_collection.save();
frames_keep = set(map(lambda x:
int(os.path.splitext(x.split('_')[-2])[0])-1, aligned_cams))
for frame_index in frames_keep:
new_image_collection.images.add(imageList[frame_index])
# frames_remove = set(xrange(len(imageList))) - frames_keep
#
# for remove_index in list(frames_remove):
# #The frame number refers to the nth image in the image collection,
# #so frame_00100.tif is the 100th image, starting the index at one
# #See local_name above
#
# #remove the images sfm threw away
# new_image_collection.remove(imageList[remove_index])
image_collection = new_image_collection
frames_keep = list(frames_keep)
else:
frames_keep = xrange(len(aligned_cams))
#---Update the camera models in the database.---
for camera_index, frame_index in enumerate(frames_keep):
krt = Krt.load(aligned_cams[camera_index])
image = imageList[frame_index].history(history)
save_krt(self.request.id, image, krt.k, krt.r, krt.t, [0,0,0],
srid=4326)
#---Update scene information important for the no-metadata case ---
scene_filename = os.path.join(processing_dir, 'model', 'uscene.xml')
boxm_scene = boxm2_scene_adaptor.boxm2_scene_adaptor(scene_filename)
scene.bbox_min = 'POINT(%0.15f %0.15f %0.15f)' % boxm_scene.bbox[0]
scene.bbox_max = 'POINT(%0.15f %0.15f %0.15f)' % boxm_scene.bbox[1]
#This is not a complete or good function really... but it will get me the
#information I need.
scene_dict = load_xml(scene_filename)
block = scene_dict['block']
scene.default_voxel_size='POINT(%f %f %f)' % \
(float(block.at['dim_x']), float(block.at['dim_y']),
float(block.at['dim_z']))
scene.save()
return oid.id;
def run_build_voxel_model_bp(self,
image_collection_id,
scene_id,
bbox,
skip_frames,
cleanup=True,
history=None):
from distutils.dir_util import remove_tree
from shutil import move
import random
from vsi.tools.redirect import StdRedirect, Logger as LoggerWrapper
from voxel_globe.meta import models
from voxel_globe.tools.camera import get_krt
import voxel_globe.tools
from boxm2_scene_adaptor import boxm2_scene_adaptor
from vil_adaptor import load_image
from vpgl_adaptor import load_perspective_camera
from voxel_globe.tools.wget import download as wget
from vsi.vxl.create_scene_xml import create_scene_xml
from vsi.tools.dir_util import copytree
with StdRedirect(
open(
os.path.join(voxel_globe.tools.log_dir(), self.request.id) +
'_out.log', 'w'),
open(
os.path.join(voxel_globe.tools.log_dir(), self.request.id) +
'_err.log', 'w')):
openclDevice = os.environ['VIP_OPENCL_DEVICE']
opencl_memory = os.environ.get('VIP_OPENCL_MEMORY', None)
if opencl_memory:
opencl_memory = int(opencl_memory)
scene = models.Scene.objects.get(id=scene_id)
imageCollection = models.ImageCollection.objects.get(\
id=image_collection_id).history(history)
imageList = imageCollection.images.all()
with voxel_globe.tools.task_dir('voxel_world') as processing_dir:
logger.warning(bbox)
if bbox['geolocated']:
create_scene_xml(
openclDevice,
3,
float(bbox['voxel_size']),
lla1=(float(bbox['x_min']), float(bbox['y_min']),
float(bbox['z_min'])),
lla2=(float(bbox['x_max']), float(bbox['y_max']),
float(bbox['z_max'])),
origin=scene.origin,
model_dir='.',
number_bins=1,
output_file=open(os.path.join(processing_dir, 'scene.xml'),
'w'),
n_bytes_gpu=opencl_memory)
else:
create_scene_xml(
openclDevice,
3,
float(bbox['voxel_size']),
lvcs1=(float(bbox['x_min']), float(bbox['y_min']),
float(bbox['z_min'])),
lvcs2=(float(bbox['x_max']), float(bbox['y_max']),
float(bbox['z_max'])),
origin=scene.origin,
model_dir='.',
number_bins=1,
output_file=open(os.path.join(processing_dir, 'scene.xml'),
'w'),
n_bytes_gpu=opencl_memory)
counter = 1
imageNames = []
cameraNames = []
os.mkdir(os.path.join(processing_dir, 'local'))
#Prepping
for image in imageList:
