def order_status(request, task_id):
import urllib2, json, os
from celery.result import AsyncResult
task = AsyncResult(task_id)
#u = urllib2.urlopen('http://%s:%s/api/task/info/%s' % (os.environ['VIP_FLOWER_HOST'],
# os.environ['VIP_FLOWER_PORT'],
# task_id))
#status = json.loads(u.read());
#status['task_id'] = status['task-id']
#jinja2 limitation
status = {
'task': task
}
if task.state == 'PROCESSING' and task.result[
'stage'] == 'generate match points':
from vsi.iglob import glob
status['mat'] = len(
glob(os.path.join(task.result['processing_dir'], '*.mat'), False))
status['sift'] = len(
glob(os.path.join(task.result['processing_dir'], '*.sift'), False))
return render(request, 'order/visualsfm/html/order_status.html', status)
def order_status(request, task_id):
import urllib2, json, os
from celery.result import AsyncResult
task = AsyncResult(task_id)
status = {'task': task}
if task.state == 'PROCESSING' and task.result[
'stage'] == 'generate match points':
from vsi.iglob import glob
status['mat'] = len(
glob(os.path.join(task.result['processing_dir'], '*.mat'), False))
status['sift'] = len(
glob(os.path.join(task.result['processing_dir'], '*.sift'), False))
return render(request, 'visualsfm/html/order_status.html', status)
def order_status(request, task_id):
import urllib2, json, os
from celery.result import AsyncResult
task = AsyncResult(task_id);
#u = urllib2.urlopen('http://%s:%s/api/task/info/%s' % (os.environ['VIP_FLOWER_HOST'],
# os.environ['VIP_FLOWER_PORT'],
# task_id))
#status = json.loads(u.read());
#status['task_id'] = status['task-id']
#jinja2 limitation
status = {'task': task};
if task.state == 'PROCESSING' and task.result['stage'] == 'generate match points':
from vsi.iglob import glob
status['mat'] = len(glob(os.path.join(task.result['processing_dir'], '*.mat'), False))
status['sift'] = len(glob(os.path.join(task.result['processing_dir'], '*.sift'), False))
return render(request, 'order/visualsfm/html/order_status.html',
status)
def run(self):
import csv
from django.contrib.gis import geos
from vsi.iglob import glob
from voxel_globe.meta.models import ControlPoint
csv_files = glob(os.path.join(self.ingest_dir, '*.csv'), False)
for csv_file in csv_files:
with open(csv_file, 'r') as fid:
reader = csv.reader(fid, delimiter=',')
for line in reader:
try:
point = geos.Point(float(line[2]),float(line[3]), float(line[4]),
srid=int(line[1]))
ControlPoint.create(name=line[0], description="Ingested point",
point=point, apparentPoint=point,
service_id=self.task.request.id).save()
except:
pass
def ingest_data(self, uploadSession_id, imageDir):
''' task for the ingest route, to ingest the data an upload sessions points to '''
import voxel_globe.ingest.models as IngestModels
from .tools import loadAdjTaggedMetadata
import numpy
from voxel_globe.tools.camera import save_krt
from PIL import Image
uploadSession = IngestModels.UploadSession.objects.get(id=uploadSession_id);
#directories = uploadSession.directory.all();
#imageDirectory = directories.filter(name='image')
#metaDirectory = directories.filter(name='meta')
metadataFilename = glob(os.path.join(imageDir, '*', '*_adj_tagged_images.txt'), False);
