def roi_init(NITF_path,
camera,
scene,
convert_to_8bit,
params_fname,
margin=0):
boxm2_batch.init_process("boxm2RoiInitProcess")
boxm2_batch.set_params_process(params_fname)
boxm2_batch.set_input_string(0, NITF_path)
boxm2_batch.set_input_from_db(1, camera)
boxm2_batch.set_input_from_db(2, scene)
boxm2_batch.set_input_bool(3, convert_to_8bit)
boxm2_batch.set_input_int(4, margin)
result = boxm2_batch.run_process()
if result:
(id, type) = boxm2_batch.commit_output(0)
local_cam = dbvalue(id, type)
(id, type) = boxm2_batch.commit_output(1)
cropped_image = dbvalue(id, type)
(id, type) = boxm2_batch.commit_output(2)
uncertainty = boxm2_batch.get_output_float(id)
else:
local_cam = 0
cropped_image = 0
uncertainty = 0
return result, local_cam, cropped_image, uncertainty
def export_stack(scene,cache,outdir,isopacityonly= True):
boxm2_batch.init_process("boxm2ExportStackImagesProcess");
boxm2_batch.set_input_from_db(0,scene);
boxm2_batch.set_input_from_db(1,cache);
boxm2_batch.set_input_string(2,outdir);
boxm2_batch.set_input_bool(3,isopacityonly);
boxm2_batch.run_process();
def render_z_image(scene,
cache,
cam,
ni=1280,
nj=720,
normalize=False,
device=None):
if cache.type == "boxm2_cache_sptr":
print "boxm2_batch CPU render depth not yet implemented"
elif cache.type == "boxm2_opencl_cache_sptr" and device:
boxm2_batch.init_process("boxm2OclRenderExpectedZImageProcess")
boxm2_batch.set_input_from_db(0, device)
boxm2_batch.set_input_from_db(1, scene)
boxm2_batch.set_input_from_db(2, cache)
boxm2_batch.set_input_from_db(3, cam)
boxm2_batch.set_input_unsigned(4, ni)
boxm2_batch.set_input_unsigned(5, nj)
boxm2_batch.set_input_bool(6, normalize)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
z_exp_image = dbvalue(id, type)
(id, type) = boxm2_batch.commit_output(1)
z_var_image = dbvalue(id, type)
return z_exp_image, z_var_image
else:
print "ERROR: Cache type not recognized: ", cache.type
def update_grey(scene,
cache,
cam,
img,
device=None,
ident="",
mask=None,
update_alpha=True,
var=-1.0):
#If no device is passed in, do cpu update
if cache.type == "boxm2_cache_sptr":
print "boxm2_batch CPU update"
boxm2_batch.init_process("boxm2CppUpdateImageProcess")
boxm2_batch.set_input_from_db(0, scene)
boxm2_batch.set_input_from_db(1, cache)
boxm2_batch.set_input_from_db(2, cam)
boxm2_batch.set_input_from_db(3, img)
return boxm2_batch.run_process()
elif cache.type == "boxm2_opencl_cache_sptr" and device:
print("boxm2_batch GPU update")
boxm2_batch.init_process("boxm2OclUpdateProcess")
boxm2_batch.set_input_from_db(0, device)
boxm2_batch.set_input_from_db(1, scene)
boxm2_batch.set_input_from_db(2, cache)
boxm2_batch.set_input_from_db(3, cam)
boxm2_batch.set_input_from_db(4, img)
boxm2_batch.set_input_string(5, ident)
if mask:
boxm2_batch.set_input_from_db(6, mask)
boxm2_batch.set_input_bool(7, update_alpha)
boxm2_batch.set_input_float(8, var)
return boxm2_batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
def update_grey_with_alt(scene,
cache,
cam,
img,
device=None,
ident="",
mask=None,
update_alpha=True,
var=-1.0,
alt_prior=None,
alt_density=None):
#If no device is passed in, do cpu update
if cache.type == "boxm2_cache_sptr":
print "ERROR: CPU update not implemented for update_with_alt"
elif cache.type == "boxm2_opencl_cache_sptr" and device:
print("boxm2_batch GPU update with alt")
