def cameras_krt(request):
if request.method == 'POST':
form = forms.TiePointForm(request.POST)
if form.is_valid():
from StringIO import StringIO
import math
import json
import zipfile
import numpy as np
from voxel_globe.tools.camera import get_krt
image_collection = form.cleaned_data['image_collection']
_, _, _, origin = get_krt(
image_collection.images.all()[0].history())
krts = []
name_format = 'frame_%%0%dd.txt' % int(
math.ceil(
math.log10(
max(image_collection.images.all().values_list(
'id', flat=True)))))
zip_s = StringIO()
with zipfile.ZipFile(zip_s, 'w', zipfile.ZIP_DEFLATED) as zipper:
for image in image_collection.images.all():
k, r, t, _ = get_krt(image.history(), origin=origin)
krt_s = StringIO()
np.savetxt(krt_s, np.array(k))
krt_s.write('\n')
np.savetxt(krt_s, np.array(r))
krt_s.write('\n')
np.savetxt(krt_s, np.array(t).T)
zipper.writestr(name_format % image.id, krt_s.getvalue())
zipper.writestr(
'scene.json',
json.dumps({
'origin': origin,
'longitude': origin[0],
'latitude': origin[1],
'altitude': origin[2]
}))
response = HttpResponse(zip_s.getvalue(),
content_type='application/zip')
response['Content-Length'] = len(response.content)
response['Content-Disposition'] = 'attachment; ' + \
'filename=cameras_%d.zip' % image_collection.id
return response
else:
form = forms.TiePointForm()
return render(
request, 'main/form.html', {
'title': 'Voxel Globe - Download',
'page_title':
'Voxel Globe - Download Cameras for Image Collection',
'form': form
})
def cameras_krt(request):
if request.method == 'POST':
form = forms.CameraForm(request.POST)
if form.is_valid():
from StringIO import StringIO
import math
import json
import zipfile
import numpy as np
from voxel_globe.tools.camera import get_krt
image_set = form.cleaned_data['image_set']
camera_set = form.data['camera_set']
_,_,_,origin = get_krt(image_set.images.all()[0], camera_set)
krts = []
name_format = 'frame_%%0%dd.txt' % int(math.ceil(math.log10(max(image_set.images.all().values_list('id', flat=True)))))
zip_s = StringIO()
with zipfile.ZipFile(zip_s, 'w', zipfile.ZIP_DEFLATED) as zipper:
for image in image_set.images.all():
k,r,t,_ = get_krt(image, camera_set, origin=origin)
krt_s = StringIO()
np.savetxt(krt_s, np.array(k))
krt_s.write('\n')
np.savetxt(krt_s, np.array(r))
krt_s.write('\n')
np.savetxt(krt_s, np.array(t).T)
zipper.writestr(name_format % image.id, krt_s.getvalue())
zipper.writestr('scene.json', json.dumps({'origin':origin, 'longitude':origin[0], 'latitude':origin[1], 'altitude':origin[2]}))
response = HttpResponse(zip_s.getvalue(), content_type='application/zip')
response['Content-Length'] = len(response.content)
response['Content-Disposition'] = 'attachment; ' + \
'filename=cameras_%d.zip' % image_set.id
return response
else:
form = forms.CameraForm()
return render(request, 'main/form.html',
{'form':form,
'action': '/download/cameras'})
def tiepoint_error_calculation(self,
image_collection_id,
scene_id,
history=None):
from PIL import Image
import numpy as np
import vpgl_adaptor
from voxel_globe.meta import models
import voxel_globe.tools
from voxel_globe.tools.camera import get_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,
'scene': scene_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 not '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_xyz = list(models.Scene.objects.get(id=scene_id).origin)
lvcs = vpgl_adaptor.create_lvcs(origin_xyz[1], origin_xyz[0],
origin_xyz[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 = []
points_triangulate = []
for correspondence in xml['Correspondences']['Correspondence']:
points_triangulate_id.append(int(correspondence.at['id']))
points_triangulate.append(
(float(correspondence['corr_world_point'].at['X']),
float(correspondence['corr_world_point'].at['Y']),
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 = []
for point_id in points_triangulate_id:
point = [
v['lvcs'] for k, v in control_points.iteritems()
if v['id'] == point_id
]
points_orig.append(point[0])
points_orig = np.array(points_orig)
points_triangulate = np.array(points_triangulate)
error = np.linalg.norm((points_orig - points_triangulate),
axis=0) / (points_orig.shape[0]**0.5)
result = {}
result['error'] = list(error)
result['horizontal_accuracy'] = 2.4477 * 0.5 * (error[0] + error[1])
result['vertical_accuracy'] = 1.96 * error[2]
return result
def 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 run_build_voxel_model_bp(self,
image_collection_id,
scene_id,
bbox,
skip_frames,
cleanup=True,
history=None):
from distutils.dir_util import remove_tree
from shutil import move
import random
from vsi.tools.redirect import StdRedirect, Logger as LoggerWrapper
from voxel_globe.meta import models
from voxel_globe.tools.camera import get_krt
import voxel_globe.tools
from boxm2_scene_adaptor import boxm2_scene_adaptor
from vil_adaptor import load_image
from vpgl_adaptor import load_perspective_camera
from voxel_globe.tools.wget import download as wget
from vsi.vxl.create_scene_xml import create_scene_xml
from vsi.tools.dir_util import copytree
with StdRedirect(
open(
os.path.join(voxel_globe.tools.log_dir(), self.request.id) +
'_out.log', 'w'),
open(
os.path.join(voxel_globe.tools.log_dir(), self.request.id) +
'_err.log', 'w')):
openclDevice = os.environ['VIP_OPENCL_DEVICE']
opencl_memory = os.environ.get('VIP_OPENCL_MEMORY', None)
if opencl_memory:
opencl_memory = int(opencl_memory)
scene = models.Scene.objects.get(id=scene_id)
imageCollection = models.ImageCollection.objects.get(\
id=image_collection_id).history(history)
imageList = imageCollection.images.all()
with voxel_globe.tools.task_dir('voxel_world') as processing_dir:
logger.warning(bbox)
if bbox['geolocated']:
create_scene_xml(
openclDevice,
3,
float(bbox['voxel_size']),
lla1=(float(bbox['x_min']), float(bbox['y_min']),
float(bbox['z_min'])),
lla2=(float(bbox['x_max']), float(bbox['y_max']),
float(bbox['z_max'])),
origin=scene.origin,
model_dir='.',
number_bins=1,
output_file=open(os.path.join(processing_dir, 'scene.xml'),
'w'),
n_bytes_gpu=opencl_memory)
else:
create_scene_xml(
openclDevice,
3,
float(bbox['voxel_size']),
lvcs1=(float(bbox['x_min']), float(bbox['y_min']),
float(bbox['z_min'])),
lvcs2=(float(bbox['x_max']), float(bbox['y_max']),
float(bbox['z_max'])),
origin=scene.origin,
model_dir='.',
number_bins=1,
output_file=open(os.path.join(processing_dir, 'scene.xml'),
'w'),
n_bytes_gpu=opencl_memory)
counter = 1
imageNames = []
cameraNames = []
os.mkdir(os.path.join(processing_dir, 'local'))
#Prepping
for image in imageList:
self.update_state(state='INITIALIZE',
meta={
'stage': 'image fetch',
'i': counter,
'total': len(imageList)
})
image = image.history(history)
(K, R, T, o) = get_krt(image.history(history), history=history)
krtName = os.path.join(processing_dir, 'local',
'frame_%05d.krt' % counter)
with open(krtName, 'w') as fid:
print >> fid, (("%0.18f " * 3 + "\n") * 3) % (
K[0, 0], K[0, 1], K[0, 2], K[1, 0], K[1, 1], K[1, 2],
K[2, 0], K[2, 1], K[2, 2])
print >> fid, (("%0.18f " * 3 + "\n") * 3) % (
R[0, 0], R[0, 1], R[0, 2], R[1, 0], R[1, 1], R[1, 2],
R[2, 0], R[2, 1], R[2, 2])
print >> fid, ("%0.18f " * 3 + "\n") % (T[0, 0], T[1, 0],
T[2, 0])
imageName = image.originalImageUrl
extension = os.path.splitext(imageName)[1]
localName = os.path.join(processing_dir, 'local',
'frame_%05d%s' % (counter, extension))
wget(imageName, localName, secret=True)
counter += 1
imageNames.append(localName)
cameraNames.append(krtName)
variance = 0.06
vxl_scene = boxm2_scene_adaptor(
os.path.join(processing_dir, "scene.xml"), openclDevice)
current_level = 0
loaded_imgs = []
loaded_cams = []
for i in range(0, len(imageNames), skip_frames):
logger.debug("i: %d img name: %s cam name: %s", i,
imageNames[i], cameraNames[i])
self.update_state(state='PRELOADING',
meta={
'stage': 'image load',
'i': i,
'total': len(imageNames)
})
img, ni, nj = load_image(imageNames[i])
loaded_imgs.append(img)
pcam = load_perspective_camera(cameraNames[i])
loaded_cams.append(pcam)
refine_cnt = 5
refine_device = openclDevice[0:3]
if refine_device == 'cpu':
refine_device = 'cpp'
for rfk in range(0, refine_cnt, 1):
pair = zip(loaded_imgs, loaded_cams)
random.shuffle(pair)
for idx, (img, cam) in enumerate(pair):
self.update_state(state='PROCESSING',
meta={
'stage': 'update',
'i': rfk + 1,
'total': refine_cnt,
'image': idx + 1,
'images': len(loaded_imgs)
})
logger.debug("refine_cnt: %d, idx: %d", rfk, idx)
vxl_scene.update(cam,
img,
True,
True,
None,
openclDevice[0:3],
variance,
tnear=1000.0,
tfar=100000.0)
for bp_index in range(2):
pass #height map update?
