def writing_pgpatch_to_base(spec_array,
schema,
conn,
cur,
writing_query,
file_name,
schemas,
additional_offset=None):
""" this function writes pgpatch one by one in a given table"""
import pg_pointcloud_classes as pgp
import psycopg2
import sys
import numpy as np
connection_string = """host=localhost dbname=test_pointcloud user=postgres password=postgres port=5433"""
#shitty move, casting back to numpy double should not be necessary !
numpy_double, schema = pgp.patch_numpy_to_numpy_double(
np.array(spec_array), schema, use_scale_offset=False)
numpy_double = np.array(numpy_double)
if additional_offset != None:
numpy_double[:] += np.array(additional_offset)
pgpatch = pgp.numpy_double_to_WKB_patch(np.array(numpy_double), schema)
cur.execute(writing_query, (
file_name,
pgpatch,
))
conn.commit()
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 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 writing_pgpatch_to_base(spec_array,schema,conn, cur, writing_query,file_name, schemas, additional_offset=None):
""" this function writes pgpatch one by one in a given table"""
import pg_pointcloud_classes as pgp
import psycopg2
import sys
import numpy as np
connection_string = """host=localhost dbname=test_pointcloud user=postgres password=postgres port=5433"""
#shitty move, casting back to numpy double should not be necessary !
numpy_double, schema = pgp.patch_numpy_to_numpy_double(np.array(spec_array), schema,use_scale_offset=False)
numpy_double = np.array(numpy_double)
if additional_offset != None:
numpy_double[:] += np.array(additional_offset)
pgpatch = pgp.numpy_double_to_WKB_patch(np.array(numpy_double), schema)
cur.execute(writing_query,(file_name,pgpatch,) )
conn.commit()