boxm2_batch.init_process("boxm2OclUpdateWithAltProcess")
boxm2_batch.set_input_from_db(0, device)
boxm2_batch.set_input_from_db(1, scene)
boxm2_batch.set_input_from_db(2, cache)
boxm2_batch.set_input_from_db(3, cam)
boxm2_batch.set_input_from_db(4, img)
boxm2_batch.set_input_string(5, ident)
if mask:
boxm2_batch.set_input_from_db(6, mask)
boxm2_batch.set_input_bool(7, update_alpha)
boxm2_batch.set_input_float(8, var)
boxm2_batch.set_input_from_db(9, alt_prior)
boxm2_batch.set_input_from_db(10, alt_density)
boxm2_batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
def get_geocam_footprint(geocam, geotiff_filename, out_kml_filename,init_finish=True):
boxm2_batch.init_process('vpglGeoFootprintProcess');
boxm2_batch.set_input_from_db(0,geocam);
boxm2_batch.set_input_string(1,geotiff_filename);
boxm2_batch.set_input_string(2,out_kml_filename);
boxm2_batch.set_input_bool(3,init_finish);
boxm2_batch.run_process();
def export_points_and_normals(
scene,
cache,
file_out,
save_aux=True,
prob_thresh=0.0,
vis_thresh=0.0,
nmag_thresh=0.0,
exp_thresh=0.0,
bbox_file="",
):
if cache.type == "boxm2_cache_sptr":
print ("Exporting to oriented point cloud")
boxm2_batch.init_process("boxm2ExportOrientedPointCloudProcess")
boxm2_batch.set_input_from_db(0, scene)
boxm2_batch.set_input_from_db(1, cache)
boxm2_batch.set_input_string(2, file_out)
# output aux (prob, vis, normal magnitdue)
boxm2_batch.set_input_bool(3, save_aux)
boxm2_batch.set_input_float(4, vis_thresh) # vis threshold
boxm2_batch.set_input_float(5, nmag_thresh) # nmag threshold
boxm2_batch.set_input_float(6, prob_thresh) # prob threshold
# boxm2_batch.set_input_float(7,exp_thresh); #exp threshold
# boxm2_batch.set_input_string(8, bbox_file); #bb filename
boxm2_batch.set_input_string(7, bbox_file) # bb filename
return boxm2_batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
def export_points_and_normals(scene,
cache,
file_out,
save_aux=True,
prob_thresh=0.0,
vis_thresh=0.0,
nmag_thresh=0.0,
exp_thresh=0.0,
bbox_file=""):
if cache.type == "boxm2_cache_sptr":
print("Exporting to oriented point cloud")
boxm2_batch.init_process("boxm2ExportOrientedPointCloudProcess")
boxm2_batch.set_input_from_db(0, scene)
boxm2_batch.set_input_from_db(1, cache)
boxm2_batch.set_input_string(2, file_out)
boxm2_batch.set_input_bool(3, save_aux)
#output aux (prob, vis, normal magnitdue)
boxm2_batch.set_input_float(4, vis_thresh)
#vis threshold
boxm2_batch.set_input_float(5, nmag_thresh)
#nmag threshold
boxm2_batch.set_input_float(6, prob_thresh)
#prob threshold
boxm2_batch.set_input_float(7, exp_thresh)
#exp threshold
boxm2_batch.set_input_string(8, bbox_file)
#bb filename
return boxm2_batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
def get_geocam_footprint(geocam, geotiff_filename, out_kml_filename,init_finish=True):
boxm2_batch.init_process('vpglGeoFootprintProcess')
boxm2_batch.set_input_from_db(0,geocam);
boxm2_batch.set_input_string(1,geotiff_filename);
boxm2_batch.set_input_string(2,out_kml_filename);
boxm2_batch.set_input_bool(3,init_finish);
boxm2_batch.run_process()
def export_stack(scene,cache,outdir,isopacityonly= True):
boxm2_batch.init_process("boxm2ExportStackImagesProcess");
boxm2_batch.set_input_from_db(0,scene);
boxm2_batch.set_input_from_db(1,cache);
boxm2_batch.set_input_string(2,outdir);
boxm2_batch.set_input_bool(3,isopacityonly);
boxm2_batch.run_process();
