def build_binary_expression(node):
if len(node.children) != 3:
logging.error('FATAL ERROR: build_binary_expression()')
sys.exit(1)
valid_operators = {
'EqualOperator',
'NotEqualOperator',
'AndOperator',
'OrOperator',
'LessThanOperator',
'LessThanEqualOperator',
'GreaterThanOperator',
'GreaterThanEqualOperator',
'BinaryAndOperator',
'BinaryOrOperator',
'AddOperator',
'SubtractOperator',
'MultiplyOperator',
'DivideOperator',
'ModuloOperator',
}
lhs = build_expr(node[0])
rhs = build_expr(node[2])
# Check that the operator is valid, and convert it to a corresponding
# expression node (done by stripping "Operator" and replacing it with
# "Expression").
if node[1].name in valid_operators:
node_name = node[1].name[:-len('Operator')] + 'Expression'
return ASTNode(node_name, None, [lhs, rhs])
else:
logging.error('FATAL ERROR: Invalid operator %s encountered in build_binary_expression()' % node[1].name)
sys.exit(1)
def get_arraytype(typ):
if is_array_type(typ):
return typ[:-2]
else:
logging.error("FATAL ERROR: non-array type %s provided to get_arraytype" %
typ)
sys.exit(-1)
def check_method_forwardreference(pkg_index, type_index, cls_idx):
""" check forward referencing of methods in field initializers """
for pkg_name in pkg_index:
for cu_env in pkg_index[pkg_name]:
if cu_env['ClassDeclaration'] == None:
continue
typedecl_env = cu_env['ClassDeclaration']
for field_decl in typedecl_env.node.select(['Fields',
'FieldDeclaration']):
# get all methods declared after this field:
methods_obj_blacklist = []
for m in typedecl_env.methods:
for method_decl in typedecl_env.methods[m]:
if method_decl.decl_order > field_decl.decl_order:
methods_obj_blacklist.append(method_decl.obj)
# for each field initializer, find method invocations
for mi_node in \
field_decl.find_child('Initializer').select(['MethodInvocation']):
# for each method invocation, ensure it's not declared after
# this field
if mi_node.decl.obj.declared_in.name == cu_env.canon \
and mi_node.decl.obj in methods_obj_blacklist:
logging.error("Illegal Forward reference to method %s before its declaration" \
% (mi_node.decl.obj))
sys.exit(42)
def gen_add_expr(info, node, method_obj):
output = []
# Numbers, just add.
if primitives.is_numeric(node[0].typ) and primitives.is_numeric(node[1].typ):
output.extend(gen_binary_expr_common(info, node, method_obj))
output.append('add eax, ebx')
# If they are objects, we do the following:
# 1. Use String.valueOf() on each operand.
# 2. Do a LHS.concat(RHS), which returns a new string.
elif node[0].typ == 'java.lang.String' or node[1].typ == 'java.lang.String':
# Convert LHS to a string.
output.extend(gen_string_valueof(info, node[0], method_obj))
output.append('push eax')
# Convert RHS to a string.
output.extend(gen_string_valueof(info, node[1], method_obj))
output.append('push eax')
# Receiver is LHS, Argument is RHS (already done!), just need to call
# the correct method.
output.append('call METHOD~java.lang.String.concat~java.lang.String')
# Jump back.
output.append('add esp, 8')
else:
logging.error('FATAL ERROR: invalid add')
sys.exit(1)
return output
def typecheck_add(node, c, method, t_i, c_i):
expected_node = ['AddExpression', 'SubtractExpression']
if node.name not in expected_node:
logging.error("FATAL ERROR: expected", expected_node)
sys.exit(1)
if len(node.children) == 0:
logging.error("FATAL ERROR: %s has no children" % expected_node)
sys.exit(1)
elif len(node.children) != 2:
logging.error('FATAL ERROR: typecheck_add() on expression %s with %d children' %
(node.name, len(node.children)))
sys.exit(1)
else:
t1 = typecheck_expr(node[0], c, method, t_i, c_i)
t2 = typecheck_expr(node[1], c, method, t_i, c_i)
if node.name == 'AddExpression' and \
(t1 == "java.lang.String" or t2 == "java.lang.String"):
if t1 != "Void" and t2 != "Void":
node.typ = 'java.lang.String'
return node.typ
else:
logging.error("typecheck failed: string add void")
sys.exit(42)
elif primitives.is_numeric(t1) and primitives.is_numeric(t2):
node.typ = 'Int'
return node.typ
else:
logging.error("typecheck failed: Add:", t1, t2)
sys.exit(42)
def replace(m1, list_of_m):
if isinstance(m1, Field):
return
for m2 in list_of_m:
# A nonstatic method must not replace a static method (JLS 8.4.6.1, dOvs well-formedness constraint 5)
if ("Static" not in m1.mods and "Static" in m2.mods):
logging.error("a nonstatic method (%s) replaced a static method (%s)" % (m1, m2))
sys.exit(42)
if ("Static" in m1.mods and "Static" not in m2.mods):
logging.error("a static method (%s) replaced a nonstatic method (%s)" % (m1, m2))
sys.exit(42)
# A method must not replace a method with a different return type. (JLS 8.1.1.1, 8.4, 8.4.2, 8.4.6.3, 8.4.6.4, 9.2, 9.4.1, dOvs well-formedness constraint 6)
if m1.type != m2.type:
logging.error("a method (%s) replaced a method (%s) with a different return type" % (m1, m2))
sys.exit(42)
# A protected method must not replace a public method. (JLS 8.4.6.3, dOvs well-formedness constraint 7)
if "Protected" in m1.mods and "Public" in m2.mods:
print(list_of_m)
logging.error("a protected method (%s) replaced a public method (%s)" % (m1, m2))
sys.exit(42)
# A method must not replace a final method. (JLS 8.4.3.3, dOvs well-formedness constraint 9)
if "Final" in m2.mods:
logging.error("a method (%s) replaced a final method (%s)" % (m1, m2))
sys.exit(42)
def on_status(self, status):
if self.storage == 'stdout':
print self.to_string(status)
elif self.storage == 'sql':
try:
self.to_sql(status)
except db.DataError as ex:
logging.error(ex)
raise ex
def get_field_offset(self, node):
if node.name != "FieldAccess":
logging.error("get_field_offset")
sys.exit(1)
receiver_type = node.find_child("FieldReceiver")[0].typ
field_name = node.find_child("FieldName")[0].value.value
return self.get_field_offset_from_field_name(receiver_type, field_name)
def on_status(self, status):
if 'stdout' in self.storage:
print self.status_to_string(status)
if 'sql' in self.storage:
try:
self.to_sql(status)
except db.DataError as ex:
logging.error(ex)
raise ex
def build_method_params(method_node):
params = {}
for p in method_node.select(['Parameters', 'Parameter']):
param_name = p[1].value.value
if param_name in params:
logging.error('Two params=%s have cannot have the same name'
% (param_name))
sys.exit(42)
params[param_name] = p
return params
def cycle_detection(c, seen_so_far):
l = list(c.implements)
if c.extends != None:
l.append(c.extends)
for cc in l:
if cc in seen_so_far:
logging.error("Cycle detected in super class hierarchy for %s"
% c.name)
sys.exit(42)
cycle_detection(cc, list(seen_so_far)+[cc])
def call_remote(self, url):
req = urllib2.Request(url)
try:
response = urllib2.urlopen(req)
except urllib2.URLError:
result = None
logging.error("Error while requesting: " + url)
self.flash(_("Cannot connect to server"))
else:
json_data = response.read()
result = json.loads(json_data)
return result
def typecheck_while(node, c, method, t_i, c_i):
if node.name != 'WhileStatement':
logging.error('FATAL ERROR: typecheck_while')
sys.exit(1)
expr_type = typecheck_expr(node[0], c, method, t_i, c_i)
if expr_type != 'Boolean':
logging.error('Type of expression for \'while\' must be a Boolean')