logger.debug("writing cache: %d", rfk)
vxl_scene.write_cache()
logger.debug("wrote cache: %d", rfk)
if rfk < refine_cnt - 1:
self.update_state(state='PROCESSING',
meta={
'stage': 'refine',
'i': rfk,
'total': refine_cnt
})
logger.debug("refining %d...", rfk)
vxl_scene.refine(0.3, refine_device)
vxl_scene.write_cache()
with open(os.path.join(processing_dir, "scene_color.xml"),
'w') as fid:
lines = open(os.path.join(processing_dir, "scene.xml"),
'r').readlines()
lines = [
line.replace('boxm2_mog3_grey', 'boxm2_gauss_rgb').replace(
'boxm2_num_obs', 'boxm2_num_obs_single')
for line in lines
]
fid.writelines(lines)
vxl_scene = boxm2_scene_adaptor(
os.path.join(processing_dir, "scene_color.xml"), openclDevice)
for idx, (img, cam) in enumerate(pair):
self.update_state(state='PROCESSING',
meta={
'stage': 'color_update',
'i': rfk + 1,
'total': refine_cnt,
'image': idx + 1,
'images': len(loaded_imgs)
})
logger.debug("color_paint idx: %d", idx)
vxl_scene.update(cam,
img,
False,
False,
None,
openclDevice[0:3],
tnear=1000.0,
tfar=100000.0)
vxl_scene.write_cache()
with voxel_globe.tools.storage_dir(
'voxel_world') as voxel_world_dir:
copytree(processing_dir,
voxel_world_dir,
ignore=lambda x, y: ['images'])
models.VoxelWorld.create(
name='%s world (%s)' %
(imageCollection.name, self.request.id),
origin=scene.origin,
directory=voxel_world_dir,
service_id=self.request.id).save()
def generate_error_point_cloud(self, voxel_world_id, prob=0.5, history=None):
from glob import glob
import json
import numpy as np
from boxm2_adaptor import load_cpp, render_depth, cast_3d_point, \
cast_3d_point_pass2, write_cache, \
create_stream_cache
from boxm2_scene_adaptor import boxm2_scene_adaptor
from vpgl_adaptor import create_perspective_camera_krt, persp2gen, \
compute_direction_covariance
from boxm2_mesh_adaptor import batch_compute_3d_points, gen_error_point_cloud
from vsi.tools.redirect import Redirect, Logger as LoggerWrapper
import voxel_globe.tools
import voxel_globe.meta.models as models
from voxel_globe.tools.camera import get_krt
with Redirect(stdout_c=LoggerWrapper(logger, lvl=logging.INFO),
stderr_c=LoggerWrapper(logger, lvl=logging.WARNING)):
self.update_state(state='SETUP', meta={'pid':os.getpid()})
voxel_world = models.VoxelWorld.objects.get(id=voxel_world_id)
service_inputs = json.loads(voxel_world.service.inputs)
image_collection = models.ImageCollection.objects.get(
id=service_inputs[0][0])
images = image_collection.images.all()
scene = models.Scene.objects.get(id=service_inputs[0][1])
voxel_world_dir = voxel_world.directory
scene_filename = os.path.join(voxel_world_dir, 'scene.xml')
opencl_device = os.environ['VIP_OPENCL_DEVICE']
scene_gpu = boxm2_scene_adaptor(scene_filename, opencl_device)
scene_cpp = boxm2_scene_adaptor(scene_filename, 'cpp')
type_id_fname = "type_names_list.txt"
image_id_fname = "image_list.txt"
std_dev_angle = 0.1
cov_c_path = 'cov_c.txt'
cov_c = 0*np.eye(3)*0.8**2
with voxel_globe.tools.task_dir('generate_error_point_cloud', cd=True) \
as processing_dir:
np.savetxt(cov_c_path, cov_c)
for index, image in enumerate(images):
self.update_state(state='PROCESSING',
meta={'stage':'casting', 'image':index+1,
'total':len(images)})
k,r,t,o = get_krt(image.history(history))
perspective_camera = create_perspective_camera_krt(k, r, t)
(depth_image, variance_image, _) = render_depth(scene_gpu.scene,
scene_gpu.opencl_cache, perspective_camera,
image.imageWidth, image.imageHeight,
scene_gpu.device)
cov_v_path = 'cov_%06d.txt' % index
appearance_model = 'image_%06d' % index
generic_camera = persp2gen(perspective_camera, image.imageWidth,
image.imageHeight)
compute_direction_covariance(perspective_camera, std_dev_angle,
cov_v_path)
cast_3d_point(scene_cpp.scene,scene_cpp.cpu_cache,perspective_camera,
generic_camera,depth_image,variance_image,appearance_model)
cast_3d_point_pass2(scene_cpp.scene,scene_cpp.cpu_cache,generic_camera,
appearance_model,cov_c_path,cov_v_path)
write_cache(scene_cpp.cpu_cache, 1)
self.update_state(state='PROCESSING',
meta={'stage':'compute error'})
with open(image_id_fname, 'w') as fid:
print >>fid, len(images)
for index, image in enumerate(images):
print >>fid, 'image_%06d' % (index)
with open(type_id_fname,"w") as fid:
print >>fid, 2
print >>fid, "boxm2_point"
print >>fid, "boxm2_covariance"
mem=3.0
stream_cache = create_stream_cache(scene_cpp.scene, type_id_fname,
image_id_fname, mem)
batch_compute_3d_points(scene_cpp.scene, scene_cpp.cpu_cache, stream_cache)
self.update_state(state='EXPORTING',
meta={'stage':'ply'})
with voxel_globe.tools.storage_dir('generate_error_point_cloud') \
as storage_dir:
ply_filename = os.path.join(storage_dir, 'model.ply')
gen_error_point_cloud(scene_cpp.scene, scene_cpp.cpu_cache,
ply_filename, 0.5, True)
potree_filename = os.path.join(storage_dir, 'potree.ply')
with voxel_globe.tools.image_dir('point_cloud') as potree_dir:
convert_ply_to_potree(ply_filename, potree_dir)
models.PointCloud.create(name='%s point cloud' % image_collection.name,
service_id=self.request.id, origin=voxel_world.origin,
potree_url='%s://%s:%s/%s/point_cloud/%s/cloud.js' % \
(env['VIP_IMAGE_SERVER_PROTOCOL'], env['VIP_IMAGE_SERVER_HOST'],
env['VIP_IMAGE_SERVER_PORT'], env['VIP_IMAGE_SERVER_URL_PATH'],
os.path.basename(potree_dir)),
directory=storage_dir).save()
voxel_files = lambda x: glob(os.path.join(voxel_world_dir, x))
cleanup_files = []
cleanup_files += voxel_files('boxm2_covariance_*.bin')
cleanup_files += voxel_files('boxm2_point_*.bin')
cleanup_files += voxel_files('float16_image_*.bin')
for cleanup_file in cleanup_files:
os.remove(cleanup_file)
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_error_calculation(self, image_collection_id, scene_id, history=None):
from PIL import Image
import numpy as np
import vpgl_adaptor
from voxel_globe.meta import models
import voxel_globe.tools
from voxel_globe.tools.camera import get_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, 'scene':scene_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 not '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_xyz = list(models.Scene.objects.get(id=scene_id).origin)
lvcs = vpgl_adaptor.create_lvcs(origin_xyz[1], origin_xyz[0], origin_xyz[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=[]
points_triangulate=[]
for correspondence in xml['Correspondences']['Correspondence']:
points_triangulate_id.append(int(correspondence.at['id']))
points_triangulate.append((float(correspondence['corr_world_point'].at['X']),
float(correspondence['corr_world_point'].at['Y']),
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 = []
for point_id in points_triangulate_id:
point = [v['lvcs'] for k,v in control_points.iteritems() if v['id'] == point_id]
