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 create_all_view_directions(self):
for i, img in enumerate(self.imgList):
img, ni, nj = load_image(self.imgList[i])
cam = vpgl_adaptor.load_perspective_camera(self.camList[i])
self.create_view_directions(cam, ni, nj, i)
remove_from_db([img, cam])
self.write_cache(True)
def render_changes(scene, img_glob, cam_glob, outdir, n=1, raybelief="", max_mode=False):
# make sure imgglob and camglob are string lists and same size
if isinstance(img_glob, str) and isinstance(cam_glob, str):
img_glob = [img_glob]
cam_glob = [cam_glob]
assert len(img_glob) == len(cam_glob)
# make sure outdir exists
if not os.path.exists(outdir):
os.makedirs(outdir)
print "Rendering images: ", img_glob, " into ", outdir
# load change for each image
for idx, img in enumerate(img_glob):
# grab image number of ground truth image
imgnum, ext = os.path.splitext(basename(img))
pcam = vpgl.load_perspective_camera(cam_glob[idx])
rimg, ni, nj = vil.load_image(img)
# render exp
expimg = scene.render(pcam, ni, nj)
# render change detection
cd_fname = outdir + "/cd_" + imgnum + ".tiff"
cd_img = scene.change_detect(
pcam, rimg, expimg, n, raybelief, max_mode)
vil.save_image(cd_img, cd_fname)
# clean up
bbas.remove_from_db([rimg, expimg, cd_img])
def update_all_alphas_with_cubic(self):
for i, img in enumerate(self.imgList):
cam = vpgl_adaptor.load_perspective_camera(self.camList[i])
img, ni, nj = vil_adaptor.load_image(self.imgList[i])
self.update_alpha_with_cubic(cam, img)
remove_from_db([img, cam])
self.write_cache()
def create_all_view_directions(self):
for i, img in enumerate(self.imgList):
img, ni, nj = load_image(self.imgList[i])
cam = vpgl_adaptor.load_perspective_camera(self.camList[i])
self.create_view_directions(cam, ni, nj, i)
remove_from_db([img, cam])
self.write_cache(True)
def update_all_alphas_with_cubic(self):
for i, img in enumerate(self.imgList):
cam = vpgl_adaptor.load_perspective_camera(self.camList[i])
img, ni, nj = vil_adaptor.load_image(self.imgList[i])
self.update_alpha_with_cubic(cam, img)
remove_from_db([img, cam])
self.write_cache()
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 store_all_uncertainty_aux(self, offset=0):
for i, img in enumerate(self.imgList):
cam = vpgl_adaptor.load_perspective_camera(self.camList[i])
img, ni, nj = vil_adaptor.load_image(self.imgList[i])
gcam = vpgl_adaptor.persp2gen(cam, ni, nj)
self.uncertainty_per_view(gcam, img, i + offset)
remove_from_db([img, cam, gcam])
self.write_cache()
def store_all_uncertainty_aux(self, offset=0):
for i, img in enumerate(self.imgList):
cam = vpgl_adaptor.load_perspective_camera(self.camList[i])
img, ni, nj = vil_adaptor.load_image(self.imgList[i])
gcam = vpgl_adaptor.persp2gen(cam, ni, nj)
self.uncertainty_per_view(gcam, img, i + offset)
remove_from_db([img, cam, gcam])
self.write_cache()
def updateScene(self, stepNum):
# Should initialize a GPU
scenePath = os.path.abspath(self.config.uSceneFileName)
scene = boxm2_scene_adaptor.boxm2_scene_adaptor(scenePath, "gpu0")
# Get list of imgs and cams
imgsDir = os.path.abspath(
os.path.join(self.config.NVMOutputDirName, "imgs"))
camsDir = os.path.abspath(
os.path.join(self.config.NVMOutputDirName, "cams"))
if False == os.path.isdir(imgsDir):
log("The folder %s does not exist." % (imgsDir))
return -stepNum
if False == os.path.isdir(camsDir):
log("The folder %s does not exist." % (camsDir))
return -stepNum
imgFiles = getAllFilesInSubDirs(imgsDir)
camFiles = getAllFilesInSubDirs(camsDir)
if len(imgFiles) != len(camFiles):
log("The number of files in %s is %d." % (imgsDir, len(imgFiles)))
log("The number of fiels in %s is %d." % (camsDir, len(camFiles)))
log("These numbers should match and they do not.")
return -stepNum
if len(imgFiles) == 0:
log("No image files were found in %s." % (imgsDir))
return -stepNum
imgFiles.sort()
camFiles.sort()
# Make two passes over the image set
for p in xrange(0, self.config.updateScenePassCount):
frames = range(0, len(imgFiles), 1)
if self.config.randomizeUpdateOrder:
random.shuffle(frames)
for idx, i in enumerate(frames):
pcam = vpgl.load_perspective_camera(
os.path.join(camsDir, camFiles[i]))
img, ni, nj = vil.load_image(os.path.join(
imgsDir, imgFiles[i]))
scene.update(pcam, img, 1, "", "gpu0")
if 0 == (
idx % 15
): # No idea what is special about the magic number 15
scene.refine()
scene.write_cache()
return 0
def cpu_batch_paint(self, imgs, cams):
if (self.str_cache is None):
self.create_stream_cache(imgs)