sys.exit(42)
return None
def typecheck_mult(node, c, method, t_i, c_i):
expected_node = ['MultiplyExpression', 'DivideExpression', 'ModuloExpression']
if node.name not in expected_node:
logging.error("FATAL ERROR: expected", expected_node)
sys.exit(1)
if len(node.children) == 0:
logging.error("FATAL ERROR: %s has no children in typecheck_mult()" %
expected_node)
sys.exit(1)
elif len(node.children) != 2:
logging.error('FATAL ERROR: typecheck_mult got %s node with %d children' %
(node.name, len(node.children)))
sys.exit(1)
else:
t1 = typecheck_expr(node[0], c, method, t_i, c_i)
t2 = typecheck_expr(node[1], c, method, t_i, c_i)
if primitives.is_numeric(t1) and primitives.is_numeric(t2):
node.typ = 'Int'
return node.typ
else:
logging.error("typecheck failed: mult/div/mod not num")
sys.exit(42)
def sell(branch, isdn, amount):
result = None
try:
xml = (
'<request><branch>%(branch)s</branch><customer system="ISDN">%(isdn)s</customer><point>%(point)s</point><smsPrefix>my.ubedn.mn</smsPrefix><smsSuffix>UBEDN</smsSuffix><product>tsaxilgaanii tulbur</product><productType></productType><description>tsaxilgaanii tulbur tulugdsun.</description></request>'
% {"branch": branch, "isdn": isdn, "point": amount}
)
result = api_request("sell", xml)
except Exception as ex:
logging.error("[candy api] [sell] error: %s" % ex)
return result
def typecheck_relational(node, c, method, t_i, c_i):
expected_node = ['LessThanExpression', 'LessThanEqualExpression',
'GreaterThanExpression', 'GreaterThanEqualExpression']
if node.name not in expected_node:
logging.error("FATAL ERROR: typecheck_relational() expected", expected_node)
sys.exit(1)
if len(node.children) == 0:
logging.error("FATAL ERROR: has no children", expected_node)
sys.exit(1)
elif len(node.children) != 2:
logging.error('FATAL ERROR: typecheck_relational() on expression %s with %d children' %
(node.name, len(node.children)))
sys.exit(1)
else:
t1 = typecheck_expr(node[0], c, method, t_i, c_i)
t2 = typecheck_expr(node[1], c, method, t_i, c_i)
if primitives.is_numeric(t1) and primitives.is_numeric(t2):
node.typ = 'Boolean'
return node.typ
else:
logging.error("typecheck failed: Relational:", t1, t2)
sys.exit(42)
def get_method_offset(self, node):
if node.name != "MethodInvocation":
logging.error("get_method_offset")
sys.exit(1)
method_name = node.find_child("MethodName")[0].value.value
params = []
for child in node.find_child("Arguments").children:
params.append(child.typ)
method = class_hierarchy.Temp_Method(method_name, params)
offset = self.method_index.index(method) * 4
return offset
def build_field_index(class_index):
field_index = {}
for c in class_index.values():
# print(c)
field_index[c.name] = []
for m in get_all_fields(c):
if isinstance(m, class_hierarchy.Field):
field_index[c.name].append(m)
# print(" ",field_index[c.name].index(m), field_index[c.name])
else:
logging.error("build_field_index")
sys.exit(1)
return field_index
def typecheck_equality(node, c, method, t_i, c_i):
expected_node = ['EqualExpression', 'NotEqualExpression']
if node.name not in expected_node:
logging.error("FATAL ERROR: expected", expected_node)
sys.exit(1)
if len(node.children) == 0:
logging.error("FATAL ERROR: has no children", expected_node)
sys.exit(1)
elif len(node.children) != 2:
logging.error('FATAL ERROR: typecheck_equality on %d children' %
len(node.children))
sys.exit(1)
else:
t1 = typecheck_expr(node[0], c, method, t_i, c_i)
t2 = typecheck_expr(node[1], c, method, t_i, c_i)
if primitives.is_numeric(t1) and primitives.is_numeric(t2):
node.typ = "Boolean"
return node.typ
elif t1 == "Boolean" and t2 == "Boolean":
node.typ = "Boolean"
return node.typ
elif is_assignable(t1, t2, c_i) or is_assignable(t2, t1, c_i):
node.typ = "Boolean"
return node.typ
else:
logging.error("typecheck failed: equality between", t1, t2)
sys.exit(42)
def get_parse_tree(tokens, filename, options):
global parse_table
parse_tree = parser.parse(tokens, parse_table)
if parse_tree == False:
logging.error("Could not parse file %s" % filename)
sys.exit(42)
if options.stage == 'parser':
if options.include_stdlib == False or filename not in opts.stdlib_files or \
options.print_stdlib == True:
print("Unweeded parse tree for %s:" % filename)
parse_tree.pprint()
return parse_tree
def typecheck_cast_expression(node, c, method, t_i, c_i):
if node.name != 'CastExpression':
logging.error('FATAL: Invalid node %s for typecheck_cast_expression' %
node.name)
sys.exit(1)
expr_type = typecheck_expr(node[1], c, method, t_i, c_i)
if (primitives.is_numeric(expr_type) and primitives.is_numeric(node[0].canon)) \
or is_assignable(expr_type, node[0].canon, c_i) \
or is_assignable(node[0].canon, expr_type, c_i):
return node[0].canon
else:
logging.error('Cast expression of type %s into %s' %
(expr_type, node[0].canon))
sys.exit(42)
def get_balance(isdn):
result = None
try:
xml = "<request><customer>%(isdn)s</customer><customer.system>ISDN</customer.system></request>" % {"isdn": isdn}
result = api_request("customer", xml)
if result and not result.has_error:
result.balance = None
root = xml_et.fromstring(result.body)
result.balance = int(float(root.find("balance").text))
except Exception as ex:
logging.error("[candy api] [get balance] error: %s" % ex)
return result
def typecheck_postfix(node, c, method, t_i, c_i):
expected_node = 'PostfixExpression'
if node.name != expected_node:
logging.error("FATAL ERROR: typecheck_postfix() got", node.name)
sys.exit(1)
if len(node.children) == 0:
logging.error("FATAL ERROR")
sys.exit(1)
if node[0].name == 'Name':
node.typ = typecheck_name(node[0])
else:
node.typ = typecheck_expr(node[0], c, method, t_i, c_i)
return node.typ
def typecheck_for(node, c, method, t_i, c_i):
if node.name != 'ForStatement':
logging.error('FATAL ERROR: typecheck_for')
sys.exit(1)
# If there's no 'ForCondition', don't need to do anything.
if len(node[1].children) == 0:
return None
else:
expr_type = typecheck_expr(node[1][0], c, method, t_i,
c_i)
if expr_type != 'Boolean':
logging.error('Type of expression for \'for\' must be a Boolean')
sys.exit(42)
return None
def typecheck_creation(node, c, method, t_i, c_i):
expected_node = 'CreationExpression'
if node.name != expected_node:
logging.error("FATAL ERROR: expected", expected_node)
sys.exit(1)
creation_type = node[0].canon
if is_array_type(creation_type):
if len(node[1].children) > 1:
logging.error('Too many args to array creation')
sys.exit(42)
if len(node[1].children) == 1:
expr_type = typecheck_expr(node[1][0], c,
method, t_i, c_i)
if not primitives.is_numeric(expr_type):
logging.error('Invalid array creation argument')
sys.exit(42)
node.typ = creation_type
return node.typ
else:
cons_name = creation_type.split('.')[-1]
# Check that we don't call constructor of an abstract class.
if 'Abstract' in c_i[creation_type].mods:
logging.error('Cannot call constructor of abstract class')
sys.exit(42)
arg_types = []
for arg_expr in node[1].children:
arg_types.append(typecheck_expr(arg_expr, c, method,
t_i, c_i))
cons_decl = name_resolve.constructor_accessable(c_i, t_i,
creation_type, cons_name, arg_types, c.name, False)
cons = class_hierarchy.Temp_Constructor(cons_name, arg_types)