points_orig.append(point[0])
points_orig = np.array(points_orig)
points_triangulate = np.array(points_triangulate)
error = np.linalg.norm((points_orig-points_triangulate), axis=0)/(points_orig.shape[0]**0.5)
result={}
result['error'] = list(error)
result['horizontal_accuracy'] = 2.4477*0.5*(error[0]+error[1])
result['vertical_accuracy'] = 1.96*error[2]
return result
def generate_error_point_cloud(
self, voxel_world_id, prob=0.5, position_error_override=None, orientation_error_override=None, history=None
):
from glob import glob
import json
import numpy as np
from boxm2_adaptor import (
load_cpp,
render_depth,
cast_3d_point,
cast_3d_point_pass2,
write_cache,
accumulate_3d_point_and_cov,
normalize_3d_point_and_cov,
)
from boxm2_scene_adaptor import boxm2_scene_adaptor
from vpgl_adaptor import create_perspective_camera_krt, persp2gen, compute_direction_covariance
from boxm2_mesh_adaptor import gen_error_point_cloud
from vsi.tools.redirect import StdRedirect, Logger as LoggerWrapper
import voxel_globe.tools
import voxel_globe.meta.models as models
from voxel_globe.tools.camera import get_krt
with StdRedirect(
open(os.path.join(voxel_globe.tools.log_dir(), self.request.id) + "_out.log", "w"),
open(os.path.join(voxel_globe.tools.log_dir(), self.request.id) + "_err.log", "w"),
):
self.update_state(state="SETUP", meta={"pid": os.getpid()})
voxel_world = models.VoxelWorld.objects.get(id=voxel_world_id)
service_inputs = json.loads(voxel_world.service.inputs)
image_collection = models.ImageCollection.objects.get(id=service_inputs[0][0])
images = image_collection.images.all()
scene = models.Scene.objects.get(id=service_inputs[0][1])
voxel_world_dir = voxel_world.directory
scene_filename = os.path.join(voxel_world_dir, "scene_color.xml")
opencl_device = os.environ["VIP_OPENCL_DEVICE"]
scene_gpu = boxm2_scene_adaptor(scene_filename, opencl_device)
scene_cpp = boxm2_scene_adaptor(scene_filename, "cpp")
type_id_fname = "type_names_list.txt"
image_id_fname = "image_list.txt"
std_dev_angle_default = 0.1
cov_c_path = "cov_c.txt"
cov_c_default = 0.8
with voxel_globe.tools.task_dir("generate_error_point_cloud", cd=True) as processing_dir:
for index, image in enumerate(images):
self.update_state(
state="PROCESSING", meta={"stage": "casting", "image": index + 1, "total": len(images)}
)
k, r, t, o = get_krt(image.history(history))
attributes = image.history(history).camera.history(history).attributes
cov_c = attributes.get("position_error", std_dev_angle_default)
if position_error_override is not None:
cov_c = position_error_override
std_dev_angle = attributes.get("orientation_error", cov_c_default)
if orientation_error_override is not None:
std_dev_angle = orientation_error_override
cov_c = np.eye(3) * cov_c ** 2
np.savetxt(cov_c_path, cov_c)
perspective_camera = create_perspective_camera_krt(k, r, t)
self.update_state(
state="PROCESSING", meta={"stage": "pre render", "image": index + 1, "total": len(images)}
)
(depth_image, variance_image, _) = render_depth(
scene_gpu.scene,
scene_gpu.opencl_cache,
perspective_camera,
image.imageWidth,
image.imageHeight,
scene_gpu.device,
)
self.update_state(
state="PROCESSING", meta={"stage": "post_render", "image": index + 1, "total": len(images)}
)
cov_v_path = "cov_%06d.txt" % index
appearance_model = "image"
self.update_state(
state="PROCESSING", meta={"stage": "pre_persp2gen", "image": index + 1, "total": len(images)}
)
generic_camera = persp2gen(perspective_camera, image.imageWidth, image.imageHeight)
self.update_state(
state="PROCESSING", meta={"stage": "pre_covar", "image": index + 1, "total": len(images)}
)
compute_direction_covariance(perspective_camera, std_dev_angle, cov_v_path)
self.update_state(
state="PROCESSING", meta={"stage": "pre_cast1", "image": index + 1, "total": len(images)}
)
cast_3d_point(
scene_cpp.scene,
scene_cpp.cpu_cache,
perspective_camera,
generic_camera,
depth_image,
variance_image,
appearance_model,
)
self.update_state(
state="PROCESSING", meta={"stage": "pre_cast2", "image": index + 1, "total": len(images)}
)
cast_3d_point_pass2(
scene_cpp.scene, scene_cpp.cpu_cache, generic_camera, appearance_model, cov_c_path, cov_v_path
)
self.update_state(
state="PROCESSING", meta={"stage": "pre_accumulate", "image": index + 1, "total": len(images)}
)
accumulate_3d_point_and_cov(scene_cpp.scene, scene_cpp.cpu_cache, appearance_model)
# self.update_state(state='PROCESSING',
# meta={'stage':'pre_write', 'image':index+1,
# 'total':len(images)})
# write_cache(scene_cpp.cpu_cache, 1)
self.update_state(
state="PROCESSING", meta={"stage": "post_write", "image": index + 1, "total": len(images)}
)
self.update_state(state="PROCESSING", meta={"stage": "compute error"})
normalize_3d_point_and_cov(scene_cpp.scene, scene_cpp.cpu_cache)
self.update_state(state="PROCESSING", meta={"stage": "pre_write", "image": index + 1, "total": len(images)})
write_cache(scene_cpp.cpu_cache, 1)
self.update_state(state="EXPORTING", meta={"stage": "ply"})
with voxel_globe.tools.storage_dir("generate_error_point_cloud") as storage_dir:
ply_filename = os.path.join(storage_dir, "model.ply")
gen_error_point_cloud(scene_cpp.scene, scene_cpp.cpu_cache, ply_filename, prob)
potree_filename = os.path.join(storage_dir, "potree.ply")
with voxel_globe.tools.image_dir("point_cloud") as potree_dir:
convert_ply_to_potree(ply_filename, potree_dir)
models.PointCloud.create(
name="%s point cloud" % image_collection.name,
service_id=self.request.id,
origin=voxel_world.origin,
potree_url="%s://%s:%s/%s/point_cloud/%s/cloud.js"
% (
env["VIP_IMAGE_SERVER_PROTOCOL"],
env["VIP_IMAGE_SERVER_HOST"],
env["VIP_IMAGE_SERVER_PORT"],
env["VIP_IMAGE_SERVER_URL_PATH"],
os.path.basename(potree_dir),
),
filename=ply_filename,
).save()
voxel_files = lambda x: glob(os.path.join(voxel_world_dir, x))
cleanup_files = []
cleanup_files += voxel_files("boxm2_covariance_*.bin")
cleanup_files += voxel_files("boxm2_point_*.bin")
cleanup_files += voxel_files("float16_image_*.bin")
for cleanup_file in cleanup_files:
os.remove(cleanup_file)
def run_build_voxel_model_bp(self,
image_set_id,
camera_set_id,
scene_id,
bbox,
skip_frames,
cleanup=True):
import random
import glob
import math
import numpy as np
from vsi.tools.redirect import StdRedirect
from voxel_globe.meta import models
from voxel_globe.tools.camera import get_krt
import voxel_globe.tools
from boxm2_scene_adaptor import boxm2_scene_adaptor
import brl_init
from vil_adaptor_boxm2_batch import load_image
import vpgl_adaptor_boxm2_batch as vpgl
from vsi.vxl.create_scene_xml import create_scene_xml
from vsi.tools.dir_util import copytree
from vsi.tools.file_util import lncp
def rectint(recta, rectb):
lx = max(recta[0], rectb[0])
