def upload_bag(obj, bag_file, couchdb_url=dbtools.DEFAULT_SERVER_URL):
''' Uploads a bag to the couch of your choice, associates it with the object description given
@param obj: An instance of object_rocognition.models.Object
@param bag_file: A file like object that looks like a ros bag.
'''
couch = couchdb.Server(couchdb_url)
dbs = dbtools.init_object_databases(couch)
objects = dbs['objects']
bags = dbs['bags']
obj.store(objects)
bag = models.Bag(object_id=obj.id,
author_name=obj.author_name,
author_email=obj.author_email,
)
bag.store(bags)
bags.put_attachment(doc=bag, content=bag_file, filename='data.bag', content_type='application/octet-stream')
return bag
session.bag_id = bag.id
if args.commit:
session.store(sessions)
print "running graph"
plasm = connect_observation_calc(sync, args.commit, str(session.object_id), str(session.id), args.visualize)
sched = ecto.schedulers.Threadpool(plasm)
sched.execute()
return session
finally:
print "Removing tmp_file:", tmp_file.name
os.remove(tmp_file.name)
if "__main__" == __name__:
args = parse_args()
couch = couchdb.Server(args.db_root)
dbs = dbtools.init_object_databases(couch)
bags = dbs['bags']
if args.compute_all:
models.sync_models(dbs)
results = models.Bag.all(bags)
for bag in results:
existing_sessions = models.Session.by_bag_id(dbs['sessions'], key=bag.id)
if(len(existing_sessions) == 0):
obj = models.Object.load(dbs['objects'], bag.object_id)
print "Computing session for:", obj.object_name, "\ndescription:", obj.description
compute_for_bag(bag, bags, args)
else:
print "Skipping bag:", bag.id, "Already computed %d sessions" % len(existing_sessions)
else:
bag = models.Bag.load(bags, args.bag_id)
if bag == None or bag.id == None:
type=str,
help='Remote db to copy from.')
parser.add_argument('--remote_collection',
dest='remote_collection',
type=str,
help='Remote collection.')
object_recognition.dbtools.add_db_options(parser)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
couch = couchdb.Server(args.db_root)
remote_couch = couchdb.Server(args.remote)
local_db = dbtools.init_object_databases(couch)
remote_db = remote_couch[args.remote_collection]
results = remote_db.view('_all_docs')
total_docs = len(results)
i = 1
for x in results:
doc = remote_db.get(x.id)
doc = doc.copy()
attachments = doc.get('_attachments', False)
if attachments:
del doc['_attachments']
if x.id not in local_db:
(doc_id, rev) = local_db.save(doc)
if attachments:
for key, val in attachments.iteritems():
def parse_args():
parser = argparse.ArgumentParser(description='Copies docs from one db to another.')
parser.add_argument('--remote', dest='remote', type=str,
help='Remote db to copy from.')
parser.add_argument('--remote_collection',
dest='remote_collection', type=str,
help='Remote collection.')
object_recognition.dbtools.add_db_options(parser)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
couch = couchdb.Server(args.db_root)
remote_couch = couchdb.Server(args.remote)
local_db = dbtools.init_object_databases(couch)
remote_db = remote_couch[args.remote_collection]
results = remote_db.view('_all_docs')
total_docs = len(results)
i = 1
for x in results:
doc = remote_db.get(x.id)
doc = doc.copy()
attachments = doc.get('_attachments',False)
if attachments:
del doc['_attachments']
if x.id not in local_db:
(doc_id,rev) = local_db.save(doc)
if attachments:
for key,val in attachments.iteritems():
from ecto_object_recognition import capture
from object_recognition import models, dbtools
import ecto_opencv
import os
def parse_args():
import argparse
parser = argparse.ArgumentParser(description='Train MyAlgorithm on views from the database.')
parser.add_argument('objects', metavar='OBJECT', type=str, nargs='+',
help='Object ids to train.')
dbtools.add_db_options(parser)
args = parser.parse_args()
return args
args = parse_args()
db = dbtools.init_object_databases(couchdb.Server(args.db_root))
for object_id in args.objects:
#get a list of observation ids for a particular object id.
obs_ids = models.find_all_observations_for_object(db, object_id)
if not obs_ids:
print 'No observations found for object %s.' % object_id
continue
plasm = ecto.Plasm()
#the db_reader transforms observation id into a set of image,depth,mask,K,R,T
db_reader = capture.ObservationReader("db_reader", db_params=dbtools.args_to_db_params(args))
#this iterates over all of the observation ids.
observation_dealer = ecto.Dealer(tendril=db_reader.inputs.at('observation'), iterable=obs_ids)
import os
import math
import subprocess
import couchdb
import ecto
from ecto_opencv import highgui
import object_recognition
from object_recognition import dbtools, models, capture
db_url = dbtools.DEFAULT_SERVER_URL
#database ritual
couch = couchdb.Server(db_url)
dbs = dbtools.init_object_databases(couch)
sessions = dbs['sessions']
observations = dbs['observations']
results = models.Session.all(sessions)
obs_ids = []
for session in results:
obs_ids += models.find_all_observations_for_session(
observations, session.id)
if len(obs_ids) == 0:
raise RuntimeError("There are no observations available.")
db_reader = capture.ObservationReader('db_reader',
db_params=ObjectDbParameters({
'type':
emit(null,doc)
}
""",
)
@classmethod
def sync(cls, db):
cls.all.sync(db)
cls.by_object_id.sync(db)
def sync_models(dbs):
Bag.sync(dbs["bags"])
Object.sync(dbs["objects"])
Session.sync(dbs["sessions"])
Observation.sync(dbs["observations"])
if __name__ == "__main__":
couch = couchdb.Server(DEFAULT_SERVER_URL)
dbs = init_object_databases(couch)
obj = Object(
object_id="TestObject",
object_name="A test object.",
description="test objects are fun.",
tags=["test", "object", "tod"],
)
obj.store(dbs["objects"])
print obj
print dbs