# TODO: Check that cons is not protected, and if it is, we have access
# to call it.
if cons_decl != None and cons in c_i[creation_type].declare:
node.typ = creation_type
return node.typ
else:
logging.error('Invalid constructor call')
sys.exit(42)
def build_statement(node):
if_statements = [
'IfThenStatement',
'IfThenElseStatement',
'IfThenElseStatementNoShortIf',
]
if node[0].name == 'StatementWithoutTrailingSubstatement':
return build_statement_wts(node[0])
elif node[0].name in if_statements:
return build_if_statement(node[0])
elif node[0].name in ['WhileStatement', 'WhileStatementNoShortIf']:
return build_while_statement(node[0])
elif node[0].name in ['ForStatement', 'ForStatementNoShortIf']:
return build_for_statement(node[0])
else:
logging.error('AST: invalid node in build_statement()')
sys.exit(1)
def __make_station(self, s):
try:
prefix, name, bitrate, mimetype, location, genre = File.unpack_path(
s)
except:
logging.error("error decoding iradio path: %s\n%s", path,
logging.stacktrace())
return None
f = File(self)
f.name = name
f.info = "Bitrate: %s kb" % bitrate
f.resource = location
f.path = s
f.mimetype = mimetype
f.icon = theme.shoutcast_station.get_path()
return f
def is_widening_conversion(type1, type2):
if type1 == type2:
return True
elif type2 == 'Byte':
return type1 == 'Short' or type1 == 'Int'
elif type2 == 'Short':
return type1 == 'Int'
elif type2 == 'Char':
return type1 == 'Int'
elif type2 == 'Int':
return type1 == 'Int'
else:
logging.error('FATAL ERROR: Invalid type (%s) for is_widening_conversion' % type2)
sys.exit(1)
def sell_confirm(branch, isdn, tan):
result = None
try:
xml = (
'<request><branch>%(branch)s</branch><customer system="ISDN">%(isdn)s</customer><tancode>%(tan)s</tancode></request>'
% {"branch": branch, "isdn": isdn, "tan": tan}
)
result = api_request("sell/confirm", xml)
if result and not result.has_error:
result.trans_id = None
root = xml_et.fromstring(result.body)
result.trans_id = root.find("transactionId").text
except Exception as ex:
logging.error("[candy api] [sell confirm] error: %s" % ex)
return result
def __init__(self, location, dom):
"""
Creates a new DeviceDescription object from the given DOM tree of a
device description.
@since: 0.96
@param location: URL of the device location
@param dom: the DOM tree of the description XML (see L{utils.MiniXML})
"""
self.__dom = dom
# location of the device description
self.__location = location
# prefix for accessing relative URLs
self.__url_base = ""
self.__device_type = ""
self.__udn = ""
self.__friendly_name = ""
self.__manufacturer = ""
self.__model_name = ""
self.__model_number = ""
self.__model_description = ""
self.__presentation_url = ""
self.__url_base = ""
# mapping of service type to (SCPDURL, controlURL, eventSubURL)
self.__services = {}
# mapping of service type to a SOAP proxy
self.__service_proxies = {}
self.__icon_url = ""
try:
self.__parse_description(dom)
except:
import traceback
traceback.print_exc()
logging.error("[upnp] invalid device description:\n%s", str(dom))
def __on_receive_xml(self, data, amount, total, xml, category, query,
is_toc, cb, *args):
if (data == None and not xml):
# error
logging.error("error downloading XML data")
self.__current_folder.message = "content not available"
cb(None, *args)
elif (not data):
# finished loading
#self.__cache_search_result(url, xml[0])
if (is_toc):
gobject.timeout_add(0, self.__parse_toc_xml, xml[0], category,
query, cb, *args)
else:
gobject.timeout_add(0, self.__parse_page_xml, xml[0], cb,
*args)
else:
xml[0] += data
def _set_thumbnail(self, f, pbuf):
"""
Saves the given pixbuf as thumbnail for the given file.
Thumbnailers may use this method for caching thumbnails.
"""
# always scale down large thumbnails
if (pbuf.get_width() > 200 or pbuf.get_height() > 200):
_PBUF.fill(0x00000000)
pixbuftools.fit_pbuf(_PBUF, pbuf, 0, 0, 200, 200, True)
pbuf = _PBUF
#end if
path = self.__get_thumbnail_path(f)
try:
pbuf.save(path, "jpeg")
return path
except:
logging.error("cannot save thumbnail:\n%s", logging.stacktrace())
return ""
def parse(tokens, parse_table):
#logging.info("PARSE: %s" % (tokens))
tree = None
stack = []
node_stack = [] # keep nodes in a parallel stack cause its easier suck it
for a in tokens:
#logging.info(">>PARSE LOOP")
#logging.info(" " + str(a))
#logging.info(" " + str(stack))
# Reduce
production = parse_reduce(parse_table, stack, a)
while production != False:
children = []
# Remove RHS from stack
for p in reversed(production[1]):
if stack.pop()[0] != p:
logging.error("PARSE ERROR: Stack did not match production ('%s')" % (p))
sys.exit(1)
children.insert(0, node_stack.pop())
stack.append((production[0], None))
node_stack.append(node.Node(production[0], None, children))
#logging.info("#PARSE REDUCE : %s" % (stack))
production = parse_reduce(parse_table, stack, a)
# Reject?
if parse_reject(parse_table, stack + [a]):
#logging.info("#FAIL : %s" % (stack + [a]))
return False
# Shift
stack.append(a)
node_stack.append(node.Node(a[0], a, []))
#logging.info("#PARSE SHIFT : %s" % (stack))
#logging.info("#NODE STACK : %s" % (node_stack))
# Accept
return node.Node("ROOT", children=node_stack)
def load_bbox(depth_dir, view):
"""
Internal helper function trying to load bounding box information from file.
"""
base_filename = os.path.join(depth_dir, "%05d" % view)
if os.path.exists(base_filename + ".npz"):
npz_dict = np.load(base_filename + ".npz")
if 'bbox' in npz_dict:
crop = npz_dict['bbox']
else:
crop = None
else:
crop = None
if crop is None:
crop_files = glob(base_filename + "_bbox*")
if len(crop_files) == 1:
crop = np.load(crop_files[0])
elif len(crop_files) > 1:
error("Crop file base '%s_bbox' matches multiple files" %
base_filename)
return crop
def typecheck_local_var_decl(node, c, method, t_i, c_i):
type_node = node.find_child('Type')
init_node = node.find_child('Initializer')
if type_node == None or init_node == None:
logging.error('FATAL ERROR: typecheck_var_decl')
sys.exit(1)
# Extract type from Type node.
var_type = type_node.canon
initializer_type = var_type
if len(init_node.children) == 1:
initializer_type = typecheck_expr(init_node[0], c, method, t_i, c_i)
if is_assignable(var_type, initializer_type, c_i):
node.typ = var_type
return node.typ
else:
logging.error('Invalid initializer for variable of type %s to %s' % (initializer_type, var_type))
sys.exit(42)
def get_file(self, path):
try:
name, location = self.__decode_path(path)
except:
logging.error("error decoding iradio path: %s\n%s", path,
logging.stacktrace())
return None
f = File(self)
f.name = name
f.info = location
f.path = path
f.resource = location
if (location.endswith(".ram") or location.endswith(".rm")):
f.mimetype = "application/vnd.rn-realmedia"
else:
f.mimetype = "audio/x-unknown"
f.icon = theme.iradio_device
return f
def __get_video(self, f):
"""
Returns the video URL.
"""
if (f.resource == _REGION_BLOCKED):
self.emit_message(msgs.UI_ACT_SHOW_INFO,
"This video is not available in your country.")
return ("", "")
try:
fmts = self.__get_flv(f.resource)
except:
logging.error("could not retrieve video\n%s", logging.stacktrace())
return ""
#if (not 18 in fmts): fmts.append(18)
f_ids = fmts.keys()
f_ids.sort(formats.comparator)
# filter out incompatible formats
if (platforms.MAEMO5):
f_ids = [f for f in f_ids if f in _N900_FORMAT_WHITELIST]
elif (platforms.MAEMO4):
f_ids = [f for f in f_ids if f in _N8x0_FORMAT_WHITELIST]
# retrieve high-quality version, if desired
if (len(f_ids) > 1):
qtype = self.__ask_for_quality(f_ids)
elif (len(f_ids) == 1):
qtype = f_ids[0]
else:
qtype = 5
print "Requested Video Quality:", qtype
flv = fmts[qtype]
ext = "." + formats.get_container(qtype)
logging.info("found FLV: %s", flv)
return flv + "&ext=" + ext
def delete_cluster(proxy):
"""Idempotently delete the cluster defined in [cluster_section].