def vrml_filter_ply(vrml_filename, ply_file, points_file, dist_threshold,nearest=False):
boxm2_batch.init_process("bvrmlFilteredPlyProcess");
boxm2_batch.set_input_string(0,vrml_filename);
boxm2_batch.set_input_string(1,ply_file);
boxm2_batch.set_input_string(2,points_file);
boxm2_batch.set_input_float(3,dist_threshold);
boxm2_batch.set_input_bool(4,nearest);
boxm2_batch.run_process();
def write_cache(cache, do_clear=0):
if cache.type == "boxm2_cache_sptr":
boxm2_batch.init_process("boxm2WriteCacheProcess")
boxm2_batch.set_input_from_db(0, cache)
boxm2_batch.set_input_bool(1, do_clear)
boxm2_batch.run_process()
else:
print "ERROR: Cache type needs to be boxm2_cache_sptr, not ", cache.type
def truncate_nitf_image(img, is_short = True):
boxm2_batch.init_process("bripTruncateNITFBitProcess");
boxm2_batch.set_input_from_db(0, img);
boxm2_batch.set_input_bool(1, is_short);
boxm2_batch.run_process();
(id, type) = boxm2_batch.commit_output(0);
out_img = dbvalue(id, type);
return out_img;
def truncate_nitf_image(img, is_short=True, is_scale=True):
boxm2_batch.init_process("bripTruncateNITFBitProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_bool(1, is_short)
boxm2_batch.set_input_bool(2, is_scale)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
out_img = dbvalue(id, type)
return out_img
def compute_camera_to_world_homography(cam,plane,inverse = False):
boxm2_batch.init_process("vpglComputeImageToWorldHomographyProcess");
boxm2_batch.set_input_from_db(0, cam);
boxm2_batch.set_input_float_array(1, plane);
boxm2_batch.set_input_bool(2, inverse);
boxm2_batch.run_process();
(id, type) = boxm2_batch.commit_output(0);
homg2d = boxm2_batch.get_bbas_1d_array_float(id);
return homg2d
def compute_camera_to_world_homography(cam, plane, inverse=False):
boxm2_batch.init_process("vpglComputeImageToWorldHomographyProcess")
boxm2_batch.set_input_from_db(0, cam)
boxm2_batch.set_input_float_array(1, plane)
boxm2_batch.set_input_bool(2, inverse)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
homg2d = boxm2_batch.get_bbas_1d_array_float(id)
return homg2d
def threshold_image(img, value, threshold_above=True):
boxm2_batch.init_process("vilThresholdImageProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_float(1, value)
boxm2_batch.set_input_bool(2, threshold_above)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
mask = dbvalue(id, type)
return mask
def threshold_image(img, value, threshold_above=True):
boxm2_batch.init_process("vilThresholdImageProcess")
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_float(1,value)
boxm2_batch.set_input_bool(2,threshold_above)
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
mask = dbvalue(id,type)
return mask
def add_to_filter_bank(tclsf, img_name, plane, filter_folder, filter_name, is_gauss_smooth = True):
boxm2_batch.init_process("sdetAddtoFilterBankProcess");
boxm2_batch.set_input_from_db(0,tclsf); # classifier instance
boxm2_batch.set_input_string(1,img_name);
boxm2_batch.set_input_unsigned(2,plane); ## pass which plane to extract the gauss response from
boxm2_batch.set_input_string(3,filter_folder);
boxm2_batch.set_input_string(4,filter_name); ## pass a unique name to be used to write to filter_folder
boxm2_batch.set_input_bool(5,is_gauss_smooth)
boxm2_batch.run_process();
def estimate_shadows(image, metadata, atmospheric_params):