if not len(metadataFilename) == 1:
logger.error('Only one metadatafile should have been found, found %d instead', len(metadataFilename));
try:
metadataFilename = metadataFilename[0]
(day, timeOfDay) = os.path.split(metadataFilename)[1].split(' ');
timeOfDay = timeOfDay.split('_', 1)[0];
except:
metadataFilename = os.devnull;
day = 'NYA'
timeOfDay = 'NYA'
imageCollection = voxel_globe.meta.models.ImageCollection.create(name="Arducopter Upload %s %s %s (%s)" % (uploadSession.name, day, timeOfDay, uploadSession_id), service_id = self.request.id);
imageCollection.save();
for d in glob(os.path.join(imageDir, '*'+os.path.sep), False):
files = glob(os.path.join(d, '*.jpg'), False);
files.sort()
for f in files:
self.update_state(state='PROCESSING',
meta={'stage':'File %s of %d' % (f, len(files))})
logger.debug('Processing %s of %s', f, len(files))
zoomifyName = f[:-4] + '_zoomify'
pid = Popen(['vips', 'dzsave', f, zoomifyName, '--layout', 'zoomify'])
pid.wait();
#convert the slashes to URL slashes
relFilePath = urllib.pathname2url(os.path.relpath(f, env['VIP_IMAGE_SERVER_ROOT']));
basename = os.path.split(f)[-1]
relZoomPath = urllib.pathname2url(os.path.relpath(zoomifyName, env['VIP_IMAGE_SERVER_ROOT']));
image = Image.open(f)
if image.bits == 8:
pixel_format = 'b';
if image.bits == 16:
pixel_format = 's';
if image.bits == 32:
if image.mode == "I":
pixel_format = 'i';
elif image.mode == "F":
pixel_format = 'f'
img = voxel_globe.meta.models.Image.create(
name="Arducopter Upload %s (%s) Frame %s" % (uploadSession.name, uploadSession_id, basename),
imageWidth=image.size[0], imageHeight=image.size[1],
numberColorBands=image.layers, pixelFormat=pixel_format, fileFormat='zoom',
imageUrl='%s://%s:%s/%s/%s/' % (env['VIP_IMAGE_SERVER_PROTOCOL'],
env['VIP_IMAGE_SERVER_HOST'],
env['VIP_IMAGE_SERVER_PORT'],
env['VIP_IMAGE_SERVER_URL_PATH'],
relZoomPath),
originalImageUrl='%s://%s:%s/%s/%s' % (env['VIP_IMAGE_SERVER_PROTOCOL'],
env['VIP_IMAGE_SERVER_HOST'],
env['VIP_IMAGE_SERVER_PORT'],
env['VIP_IMAGE_SERVER_URL_PATH'],
relFilePath),
service_id = self.request.id);
img.save();
imageCollection.images.add(img);
self.update_state(state='Processing', meta={'stage':'metadata'})
metadata = loadAdjTaggedMetadata(metadataFilename);
for meta in metadata:
try:
img = imageCollection.images.get(name__icontains='Frame %s'%meta.filename)
k = numpy.eye(3);
k[0,2] = img.imageWidth/2;
k[1,2] = img.imageHeight/2;
r = numpy.eye(3);
t = [0, 0, 0];
origin = meta.llh_xyz;
save_krt(self.request.id, img, k, r, t, origin, srid=7428);
except Exception as e:
logger.warning('%s', e)
logger.error('Could not match metadata entry for %s' % meta.filename)
averageGps = numpy.mean(numpy.array(map(lambda x:x.llh_xyz, metadata)), 0);
voxel_globe.meta.models.Scene.create(name="Arducopter origin %s (%s)" % (uploadSession.name, uploadSession_id),
service_id = self.request.id,
origin='SRID=%d;POINT(%0.12f %0.12f %0.12f)' % \
(7428, averageGps[0], averageGps[1], averageGps[2])).save()
uploadSession.delete()
def add_arducopter_images(self, *args, **kwargs):
