def run(self):
from glob import glob
from vsi.io.krt import Krt as KrtCamera
from voxel_globe.tools.camera import save_krt
self.task.update_state(state='Processing', meta={'stage':'metadata'})
self.parse_json()
metadata_filenames = glob(os.path.join(self.ingest_dir, '*'))
krts={}
for metadata_filename in metadata_filenames:
if os.stat(metadata_filename).st_size <= Krt.MAX_SIZE:
try:
krt_1 = KrtCamera.load(metadata_filename)
krts[os.path.basename(metadata_filename)] = krt_1
except: #Hopefully non-krts throw an exception when loading
import traceback as tb
logger.debug('Non-KRT parsed: %s', tb.format_exc())
matches = match_images(self.image_collection.images.all(), krts.keys(),
self.json_config)
for match in matches:
krt_1 = krts[match]
logger.debug('%s matched to %s', match, matches[match].original_filename)
save_krt(self.task.request.id, matches[match], krt_1.k, krt_1.r, krt_1.t,
self.origin_xyz)
self.save_scene()
def run(self):
from glob import glob
from vsi.io.krt import Krt as KrtCamera
from voxel_globe.tools.camera import save_krt
self.task.update_state(state='Processing', meta={'stage': 'metadata'})
self.parse_json()
metadata_filenames = glob(os.path.join(self.ingest_dir, '*'))
krts = {}
for metadata_filename in metadata_filenames:
if os.stat(metadata_filename).st_size <= Krt.MAX_SIZE:
try:
krt_1 = KrtCamera.load(metadata_filename)
krts[os.path.basename(metadata_filename)] = krt_1
except: #Hopefully non-krts throw an exception when loading
import traceback as tb
logger.debug('Non-KRT parsed: %s', tb.format_exc())
matches = match_images(self.image_collection.images.all(), krts.keys(),
self.json_config)
for match in matches:
krt_1 = krts[match]
logger.debug('%s matched to %s', match,
matches[match].original_filename)
save_krt(self.task.request.id, matches[match], krt_1.k, krt_1.r,
krt_1.t, self.origin_xyz)
self.save_scene()
def main():
import matplotlib
args = parse_args()
plot_scene = PlotScene()
if args.limits:
xyz = [float(i) for i in args.limits]
plot_scene.set_limits(xyz[0], xyz[1],\
xyz[2], xyz[3],\
xyz[4], xyz[5])
z_min = None
if args.scene:
scene = boxm2_scene_adaptor(args.scene, 'cpp')
plot_scene.draw_scene_box(scene)
z_min = scene.bbox[0][2]
if (args.limits and not z_min):
z_min = xyz[4]
if args.cameras:
cameras = []
camera_files = [
x for y in map(lambda x: glob(x), args.cameras) for x in y
]
camera_files = natural_sorted(camera_files)
for camera_file in camera_files:
krt = Krt.load(camera_file)
cameras.append(krt)
plot_scene.draw_cameras(cameras, z_min)
if args.cameras and args.diff:
cameras = []
camera_files = [
x for y in map(lambda x: glob(x), args.diff) for x in y
]
camera_files = natural_sorted(camera_files)
for camera_file in camera_files:
krt = Krt.load(camera_file)
cameras.append(krt)
plot_scene.draw_cameras(cameras, z_min, 'g')
plt.show()
def run(self):
from glob import glob
from vsi.io.krt import Krt as KrtCamera
self.task.update_state(state='Processing', meta={'stage':'metadata'})
self.create_camera_set()
self.parse_json()
metadata_filenames = glob(os.path.join(self.ingest_dir, '*'))
krts={}
for metadata_filename in metadata_filenames:
if os.stat(metadata_filename).st_size <= Krt.MAX_SIZE:
try:
krt_1 = KrtCamera.load(metadata_filename)
krts[os.path.basename(metadata_filename)] = krt_1
except: #Hopefully non-krts throw an exception when loading
import traceback as tb
logger.debug('Non-KRT parsed: %s', tb.format_exc())
matches = match_images(self.image_set.images.all(), krts.keys(),
self.json_config)
matching_attributes = match_attributes(self.image_set.images.all(),
self.json_config)
cameras = []
for match in matches:
krt_1 = krts[match]
attributes = ''
logger.debug('%s matched to %s', match, matches[match].filename_path)
camera = save_krt(self.task.request.id, matches[match], krt_1.k, krt_1.r,
krt_1.t, self.origin_xyz, srid=self.srid,
attributes=matching_attributes.get(
os.path.basename(matches[match].filename_path),
{}))
self.camera_set.cameras.add(camera)
self.save_scene()
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 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 runVisualSfm(self, imageSetId, sceneId, cleanup=True):