boxm2_batch.init_process("bradEstimateShadowsProcess")
boxm2_batch.set_input_from_db(0, image)
boxm2_batch.set_input_from_db(1, metadata)
boxm2_batch.set_input_from_db(2, atmospheric_params)
boxm2_batch.set_input_bool(3, False) # False returns probability value
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
shadow_probs = dbvalue(id, type)
return shadow_probs
def threshold_image_inside(img, min_thres, max_thres, threshold_inside=True):
boxm2_batch.init_process("vilThresholdImageInsideProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_float(1, min_thres)
boxm2_batch.set_input_float(2, max_thres)
boxm2_batch.set_input_bool(3, threshold_inside)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
mask = dbvalue(id, type)
return mask
def threshold_image_inside(img, min_thres, max_thres, threshold_inside=True):
boxm2_batch.init_process("vilThresholdImageInsideProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_float(1, min_thres)
boxm2_batch.set_input_float(2, max_thres)
boxm2_batch.set_input_bool(3, threshold_inside)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
mask = dbvalue(id, type)
return mask
def estimate_shadows(image, metadata, atmospheric_params):
boxm2_batch.init_process("bradEstimateShadowsProcess")
boxm2_batch.set_input_from_db(0,image)
boxm2_batch.set_input_from_db(1,metadata)
boxm2_batch.set_input_from_db(2,atmospheric_params)
boxm2_batch.set_input_bool(3,False) # False returns probability value
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
shadow_probs = dbvalue(id,type)
return shadow_probs
def texture_training(tclsf, image, exp_poly_path,texton_dict_path, name_of_category="mod", compute_textons=True):
boxm2_batch.init_process("sdetTextureTrainingProcess");
boxm2_batch.set_input_from_db(0,tclsf);
boxm2_batch.set_input_bool(1,compute_textons);# compute textons
boxm2_batch.set_input_from_db(2,image);
boxm2_batch.set_input_string(3,exp_poly_path);
boxm2_batch.set_input_string(4,name_of_category);
boxm2_batch.set_input_string(5,texton_dict_path);
boxm2_batch.run_process();
# write out the texton dictionary on finish
boxm2_batch.finish_process();
def convert_reflectance_to_digital_count(reflectance_image, metadata, atmospheric_params, normalize_0_1 = False, max_digital_count = 2047):
boxm2_batch.init_process("bradConvertReflectanceToDigitalCountProcess")
boxm2_batch.set_input_from_db(0,reflectance_image)
boxm2_batch.set_input_from_db(1,metadata)
boxm2_batch.set_input_from_db(2,atmospheric_params)
boxm2_batch.set_input_bool(3,normalize_0_1)
boxm2_batch.set_input_unsigned(4,max_digital_count)
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
output_img = dbvalue(id,type)
return output_img
def create_mask_image(scene, camera, ni, nj, ground_plane_only=False):
boxm2_batch.init_process("boxm2CreateSceneMaskProcess")
boxm2_batch.set_input_from_db(0, scene)
boxm2_batch.set_input_from_db(1, camera)
boxm2_batch.set_input_unsigned(2, ni)
boxm2_batch.set_input_unsigned(3, nj)
boxm2_batch.set_input_bool(4, ground_plane_only)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
mask = dbvalue(id, type)
return mask
def make_inside_empty(scene, cache, device, use_sum=False):
if cache.type == "boxm2_opencl_cache_sptr":
print("Flipping Normal")
boxm2_batch.init_process("boxm2OclMakeInsideVoxelsEmptyProcess")
boxm2_batch.set_input_from_db(0, device)
boxm2_batch.set_input_from_db(1, scene)