Section of the configuration is 'cluster' by default. Use the
cluster_section option on the cluster subcommand to specifiy a
different section from the config. E.g.:
$> cumulus cluster --cluster_section 'other_section' delete
"""
try:
_id = proxy.cluster['_id']
logging.info('Deleting cluster "%s"' % proxy.cluster_name)
try:
del proxy.cluster
logging.info('Finished deleting cluster "%s" (%s)' %
(proxy.cluster_name, _id))
except RuntimeError as e:
logging.error(e.message)
except TypeError:
logging.info('No cluster "%s" found. Skipping' % proxy.cluster_name)
def delete_profile(proxy):
"""Idempotently delete the profile defined in [profile_section].
Section of the configuration is 'profile' by default. Use the
profile_section option on the profile subcommand to specifiy a
different section from the config. E.g.:
$> cumulus profile --profile_section 'other_section' delete
"""
try:
_id = proxy.profile['_id']
logging.info('Deleting profile "%s"' % proxy.profile_name)
try:
del proxy.profile
logging.info('Finished deleting profile "%s" (%s)' %
(proxy.profile_name, _id))
except RuntimeError as e:
logging.error(e.message)
except TypeError:
logging.info('Found no profile "%s", Skipping.' % proxy.profile_name)
def __parse_genres(self, data):
"""
Parses the XML list of genres.
"""
genres = []
try:
dtree = ElementTree.parse(StringIO(data))
for i in dtree.getiterator():
if i.tag == "genre":
for j,n in i.items():
if j == "name":
genres.append(n)
except:
self.call_service(msgs.UI_ACT_SHOW_INFO,
"An error occured while loading the list of genres.\n" \
"Check your internet connection and try again.")
logging.error("SHOUTcast genre listing download failed:\n%s",
logging.stacktrace())
genres.sort()
return genres
def evaluate(self, simulations, observations, experiment_state,
data_adapter):
if not hasattr(experiment_state.materials, "base_weights"):
error(
"Calculating weight smoothness on a material representation that doesn't have weights"
)
N = experiment_state.locations.get_point_count()
locations = experiment_state.locations.location_vector() # Nx3
albedo = experiment_state.materials.get_brdf_parameters()[
'diffuse'] # Nx3
bilateral_positions = torch.cat((locations / 0.1, albedo / 0.01),
dim=1) # Nx6
weights = experiment_state.materials.base_weights
smoothed_weights = permutohedral_filter(
weights[None], bilateral_positions[None],
torch.ones(1, N, dtype=torch.float, device=locations.device),
False)[0]
return (smoothed_weights - weights).abs()
def __load_index(self):
"""
Deserializes the index from file.
"""
try:
import cPickle
fd = open(_INDEX_FILE, "rb")
except:
return
try:
magic, data = cPickle.load(fd)
except:
logging.error(logging.stacktrace())
return
finally:
fd.close()
# ignore the file if it isn't compatible
if (magic == _MAGIC):
self.__current_id, self.__entries, self.__indices = data
self.__is_dirty = False
def load(self, f):
self.__lyrics = ""
self.__have_cover = False
stopwatch = logging.stopwatch()
self.__player = self.call_service(msgs.MEDIA_SVC_GET_OUTPUT)
self.__player.connect_status_changed(self.__on_change_player_status)
self.__player.connect_volume_changed(self.__on_change_player_volume)
self.__player.connect_position_changed(self.__on_update_position)
self.__player.connect_tag_discovered(self.__on_discovered_tags)
self.__player.connect_error(self.__on_error)
logging.profile(stopwatch, "[audioplayer] connected audio output")
try:
stopwatch = logging.stopwatch()
self.__context_id = self.__player.load_audio(f)
logging.profile(stopwatch, "[audioplayer] loaded media file: %s", f)
except:
logging.error("error loading media file: %s\n%s",
f, logging.stacktrace())
stopwatch = logging.stopwatch()
self.__current_file = f
logging.profile(stopwatch, "[audioplayer] loaded track info")
# load bookmarks
self.__progress.set_bookmarks(media_bookmarks.get_bookmarks(f))
self.emit_message(msgs.MEDIA_EV_LOADED, self, f)
t = threading.Thread(target = self.__load_track_info, args = [f])
t.setDaemon(True)
gobject.idle_add(lambda *x:t.start() and False)
if (self.__offscreen_buffer):
self.render_buffered(self.__offscreen_buffer)
def load(self, f):
self.render()
self.__wait_for_dsp()
self.__player = self.call_service(msgs.MEDIA_SVC_GET_OUTPUT)
self.__player.connect_status_changed(self.__on_status_changed)
self.__player.connect_position_changed(self.__on_update_position)
self.__player.connect_volume_changed(self.__on_change_player_volume)
self.__player.connect_aspect_changed(
self.__on_change_player_aspect_ratio)
self.__player.connect_error(self.__on_error)
self.__player.set_window(self.__screen.get_xid())
# loading may fail, so we need to setup a handler that frees the DSP
# semaphore after some time
if (self.__load_failed_handler):
gobject.source_remove(self.__load_failed_handler)
self.__load_failed_handler = gobject.timeout_add(
30000, self.__on_load_failed)
try:
self.__progress.set_message("Loading")
self.__context_id = self.__player.load_video(f)
self.__current_file = f
# load bookmarks
self.__progress.set_bookmarks(media_bookmarks.get_bookmarks(f))
self.emit_message(msgs.MEDIA_EV_LOADED, self, f)
except:
self.__progress.set_message("Error")
logging.error("[videoplayer] error loading media file: %s\n%s", f,
logging.stacktrace())
def handle_HTTPSERVER_SVC_BIND(self, owner, addr, port):
if ((addr, port) in self.__listeners):
return "address already in use"
try:
sock = socket.socket(socket.AF_INET)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
sock.bind((addr, port))
sock.listen(1)
except:
logging.error("[httpserv] error binding to %s:%d\n%s", addr, port,
logging.stacktrace())
return "could not bind to address"
iowatch = gobject.io_add_watch(sock, gobject.IO_IN,
self.__on_new_client,
owner)
self.__listeners[(addr, port)] = _Listener(owner, sock, iowatch)
logging.info("[httpserv] bound to TCP %s:%d", addr, port)
return ""
def _get_info_set(self, criterion=lambda x: True):
"""
Format the image indices and related lighting information as prescribed by general_settings.batch_size.
Outputs:
indices, light_infos python lists containing, in parallel:
batch_indices (long) torch.tensor containing indices into the observation list
batch_light_infos (long) torch.tensor containing information required for the lighting model
Typically indices into the light list
"""
device = torch.device(general_settings.device_name)
training_indices = []
training_light_infos = []
for image_index, image in enumerate(self.images):
if criterion(image):
training_indices.append(image_index)
training_light_infos.append(image.light_info)
training_indices = torch.tensor(training_indices,
dtype=torch.long,
device=device)
training_light_infos = torch.tensor(training_light_infos,
dtype=torch.long,
device=device)
if training_light_infos.min() < 0:
error("Trying to reconstruct an image without a light source.")
batch_size = general_settings.batch_size
training_indices = [
training_indices[i0:min(i0 + batch_size, len(training_indices))]
for i0 in range(0, len(training_indices), batch_size)
]
training_light_infos = [
training_light_infos[i0:min(i0 +
batch_size, len(training_light_infos))]
for i0 in range(0, len(training_light_infos), batch_size)
]
return training_indices, training_light_infos
def higo_baseline(experiment_state, data_adapter, cache_path, higo_settings):
"""
Reimplementation of the Higo et al. 2009 paper
Tomoaki Higo, Yasuyuki Matsushita, Neel Joshi, and Katsushi Ikeuchi
A hand-held photometric stereo camera for 3-d modeling
ICCV2009
Uses the PyMaxFlow library for graphcut problem: http://pmneila.github.io/PyMaxflow/.
This library is installed as a python package by the installEnv.sh script.