rx = min(recta[2], rectb[2])
by = max(recta[1], rectb[1])
ty = min(recta[3], rectb[3])
if lx > rx or by > ty:
return [0, 0, 0, 0], 0
else:
return [lx, by, rx, ty], (rx - lx) * (ty - by)
def generate_subsetim(scene, camfiles, ni, nj):
subsetIdx = []
refIndices = []
minDepOverlap = 0.25
minRefOverlap = 0.5
minIndepAngle = 5.0
minRefAngle = 5.0
maxRefAngle = 15.0
minRefIndepAngle = 5.0
cosMinIndepAngle = math.cos(minIndepAngle * math.pi / 180.0)
cosMinRefAngle = math.cos(minRefAngle * math.pi / 180.0)
cosMaxRefAngle = math.cos(maxRefAngle * math.pi / 180.0)
cosMinRefIndepAngle = math.cos(minRefIndepAngle * math.pi / 180.0)
bbox = scene.bbox
grect = [
scene.bbox[0][0], scene.bbox[0][1], scene.bbox[1][0],
scene.bbox[1][1]
]
worldoverlaps = []
camrects = []
cams = []
princAxis = []
for camfile in camfiles:
pcam = vpgl.load_perspective_camera(camfile)
prx, pry, prz = vpgl.get_backprojected_ray(pcam, ni / 2, nj / 2)
princAxis.append([prx, pry, prz])
Hmat = vpgl.compute_camera_to_world_homography(
pcam, [0, 0, 1, -bbox[0][2]])
H = np.array(Hmat).reshape([3, 3])
ps = np.dot(
H,
np.transpose([[0, 0, 1], [ni, 0, 1], [ni, nj, 1], [0, nj, 1]]))
xs = ps[0, :] / ps[2, :]
ys = ps[1, :] / ps[2, :]
rect = [min(xs), min(ys), max(xs), max(ys)]
area = (rect[2] - rect[0]) * (rect[3] - rect[1])
crect, carea = rectint(rect, grect)
#print crect,carea
if (carea > 0):
cams.append(pcam)
camrects.append(crect)
worldoverlaps.append(carea / area)
usedcams = [False] * len(cams)
for i in range(0, len(cams)):
randidx = random.randint(0, len(cams) - 1)
while usedcams[randidx]:
randidx = (randidx + 1) % len(cams)
usedcams[randidx] = True
dep = False
for c2 in range(0, len(subsetIdx)):
cosAngle = np.dot(princAxis[randidx], princAxis[subsetIdx[c2]])
if cosAngle > cosMinIndepAngle:
rectc2 = camrects[subsetIdx[c2]]
overlap, oarea = rectint(camrects[randidx], rectc2)
tarea = (rectc2[2] - rectc2[0]) * (rectc2[3] - rectc2[1])
if (oarea / tarea > minDepOverlap):
dep = True
break
if dep:
continue
theseRefIndices = []
for c3 in range(0, len(cams)):
#Check angle disparity
cosAngle2 = np.dot(princAxis[randidx], princAxis[c3])
if (cosAngle2 > cosMinRefAngle or cosAngle2 < cosMaxRefAngle):
continue
# Check that a similar viewpoint isn't already used for reference
refDep = False
for c4 in range(0, len(theseRefIndices)):
#Check angle disparity
cosAngle3 = np.dot(princAxis[theseRefIndices[c4]],
princAxis[c3])
if (cosAngle3 > cosMinRefIndepAngle):
refDep = True
break
#If similar viewpoint don't add
if (refDep):
continue
theseRefIndices.append(c3)
#If at least one reference image save this viewpoint
if len(theseRefIndices) > 0:
subsetIdx.append(randidx)
refIndices.append(theseRefIndices)
return subsetIdx, refIndices
def update_bp(scene,
images,
cameras,
do_update_image=True,
do_update_hmap=False):
_, ni, nj = load_image(images[0])
frames, refimages = generate_subsetim(scene, cameras, ni, nj)
for file_name in glob.glob(os.path.join(scene.model_dir,
'boxm2_*.bin')):
os.remove(file_name)
scene.init_uniform_prob()
sradius = 16
idents = []
weights = []
if do_update_image:
idents.append("if")
weights.append(1.0)
if do_update_hmap:
idents.append("hf")
weights.append(2.0)
for idx, i in enumerate(frames):
if do_update_image:
print "Iteration ", idx, "Image ", images[i]
####load image and camera
viewid = os.path.splitext(os.path.basename(images[i]))[0]
#### forming an app model using the neighbor images
for lindex in refimages[idx]:
lcam = vpgl.load_perspective_camera(cameras[lindex])
limg, ni, nj = load_image(images[lindex])
scene.update(lcam, limg, False, True, None, "gpu0", 0.05,
viewid)
scene.update_if(False, viewid) # subtracting the image factor
scene.fuse_factors(idents, weights)
pcam = vpgl.load_perspective_camera(cameras[i])
img, ni, nj = load_image(images[i])
scene.compute_pre_post(pcam, img, viewid, 100000, 100000)
# computing the new image factor
scene.update_if(True, viewid) # adding the image factor
scene.fuse_factors(idents, weights)
if do_update_hmap and idx % 2 == 0:
scene.update_hf(False) # subtracting the height-map factor
scene.fuse_factors(idents, weights)
zimg, zvar, ximg, yimg, probimg = scene.render_height_map()
#save_image(zimg, "./zimg.tif")
scene.compute_hmapf(zimg, zvar, ximg, yimg,
sradius) # computing the height-map factor
scene.update_hf(True) # adding the height-map factor
scene.fuse_factors(idents, weights)
scene.write_cache()
def refine(scene):
scene.refine(0.3)
for filename in glob.glob(os.path.join(scene.model_dir, '[a-b]*.bin')):
os.remove(filename)
scene.write_cache()
with StdRedirect(
open(
os.path.join(voxel_globe.tools.log_dir(), self.request.id) +
'_out.log', 'w'),
open(
os.path.join(voxel_globe.tools.log_dir(), self.request.id) +
'_err.log', 'w')):
openclDevice = os.environ['VIP_OPENCL_DEVICE']
opencl_memory = os.environ.get('VIP_OPENCL_MEMORY', None)
if opencl_memory:
opencl_memory = int(opencl_memory)
scene = models.Scene.objects.get(id=scene_id)
imageSet = models.ImageSet.objects.get(id=image_set_id)
imageList = imageSet.images.all()
with voxel_globe.tools.task_dir('voxel_world') as processing_dir:
logger.warning(bbox)
create_scene_xml(openclDevice,
0,
float(bbox['voxel_size']),
lvcs1=(float(bbox['x_min']), float(bbox['y_min']),
float(bbox['z_min'])),
lvcs2=(float(bbox['x_max']), float(bbox['y_max']),
float(bbox['z_max'])),
origin=scene.origin,
model_dir='.',
number_bins=1,
output_file=open(
os.path.join(processing_dir, 'scene.xml'),
'w'),
n_bytes_gpu=opencl_memory)
counter = 1
imageNames = []
cameraNames = []
os.mkdir(os.path.join(processing_dir, 'local'))
#Prepping
self.update_state(state='INITIALIZE',
meta={
'image_set_name': imageSet.name,
'stage': 'camera fetch'
})
for image in imageList:
(K, R, T, o) = get_krt(image, camera_set_id)
krtName = os.path.join(processing_dir, 'local',
'frame_%05d.krt' % counter)
with open(krtName, 'w') as fid:
print >> fid, (("%0.18f " * 3 + "\n") * 3) % (
K[0, 0], K[0, 1], K[0, 2], K[1, 0], K[1, 1], K[1, 2],
K[2, 0], K[2, 1], K[2, 2])
print >> fid, (("%0.18f " * 3 + "\n") * 3) % (
R[0, 0], R[0, 1], R[0, 2], R[1, 0], R[1, 1], R[1, 2],
R[2, 0], R[2, 1], R[2, 2])
print >> fid, ("%0.18f " * 3 + "\n") % (T[0, 0], T[1, 0],
T[2, 0])
imageName = image.filename_path
extension = os.path.splitext(imageName)[1]
localName = os.path.join(processing_dir, 'local',
'frame_%05d%s' % (counter, extension))
lncp(imageName, localName)
counter += 1
imageNames.append(localName)
cameraNames.append(krtName)
variance = 0.06
vxl_scene = boxm2_scene_adaptor(
os.path.join(processing_dir, "scene.xml"), openclDevice)