boxm2_batch.set_input_from_db(2, cache)
boxm2_batch.set_input_bool(3, use_sum)
return boxm2_batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
def flip_normals(scene, cache, device, use_sum=False):
if cache.type == "boxm2_opencl_cache_sptr":
print("Flipping Normal")
boxm2_batch.init_process("boxm2OclFlipNormalsUsingVisProcess")
boxm2_batch.set_input_from_db(0, device)
boxm2_batch.set_input_from_db(1, scene)
boxm2_batch.set_input_from_db(2, cache)
boxm2_batch.set_input_bool(3, use_sum)
return boxm2_batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
def flip_normals(scene, cache, device, use_sum=False):
if cache.type == "boxm2_opencl_cache_sptr":
print("Flipping Normal")
boxm2_batch.init_process("boxm2OclFlipNormalsUsingVisProcess")
boxm2_batch.set_input_from_db(0, device)
boxm2_batch.set_input_from_db(1, scene)
boxm2_batch.set_input_from_db(2, cache)
boxm2_batch.set_input_bool(3, use_sum)
return boxm2_batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
def estimate_atmospheric_parameters(image, metadata, mean_reflectance = None, constrain_parameters=None):
boxm2_batch.init_process("bradEstimateAtmosphericParametersProcess")
boxm2_batch.set_input_from_db(0,image)
boxm2_batch.set_input_from_db(1,metadata)
if mean_reflectance != None:
boxm2_batch.set_input_float(2,mean_reflectance)
if constrain_parameters != None:
boxm2_batch.set_input_bool(3, constrain_parameters)
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
atm_params = dbvalue(id,type)
return atm_params
def make_inside_empty(scene, cache, device, use_sum=False):
if cache.type == "boxm2_opencl_cache_sptr":
print("Flipping Normal")
boxm2_batch.init_process("boxm2OclMakeInsideVoxelsEmptyProcess")
boxm2_batch.set_input_from_db(0, device)
boxm2_batch.set_input_from_db(1, scene)
boxm2_batch.set_input_from_db(2, cache)
boxm2_batch.set_input_bool(3, use_sum)
return boxm2_batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
def vrml_filter_ply(vrml_filename,
ply_file,
points_file,
dist_threshold,
nearest=False):
boxm2_batch.init_process("bvrmlFilteredPlyProcess")
boxm2_batch.set_input_string(0, vrml_filename)
boxm2_batch.set_input_string(1, ply_file)
boxm2_batch.set_input_string(2, points_file)
boxm2_batch.set_input_float(3, dist_threshold)
boxm2_batch.set_input_bool(4, nearest)
boxm2_batch.run_process()
def correct_cam_rotation(img, pcam, exp_img, cone_half_angle, n_steps, refine=True):
boxm2_batch.init_process('icamCorrectCamRotationProcess')
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_from_db(1,pcam)
boxm2_batch.set_input_from_db(2,exp_img)
boxm2_batch.set_input_float(3,cone_half_angle)
boxm2_batch.set_input_unsigned(4,n_steps)
boxm2_batch.set_input_bool(5,refine)
boxm2_batch.run_process()
(m_id,m_type) = boxm2_batch.commit_output(0)
mapped_img = dbvalue(m_id,m_type);
(c_id,c_type) = boxm2_batch.commit_output(1)
corr_cam = dbvalue(c_id,c_type);
return mapped_img, corr_cam
def vrml_write_box(vrml_filename, bbox, is_wire, r, g, b):
boxm2_batch.init_process("bvrmlWriteBoxProcess");
boxm2_batch.set_input_string(0,vrml_filename);
boxm2_batch.set_input_double(1,bbox[0]); # minx
boxm2_batch.set_input_double(2,bbox[1]); # miny
boxm2_batch.set_input_double(3,bbox[2]); # minz
boxm2_batch.set_input_double(4,bbox[3]); # maxx
boxm2_batch.set_input_double(5,bbox[4]); # maxy
boxm2_batch.set_input_double(6,bbox[5]); # maxz