"""
if not isinstance(experiment_state.locations, DepthMapParametrization):
error("Higo et al. 2009 requires a depth map parametrization.")
os.makedirs(cache_path, exist_ok=True)
device = torch.device(general_settings.device_name)
with torch.no_grad():
step_size = higo_settings['step_size']
step_radius = higo_settings['step_radius']
depth_range = step_size * step_radius # 2.5cm
nr_steps = 2 * step_radius + 1
eta = higo_settings['eta'] * general_settings.intensity_scale
lambda_n = higo_settings['lambda_n']
lambda_s = higo_settings['lambda_s'] * step_size * 1000
lambda_1 = higo_settings['lambda_1']
lambda_2 = higo_settings['lambda_2']
surface_constraint_threshold = 0.005 # 5mm
surface_constraint_threshold = surface_constraint_threshold / (
depth_range / nr_steps)
surface_constraint_penalization = 0.010 # 1cm
## 1) calculate the photometric loss volume, and the depth/normal hypothesis volume
N_pixels = experiment_state.locations.get_point_count()
photo_loss_volume = torch.zeros(N_pixels, nr_steps).to(device)
depth_volume = torch.zeros(N_pixels, nr_steps, 1).to(device)
normal_volume = torch.zeros(N_pixels, nr_steps, 3).to(device)
diffuse_volume = torch.zeros(N_pixels, nr_steps, 3).to(device)
# we need multiples of the step size for the graph cut later on
initial_depth = experiment_state.locations.implied_depth_image().clone(
)
initial_depth.div_(step_size).round_().mul_(step_size)
for offset_idx in tqdm(
range(nr_steps),
desc="Solving all RANSAC problems (photometric loss volume)"):
depth_offset = -depth_range + offset_idx * step_size
cache_file = os.path.join(cache_path, "%8.6f.npz" % depth_offset)
depth = initial_depth + depth_offset
if os.path.exists(cache_file):
cached = np.load(cache_file)
normals = to_torch(cached['normals'])
inliers_N = to_torch(cached['inliers_N'])
inlier_photometric_error = to_torch(
cached['inlier_photometric_error'])
albedo = to_torch(cached['diffuse'])
else:
spoofed_experiment_state = ExperimentState.copy(
experiment_state)
spoofed_experiment_state.locations = DepthMapParametrization()
spoofed_experiment_state.locations.initialize(
depth,
experiment_state.locations.mask,
experiment_state.locations.invK,
experiment_state.locations.invRt,
)
normals, albedo, inliers, residuals = closed_form_lambertian_solution(
spoofed_experiment_state,
data_adapter,
sample_radius=0,
shadows_occlusions=False,
verbose=False)
inlier_photometric_error = (residuals * inliers).abs().sum(
dim=1).sum(dim=1)
inliers_N = inliers.squeeze().sum(dim=1)
np.savez_compressed(
cache_file,
normals=to_numpy(normals),
diffuse=to_numpy(albedo),
inliers_N=to_numpy(inliers_N),
inlier_photometric_error=to_numpy(
inlier_photometric_error),
)
depth_volume[:, offset_idx,
0] = experiment_state.locations.create_vector(depth)
normal_volume[:, offset_idx] = normals
diffuse_volume[:, offset_idx] = albedo.squeeze()
photo_loss_volume[:,
offset_idx] = eta * inlier_photometric_error / inliers_N - inliers_N
# precalculation of neighbour relationships
mask = experiment_state.locations.mask
py, px = torch.meshgrid(
[torch.arange(0, mask.shape[0]),
torch.arange(0, mask.shape[1])])
pixels = torch.stack((
px[mask.squeeze()],
py[mask.squeeze()],
), dim=0).to(device)
indices = torch.zeros(*mask.shape[:2], 1).long().to(device) - 1
indices[mask] = torch.arange(N_pixels).to(device)[:, None]
indices = torch.nn.functional.pad(indices,
pad=(0, 0, 1, 1, 1, 1),
value=-1)
neighbours = []
for offset in [[-1, 0], [1, 0], [0, -1], [0, 1]]:
offset_pixels = pixels + 1 # because of the padding
for c in range(2):
offset_pixels[c, :] += offset[c]
offset_linidces = offset_pixels[
0, :] + offset_pixels[1, :] * indices.shape[1]
neighbours.append(indices.flatten()[offset_linidces])
neighbours = torch.stack(neighbours, dim=1)
surface_constrain_cachefile = os.path.join(
cache_path, "surface_normal_constraint.npz")
## 2) calculate the surface normal constraint loss volume:
if not os.path.exists(surface_constrain_cachefile):
# we add in a nonsense 'neighbour' that will never be able to win, for implementational cleanliness
surface_constraint_volume = torch.zeros(N_pixels,
nr_steps).to(device)
neighbours_n = neighbours.clone()
neighbours_n[neighbours_n < 0] = N_pixels
depth_volume_n = torch.cat(
(depth_volume, torch.zeros(1, nr_steps, 1).to(device)))
normal_volume_n = torch.cat(
(normal_volume, torch.ones(1, nr_steps, 3).to(device)))
pixel_locs = torch.cat(
(pixels.float(), torch.ones(1, pixels.shape[1]).to(device)),
dim=0)
pixel_locs_n = torch.cat(
(pixel_locs, torch.zeros(pixel_locs.shape[0], 1).to(device)),
dim=1)
for offset_idx in tqdm(
range(nr_steps),
desc="Generating the surface constraint loss volume"):
hypothesis_points = experiment_state.locations.invK @ (
pixel_locs * depth_volume[None, :, offset_idx, 0])
hypothesis_normals = normal_volume[:,
offset_idx].transpose(0, 1)
for n_idx in range(4):
these_neighbours = neighbours_n[:, n_idx]
n_pixel_locs = pixel_locs_n[:, these_neighbours]
best_label_points = torch.zeros(3, N_pixels).to(device)
best_label_normals = torch.zeros(3, N_pixels).to(device)
best_label_offsets = torch.zeros(N_pixels).to(device)
best_label_pdists = torch.zeros(N_pixels).to(
device) + np.inf
for n_offset_idx in range(nr_steps):
n_hypothesis_points = experiment_state.locations.invK @ (
n_pixel_locs *
depth_volume_n[None, these_neighbours,
n_offset_idx, 0])
n_hypothesis_normals = normal_volume_n[
these_neighbours, n_offset_idx].transpose(0, 1)
n_hypothesis_pdists = (
hypothesis_normals *
(hypothesis_points -
n_hypothesis_points)).abs().sum(dim=0)
better_matches = n_hypothesis_pdists < best_label_pdists
best_label_offsets[better_matches] = n_offset_idx
best_label_pdists[
better_matches] = n_hypothesis_pdists[
better_matches]
best_label_points[:,
better_matches] = n_hypothesis_points[:,
better_matches]
best_label_normals[:,
better_matches] = n_hypothesis_normals[:,
better_matches]
hypothesis_ldists = (best_label_offsets - offset_idx).abs()
valid_best_labels = hypothesis_ldists < surface_constraint_threshold
hypothesis_pdists = (
best_label_normals *
(hypothesis_points - best_label_points)).abs().sum(
dim=0)
# we don't have parallel depth planes, however!
surface_constraint_volume[
valid_best_labels,
offset_idx] += hypothesis_pdists[valid_best_labels]
surface_constraint_volume[
valid_best_labels == False,
offset_idx] = surface_constraint_penalization
np.savez_compressed(
surface_constrain_cachefile,
surface_constraint_volume=to_numpy(surface_constraint_volume),
)
else:
cached = np.load(surface_constrain_cachefile)
surface_constraint_volume = to_torch(
cached['surface_constraint_volume'])
# at this point we can calculate the unary result, i.e. without TV-1 depth smoothness
unary_loss_volume = photo_loss_volume + lambda_n * surface_constraint_volume
winners_unary = torch.argmin(unary_loss_volume, dim=1)
graphcut_cache_file = os.path.join(cache_path, "higo_graphcut.npz")
if not os.path.exists(graphcut_cache_file):