images = glob(path_join(env['VIP_PROJECT_ROOT'], 'images', '1fps*', ''), False);
images.sort();
imageCollection = [];
for image in images:
image = os.path.basename(os.path.dirname(image));
frameNum = image[11:15]
if voxel_globe.meta.models.Image.objects.filter(name="Arducopter Mission 2 Frame:%s" % frameNum):
raise Exception('Already exists');
img = voxel_globe.meta.models.Image.create(name="Arducopter Mission 2 Frame:%s" % frameNum, imageWidth=4096, imageHeight=2160,
numberColorBands=3, pixelFormat='b', fileFormat='zoom',
imageUrl='%s://%s:%s/%s/%s/' % (env['VIP_IMAGE_SERVER_PROTOCOL'],
env['VIP_IMAGE_SERVER_HOST'],
env['VIP_IMAGE_SERVER_PORT'],
env['VIP_IMAGE_SERVER_URL_PATH'],
image),
originalImageUrl='%s://%s:%s/%s/%s.jpg' % (env['VIP_IMAGE_SERVER_PROTOCOL'],
env['VIP_IMAGE_SERVER_HOST'],
env['VIP_IMAGE_SERVER_PORT'],
env['VIP_IMAGE_SERVER_URL_PATH'],
image),
service_id = self.request.id);
img.save();
imageCollection.append(img.id);
ic = voxel_globe.meta.models.ImageCollection.create(name="Arducopter Mission 2", service_id = self.request.id);
ic.save();
ic.images.add(*imageCollection);
ic = voxel_globe.meta.models.ImageCollection.create(name="Arducopter Mission 2 short", service_id = self.request.id);
ic.save();
ic.images.add(*imageCollection[101:151]);
with open(path_join(env['VIP_PROJECT_ROOT'], 'images', 'Contractor_Survey_NorthA_List.csv'), 'r') as fid:
lines = fid.readlines();
lines = map(lambda x: x.split(','), lines);
for line in lines[3:]:
name = line[1];
desc = line[2];
lat = float(line[3]) + float(line[4])/60.0 + float(line[5])/3600.0;
if line[6] == 'S':
lat = -lat;
lon = float(line[8]) + float(line[9])/60.0 + float(line[10])/3600.0;
if line[11] == 'W':
lon = -lon;
alt = float(line[13]);
point = geos.Point(lon, lat, alt)
tp = voxel_globe.meta.models.ControlPoint.create(name=name,
description=desc,
point=point,
apparentPoint=point)
tp.service_id = self.request.id;
tp.save();
print '********** Populating arducopter cameras **********'
add_sample_cameras(self, path_join(env['VIP_PROJECT_ROOT'], 'images', 'cannon_cameras_gps.txt'), srid=7428)
voxel_globe.meta.models.Scene.create(name="Arducopter Mission 2 origin", service_id = self.request.id,
origin='SRID=%d;POINT(%0.12f %0.12f %0.12f)' % (7428, -92.215197, 37.648858, 300)).save()
def run_test(self, pattern, result, resultI):
self.assertEqual(len(glob(pattern, True)), result)
self.assertEqual(len(glob(pattern, False)), resultI)
self.assertItemsEqual(glob(pattern), glob_orig(pattern))
def ingest_data(self, uploadSession_id, imageDir):
''' task for the ingest route, to ingest the data an upload sessions points
to '''
import voxel_globe.ingest.models as IngestModels
from voxel_globe.tools.camera import save_krt
from PIL import Image
from datetime import datetime, timedelta
from .tools import split_clif
uploadSession = IngestModels.UploadSession.objects.get(id=uploadSession_id);
metadataFilenames = glob(os.path.join(imageDir, '*.txt'), False);
metadataFilenames = sorted(metadataFilenames, key=lambda s:s.lower())
metadataBasenames = map(lambda x:os.path.basename(x).lower(), metadataFilenames)
#In case none of them succeeded...