boxm2_batch.set_input_bool(7,is_wire);
boxm2_batch.set_input_float(8,r);
boxm2_batch.set_input_float(9,g);
boxm2_batch.set_input_float(10,b);
boxm2_batch.run_process();
def vrml_write_box(vrml_filename, bbox, is_wire, r, g, b):
boxm2_batch.init_process("bvrmlWriteBoxProcess")
boxm2_batch.set_input_string(0, vrml_filename)
boxm2_batch.set_input_double(1, bbox[0]) # minx
boxm2_batch.set_input_double(2, bbox[1]) # miny
boxm2_batch.set_input_double(3, bbox[2]) # minz
boxm2_batch.set_input_double(4, bbox[3]) # maxx
boxm2_batch.set_input_double(5, bbox[4]) # maxy
boxm2_batch.set_input_double(6, bbox[5]) # maxz
boxm2_batch.set_input_bool(7, is_wire)
boxm2_batch.set_input_float(8, r)
boxm2_batch.set_input_float(9, g)
boxm2_batch.set_input_float(10, b)
boxm2_batch.run_process()
def convert_reflectance_to_digital_count(reflectance_image,
metadata,
atmospheric_params,
normalize_0_1=False,
max_digital_count=2047):
boxm2_batch.init_process("bradConvertReflectanceToDigitalCountProcess")
boxm2_batch.set_input_from_db(0, reflectance_image)
boxm2_batch.set_input_from_db(1, metadata)
boxm2_batch.set_input_from_db(2, atmospheric_params)
boxm2_batch.set_input_bool(3, normalize_0_1)
boxm2_batch.set_input_unsigned(4, max_digital_count)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
output_img = dbvalue(id, type)
return output_img
def update_multi(scene, mcache, cam, img, ident="", update_alpha=True, var=-1.0, update_app=True, tnear=100000.0, tfar=0.000001):
# If no device is passed in, do cpu update
print("boxm2_batch multi GPU update")
boxm2_batch.init_process("boxm2MultiUpdateProcess")
boxm2_batch.set_input_from_db(0, mcache)
boxm2_batch.set_input_from_db(1, scene)
boxm2_batch.set_input_from_db(2, cam)
boxm2_batch.set_input_from_db(3, img)
boxm2_batch.set_input_string(4, ident)
boxm2_batch.set_input_bool(5, update_alpha)
boxm2_batch.set_input_float(6, var)
boxm2_batch.set_input_bool(7, update_app)
boxm2_batch.set_input_float(8, tnear)
boxm2_batch.set_input_float(9, tfar)
return boxm2_batch.run_process()
def estimate_atmospheric_parameters(image,
metadata,
mean_reflectance=None,
constrain_parameters=None):
boxm2_batch.init_process("bradEstimateAtmosphericParametersProcess")
boxm2_batch.set_input_from_db(0, image)
boxm2_batch.set_input_from_db(1, metadata)
if mean_reflectance != None:
boxm2_batch.set_input_float(2, mean_reflectance)
if constrain_parameters != None:
boxm2_batch.set_input_bool(3, constrain_parameters)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
atm_params = dbvalue(id, type)
return atm_params
def train_classifier(tclsf, poly_file, category_name, dictionary_name, compute_category_textons, finish_and_write_dictionary):
boxm2_batch.init_process("sdetTextureTrainingProcess2");
boxm2_batch.set_input_from_db(0,tclsf);
if compute_category_textons:
boxm2_batch.set_input_bool(1,1); # compute the textons if this is the last polygon file for the category
else:
boxm2_batch.set_input_bool(1,0);
boxm2_batch.set_input_string(2, poly_file);
boxm2_batch.set_input_string(3, category_name);
boxm2_batch.set_input_string(4, dictionary_name);
boxm2_batch.run_process();
(tclsf_id, tclsf_type)=boxm2_batch.commit_output(0);
tclsf = dbvalue(tclsf_id, tclsf_type);
if finish_and_write_dictionary:
boxm2_batch.finish_process();
return tclsf
def compute_sun_affine_camera(scene, sun_az, sun_el, astro_coords=True):