# 3) Graph-cut magic. TV-1 optimization on the depth
# because we now have discretized depth values, there is only a finite number of labels to optimize over.
# As such, we can use the graph construction from "Stereo Without Epipolar Lines: A Maximum-Flow Formulation"
depth_min = depth_volume.min()
depth_max = depth_volume.max()
n_hyps = round((depth_max - depth_min).item() / step_size) + 1
depth_hypotheses = depth_min + (
depth_max - depth_min
) * torch.arange(n_hyps).float().to(device) / (n_hyps - 1)
depth_hypotheses.div_(step_size).round_().mul_(step_size)
# make it amenable to graphcut optimization, i.e. all positive values
safe_unary_loss_volume = unary_loss_volume.clone()
safe_unary_loss_volume = safe_unary_loss_volume - (
safe_unary_loss_volume.min() - 1)
# a value definitely higher than the optimal solution's loss
cost_upper_bound = safe_unary_loss_volume.sum(
dim=0).sum().item() + 1
# create the bigger volume of unary weights
# because of the way graphcut imposes smoothness cost this is the easiest to implement
full_unary_loss_volume = torch.zeros(
len(unary_loss_volume), n_hyps).to(device) + cost_upper_bound
for step in range(nr_steps):
# fill in these unary losses in the correct position in the full volume
full_idces = ((depth_volume[:, step] - depth_min) /
step_size).round().long()
full_values = safe_unary_loss_volume[:, step, None]
full_unary_loss_volume.scatter_(dim=1,
index=full_idces,
src=full_values)
full_offsets = (depth_volume[:, 0, 0] -
depth_min).div_(step_size).round_()
import maxflow
graph = maxflow.GraphFloat()
node_ids = graph.add_grid_nodes((N_pixels, n_hyps + 1))
for hyp in tqdm(
range(1, n_hyps + 1),
desc="Building optimization graph - unary weights"):
nodepairs = node_ids[:, hyp - 1:hyp + 1]
edgeweights = to_numpy(full_unary_loss_volume[:, hyp - 1:hyp])
graph.add_grid_edges(nodepairs,
weights=edgeweights,
structure=np.array([[0, 0, 0], [0, 0, 1],
[0, 0, 0]]),
symmetric=1)
# build terminal edges
for x in tqdm(range(N_pixels),
desc="Building optimization graph - terminal edges"):
graph.add_tedge(node_ids[x, 0], cost_upper_bound, 0)
graph.add_tedge(node_ids[x, n_hyps], 0, cost_upper_bound)
# debug test: not including the smoothness loss *should* mean that we get exactly the unary winners
# print("Starting unary maxflow calculation...")
# tic = time.time()
# unary_max_flow = graph.maxflow()
# print("Finished in %ss" % (time.time() - tic))
# unary_min_cut = graph.get_grid_segments(node_ids)
# winners_unary_test = np.nonzero(unary_min_cut[:,1:] != unary_min_cut[:,:-1])[1]
# assert np.all(winners_unary_test == to_numpy(winners_unary) + to_numpy(full_offsets)), "Issue building the graph: unary solution does not match unary WTA"
no_neighbour_node = graph.add_nodes(1)
neighbours_g = to_numpy(neighbours)
neighbours_g[neighbours_g < 0] = len(neighbours)
node_ids_g = np.concatenate(
(node_ids, np.ones((1, n_hyps + 1), dtype=node_ids.dtype) *
no_neighbour_node),
axis=0)
for n_idx in range(4):
neighbour_ids = np.take(node_ids_g[:, :-1],
indices=neighbours_g[:, n_idx],
axis=0)
nodepairs = np.stack((node_ids[:, :-1], neighbour_ids), axis=2)
edgeweights = lambda_s
candidates = nodepairs[:, :, 1] != no_neighbour_node
candidates = to_numpy((depth_volume[:, 0] - step_size * 3 <=
depth_hypotheses[None]) *
(depth_volume[:, -1] + step_size * 3 >=
depth_hypotheses[None])) * candidates
graph.add_grid_edges(nodepairs[candidates].reshape(-1, 2),
weights=edgeweights,
structure=np.array([[0, 0, 0], [0, 0, 1],
[0, 0, 0]]),
symmetric=0)
print("Starting full maxflow calculation...")
tic = time.time()
max_flow = graph.maxflow()
print("Finished in %ss" % (time.time() - tic))
min_cut = graph.get_grid_segments(node_ids)
nonzeroes = np.nonzero(min_cut[:, 1:] != min_cut[:, :-1])
unique_nonzeroes = np.unique(nonzeroes[0], return_index=True)
winners_graphcut = nonzeroes[1][unique_nonzeroes[1]]
winners_graphcut = winners_graphcut - to_numpy(full_offsets)
np.savez_compressed(
graphcut_cache_file,
winners_graphcut=winners_graphcut,
)
else:
cached = np.load(graphcut_cache_file)
winners_graphcut = cached['winners_graphcut']
winners_graphcut = to_torch(winners_graphcut).long()
#4) Depth refinement step
def tangent_vectors(depth_image, invK, inv_extrinsics=None):
"""
Given HxWx1 depth map, return quick-and-dirty the HxWx3 LR and UP tangents
Takes the weighted left-right and up-down neighbour points as spanning the local plane.
"""
assert len(depth_image.shape) == 3, "Depth map should be H x W x 1"
assert depth_image.shape[2] == 1, "Depth map should be H x W x 1"
H = depth_image.shape[0]
W = depth_image.shape[1]
data_shape = list(depth_image.shape)
world_coords = depth_map_to_locations(depth_image, invK,
inv_extrinsics)
depth1 = depth_image[:-2, 1:-1] * depth_image[
1:-1, 1:-1] * depth_image[2:, 1:-1] * depth_image[
1:-1, :-2] * depth_image[1:-1, 2:]
depth2 = depth_image[:-2, :-2] * depth_image[:-2, 2:] * depth_image[
2:, :-2] * depth_image[2:, 2:]
depth_mask = (depth1 * depth2 == 0).float()
ud_vectors = (world_coords[:-2,1:-1,:] * 2 + world_coords[:-2,0:-2,:] + world_coords[:-2,2:,:]) \
- (world_coords[2:,1:-1,:] * 2 + world_coords[2:,0:-2,:] + world_coords[2:,2:,:])
ud_vectors = ud_vectors / (depth_mask +
ud_vectors.norm(dim=2, keepdim=True))
lr_vectors = (world_coords[1:-1,:-2,:] * 2 + world_coords[0:-2,:-2,:] + world_coords[2:,:-2,:]) \
- (world_coords[1:-1,2:,:] * 2 + world_coords[0:-2,2:,:] + world_coords[2:,2:,:])
lr_vectors = lr_vectors / (depth_mask +
lr_vectors.norm(dim=2, keepdim=True))
repad = lambda x: torch.nn.functional.pad(x, pad=(0, 0, 1, 1, 1, 1))
return repad(lr_vectors), repad(ud_vectors), repad(
(depth_mask == 0).float())
def get_laplacian(depth_image, mask):
kernel = to_torch(
np.array([
[-0.25, -0.50, -0.25],
[-0.50, 3.00, -0.50],
[-0.25, -0.50, -0.25],
])).float()
laplacian = torch.nn.functional.conv2d(
depth_image[None, None, :, :, 0], kernel[None, None])[0, 0, :, :,
None]
laplacian_mask = torch.nn.functional.conv2d(
mask[None, None, :, :, 0].float(),
torch.ones_like(kernel)[None, None])[0, 0, :, :, None] == 9
repad = lambda x: torch.nn.functional.pad(x, pad=(0, 0, 1, 1, 1, 1))
return repad(laplacian * laplacian_mask.float())
depth_estimate = torch.gather(depth_volume,
dim=1,
index=winners_graphcut[:, None, None])
depth_estimate.requires_grad_(True)
center_mask = mask.view(*mask.shape[:2], 1)
with torch.no_grad():
normal_estimate = torch.gather(normal_volume,
dim=1,
index=winners_graphcut[:, None,
None].expand(
-1, -1, 3))
normal_image = torch.zeros(*center_mask.shape[:2], 3).to(device)
normal_image.masked_scatter_(center_mask, normal_estimate)
depth_estimate_initial = depth_estimate.clone()
depth_image_initial = torch.zeros(*center_mask.shape[:2], 1).to(device)
depth_image_initial.masked_scatter_(center_mask,
depth_estimate_initial)
pixel_locs = torch.cat(
(pixels.float(), torch.ones(1, pixels.shape[1]).to(device)), dim=0)
loop = tqdm(range(1000), desc="Depth refinement")
loss_evolution = []
optimizer = torch.optim.Adam([depth_estimate],
eps=1e-5,
lr=0.0001,
betas=[0.9, 0.99])
for iteration in loop:
# term 1: position error
initial_points = experiment_state.locations.invK @ (
pixel_locs * depth_estimate_initial.view(1, -1))
current_points = experiment_state.locations.invK @ (
pixel_locs * depth_estimate.view(1, -1))
position_diff = (initial_points - current_points).abs()
position_error = (position_diff**2).sum()
# term 2: the normal error
depth_image = torch.zeros(*center_mask.shape[:2], 1).to(device)
depth_image.masked_scatter_(center_mask, depth_estimate)
lr_img, ud_img, mask_img = tangent_vectors(
depth_image, experiment_state.locations.invK,
experiment_state.locations.invRt)
lr = lr_img[center_mask.expand_as(lr_img)].view(-1, 1, 3)
ud = ud_img[center_mask.expand_as(ud_img)].view(-1, 1, 3)
mask = mask_img[center_mask].view(-1, 1)
normal_error = (mask * (((lr * normal_estimate).sum(dim=2)**2) +
((ud * normal_estimate).sum(dim=2)**2))).sum()
# term 3: smoothness constraint
laplacian = get_laplacian(depth_image, center_mask)
laplacian_masked = laplacian # * (laplacian.abs() < 5 * step_size).float()
smoothness_constraint = laplacian_masked.abs().sum()
# the backprop
total_loss = 1e5 * position_error + 10 * normal_error + 3000 * smoothness_constraint
loss_evolution.append(total_loss.item())
