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 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 match_images(images, camera_names, json_config={}):
''' returns dictionary camera_name:image pairs
json example:
{"image_camera_match":
{"frame_00008.png": "frame_00001.txt",
"frame_00019.png": "frame_00002.txt",
"frame_00022.png": "frame_00003.txt"}
}
'''
matches = {}
try:
json_config = json_config['image_camera_match']
except (TypeError, KeyError):
json_config = {}
#lowercase everything... THANK YOU WINDOWS! 8:(
camera_safe_names_ext = [x.lower() for x in camera_names]
camera_safe_names = [os.path.splitext(x)[0] for x in camera_safe_names_ext]
image_safe_names_ext = [s.original_filename.lower() for s in images]
json_image_safe_names = [s.lower() for s in json_config]
for image_index, image in enumerate(images):
#First check the json_config to see if the camera/image is defined there.
#This takes priority
try:
json_image_index = json_image_safe_names.index(
image_safe_names_ext[image_index])
json_image_name = json_config.keys()[json_image_index]
json_camera_name = json_config[json_image_name]
camera_index = camera_safe_names_ext.index(
json_camera_name.lower())
camera_name = camera_names[camera_index]
matches[camera_name] = image
continue
except ValueError:
pass
#Second, guess that the filenames without extension match
try:
image_name = os.path.splitext(image_safe_names_ext[image_index])[0]
#strip the extension to check against camera name
camera_index = camera_safe_names.index(image_name)
camera_name = camera_names[camera_index]
matches[camera_name] = image
continue
except ValueError:
pass
logger.debug('No camera matched for %s', image.original_filename)
#Third if there are NO matches, guess that when sorted in lowered alpha order
#that they match that way... NOT the best, but it's a nice idea...
if not matches:
from vsi.tools.natural_sort import natural_sorted
camera_indexes = [
i[0] for i in natural_sorted(enumerate(camera_safe_names_ext),
key=lambda x: x[1])
]
image_indexes = [
i[0] for i in natural_sorted(enumerate(image_safe_names_ext),
key=lambda x: x[1])
]
number_matches = min(len(camera_indexes), len(image_indexes))
for match_index in range(number_matches):
image_index = image_indexes[match_index]
camera_index = camera_indexes[match_index]
matches[camera_names[camera_index]] = images[image_index]
logger.info('%d out of %d images matched to cameras', len(matches),
len(images))
return matches
def match_images(images, camera_names, json_config={}):
''' returns dictionary camera_name:image pairs
json example:
{"image_camera_match":
{"frame_00008.png": "frame_00001.txt",
"frame_00019.png": "frame_00002.txt",
"frame_00022.png": "frame_00003.txt"}
}
'''
matches = {}
try:
json_config=json_config['image_camera_match']
except (TypeError, KeyError):
json_config={}
#lowercase everything... THANK YOU WINDOWS! 8:(
camera_safe_names_ext = [x.lower() for x in camera_names]
camera_safe_names = [os.path.splitext(x)[0] for x in camera_safe_names_ext]
image_safe_names_ext = [os.path.basename(s.filename_path).lower() for s in images]
json_image_safe_names = [s.lower() for s in json_config]
for image_index, image in enumerate(images):
#First check the json_config to see if the camera/image is defined there.
#This takes priority
try:
json_image_index = json_image_safe_names.index(image_safe_names_ext[image_index])
json_image_name = json_config.keys()[json_image_index]
json_camera_name = json_config[json_image_name]
camera_index = camera_safe_names_ext.index(json_camera_name.lower())
camera_name = camera_names[camera_index]
matches[camera_name] = image
continue
except ValueError:
pass
#Second, guess that the filenames without extension match
try:
image_name = os.path.splitext(image_safe_names_ext[image_index])[0]
#strip the extension to check against camera name
camera_index = camera_safe_names.index(image_name)
camera_name = camera_names[camera_index]
matches[camera_name] = image
continue
except ValueError:
pass
logger.debug('No camera matched for %s', image.filename_path)
#Third if there are NO matches, guess that when sorted in lowered alpha order
#that they match that way... NOT the best, but it's a nice idea...
if not matches:
from vsi.tools.natural_sort import natural_sorted
camera_indexes = [i[0] for i in natural_sorted(
enumerate(camera_safe_names_ext), key=lambda x:x[1])]
image_indexes = [i[0] for i in natural_sorted(
enumerate(image_safe_names_ext), key=lambda x:x[1])]
number_matches = min(len(camera_indexes), len(image_indexes))
for match_index in range(number_matches):
image_index = image_indexes[match_index]
camera_index = camera_indexes[match_index]
matches[camera_names[camera_index]] = images[image_index]
logger.info('%d out of %d images matched to cameras', len(matches),
len(images))
return matches
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()