boxm2_batch.init_process("boxm2ComputeSunAffineCameraProcess")
boxm2_batch.set_input_from_db(0, scene)
boxm2_batch.set_input_float(1, sun_el)
boxm2_batch.set_input_float(2, sun_az)
boxm2_batch.set_input_bool(3, astro_coords)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
sun_cam = dbvalue(id, type)
(ni_id, type) = boxm2_batch.commit_output(1)
(nj_id, type) = boxm2_batch.commit_output(2)
ni = boxm2_batch.get_output_unsigned(ni_id)
nj = boxm2_batch.get_output_unsigned(nj_id)
boxm2_batch.remove_data(ni_id)
boxm2_batch.remove_data(nj_id)
return sun_cam, ni, nj
def texture_training(tclsf,
image,
exp_poly_path,
texton_dict_path,
name_of_category="mod",
compute_textons=True):
boxm2_batch.init_process("sdetTextureTrainingProcess")
boxm2_batch.set_input_from_db(0, tclsf)
boxm2_batch.set_input_bool(1, compute_textons) # compute textons
boxm2_batch.set_input_from_db(2, image)
boxm2_batch.set_input_string(3, exp_poly_path)
boxm2_batch.set_input_string(4, name_of_category)
boxm2_batch.set_input_string(5, texton_dict_path)
boxm2_batch.run_process()
# write out the texton dictionary on finish
boxm2_batch.finish_process()
def change_detect(scene,
cache,
cam,
img,
exp_img,
device=None,
rgb=False,
n=1,
raybelief="",
max_mode=False):
if cache.type == "boxm2_cache_sptr":
print "boxm2_batch CPU change detection"
boxm2_batch.init_process("boxm2CppChangeDetectionProcess")
boxm2_batch.set_input_from_db(0, scene)
boxm2_batch.set_input_from_db(1, cache)
boxm2_batch.set_input_from_db(2, cam)
boxm2_batch.set_input_from_db(3, img)
boxm2_batch.set_input_from_db(4, exp_img)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
cd_img = dbvalue(id, type)
return cd_img
elif cache.type == "boxm2_opencl_cache_sptr" and device:
print "boxm2_batch GPU change detection"
boxm2_batch.init_process("boxm2OclChangeDetectionProcess")
boxm2_batch.set_input_from_db(0, device)
boxm2_batch.set_input_from_db(1, scene)
boxm2_batch.set_input_from_db(2, cache)
boxm2_batch.set_input_from_db(3, cam)
boxm2_batch.set_input_from_db(4, img)
boxm2_batch.set_input_from_db(5, exp_img)
boxm2_batch.set_input_int(6, n)
boxm2_batch.set_input_string(7, raybelief)
boxm2_batch.set_input_bool(8, max_mode)
boxm2_batch.run_process()
if not rgb:
(id, type) = boxm2_batch.commit_output(0)
cd_img = dbvalue(id, type)
else:
(id, type) = boxm2_batch.commit_output(1)
cd_img = dbvalue(id, type)
return cd_img
else:
print "ERROR: Cache type not recognized: ", cache.type
def correct_cam_rotation(img,
pcam,
exp_img,
cone_half_angle,
n_steps,
refine=True):
boxm2_batch.init_process('icamCorrectCamRotationProcess')
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_from_db(1, pcam)
boxm2_batch.set_input_from_db(2, exp_img)
boxm2_batch.set_input_float(3, cone_half_angle)
boxm2_batch.set_input_unsigned(4, n_steps)
boxm2_batch.set_input_bool(5, refine)
boxm2_batch.run_process()
(m_id, m_type) = boxm2_batch.commit_output(0)
mapped_img = dbvalue(m_id, m_type)
(c_id, c_type) = boxm2_batch.commit_output(1)
corr_cam = dbvalue(c_id, c_type)
return mapped_img, corr_cam
def update_rgb(scene, cache, cam, img, device=None, mask="", updateAlpha=True):
#If no device is passed in, do cpu update
if cache.type == "boxm2_cache_sptr":
print "boxm2_batch rgb CPU update not implemented"
elif cache.type == "boxm2_opencl_cache_sptr" and device:
print("boxm2_batch GPU update")