loop.set_description("Depth refinement | loss %8.6f" %
loss_evolution[-1])
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
plt.clf()
plt.plot(loss_evolution)
plt.savefig(os.path.join(cache_path, "higo_refinement_loss.png"))
plt.close()
# now return a new experiment_state
new_experiment_state = ExperimentState.copy(experiment_state)
new_experiment_state.locations = DepthMapParametrization()
new_experiment_state.locations.initialize(
experiment_state.locations.create_image(depth_estimate[:,
0]).squeeze(),
experiment_state.locations.mask,
experiment_state.locations.invK,
experiment_state.locations.invRt,
)
winner_normals = torch.gather(normal_volume,
dim=1,
index=winners_graphcut[:, None, None].expand(
-1, -1, 3)).squeeze()
winner_diffuse = torch.gather(diffuse_volume,
dim=1,
index=winners_graphcut[:, None, None].expand(
-1, -1, 3)).squeeze()
new_experiment_state.normals = experiment_state.normals.__class__(
new_experiment_state.locations)
new_experiment_state.normals.initialize(winner_normals)
new_experiment_state.materials = experiment_state.materials.__class__(
new_experiment_state.brdf)
new_experiment_state.materials.initialize(winner_diffuse.shape[0],
winner_diffuse,
winner_diffuse.device)
new_experiment_state.materials.brdf_parameters['specular'][
'albedo'].data.zero_()
new_experiment_state.materials.brdf_parameters['specular'][
'roughness'].data.fill_(0.1)
return new_experiment_state
async def on_message(self, message):
try:
def check(m):
flag = False
m_split = m.content.translate(
{ord(i): None
for i in '",.?!;:-_`*\''}).upper().split(" ")
banned_words = [
"NIGGER", "N***A", "AUTISTIC", "RETARD", "RETARDED",
"AIDS", "SPASTIC", "SPAZ", "RETARDS"
]
for word in banned_words:
if word in m_split:
flag = True
#print(m.content)
return flag
if check(message):
await message.delete()
return 0
message.content = message.content.replace(
"@!", "@"
) #crappy fix for the mention inconsistency between platforms
if len(message.content) == 0:
return
if isinstance(message.channel, discord.DMChannel):
log.info("Ignoring Direct Message From %s: %s" %
(str(message.author), str(message.content)))
return
mSplit = message.content.split()
mList = []
for word in mSplit:
user = discord.utils.get(message.guild.members,
mention=word.replace("@",
"@!").replace(
"!!", "!"))
if not user is None:
if not user.nick is None:
word = word.replace("@", "@!").replace("!!", "!")
mList.append(word)
message.content = " ".join(mList)
if message.content.upper().startswith("V!UPDATE"):
await message.channel.send("Syncing DB")
for member in message.guild.members:
insert_db_user(member)
await message.channel.send("Completed DB Sync")
args = message.content.split(" ")
if message.content[0] == "$":
message.content = message.content.replace("$", "!tag ")
await self.client.process_commands(message)
if args[0].lower() in self.client.disabled_commands:
await error("[423] This command is currently disabled",
message.channel)
return False
channel = message.channel
if message.content.upper().startswith("!ENTER"):
if "Manager" in [role.name for role in message.author.roles
] and self.client.raffles:
embed = discord.Embed(
title="Raffle",
description=
"Sorry %s, you are not allowed to enter raffles." %
(message.author.mention),
color=colour.reds)
await message.channel.send(embed=embed)
elif self.client.raffles and not message.author.name in self.client.enteries:
self.client.enteries.append(message.author.name)
embed = discord.Embed(
title="Raffle",
description="**%s** has been entered!" %
(message.author.name),
color=0x00ff73)
await message.channel.send(embed=embed)
elif self.client.raffles:
embed = discord.Embed(
title="Raffle",
description=
"Hey %s! You can only enter into the same raffle once!"
% (message.author.mention),
color=colour.reds)
await message.channel.send(embed=embed)
if message.content.upper().startswith("!VOTE"):
if self.client.polls and not message.author.name in self.client.polls_enteries:
args = message.content.split(" ")
try:
choice = int(args[1])
self.client.polls_votes[choice - 1] += 1
except (IndexError, TypeError, ValueError):
embed = discord.Embed(
title="Poll",
description="Hey %s! That is not a valid option!" %
(message.author.mention),
color=colour.reds)
await message.channel.send(embed=embed)
return
self.client.polls_enteries.append(message.author.name)
embed = discord.Embed(
title="Poll",
description="**%s** you have voted for %s" %
(message.author.name,
self.client.polls_options[choice - 1]),
color=0x00ff73)
await message.channel.send(embed=embed)
elif self.client.polls:
embed = discord.Embed(
title="Poll",
description="Hey %s! You can only vote once" %
(message.author.mention),
color=colour.reds)
await message.channel.send(embed=embed)
if message.content.upper().startswith("!SUDO"):
if message.author.id == 345514405775147023:
args = message.content.split()
target = discord.utils.get(message.guild.members,
mention=args[1])
channel = self.client.get_channel(int(args[2]))
contents = " ".join(args[3:])
message.content = contents
message.author = target
message.channel = channel
await self.client.process_commands(message)
if message.guild == None:
return
words = message.content.split()
if not str(
message.author.id
) in self.client.ignore_list and not str(
message.channel.id
) in self.client.ignore_list and not message.author.id in self.client.cooldown and len(
words) > 4 and len(message.content) > 16:
multiplier = sql.db_query(
"SELECT Level FROM Members WHERE UserID = 1")[0][0]
if "Supporter" in [role.name for role in message.author.roles]:
multiplier = multiplier + 0.5
bal = sql.db_query(
"SELECT Balance FROM Members WHERE UserID = %s" %
(str(message.author.id)))[0][0]
if not "Manager" in [
role.name for role in message.author.roles
]:
currentWeeklyPoints = sql.db_query(
"SELECT weeklyActivity from Members WHERE UserID = %s"
% (str(message.author.id)))[0][0]
newWeeklyPoints = currentWeeklyPoints + 1
sql.execute_query(
"UPDATE Members set weeklyActivity = %s WHERE UserID = %s"
% (str(newWeeklyPoints), str(message.author.id)))
level = get_profile(message.author.id)[1]
self.client.cooldown.append(message.author.id)
exp_add = int(round(random.randint(15, 25) * multiplier, 0))
add_exp(message.author.id, exp_add)
channel = self.client.get_channel(547120498568331267)
await check_level_up(message.author.id, message.guild,
message.channel)
await asyncio.sleep(60)
try:
self.client.cooldown.remove(message.author.id)
except ValueError: #some people aren't always on cooldown
pass
def check(m):
flag = False
m_split = m.content.translate(
{ord(i): None
for i in '",.?!;:-_`*\''}).upper().split(" ")
banned_words = [
"F**K", "F*****G", "DICK", "BOLLOCK", "F***S", "AIDS",
"BOLLOCKS", "F****D", "W***E", "BASTARD", "SHIT",
"S******G", "C**T", "WANKER", "BASTARD", "BELLEND",
"N***A", "NIGGER", "PISS", "PISSING", "C**T", "CUNTS",
"WANKERS", "RETARDS", "RETARD", "RETARDED", "F****T",
"FKING", "FK", "CRAP", "PUSSY", "PENIS", "C**K"
]
for word in banned_words:
if word in m_split:
flag = True
return flag
except Exception as e:
log.error("Error Processing Message From %s - Error: %s" %
(str(message.author), str(e)))
def initialize(self, data_adapter, initialization_settings):
device = torch.device(general_settings.device_name)
self.locations.initialize(data_adapter.center_depth,
data_adapter.center_depth > 0,
data_adapter.center_invK,
data_adapter.center_invRt)
self.observation_poses.initialize(
torch.stack([
image.original_extrinsics[:3, :3]
for image in data_adapter.images
]).to(device),
torch.stack([
image.original_extrinsics[:3, 3:]
for image in data_adapter.images
]).to(device),
)
if initialization_settings['lights'] == "precalibrated":
with open(
initialization_settings['light_calibration_files']
['positions'], "rb") as fh:
light_positions = pickle.load(fh)[0]
light_intensities = np.load(
initialization_settings['light_calibration_files']
['intensities'])
light_attenuations = np.load(
initialization_settings['light_calibration_files']
['attenuations'])
self.light_parametrization.initialize(
torch.tensor(light_positions,
dtype=torch.float32,
device=device),
torch.tensor(light_intensities,
dtype=torch.float32,
device=device),
torch.tensor(light_attenuations,
dtype=torch.float32,
device=device),
)
else:
error("Only precalibrated lights are supported.")