# loaded_imgs = []
# loaded_cams = []
# for i in range(0, len(imageNames), skip_frames):
# logger.debug("i: %d img name: %s cam name: %s", i, imageNames[i],
# cameraNames[i])
# self.update_state(state='PRELOADING', meta={'image_set_name': imageSet.name,
# 'stage':'image load',
# 'i':i,
# 'total':len(imageNames)})
# img, ni, nj = load_image(imageNames[i])
# loaded_imgs.append(img)
# pcam = load_perspective_camera(cameraNames[i])
# loaded_cams.append(pcam)
refine_cnt = 2
for rfk in range(0, refine_cnt, 1):
self.update_state(state='PROCESSING',
meta={
'image_set_name': imageSet.name,
'stage': 'update 1'
})
update_bp(vxl_scene, imageNames, cameraNames)
# self.update_state(state='PROCESSING', meta={'image_set_name': imageSet.name,
# 'stage':'update 2'})
# update_bp(vxl_scene, imageNames, cameraNames, True, True)
# self.update_state(state='PROCESSING', meta={'image_set_name': imageSet.name,
# 'stage':'update 3'})
# update_bp(vxl_scene, imageNames, cameraNames, True, True)
if rfk < refine_cnt - 1:
self.update_state(state='PROCESSING',
meta={
'image_set_name': imageSet.name,
'stage': 'refine',
'i': rfk + 1,
'total': refine_cnt
})
refine(vxl_scene)
#Update color appearance
with open(os.path.join(processing_dir, "scene_color.xml"),
'w') as fid:
lines = open(os.path.join(processing_dir, "scene.xml"),
'r').readlines()
lines = [
line.replace('boxm2_mog3_grey', 'boxm2_gauss_rgb').replace(
'boxm2_num_obs', 'boxm2_num_obs_single')
for line in lines
]
fid.writelines(lines)
vxl_scene = boxm2_scene_adaptor(
os.path.join(processing_dir, "scene_color.xml"), openclDevice)
for idx, (image_name,
camera_name) in enumerate(zip(imageNames, cameraNames)):
self.update_state(state='PROCESSING',
meta={
'image_set_name': imageSet.name,
'stage': 'color_update',
'i': idx + 1,
'total': len(imageNames),
'images': len(imageNames)
})
img, _, _ = load_image(image_name)
pcam = vpgl.load_perspective_camera(camera_name)
logger.debug("color_paint idx: %d", idx)
vxl_scene.update(pcam,
img,
False,
False,
None,
openclDevice,
tnear=1000.0,
tfar=100000.0)
vxl_scene.write_cache()
with voxel_globe.tools.storage_dir(
'voxel_world') as voxel_world_dir:
copytree(processing_dir,
voxel_world_dir,
ignore=lambda x, y: ['local'])
models.VoxelWorld(name='%s world (%s)' %
(imageSet.name, self.request.id),
origin=scene.origin,
directory=voxel_world_dir,
service_id=self.request.id).save()
return {"image_set_name": imageSet.name}
def run_build_voxel_model(self, image_set_id, camera_set_id, scene_id, bbox,
skip_frames, cleanup=True):
import random
from vsi.tools.redirect import StdRedirect
from voxel_globe.meta import models
from voxel_globe.tools.camera import get_krt
import voxel_globe.tools
from boxm2_scene_adaptor import boxm2_scene_adaptor
import brl_init
from vil_adaptor_boxm2_batch import load_image
from vpgl_adaptor_boxm2_batch import load_perspective_camera
from vsi.vxl.create_scene_xml import create_scene_xml
from vsi.tools.dir_util import copytree
from vsi.tools.file_util import lncp
with StdRedirect(open(os.path.join(voxel_globe.tools.log_dir(),
self.request.id)+'_out.log', 'w'),
open(os.path.join(voxel_globe.tools.log_dir(),
self.request.id)+'_err.log', 'w')):
openclDevice = os.environ['VIP_OPENCL_DEVICE']
opencl_memory = os.environ.get('VIP_OPENCL_MEMORY', None)
if opencl_memory:
opencl_memory = int(opencl_memory)
scene = models.Scene.objects.get(id=scene_id)
imageSet = models.ImageSet.objects.get(\
id=image_set_id)
imageList = imageSet.images.all()
with voxel_globe.tools.task_dir('voxel_world') as processing_dir:
logger.warning(bbox)
create_scene_xml(openclDevice, 3, float(bbox['voxel_size']),
lvcs1=(float(bbox['x_min']), float(bbox['y_min']),
float(bbox['z_min'])),
lvcs2=(float(bbox['x_max']), float(bbox['y_max']),
float(bbox['z_max'])),
origin=scene.origin, model_dir='.', number_bins=1,
output_file=open(os.path.join(processing_dir, 'scene.xml'), 'w'),
n_bytes_gpu=opencl_memory)
counter = 1
imageNames = []
cameraNames = []
os.mkdir(os.path.join(processing_dir, 'local'))
#Prepping
for image in imageList:
self.update_state(state='INITIALIZE', meta={'image_set_name': imageSet.name,
'stage':'image fetch',
'i':counter,
'total':len(imageList)})
(K,R,T,o) = get_krt(image, camera_set_id)
krtName = os.path.join(processing_dir, 'local', 'frame_%05d.krt' % counter)
with open(krtName, 'w') as fid:
print >>fid, (("%0.18f "*3+"\n")*3) % (K[0,0], K[0,1], K[0,2],
K[1,0], K[1,1], K[1,2], K[2,0], K[2,1], K[2,2])
print >>fid, (("%0.18f "*3+"\n")*3) % (R[0,0], R[0,1], R[0,2],
R[1,0], R[1,1], R[1,2], R[2,0], R[2,1], R[2,2])
print >>fid, ("%0.18f "*3+"\n") % (T[0,0], T[1,0], T[2,0])
imageName = image.filename_path
extension = os.path.splitext(imageName)[1]
localName = os.path.join(processing_dir, 'local',
'frame_%05d%s' % (counter, extension))
lncp(imageName, localName)
counter += 1
imageNames.append(localName)
cameraNames.append(krtName)
variance = 0.06
vxl_scene = boxm2_scene_adaptor(os.path.join(processing_dir, "scene.xml"),
openclDevice)
loaded_imgs = []
loaded_cams = []
for i in range(0, len(imageNames), skip_frames):
logger.debug("i: %d img name: %s cam name: %s", i, imageNames[i],
cameraNames[i])
self.update_state(state='PRELOADING', meta={'image_set_name': imageSet.name,
'stage':'image load',
'i':i,
'total':len(imageNames)})
img, ni, nj = load_image(imageNames[i])
loaded_imgs.append(img)
pcam = load_perspective_camera(cameraNames[i])
loaded_cams.append(pcam)
refine_cnt = 5
for rfk in range(0, refine_cnt, 1):
pair = zip(loaded_imgs, loaded_cams)
random.shuffle(pair)
for idx, (img, cam) in enumerate(pair):
self.update_state(state='PROCESSING', meta={'image_set_name': imageSet.name,
'stage':'update',
'i':rfk+1, 'total':refine_cnt, 'image':idx+1,
'images':len(loaded_imgs)})
logger.debug("refine_cnt: %d, idx: %d", rfk, idx)
vxl_scene.update(cam,img,True,True,None,openclDevice,variance,
tnear = 1000.0, tfar = 100000.0)
logger.debug("writing cache: %d", rfk)
vxl_scene.write_cache()
logger.debug("wrote cache: %d", rfk)
if rfk < refine_cnt-1:
self.update_state(state='PROCESSING', meta={'image_set_name': imageSet.name,
'stage':'refine',
'i':rfk,
'total':refine_cnt})
logger.debug("refining %d...", rfk)
vxl_scene.refine(0.3, openclDevice)
vxl_scene.write_cache()
with open(os.path.join(processing_dir, "scene_color.xml"), 'w') as fid:
lines = open(os.path.join(processing_dir, "scene.xml"),
'r').readlines()
lines = [line.replace('boxm2_mog3_grey',
'boxm2_gauss_rgb').replace(
'boxm2_num_obs',
'boxm2_num_obs_single') for line in lines]
fid.writelines(lines)
vxl_scene = boxm2_scene_adaptor(os.path.join(processing_dir,
"scene_color.xml"),
openclDevice)
for idx, (img, cam) in enumerate(pair):
self.update_state(state='PROCESSING', meta={'image_set_name': imageSet.name,
'stage':'color_update',
'i':rfk+1, 'total':refine_cnt, 'image':idx+1,
'images':len(loaded_imgs)})