self.update_state(state='INITIALIZE',
meta={
'stage': 'image fetch',
'i': counter,
'total': len(imageList)
})
image = image.history(history)
(K, R, T, o) = get_krt(image.history(history), history=history)
krtName = os.path.join(processing_dir, 'local',
'frame_%05d.krt' % counter)
with open(krtName, '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])
imageName = image.originalImageUrl
extension = os.path.splitext(imageName)[1]
localName = os.path.join(processing_dir, 'local',
'frame_%05d%s' % (counter, extension))
wget(imageName, localName, secret=True)
counter += 1
imageNames.append(localName)
cameraNames.append(krtName)
variance = 0.06
vxl_scene = boxm2_scene_adaptor(
os.path.join(processing_dir, "scene.xml"), openclDevice)
current_level = 0
loaded_imgs = []
loaded_cams = []
for i in range(0, len(imageNames), skip_frames):
logger.debug("i: %d img name: %s cam name: %s", i,
imageNames[i], cameraNames[i])
self.update_state(state='PRELOADING',
meta={
'stage': 'image load',
'i': i,
'total': len(imageNames)
})
img, ni, nj = load_image(imageNames[i])
loaded_imgs.append(img)
pcam = load_perspective_camera(cameraNames[i])
loaded_cams.append(pcam)
refine_cnt = 5
refine_device = openclDevice[0:3]
if refine_device == 'cpu':
refine_device = 'cpp'
for rfk in range(0, refine_cnt, 1):
pair = zip(loaded_imgs, loaded_cams)
random.shuffle(pair)
for idx, (img, cam) in enumerate(pair):
self.update_state(state='PROCESSING',
meta={
'stage': 'update',
'i': rfk + 1,
'total': refine_cnt,
'image': idx + 1,
'images': len(loaded_imgs)
})
logger.debug("refine_cnt: %d, idx: %d", rfk, idx)
vxl_scene.update(cam,
img,
True,
True,
None,
openclDevice[0:3],
variance,
tnear=1000.0,
tfar=100000.0)
for bp_index in range(2):
pass #height map update?
logger.debug("writing cache: %d", rfk)
vxl_scene.write_cache()
logger.debug("wrote cache: %d", rfk)
if rfk < refine_cnt - 1:
self.update_state(state='PROCESSING',
meta={
'stage': 'refine',
'i': rfk,
'total': refine_cnt
})
logger.debug("refining %d...", rfk)
vxl_scene.refine(0.3, refine_device)
vxl_scene.write_cache()
with open(os.path.join(processing_dir, "scene_color.xml"),
'w') as fid:
lines = open(os.path.join(processing_dir, "scene.xml"),
'r').readlines()
lines = [
line.replace('boxm2_mog3_grey', 'boxm2_gauss_rgb').replace(
'boxm2_num_obs', 'boxm2_num_obs_single')
for line in lines
]
fid.writelines(lines)
vxl_scene = boxm2_scene_adaptor(
os.path.join(processing_dir, "scene_color.xml"), openclDevice)
for idx, (img, cam) in enumerate(pair):
self.update_state(state='PROCESSING',
meta={
'stage': 'color_update',
'i': rfk + 1,
'total': refine_cnt,
'image': idx + 1,
'images': len(loaded_imgs)
})
logger.debug("color_paint idx: %d", idx)
vxl_scene.update(cam,
img,
False,
False,
None,
openclDevice[0:3],
tnear=1000.0,
tfar=100000.0)
vxl_scene.write_cache()
with voxel_globe.tools.storage_dir(
'voxel_world') as voxel_world_dir:
copytree(processing_dir,
voxel_world_dir,
ignore=lambda x, y: ['images'])
models.VoxelWorld.create(
name='%s world (%s)' %
(imageCollection.name, self.request.id),
origin=scene.origin,
directory=voxel_world_dir,
service_id=self.request.id).save()
def run_build_voxel_model(self, image_collection_id, scene_id, bbox,
skip_frames, cleanup=True, history=None):
from distutils.dir_util import remove_tree
from shutil import move
import random
from vsi.tools.redirect import Redirect, Logger as LoggerWrapper
from voxel_globe.meta import models
from voxel_globe.tools.camera import get_krt
import voxel_globe.tools
from boxm2_scene_adaptor import boxm2_scene_adaptor
from vil_adaptor import load_image
from vpgl_adaptor import load_perspective_camera
from voxel_globe.tools.wget import download as wget
from vsi.vxl.create_scene_xml import create_scene_xml