date = 'NYA'
timeOfDay = 'NYA'
for metadata_filename in metadataFilenames:
#Loop through until one succeeds
try:
with open(metadata_filename, 'r') as fid:
data = fid.readline().split(',')
imu_time = float(data[6])
imu_week = int(data[7])
timestamp = datetime(1980, 1, 6) + timedelta(weeks=imu_week,
seconds=imu_time)
date = '%04d-%02d-%02d' % (timestamp.year, timestamp.month,
timestamp.day)
timeOfDay = '%02d:%02d:%02d.%06d' % (timestamp.hour, timestamp.minute,
timestamp.second,
timestamp.microsecond)
break #Break on first success
except:
pass
imageCollection = voxel_globe.meta.models.ImageCollection.create(
name="CLIF Upload %s %s %s (%s)" % (uploadSession.name, date,
timeOfDay, uploadSession_id),
service_id = self.request.id);
imageCollection.save();
llhs_xyz = []
#for d in glob(os.path.join(imageDir, '*'+os.path.sep), False):
if 1:
files = glob(os.path.join(imageDir, '*'+os.extsep+'raw'), False);
files.sort()
for index,f in enumerate(files):
self.update_state(state='PROCESSING',
meta={'stage':'File %s (%d of %d)' % (f, index+1, len(files))})
logger.debug('Processing %s (%d of %d)', f, index+1, len(files))
basename = os.path.basename(f)
img_filename = os.extsep.join([os.path.splitext(f)[0], 'png'])
with open(f, 'rb') as fid:
data = fid.read();
img = np.fromstring(data,
dtype=CLIF_DATA[CLIF_VERSION]['dtype']).reshape(
(CLIF_DATA[CLIF_VERSION]['width'],
CLIF_DATA[CLIF_VERSION]['height'])).T
img2 = Image.fromarray(img)
img2.save(img_filename)
zoomifyName = os.path.splitext(f)[0] + '_zoomify'
pid = Popen(['vips', 'dzsave', img_filename, zoomifyName, '--layout', 'zoomify'])
pid.wait();
#convert the slashes to URL slashes
relFilePath = urllib.pathname2url(os.path.relpath(img_filename, env['VIP_IMAGE_SERVER_ROOT']));
basename = os.path.split(f)[-1]
relZoomPath = urllib.pathname2url(os.path.relpath(zoomifyName, env['VIP_IMAGE_SERVER_ROOT']));
pixel_format = CLIF_DATA[CLIF_VERSION]['pixel_format']
width = CLIF_DATA[CLIF_VERSION]['width']
height = CLIF_DATA[CLIF_VERSION]['height']
bands = CLIF_DATA[CLIF_VERSION]['bands']
img = voxel_globe.meta.models.Image.create(
name="CLIF Upload %s (%s) Frame %s" % (uploadSession.name, uploadSession_id, basename),
imageWidth=width, imageHeight=height,
numberColorBands=bands, pixelFormat=pixel_format, fileFormat='zoom',
imageUrl='%s://%s:%s/%s/%s/' % (env['VIP_IMAGE_SERVER_PROTOCOL'],
env['VIP_IMAGE_SERVER_HOST'],
env['VIP_IMAGE_SERVER_PORT'],
env['VIP_IMAGE_SERVER_URL_PATH'],
relZoomPath),
originalImageUrl='%s://%s:%s/%s/%s' % (env['VIP_IMAGE_SERVER_PROTOCOL'],
env['VIP_IMAGE_SERVER_HOST'],
env['VIP_IMAGE_SERVER_PORT'],
env['VIP_IMAGE_SERVER_URL_PATH'],
relFilePath),
service_id = self.request.id);
img.save();
imageCollection.images.add(img);
metadata_filename_desired = split_clif(f)
metadata_filename_desired = '%06d-%s.txt' % (0, metadata_filename_desired[2])
if 1:
# try:
metadata_index = metadataBasenames.index(metadata_filename_desired)
metadata_filename = metadataFilenames[metadata_index]
with open(metadata_filename, 'r') as fid:
metadata = fid.readline().split(',')
llh_xyz = [float(metadata[4]), float(metadata[3]),
float(metadata[5])*CLIF_DATA[CLIF_VERSION]['altitude_conversion']]
llhs_xyz.append(llh_xyz)
k = np.eye(3);
k[0,2] = width/2;
k[1,2] = height/2;
r = np.eye(3);
t = [0, 0, 0];
origin = llh_xyz;
save_krt(self.request.id, img, k, r, t, origin, srid=4326);
# except Exception as e:
pass
averageGps = np.mean(np.array(llhs_xyz), 0);
voxel_globe.meta.models.Scene.create(name="CLIF origin %s (%s)" % (uploadSession.name, uploadSession_id),
service_id = self.request.id,
origin='SRID=%d;POINT(%0.12f %0.12f %0.12f)' % \
(4326, averageGps[0], averageGps[1], averageGps[2])).save()
uploadSession.delete()