boxm2_batch.init_process("boxm2OclUpdateColorProcess")
boxm2_batch.set_input_from_db(0, device)
boxm2_batch.set_input_from_db(1, scene)
boxm2_batch.set_input_from_db(2, cache)
boxm2_batch.set_input_from_db(3, cam)
boxm2_batch.set_input_from_db(4, img)
boxm2_batch.set_input_string(5, "")
#identifier
boxm2_batch.set_input_string(6, mask) #mask file
boxm2_batch.set_input_bool(7, updateAlpha)
boxm2_batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
def get_single_nitf_footprint(nitf_filename, out_kml_filename="",isKml = False, metafolder =""):
boxm2_batch.init_process('vpglNITFFootprintProcess2');
boxm2_batch.set_input_string(0,nitf_filename);
boxm2_batch.set_input_string(1,out_kml_filename);
boxm2_batch.set_input_bool(2,isKml);
boxm2_batch.set_input_string(3,metafolder);
boxm2_batch.run_process();
(id,type) = boxm2_batch.commit_output(0);
llon = boxm2_batch.get_output_double(id);
(id,type) = boxm2_batch.commit_output(1);
llat = boxm2_batch.get_output_double(id);
(id,type) = boxm2_batch.commit_output(2);
lele = boxm2_batch.get_output_double(id);
(id,type) = boxm2_batch.commit_output(3);
rlon = boxm2_batch.get_output_double(id);
(id,type) = boxm2_batch.commit_output(4);
rlat = boxm2_batch.get_output_double(id);
(id,type) = boxm2_batch.commit_output(5);
rele = boxm2_batch.get_output_double(id);
boxm2_batch.remove_data(id);
return llon, llat, lele, rlon, rlat, rele
def generate_xyz_from_dem(scene,
refine_level,
geotiff_dem,
geoid_height,
bilin=False):
boxm2_batch.init_process("boxm2DemToXYZProcess")
boxm2_batch.set_input_from_db(0, scene)
boxm2_batch.set_input_unsigned(1, refine_level)
boxm2_batch.set_input_string(2, geotiff_dem)
boxm2_batch.set_input_double(3, geoid_height)
boxm2_batch.set_input_bool(4, bilin)
boxm2_batch.run_process()
(xi_id, xi_type) = boxm2_batch.commit_output(0)
x_img = dbvalue(xi_id, xi_type)
(yi_id, yi_type) = boxm2_batch.commit_output(1)
y_img = dbvalue(yi_id, yi_type)
(zi_id, zi_type) = boxm2_batch.commit_output(2)
z_img = dbvalue(zi_id, zi_type)
(dem_id, dem_type) = boxm2_batch.commit_output(3)
dem_img = dbvalue(dem_id, dem_type)
(demr_id, demr_type) = boxm2_batch.commit_output(4)
dem_res_img = dbvalue(demr_id, demr_type)
return x_img, y_img, z_img, dem_img, dem_res_img
def get_single_nitf_footprint(nitf_filename,
out_kml_filename="",
isKml=False,
metafolder=""):
boxm2_batch.init_process('vpglNITFFootprintProcess2')
boxm2_batch.set_input_string(0, nitf_filename)
boxm2_batch.set_input_string(1, out_kml_filename)
boxm2_batch.set_input_bool(2, isKml)
boxm2_batch.set_input_string(3, metafolder)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
llon = boxm2_batch.get_output_double(id)
(id, type) = boxm2_batch.commit_output(1)
llat = boxm2_batch.get_output_double(id)
(id, type) = boxm2_batch.commit_output(2)
lele = boxm2_batch.get_output_double(id)
(id, type) = boxm2_batch.commit_output(3)
rlon = boxm2_batch.get_output_double(id)
(id, type) = boxm2_batch.commit_output(4)
rlat = boxm2_batch.get_output_double(id)
(id, type) = boxm2_batch.commit_output(5)
rele = boxm2_batch.get_output_double(id)
boxm2_batch.remove_data(id)
return llon, llat, lele, rlon, rlat, rele
def write_mcache(mcache, do_clear=0):
boxm2_batch.init_process("boxm2WriteMultiCacheProcess")
boxm2_batch.set_input_from_db(0, mcache)
boxm2_batch.set_input_bool(1, do_clear)
boxm2_batch.run_process()
