def compute_dim_descriptor_from_patch(uncompressed_patch, connection_string):
""" given a patch, extract points and compute dim descriptors"""
import pg_pointcloud_classes as pgp
#convert patch to numpy array
GD = pgp.create_GD_if_not_exists()
#cache mecanism for patch schema
if 'rc' not in GD: # creating the rc dict if necessary
GD['rc'] = dict()
if 'schemas' not in GD['rc']: # creating the schemas dict if necessary
GD['rc']['schemas'] = dict()
restrict_dim = ["x","y","z"]
pt_arr, (mschema,endianness, compression, npoints) = \
pgp.patch_string_buff_to_numpy(uncompressed_patch, GD['rc']['schemas'], connection_string)
#pt_arr, (mschema,endianness, compression, npoints) = pgp.patch_string_buff_to_numpy(uncompressed_patch, temp_schema, [])
numpy_double, mschema = pgp.patch_numpy_to_numpy_double( \
pt_arr[ restrict_dim], mschema,use_scale_offset=True,dim_to_use=restrict_dim)
###########
#warning: to be removed ! @TODO
#numpy_double[:,0] = numpy_double[:,0] + 649000
#numpy_double[:,1] = numpy_double[:,1] + 6840000
###########
#computing descriptors
descriptors = compute_descriptors_from_points(numpy_double)
return descriptors
def reordering_patch_following_midoc(uncompressed_patch, tot_level, stop_level,
connection_string):
""" main function : reorder patch following midoc ordering"""
import pg_pointcloud_classes as pgp
import midoc_ordering as midoc
################# only for test
temp_schema = dict()
temp_schema["1"] = artificial_schema()
#################
#convert patch to numpy array
GD = pgp.create_GD_if_not_exists()
#cache mecanism for patch schema
if 'rc' not in GD: # creating the rc dict if necessary
GD['rc'] = dict()
if 'schemas' not in GD['rc']: # creating the schemas dict if necessary
GD['rc']['schemas'] = dict()
restrict_dim = ["x", "y", "z"]
pt_arr, (mschema,endianness, compression, npoints) = \
pgp.patch_string_buff_to_numpy(uncompressed_patch, GD['rc']['schemas'], connection_string)
#pt_arr, (mschema,endianness, compression, npoints) = pgp.patch_string_buff_to_numpy(uncompressed_patch, temp_schema, [])
numpy_double, mschema = pgp.patch_numpy_to_numpy_double(
pt_arr[restrict_dim],
mschema,
use_scale_offset=True,
dim_to_use=restrict_dim)
######################
# WARNING DEBUG
# to be removed
# numpy_double [:,0] = numpy_double [:,0] #+ 649000
#numpy_double [:,1] = numpy_double [:,1] + 6840000
######################
#keep only the relevant dimensions
pt_xyz = numpy_double
num_points = npoints
#compute midoc ordering
result = midoc.order_by_octree(pt_xyz, tot_level, stop_level)
result_completed = midoc.complete_and_shuffle_result(result, num_points)
pt_per_class = midoc.count_points_per_class(result, stop_level)
#transfer ordering to full points
reordered_arr = pt_arr[result_completed[:, 0].astype('int32')]
#create new patch
wkb_ordered_patch = pgp.numpy_double_to_WKB_patch(reordered_arr, mschema)
return wkb_ordered_patch, pt_per_class
def export_one_patch(gid, connection_string, conn,cur,schemas,f ):
""" """
import pg_pointcloud_classes as pgp
import numpy as np
#get the patch
patch_query = 'SELECT pc_uncompress(patch) FROM trafi_pollu.goudron WHERE gid = %s'
cur.execute(patch_query,(gid,))
patch_tmp = cur.fetchall()
#convert patch to numpy double
numpy_arr = pgp.patch_string_buff_to_numpy(patch_tmp[0][0],schemas,connection_string)
np.save(f, np.array(numpy_arr))
numpy_arr = None
return
def export_one_patch(gid, connection_string, conn,cur,schemas,f ):
""" """
import pg_pointcloud_classes as pgp
import numpy as np
#get the patch
patch_query = 'SELECT pc_uncompress(patch) FROM tmob_20140616.riegl_pcpatch_space_int WHERE gid = %s'
cur.execute(patch_query,(gid,))
patch_tmp = cur.fetchall()
#convert patch to numpy double
numpy_arr = pgp.patch_string_buff_to_numpy(patch_tmp[0][0],schemas,connection_string)
np.save(f, np.array(numpy_arr))
numpy_arr = None
return
def reordering_patch_following_midoc(uncompressed_patch, tot_level, stop_level, connection_string):
""" main function : reorder patch following midoc ordering"""
import pg_pointcloud_classes as pgp
import midoc_ordering as midoc
################# only for test
temp_schema = dict()
temp_schema["1"]= artificial_schema()
#################
#convert patch to numpy array
GD = pgp.create_GD_if_not_exists()
#cache mecanism for patch schema
if 'rc' not in GD: # creating the rc dict if necessary
GD['rc'] = dict()
if 'schemas' not in GD['rc']: # creating the schemas dict if necessary
GD['rc']['schemas'] = dict()
restrict_dim = ["x","y","z"]
pt_arr, (mschema,endianness, compression, npoints) = \
pgp.patch_string_buff_to_numpy(uncompressed_patch, GD['rc']['schemas'], connection_string)
#pt_arr, (mschema,endianness, compression, npoints) = pgp.patch_string_buff_to_numpy(uncompressed_patch, temp_schema, [])
numpy_double, mschema = pgp.patch_numpy_to_numpy_double(pt_arr[ restrict_dim], mschema,use_scale_offset=True,dim_to_use=restrict_dim)
######################
# WARNING DEBUG
# to be removed
# numpy_double [:,0] = numpy_double [:,0] #+ 649000
#numpy_double [:,1] = numpy_double [:,1] + 6840000
######################
#keep only the relevant dimensions
pt_xyz = numpy_double
num_points = npoints
#compute midoc ordering
result = midoc.order_by_octree(pt_xyz, tot_level, stop_level)
result_completed = midoc.complete_and_shuffle_result(result, num_points)
pt_per_class = midoc.count_points_per_class(result, stop_level)
#transfer ordering to full points
reordered_arr = pt_arr[result_completed[:,0].astype('int32')]
#create new patch
wkb_ordered_patch = pgp.numpy_double_to_WKB_patch(reordered_arr, mschema)
return wkb_ordered_patch, pt_per_class
"""
import pg_pointcloud_classes as pc
patch_text = """0104000000000000000A00000060AE0A00819E03002D07000060AE0A0090A803000E09000060AE0A0005990300040B000060AE0A00A69D0300F30B000060AE0A00499D0300180C000060AE0A00EAA803001A09000060AE0A00E9B20300390A000060AE0A00C5AD0300190A000060AE0A00DCBC03008410000060AE0A003FCF0300E3140000"""
connection_string = """host=172.16.3.50 dbname=vosges user=postgres password=postgres port=5432"""
global GD
GD = {}
if 'rc' not in GD: # creating the rc dict if necessary
GD['rc'] = dict()
if 'schemas' not in GD['rc']: # creating the schemas dict if necessary
GD['rc']['schemas'] = dict()
np_points,(mschema,endianness, compression, npoints) = pc.patch_string_buff_to_numpy(patch_text, GD['rc']['schemas'], connection_string)
print np_points
pc.create_schemas_if_not_exists()
print GD['rc']['schemas']
import psycopg2
# Connect to an existing database
conn = psycopg2.connect("dbname=test_pointcloud user=postgres password=postgres port=5433")
# Open a cursor to perform database operations