logger.debug("color_paint idx: %d", idx)
vxl_scene.update(cam,img,False,False,None,openclDevice,
tnear = 1000.0, tfar = 100000.0)
vxl_scene.write_cache()
with voxel_globe.tools.storage_dir('voxel_world') as voxel_world_dir:
copytree(processing_dir, voxel_world_dir, ignore=lambda x,y:['local'])
models.VoxelWorld(
name='%s world (%s)' % (imageSet.name, self.request.id),
origin=scene.origin,
directory=voxel_world_dir,
service_id=self.request.id).save()
return {"image_set_name" : imageSet.name}
def run_build_voxel_model_bp(self, image_set_id, camera_set_id, scene_id, bbox,
skip_frames, cleanup=True):
import random
import glob
import math
import numpy as np
from vsi.tools.redirect import StdRedirect
from voxel_globe.meta import models
from voxel_globe.tools.camera import get_krt
import voxel_globe.tools
from boxm2_scene_adaptor import boxm2_scene_adaptor
import brl_init
from vil_adaptor_boxm2_batch import load_image
import vpgl_adaptor_boxm2_batch as vpgl
from vsi.vxl.create_scene_xml import create_scene_xml
from vsi.tools.dir_util import copytree
from vsi.tools.file_util import lncp
def rectint(recta,rectb):
lx = max(recta[0],rectb[0])
rx = min(recta[2],rectb[2])
by = max(recta[1],rectb[1])
ty = min(recta[3],rectb[3])
if lx > rx or by > ty :
return [0,0,0,0],0
else:
return [lx,by,rx,ty], (rx-lx)*(ty-by)
def generate_subsetim(scene,camfiles,ni,nj):
subsetIdx = []
refIndices = []
minDepOverlap = 0.25
minRefOverlap = 0.5
minIndepAngle = 5.0
minRefAngle = 5.0
maxRefAngle = 15.0
minRefIndepAngle = 5.0
cosMinIndepAngle = math.cos( minIndepAngle*math.pi/180.0 );
cosMinRefAngle = math.cos( minRefAngle*math.pi/180.0 );
cosMaxRefAngle = math.cos( maxRefAngle*math.pi/180.0 );
cosMinRefIndepAngle = math.cos( minRefIndepAngle*math.pi/180.0 );
bbox = scene.bbox
grect=[scene.bbox[0][0],scene.bbox[0][1],scene.bbox[1][0],scene.bbox[1][1]]
worldoverlaps = []
camrects = []
cams = []
princAxis = []
for camfile in camfiles:
pcam = vpgl.load_perspective_camera(camfile)
prx,pry,prz=vpgl.get_backprojected_ray(pcam,ni/2,nj/2)
princAxis.append([prx,pry,prz])
Hmat = vpgl.compute_camera_to_world_homography(pcam,[0,0,1,-bbox[0][2]])
H = np.array(Hmat).reshape([3,3])
ps = np.dot(H,np.transpose([[0,0,1],
[ni,0,1],
[ni,nj,1],
[0,nj,1]]))
xs = ps[0,:]/ps[2,:]
ys = ps[1,:]/ps[2,:]
rect = [min(xs),min(ys),max(xs),max(ys)]
area = (rect[2]-rect[0])*(rect[3]-rect[1])
crect,carea = rectint(rect,grect)
#print crect,carea
if ( carea > 0 ):
cams.append(pcam)
camrects.append(crect)
worldoverlaps.append(carea/area)
usedcams = [False]*len(cams)
for i in range(0,len(cams)):
randidx = random.randint(0,len(cams)-1)
while usedcams[randidx]:
randidx = (randidx+1)%len(cams)
usedcams[randidx]= True
dep = False
for c2 in range(0,len(subsetIdx)):
cosAngle = np.dot(princAxis[randidx], princAxis[subsetIdx[c2]] )
if cosAngle > cosMinIndepAngle :
rectc2 = camrects[subsetIdx[c2]]
overlap,oarea = rectint(camrects[randidx] , rectc2)
tarea = (rectc2[2]-rectc2[0])*(rectc2[3]-rectc2[1])
if( oarea/tarea > minDepOverlap ):
dep = True
break
if dep:
continue
theseRefIndices= []
for c3 in range(0,len(cams)):
#Check angle disparity
cosAngle2 = np.dot(princAxis[randidx],princAxis[c3] );
if( cosAngle2 > cosMinRefAngle or cosAngle2 < cosMaxRefAngle ):
continue
# Check that a similar viewpoint isn't already used for reference
refDep = False
for c4 in range(0,len(theseRefIndices)):
#Check angle disparity
cosAngle3 = np.dot(princAxis[theseRefIndices[c4]],princAxis[c3] );
if( cosAngle3 > cosMinRefIndepAngle ):
refDep = True
break
#If similar viewpoint don't add
if( refDep ):
continue
theseRefIndices.append(c3)
#If at least one reference image save this viewpoint
if len(theseRefIndices) > 0 :
subsetIdx.append( randidx );
refIndices.append( theseRefIndices );
return subsetIdx, refIndices
def update_bp(scene, images, cameras, do_update_image=True, do_update_hmap=False):
_, ni, nj = load_image (images[0])
frames,refimages = generate_subsetim(scene,cameras,ni,nj)
for file_name in glob.glob(os.path.join(scene.model_dir, 'boxm2_*.bin')):
os.remove(file_name)
scene.init_uniform_prob()
sradius = 16
idents = []
weights = []
if do_update_image:
idents.append("if")
weights.append(1.0)
if do_update_hmap:
idents.append("hf")
weights.append(2.0)
for idx, i in enumerate(frames):
if do_update_image:
print "Iteration ",idx, "Image " , images[i];
####load image and camera
viewid = os.path.splitext(os.path.basename(images[i]))[0]
#### forming an app model using the neighbor images
for lindex in refimages[idx]:
lcam = vpgl.load_perspective_camera(cameras[lindex]);
limg, ni, nj = load_image (images[lindex]);
scene.update(lcam, limg,False, True,None ,"gpu0",0.05,viewid)
scene.update_if(False,viewid) # subtracting the image factor
scene.fuse_factors(idents,weights)
pcam = vpgl.load_perspective_camera(cameras[i]);
img, ni, nj = load_image (images[i]);
scene.compute_pre_post(pcam, img,viewid,100000,100000); # computing the new image factor
scene.update_if(True,viewid) # adding the image factor
scene.fuse_factors(idents,weights)
if do_update_hmap and idx % 2 == 0:
scene.update_hf(False) # subtracting the height-map factor
scene.fuse_factors(idents,weights)
zimg,zvar,ximg,yimg,probimg = scene.render_height_map()
#save_image(zimg, "./zimg.tif")
scene.compute_hmapf(zimg,zvar,ximg,yimg,sradius) # computing the height-map factor
scene.update_hf(True) # adding the height-map factor
scene.fuse_factors(idents,weights)
scene.write_cache()
def refine(scene):
scene.refine(0.3)
for filename in glob.glob(os.path.join(scene.model_dir, '[a-b]*.bin')):
os.remove(filename)
scene.write_cache()
with StdRedirect(open(os.path.join(voxel_globe.tools.log_dir(),
self.request.id)+'_out.log', 'w'),
open(os.path.join(voxel_globe.tools.log_dir(),
self.request.id)+'_err.log', 'w')):
openclDevice = os.environ['VIP_OPENCL_DEVICE']
opencl_memory = os.environ.get('VIP_OPENCL_MEMORY', None)
if opencl_memory:
opencl_memory = int(opencl_memory)
scene = models.Scene.objects.get(id=scene_id)
imageSet = models.ImageSet.objects.get(id=image_set_id)
imageList = imageSet.images.all()
with voxel_globe.tools.task_dir('voxel_world') as processing_dir:
logger.warning(bbox)
create_scene_xml(openclDevice, 0, float(bbox['voxel_size']),
lvcs1=(float(bbox['x_min']), float(bbox['y_min']),
float(bbox['z_min'])),
lvcs2=(float(bbox['x_max']), float(bbox['y_max']),
float(bbox['z_max'])),
origin=scene.origin, model_dir='.', number_bins=1,
output_file=open(os.path.join(processing_dir, 'scene.xml'), 'w'),
n_bytes_gpu=opencl_memory)
counter = 1
imageNames = []
cameraNames = []
os.mkdir(os.path.join(processing_dir, 'local'))
#Prepping
self.update_state(state='INITIALIZE', meta={'image_set_name': imageSet.name,