from vsi.tools.dir_util import copytree, mkdtemp
with Redirect(stdout_c=LoggerWrapper(logger, lvl=logging.INFO),
stderr_c=LoggerWrapper(logger, lvl=logging.WARNING)):
openclDevice = os.environ['VIP_OPENCL_DEVICE']
opencl_memory = os.environ.get('VIP_OPENCL_MEMORY', None)
scene = models.Scene.objects.get(id=scene_id)
imageCollection = models.ImageCollection.objects.get(\
id=image_collection_id).history(history);
imageList = imageCollection.images.all();
with voxel_globe.tools.task_dir('voxel_world') as processing_dir:
logger.warning(bbox)
if bbox['geolocated']:
create_scene_xml(openclDevice, 3, float(bbox['voxel_size']),
lla1=(float(bbox['x_min']), float(bbox['y_min']),
float(bbox['z_min'])),
lla2=(float(bbox['x_max']), float(bbox['y_max']),
float(bbox['z_max'])),
origin=scene.origin, model_dir='.', number_bins=1,
output_file=open(os.path.join(processing_dir, 'scene.xml'), 'w'),
n_bytes_gpu=opencl_memory)
else:
create_scene_xml(openclDevice, 3, float(bbox['voxel_size']),
lvcs1=(float(bbox['x_min']), float(bbox['y_min']),
float(bbox['z_min'])),
lvcs2=(float(bbox['x_max']), float(bbox['y_max']),
float(bbox['z_max'])),
origin=scene.origin, model_dir='.', number_bins=1,
output_file=open(os.path.join(processing_dir, 'scene.xml'), 'w'),
n_bytes_gpu=opencl_memory)
counter = 1;
imageNames = []
cameraNames = []
os.mkdir(os.path.join(processing_dir, 'local'))
#Prepping
for image in imageList:
self.update_state(state='INITIALIZE', meta={'stage':'image fetch',
'i':counter,
'total':len(imageList)})
image = image.history(history)
(K,R,T,o) = get_krt(image.history(history), history=history)
krtName = os.path.join(processing_dir, 'local', 'frame_%05d.krt' % counter)
with open(krtName, '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]);
imageName = image.originalImageUrl;
extension = os.path.splitext(imageName)[1]
localName = os.path.join(processing_dir, 'local',
'frame_%05d%s' % (counter, extension));
wget(imageName, localName, secret=True)
counter += 1;
imageNames.append(localName)
cameraNames.append(krtName)
variance = 0.06
vxl_scene = boxm2_scene_adaptor(os.path.join(processing_dir, "scene.xml"),
openclDevice);
current_level = 0;
loaded_imgs = [];
loaded_cams = [];
for i in range(0, len(imageNames), skip_frames):
logger.debug("i: %d img name: %s cam name: %s", i, imageNames[i],
cameraNames[i])
self.update_state(state='PRELOADING', meta={'stage':'image load',
'i':i,
'total':len(imageNames)})
img, ni, nj = load_image(imageNames[i])
loaded_imgs.append(img)
pcam = load_perspective_camera(cameraNames[i])
loaded_cams.append(pcam)
refine_cnt = 5;
for rfk in range(0, refine_cnt, 1):
pair = zip(loaded_imgs, loaded_cams)
random.shuffle(pair)
for idx, (img, cam) in enumerate(pair):
self.update_state(state='PROCESSING', meta={'stage':'update',
'i':rfk+1, 'total':refine_cnt, 'image':idx+1,
'images':len(loaded_imgs)})
logger.debug("refine_cnt: %d, idx: %d", rfk, idx)
vxl_scene.update(cam,img,True,True,None,openclDevice[0:3],variance,
tnear = 1000.0, tfar = 100000.0);
logger.debug("writing cache: %d", rfk)
vxl_scene.write_cache();
logger.debug("wrote cache: %d", rfk)
if rfk < refine_cnt-1:
self.update_state(state='PROCESSING', meta={'stage':'refine',
'i':rfk,
'total':refine_cnt})
logger.debug("refining %d...", rfk)
refine_device = openclDevice[0:3]
if refine_device == 'cpu':
refine_device = 'cpp'
vxl_scene.refine(0.3, refine_device);
vxl_scene.write_cache();
voxel_world_dir = mkdtemp(dir=os.environ['VIP_STORAGE_DIR'])
copytree(processing_dir, voxel_world_dir, ignore=lambda x,y:['images'])
models.VoxelWorld.create(
name='%s world (%s)' % (imageCollection.name, self.request.id),
origin=scene.origin,
directory=voxel_world_dir,
service_id=self.request.id).save();
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