from voxel_globe.meta import models
from os import environ as env
from os.path import join as path_join
import os
import shutil
import time
from django.contrib.gis.geos import Point
from .tools import writeNvm, writeGcpFile, generateMatchPoints, runSparse,\
readNvm
import voxel_globe.tools
from voxel_globe.tools.camera import get_kto, save_krt
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 vsi.iglob import glob as 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 need to copy it locally so I can
#configure it
visualsfm_exe = os.path.join(processing_dir, 'visualsfm')
shutil.copy(find_executable('VisualSFM'), 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_set = models.ImageSet.objects.get(
id=imageSetId)
imageList = image_set.images.all()
### 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]
self.update_state(state='INITIALIZE', meta={'stage':'image fetch', 'i':x,
'total':len(imageList)})
imageName = image.filename_path
extension = os.path.splitext(imageName)[1].lower()
localName = path_join(processing_dir, 'frame_%05d%s' % (x+1, extension))
#lncp(imageName, localName)
#Stupid VisualSFM dereferences symlinks, breaking this
shutil.copyfile(imageName, localName)
#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)
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('image_url'))
# 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)})
pid = generateMatchPoints(map(lambda x:x['localName'], localImageList),
matchFilename, logger=logger,
executable=visualsfm_exe)
old_mat=None
old_sift=None
#TODO: Replace with inotify to monitor directory
while pid.poll() is None:
mat = len(glob(os.path.join(processing_dir, '*.mat'), False))
sift = len(glob(os.path.join(processing_dir, '*.sift'), False))
if mat != old_mat or \
sift != old_sift:
old_mat=mat
old_sift=sift
self.update_state(state='PROCESSING',
meta={'stage':'generate match points',
'processing_dir':processing_dir,
'sift':sift,
'mat':mat,
'total':len(imageList)})
time.sleep(5)
# 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)
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
self.update_state(state='PROCESSING', meta={'stage':'sparse SFM'})
pid = runSparse(matchFilename, sparce_filename, gcp=scene.geolocated,
shared=True, logger=logger, executable=visualsfm_exe)
pid.wait()
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'), False) +\
glob(os.path.join(processing_dir, '*.sift'), False):
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
camera_set = models.CameraSet(name="Visual SFM Geo %s" % image_set.name,
service_id = self.request.id,
images_id = imageSetId)
camera_set.save()
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']]
(k,r,t) = cam.krt(width=image.image_width, height=image.image_height)
t = t.flatten()
camera = save_krt(self.request.id, image, k, r, t, origin, srid=4326)
camera_set.cameras.add(camera)
else:
from vsi.tools.natural_sort import natural_sorted
from vsi.io.krt import 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 set
new_image_set = models.ImageSet(
name="SFM Result Subset (%s)" % image_set.name,
service_id = self.request.id)
# for image in image_set.images.all():
# new_image_set.images.add(image)
new_image_set.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_set.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 set,
# #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_set.remove(imageList[remove_index])
image_set = new_image_set
frames_keep = list(frames_keep)
else:
frames_keep = xrange(len(aligned_cams))
camera_set = models.CameraSet(name="Visual SFM %s" % image_set.name,
service_id = self.request.id,
images_id = imageSetId)
camera_set.save()
#---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]
camera = save_krt(self.request.id, image, krt.k, krt.r, krt.t, [0,0,0],
srid=4326)
camera_set.cameras.add(camera)
#---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(*boxm_scene.bbox[0])
scene.bbox_max = Point(*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(float(block.at['dim_x']),
float(block.at['dim_y']),
float(block.at['dim_z']))
scene.save()
