def render_depth(self, cam, ni=1280, nj=720, device_string=""):
dev, cache = self._get_device_cache(device_string)
expimg, varimg, visimg = boxm2_adaptor.render_depth(
self.scene, cache, cam, ni, nj, dev)
return expimg, varimg, visimg
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 render_depth(self, cam, ni=1280, nj=720, device_string=""):
dev, cache = self._get_device_cache(device_string)
expimg, varimg, visimg = boxm2_adaptor.render_depth(
self.scene, cache, cam, ni, nj, dev)
return expimg, varimg, visimg
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 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)