'stage':'camera fetch'})
for image in imageList:
(K,R,T,o) = get_krt(image, camera_set_id)
krtName = os.path.join(processing_dir, 'local', 'frame_%05d.krt' % counter)
with open(krtName, 'w') as fid:
print >>fid, (("%0.18f "*3+"\n")*3) % (K[0,0], K[0,1], K[0,2],
K[1,0], K[1,1], K[1,2], K[2,0], K[2,1], K[2,2])
print >>fid, (("%0.18f "*3+"\n")*3) % (R[0,0], R[0,1], R[0,2],
R[1,0], R[1,1], R[1,2], R[2,0], R[2,1], R[2,2])
print >>fid, ("%0.18f "*3+"\n") % (T[0,0], T[1,0], T[2,0])
imageName = image.filename_path
extension = os.path.splitext(imageName)[1]
localName = os.path.join(processing_dir, 'local',
'frame_%05d%s' % (counter, extension))
lncp(imageName, localName)
counter += 1
imageNames.append(localName)
cameraNames.append(krtName)
variance = 0.06
vxl_scene = boxm2_scene_adaptor(os.path.join(processing_dir, "scene.xml"),
openclDevice)
# loaded_imgs = []
# loaded_cams = []
# for i in range(0, len(imageNames), skip_frames):
# logger.debug("i: %d img name: %s cam name: %s", i, imageNames[i],
# cameraNames[i])
# self.update_state(state='PRELOADING', meta={'image_set_name': imageSet.name,
# 'stage':'image load',
# 'i':i,
# 'total':len(imageNames)})
# img, ni, nj = load_image(imageNames[i])
# loaded_imgs.append(img)
# pcam = load_perspective_camera(cameraNames[i])
# loaded_cams.append(pcam)
refine_cnt = 2
for rfk in range(0, refine_cnt, 1):
self.update_state(state='PROCESSING', meta={'image_set_name': imageSet.name,
'stage':'update 1'})
update_bp(vxl_scene, imageNames, cameraNames)
# self.update_state(state='PROCESSING', meta={'image_set_name': imageSet.name,
# 'stage':'update 2'})
# update_bp(vxl_scene, imageNames, cameraNames, True, True)
# self.update_state(state='PROCESSING', meta={'image_set_name': imageSet.name,
# 'stage':'update 3'})
# update_bp(vxl_scene, imageNames, cameraNames, True, True)
if rfk < refine_cnt-1:
self.update_state(state='PROCESSING',
meta={'image_set_name': imageSet.name,
'stage':'refine', 'i':rfk+1,
'total':refine_cnt})
refine(vxl_scene)
#Update color appearance
with open(os.path.join(processing_dir, "scene_color.xml"), 'w') as fid:
lines = open(os.path.join(processing_dir, "scene.xml"),
'r').readlines()
lines = [line.replace('boxm2_mog3_grey',
'boxm2_gauss_rgb').replace(
'boxm2_num_obs',
'boxm2_num_obs_single') for line in lines]
fid.writelines(lines)
vxl_scene = boxm2_scene_adaptor(os.path.join(processing_dir,
"scene_color.xml"),
openclDevice)
for idx, (image_name, camera_name) in enumerate(zip(imageNames, cameraNames)):
self.update_state(state='PROCESSING', meta={
'image_set_name': imageSet.name,
'stage':'color_update',
'i':idx+1, 'total':len(imageNames),
'images':len(imageNames)})
img, _, _ = load_image(image_name)
pcam = vpgl.load_perspective_camera(camera_name)
logger.debug("color_paint idx: %d", idx)
vxl_scene.update(pcam,img,False,False,None,openclDevice,
tnear = 1000.0, tfar = 100000.0)
vxl_scene.write_cache()
with voxel_globe.tools.storage_dir('voxel_world') as voxel_world_dir:
copytree(processing_dir, voxel_world_dir, ignore=lambda x,y:['local'])
models.VoxelWorld(
name='%s world (%s)' % (imageSet.name, self.request.id),
origin=scene.origin,
directory=voxel_world_dir,
service_id=self.request.id).save()
return {"image_set_name" : imageSet.name}
def run_build_voxel_model(self, image_collection_id, scene_id, bbox,
skip_frames, cleanup=True, history=None):
from distutils.dir_util import remove_tree
from shutil import move
import random
from vsi.tools.redirect import Redirect, Logger as LoggerWrapper
from voxel_globe.meta import models
from voxel_globe.tools.camera import get_krt
import voxel_globe.tools
from boxm2_scene_adaptor import boxm2_scene_adaptor
from vil_adaptor import load_image
from vpgl_adaptor import load_perspective_camera
from voxel_globe.tools.wget import download as wget
from vsi.vxl.create_scene_xml import create_scene_xml
from vsi.tools.dir_util import copytree, mkdtemp
with Redirect(stdout_c=LoggerWrapper(logger, lvl=logging.INFO),
stderr_c=LoggerWrapper(logger, lvl=logging.WARNING)):
openclDevice = os.environ['VIP_OPENCL_DEVICE']
opencl_memory = os.environ.get('VIP_OPENCL_MEMORY', None)
scene = models.Scene.objects.get(id=scene_id)
imageCollection = models.ImageCollection.objects.get(\
id=image_collection_id).history(history);
imageList = imageCollection.images.all();
with voxel_globe.tools.task_dir('voxel_world') as processing_dir:
logger.warning(bbox)
if bbox['geolocated']:
create_scene_xml(openclDevice, 3, float(bbox['voxel_size']),
lla1=(float(bbox['x_min']), float(bbox['y_min']),
float(bbox['z_min'])),
lla2=(float(bbox['x_max']), float(bbox['y_max']),
float(bbox['z_max'])),
origin=scene.origin, model_dir='.', number_bins=1,
output_file=open(os.path.join(processing_dir, 'scene.xml'), 'w'),
n_bytes_gpu=opencl_memory)
else:
create_scene_xml(openclDevice, 3, float(bbox['voxel_size']),
lvcs1=(float(bbox['x_min']), float(bbox['y_min']),
float(bbox['z_min'])),
lvcs2=(float(bbox['x_max']), float(bbox['y_max']),
float(bbox['z_max'])),
origin=scene.origin, model_dir='.', number_bins=1,
output_file=open(os.path.join(processing_dir, 'scene.xml'), 'w'),
n_bytes_gpu=opencl_memory)
counter = 1;
imageNames = []
cameraNames = []
os.mkdir(os.path.join(processing_dir, 'local'))
#Prepping
for image in imageList:
self.update_state(state='INITIALIZE', meta={'stage':'image fetch',
'i':counter,
'total':len(imageList)})
image = image.history(history)
(K,R,T,o) = get_krt(image.history(history), history=history)
krtName = os.path.join(processing_dir, 'local', 'frame_%05d.krt' % counter)
with open(krtName, 'w') as fid:
print >>fid, (("%0.18f "*3+"\n")*3) % (K[0,0], K[0,1], K[0,2],
K[1,0], K[1,1], K[1,2], K[2,0], K[2,1], K[2,2]);
print >>fid, (("%0.18f "*3+"\n")*3) % (R[0,0], R[0,1], R[0,2],
R[1,0], R[1,1], R[1,2], R[2,0], R[2,1], R[2,2]);
print >>fid, ("%0.18f "*3+"\n") % (T[0,0], T[1,0], T[2,0]);
imageName = image.originalImageUrl;
extension = os.path.splitext(imageName)[1]
localName = os.path.join(processing_dir, 'local',
'frame_%05d%s' % (counter, extension));
wget(imageName, localName, secret=True)
counter += 1;
imageNames.append(localName)
cameraNames.append(krtName)
variance = 0.06
vxl_scene = boxm2_scene_adaptor(os.path.join(processing_dir, "scene.xml"),
openclDevice);
current_level = 0;
loaded_imgs = [];
loaded_cams = [];
for i in range(0, len(imageNames), skip_frames):
logger.debug("i: %d img name: %s cam name: %s", i, imageNames[i],
cameraNames[i])
self.update_state(state='PRELOADING', meta={'stage':'image load',
'i':i,
'total':len(imageNames)})
img, ni, nj = load_image(imageNames[i])
loaded_imgs.append(img)
pcam = load_perspective_camera(cameraNames[i])
loaded_cams.append(pcam)
refine_cnt = 5;
for rfk in range(0, refine_cnt, 1):