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()
def ingest_data(self, uploadSession_id, imageDir):
''' task for the ingest route, to ingest the data an upload sessions points to '''
import voxel_globe.ingest.models as IngestModels
import numpy
from voxel_globe.tools.camera import save_krt
uploadSession = IngestModels.UploadSession.objects.get(id=uploadSession_id);
#directories = uploadSession.directory.all();
#imageDirectory = directories.filter(name='image')
#metaDirectory = directories.filter(name='meta')
imageCollection = voxel_globe.meta.models.ImageCollection.create(name="Generic Upload %s (%s)" % (uploadSession.name, uploadSession_id), service_id = self.request.id);
imageCollection.save();
r = numpy.eye(3);
t = [0, 0, 0];
gpsList = []
gpsList2 = []
for d in glob(os.path.join(imageDir, '*'+os.path.sep), False):
files = glob(os.path.join(d, '*'), False);
files.sort()
for f in files:
self.update_state(state='PROCESSING',
meta={'stage':'File %s of %d' % (f, len(files))})
zoomifyName = f[:-4] + '_zoomify'
pid = Popen(['vips', 'dzsave', f, zoomifyName, '--layout', 'zoomify'])
pid.wait();
#convert the slashes to URL slashes
relFilePath = urllib.pathname2url(os.path.relpath(f, env['VIP_IMAGE_SERVER_ROOT']));
basename = os.path.split(f)[-1]
relZoomPath = urllib.pathname2url(os.path.relpath(zoomifyName, env['VIP_IMAGE_SERVER_ROOT']));
with open(f, 'rb') as fid:
magic = fid.read(4)
image_info = {}
if magic == '49492A00'.decode('hex') or \
magic == '4D4D002A'.decode('hex'):
logger.debug('Tifffile: %s', f)
from tifffile import TiffFile
with TiffFile(f) as image:
if image.pages[0].dtype == 's':
image_info['dtype'] = numpy.dtype('S')
else:
image_info['dtype'] = numpy.dtype(image.pages[0].dtype)
image_info['bps'] = image.pages[0].bits_per_sample
image_info['height'] = image.pages[0].shape[0] #Yep, y,x,z order
image_info['width'] = image.pages[0].shape[1]
try:
image_info['bands'] = image.pages[0].shape[2]
except IndexError:
image_info['bands'] = 1
else:
logger.debug('Pil: %s', f)
from PIL import Image
with Image.open(f) as image:
#The getmode* commands do not give you the REAL datatypes. I need the
#REAL (numpy in this case) bps, not some random PIL designation
image_info['dtype'] = numpy.dtype(Image._MODE_CONV[image.mode][0])
#probably doesn't work well for bool... Oh well
image_info['bps'] = image_info['dtype'].itemsize*8
image_info['width'] = image.size[0] #Yep, x,y order
image_info['height'] = image.size[1]
image_info['bands'] = Image.getmodebands(image.mode)
img = voxel_globe.meta.models.Image.create(
name="Generic Upload %s (%s) Frame %s" % (uploadSession.name, uploadSession_id, basename),
imageWidth=image_info['width'],
imageHeight=image_info['height'],
numberColorBands=image_info['bands'],
pixelFormat=image_info['dtype'].char,
fileFormat='zoom',
imageUrl='%s://%s:%s/%s/%s/' % (env['VIP_IMAGE_SERVER_PROTOCOL'],
env['VIP_IMAGE_SERVER_HOST'],
env['VIP_IMAGE_SERVER_PORT'],
env['VIP_IMAGE_SERVER_URL_PATH'],
relZoomPath),
originalImageUrl='%s://%s:%s/%s/%s' % (env['VIP_IMAGE_SERVER_PROTOCOL'],
env['VIP_IMAGE_SERVER_HOST'],
env['VIP_IMAGE_SERVER_PORT'],
env['VIP_IMAGE_SERVER_URL_PATH'],
relFilePath),
service_id = self.request.id);
img.save();
imageCollection.images.add(img);
origin = [0,0,0];
logger.debug('Origin is: %s' % origin)
k = numpy.eye(3);
k[0,2] = image_info['width']/2;
k[1,2] = image_info['height']/2;
save_krt(self.request.id, img, k, r, t, origin);
voxel_globe.meta.models.Scene.create(name="Generic origin %s (%s)" % (uploadSession.name, uploadSession_id),
service_id = self.request.id,
geolocated=False,
origin='POINT(%0.12f %0.12f %0.12f)' % \
(0,0,0)).save()
uploadSession.delete()
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()