# sigma norm table?
under_estimation_probability = 0.2
batch.init_process("bstaSigmaNormTableProcess")
batch.set_input_float(0, under_estimation_probability)
batch.run_process()
(id, type) = batch.commit_output(0)
n_table = dbvalue(id, type)
# loop over images creating aux data
for idx in range(0, len(imgs)):
# load cam/img
img, ni, nj = vil_adaptor.load_image(imgs[idx])
pcam = vpgl_adaptor.load_perspective_camera(cams[idx])
gcam = vpgl_adaptor.persp2gen(pcam, ni, nj)
# create norm intensity (num rays...)
batch.init_process("boxm2CppCreateNormIntensitiesProcess")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, gcam)
batch.set_input_from_db(3, img)
batch.set_input_string(4, "img_" + "%05d" % idx)
batch.run_process()
# create aux
batch.init_process("boxm2CppCreateAuxDataOPT2Process")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, gcam)
batch.set_input_from_db(3, img)
batch.set_input_string(4, "img_" + "%05d" % idx)
batch.run_process()
self.write_cache(True)
batch.init_process("boxm2CppBatchUpdateOPT2Process")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, self.str_cache)
batch.set_input_from_db(3, n_table)
batch.run_process()
# close the files so that they can be reloaded after the next iteration
batch.init_process("boxm2StreamCacheCloseFilesProcess")
batch.set_input_from_db(0, self.str_cache)
batch.run_process()
self.write_cache()
def cpu_batch_paint(self, imgs, cams):
if (self.str_cache is None):
self.create_stream_cache(imgs)
# sigma norm table?
under_estimation_probability = 0.2
batch.init_process("bstaSigmaNormTableProcess")
batch.set_input_float(0, under_estimation_probability)
batch.run_process()
(id, type) = batch.commit_output(0)
n_table = dbvalue(id, type)
# loop over images creating aux data
for idx in range(0, len(imgs)):
# load cam/img
img, ni, nj = vil_adaptor.load_image(imgs[idx])
pcam = vpgl_adaptor.load_perspective_camera(cams[idx])
gcam = vpgl_adaptor.persp2gen(pcam, ni, nj)
# create norm intensity (num rays...)
batch.init_process("boxm2CppCreateNormIntensitiesProcess")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, gcam)
batch.set_input_from_db(3, img)
batch.set_input_string(4, "img_" + "%05d" % idx)
batch.run_process()
# create aux
batch.init_process("boxm2CppCreateAuxDataOPT2Process")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, gcam)
batch.set_input_from_db(3, img)
batch.set_input_string(4, "img_" + "%05d" % idx)
batch.run_process()
self.write_cache(True)
batch.init_process("boxm2CppBatchUpdateOPT2Process")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, self.str_cache)
batch.set_input_from_db(3, n_table)
batch.run_process()
# close the files so that they can be reloaded after the next iteration
batch.init_process("boxm2StreamCacheCloseFilesProcess")
batch.set_input_from_db(0, self.str_cache)
batch.run_process()
self.write_cache()
def write_aux_data(self, imgs, cams):
for idx in range(len(imgs)):
print '--------------------------'
print "processing image " + imgs[idx]
# load cam/img
img, ni, nj = vil_adaptor.load_image(imgs[idx])
pcam = vpgl_adaptor.load_perspective_camera(cams[idx])
gcam = vpgl_adaptor.persp2gen(pcam, ni, nj)
# update aux per view call
fname, fextension = os.path.splitext(imgs[idx])
imageID = os.path.basename(fname)
self.update_aux(img, gcam, imageID)
def write_aux_data(self, imgs, cams):
for idx in range(len(imgs)):
print '--------------------------'
print "processing image " + imgs[idx]
# load cam/img
img, ni, nj = vil_adaptor.load_image(imgs[idx])
pcam = vpgl_adaptor.load_perspective_camera(cams[idx])
gcam = vpgl_adaptor.persp2gen(pcam, ni, nj)
# update aux per view call
fname, fextension = os.path.splitext(imgs[idx])
imageID = os.path.basename(fname)
self.update_aux(img, gcam, imageID)
def render_changes(scene,
img_glob,
cam_glob,
outdir,
n=1,
raybelief="",
max_mode=False):
# make sure imgglob and camglob are string lists and same size
if isinstance(img_glob, str) and isinstance(cam_glob, str):
img_glob = [img_glob]
cam_glob = [cam_glob]
assert len(img_glob) == len(cam_glob)
# make sure outdir exists
if not os.path.exists(outdir):
os.makedirs(outdir)
print "Rendering images: ", img_glob, " into ", outdir
# load change for each image
for idx, img in enumerate(img_glob):
# grab image number of ground truth image
imgnum, ext = os.path.splitext(basename(img))
pcam = vpgl.load_perspective_camera(cam_glob[idx])
rimg, ni, nj = vil.load_image(img)
# render exp
expimg = scene.render(pcam, ni, nj)
# render change detection
cd_fname = outdir + "/cd_" + imgnum + ".tiff"
cd_img = scene.change_detect(pcam, rimg, expimg, n, raybelief,
max_mode)
vil.save_image(cd_img, cd_fname)
# clean up
bbas.remove_from_db([rimg, expimg, cd_img])
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 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(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_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 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