pair = zip(loaded_imgs, loaded_cams)
random.shuffle(pair)
for idx, (img, cam) in enumerate(pair):
self.update_state(state='PROCESSING', meta={'stage':'update',
'i':rfk+1, 'total':refine_cnt, 'image':idx+1,
'images':len(loaded_imgs)})
logger.debug("refine_cnt: %d, idx: %d", rfk, idx)
vxl_scene.update(cam,img,True,True,None,openclDevice[0:3],variance,
tnear = 1000.0, tfar = 100000.0);
logger.debug("writing cache: %d", rfk)
vxl_scene.write_cache();
logger.debug("wrote cache: %d", rfk)
if rfk < refine_cnt-1:
self.update_state(state='PROCESSING', meta={'stage':'refine',
'i':rfk,
'total':refine_cnt})
logger.debug("refining %d...", rfk)
refine_device = openclDevice[0:3]
if refine_device == 'cpu':
refine_device = 'cpp'
vxl_scene.refine(0.3, refine_device);
vxl_scene.write_cache();
voxel_world_dir = mkdtemp(dir=os.environ['VIP_STORAGE_DIR'])
copytree(processing_dir, voxel_world_dir, ignore=lambda x,y:['images'])
models.VoxelWorld.create(
name='%s world (%s)' % (imageCollection.name, self.request.id),
origin=scene.origin,
directory=voxel_world_dir,
service_id=self.request.id).save();
def generate_error_point_cloud(self,
voxel_world_id,
camera_set_id,
prob=0.5,
position_error_override=None,
orientation_error_override=None,
number_images=None):
from glob import glob
import json
import random
import numpy as np
from boxm2_adaptor import load_cpp, render_depth, cast_3d_point, \
cast_3d_point_pass2, write_cache, \
accumulate_3d_point_and_cov, \
normalize_3d_point_and_cov
from boxm2_scene_adaptor import boxm2_scene_adaptor
from vpgl_adaptor_boxm2_batch import create_perspective_camera_krt, \
persp2gen, compute_direction_covariance
from boxm2_mesh_adaptor import gen_error_point_cloud
from vsi.tools.redirect import StdRedirect, Logger as LoggerWrapper
import voxel_globe.tools
import voxel_globe.meta.models as models
from voxel_globe.tools.camera import get_krt
with StdRedirect(
open(
os.path.join(voxel_globe.tools.log_dir(), self.request.id) +
'_out.log', 'w'),
open(
os.path.join(voxel_globe.tools.log_dir(), self.request.id) +
'_err.log', 'w')):
self.update_state(state='SETUP', meta={'pid': os.getpid()})
voxel_world = models.VoxelWorld.objects.get(id=voxel_world_id)
service_inputs = json.loads(voxel_world.service.inputs)
image_set = models.ImageSet.objects.get(id=service_inputs[0][0])
images = image_set.images.all()
voxel_world_dir = voxel_world.directory
scene_filename = os.path.join(voxel_world_dir, 'scene_color.xml')
opencl_device = os.environ['VIP_OPENCL_DEVICE']
scene_gpu = boxm2_scene_adaptor(scene_filename, opencl_device)
scene_cpp = boxm2_scene_adaptor(scene_filename, 'cpp')
type_id_fname = "type_names_list.txt"
image_id_fname = "image_list.txt"
std_dev_angle_default = 0
cov_c_path = 'cov_c.txt'
cov_c_default = 0
if not number_images:
number_images = len(images)
number_images = min(len(images), number_images)
with voxel_globe.tools.task_dir('generate_error_point_cloud', cd=True) \
as processing_dir:
for index, image in enumerate(random.sample(images,
number_images)):
self.update_state(state='PROCESSING',
meta={
'stage': 'casting',
'image': index + 1,
'total': len(images)
})
k, r, t, o = get_krt(image, camera_set_id)
attributes = image.camera_set.get(
cameraset=camera_set_id).attributes
cov_c = attributes.get('position_error', cov_c_default)
if position_error_override is not None:
cov_c = position_error_override
std_dev_angle = attributes.get('orientation_error',
std_dev_angle_default)
if orientation_error_override is not None:
std_dev_angle = orientation_error_override
cov_c = np.eye(3) * cov_c**2
np.savetxt(cov_c_path, cov_c)
perspective_camera = create_perspective_camera_krt(k, r, t)
self.update_state(state='PROCESSING',
meta={
'stage': 'pre render',
'image': index + 1,
'total': len(images)
})
(depth_image, variance_image,
_) = render_depth(scene_gpu.scene, scene_gpu.opencl_cache,
perspective_camera, image.image_width,
image.image_height, scene_gpu.device)
self.update_state(state='PROCESSING',
meta={
'stage': 'post_render',
'image': index + 1,
'total': len(images)
})
cov_v_path = 'cov_%06d.txt' % index
appearance_model = 'image'
self.update_state(state='PROCESSING',
meta={
'stage': 'pre_persp2gen',
'image': index + 1,
'total': len(images)
})
generic_camera = persp2gen(perspective_camera,
image.image_width,
image.image_height)
self.update_state(state='PROCESSING',
meta={
'stage': 'pre_covar',
'image': index + 1,
'total': len(images)
})
compute_direction_covariance(perspective_camera, std_dev_angle,
cov_v_path)
self.update_state(state='PROCESSING',
meta={
'stage': 'pre_cast1',
'image': index + 1,
'total': len(images)
})
cast_3d_point(scene_cpp.scene, scene_cpp.cpu_cache,
perspective_camera, generic_camera, depth_image,
variance_image, appearance_model)
self.update_state(state='PROCESSING',
meta={
'stage': 'pre_cast2',
'image': index + 1,
'total': len(images)
})
cast_3d_point_pass2(scene_cpp.scene, scene_cpp.cpu_cache,
generic_camera, appearance_model,
cov_c_path, cov_v_path)
self.update_state(state='PROCESSING',
meta={
'stage': 'pre_accumulate',
'image': index + 1,
'total': len(images)
})
accumulate_3d_point_and_cov(scene_cpp.scene,
scene_cpp.cpu_cache,
appearance_model)
#self.update_state(state='PROCESSING',
# meta={'stage':'pre_write', 'image':index+1,
# 'total':len(images)})
#write_cache(scene_cpp.cpu_cache, 1)
self.update_state(state='PROCESSING',
meta={
'stage': 'post_write',
'image': index + 1,
'total': len(images)
})
self.update_state(state='PROCESSING',
meta={'stage': 'compute error'})
normalize_3d_point_and_cov(scene_cpp.scene, scene_cpp.cpu_cache)
self.update_state(state='PROCESSING',
meta={
'stage': 'pre_write',
'image': index + 1,
'total': len(images)
})
write_cache(scene_cpp.cpu_cache, 1)
self.update_state(state='EXPORTING', meta={'stage': 'ply'})
with voxel_globe.tools.storage_dir('generate_error_point_cloud') \
as storage_dir:
ply_filename = os.path.join(storage_dir, 'model.ply')
gen_error_point_cloud(scene_cpp.scene, scene_cpp.cpu_cache,
ply_filename, prob)
potree_filename = os.path.join(storage_dir, 'potree.ply')
with voxel_globe.tools.image_dir('point_cloud') as potree_dir:
convert_ply_to_potree(ply_filename, potree_dir)
point_cloud = models.PointCloud(name='%s point cloud' %
image_set.name,
service_id=self.request.id,
origin=voxel_world.origin)
point_cloud.filename_path = ply_filename
point_cloud.potree_dir = potree_dir
point_cloud.save()
voxel_files = lambda x: glob(os.path.join(voxel_world_dir, x))
cleanup_files = []
cleanup_files += voxel_files('boxm2_covariance_*.bin')
cleanup_files += voxel_files('boxm2_point_*.bin')
cleanup_files += voxel_files('float16_image_*.bin')
for cleanup_file in cleanup_files:
os.remove(cleanup_file)