if any([
"closed_form" in initialization_settings[entry]
for entry in initialization_settings
]):
closed_form_normals, closed_form_diffuse, _, _ = closed_form_lambertian_solution(
self, data_adapter)
points = self.locations.location_vector()
closed_form_normals = permutohedral_filter(
closed_form_normals[None], points[None] / 0.003,
torch.ones(points.shape[0]).to(points.device), False)[0]
if initialization_settings['diffuse'] == "from_closed_form":
self.materials.initialize(self.locations.get_point_count(),
closed_form_diffuse,
self.locations.device())
else:
error("Only closed-form diffuse initialization is supported.")
if initialization_settings['specular'] == "hardcoded":
self.materials.brdf_parameters['specular']['albedo'].data.fill_(
0.5)
roughness = self.materials.brdf_parameters['specular']['roughness']
roughness.data[:] = (
0.1 +
0.3 * torch.arange(start=0, end=roughness.shape[0],
step=1).view(-1, 1) / roughness.shape[0])
if 'eta' in self.materials.brdf_parameters['specular']:
self.materials.brdf_parameters['specular']['eta'].data.fill_(
1.5)
else:
error("Only hardcoded specular initialization is supported.")
if initialization_settings['normals'] == "from_closed_form":
self.normals.initialize(closed_form_normals)
elif initialization_settings['normals'] == "from_depth":
self.normals.initialize(self.locations.implied_normal_vector())
def on_resource_error(err, sender):
logging.error(f"{err}")
def __parse_stations(self, data, genre):
"""
Parses the list of stations.
"""
stations = []
next_page_url = ""
soup = BeautifulSoup(data)
resulttable = soup.find("div", {"id": "content"})
if (resulttable):
for entry in resulttable.findAll("tr"):
#print entry
station = File(self)
try:
station.name = entry.find("span", {
"class": "name"
}).a.contents[0]
except:
continue
try:
now_playing = entry.find("p", {
"class": "stream-onair"
}).contents[1]
except:
now_playing = ""
station.resource = _ICECAST_BASE + entry.find(
"td", {
"class": "tune-in"
}).find("a")["href"]
try:
bitrate = entry.find("td", {
"class": "tune-in"
}).findAll("p", {"class": "format"})[1]["title"]
except:
bitrate = "-"
try:
typename = entry.find("a", {
"class": "no-link"
}).contents[0].strip()
except:
typename = ""
if (typename == "MP3"):
station.mimetype = "audio/mpeg"
elif (typename == "AAC+"):
station.mimetype = "audio/mp4"
else:
station.mimetype = "audio/x-unknown"
station.path = "/" + genre + "/" + \
self.__encode_station(station.name,
bitrate,
station.mimetype,
station.resource,
genre)
station.info = "Bitrate: %s\n" \
"Now playing: %s" % (bitrate, now_playing)
station.icon = theme.icecast_station.get_path()
stations.append(station)
#end for
pager_tag = resulttable.find("ul", {"class": "pager"})
if (pager_tag):
link = pager_tag.findAll("a")[-1]
if (not link.contents[0].isdigit()):
# must be an arrow
next_page_url = link["href"]
#end if
#end if
#end if
if (not stations):
self.__current_folder.message = "station list not available"
logging.error("icecast station listing download failed\n%s",
logging.stacktrace())
return (stations, next_page_url)
def on_error(self, status):
logging.error(status)
def optimize(experiment_state,
data_adapter,
optimization_settings,
output_path_structure=None):
"""
Optimize the current experiment_state, given the observations in data_adapter and the optimization_settings.
Optionally saves optimization progress plots to the output_path_structure, which is formatted as
output_path_structure % plot_name.
"""
parameter_dictionary, learning_rate_dictionary, visualizer_dictionary = experiment_state.get_parameter_dictionaries(
)
device = torch.device(general_settings.device_name)
parameter_groups = []
for parameter in optimization_settings['parameters']:
if not parameter in parameter_dictionary:
error(
"Cannot optimize over %s, as it is not part of the chosen parametrization."
% parameter)
parameter_groups.append({
'params': parameter_dictionary[parameter],
'lr': learning_rate_dictionary[parameter],
})
for parameter in optimization_settings['parameters']:
if not parameter in parameter_dictionary:
error(
"Cannot optimize over %s, as it is not part of the chosen parametrization."
% parameter)
parameters = parameter_dictionary[parameter]
if not isinstance(parameters, list):
parameters = [parameters]
[
error("cannot optimize over '%s[%d]': not a leaf variable." %
(parameter, idx)) for idx, x in enumerate(parameters)
if not x.is_leaf
]
optimizer = torch.optim.Adam(parameter_groups, betas=[0.9, 0.9], eps=1e-3)
iterations = optimization_settings['iterations']
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[iterations / 2, iterations / 4 * 3], gamma=0.1)
losses = []
for loss_name, weight in optimization_settings['losses'].items():
losses.append([loss_name, LossFunctionFactory(loss_name)(), weight])
shadow_cache = {}
occlusion_cache = {}
loss_evolutions = {'Total': []}
loss_evolutions.update(
dict([(losses[loss_idx][0], []) for loss_idx in range(len(losses))]))
parameter_evolutions = defaultdict(lambda: [])
if optimization_settings['target_set'] == "training":
training_indices_batches, training_light_infos_batches = data_adapter.get_training_info(
)
elif optimization_settings['target_set'] == "testing":
training_indices_batches, training_light_infos_batches = data_adapter.get_testing_info(
)
if any([
x != "observation_poses"
for x in optimization_settings['parameters']
]):
log("Warning: optimizing non-pose parameters over the testing set."
)
else:
error(
"optimization_settings['target_set'] should be one of ('training', 'testing')"
)
total_training_views = sum([
len(training_indices) for training_indices in training_indices_batches
])
ctr_index = data_adapter.get_center_index()
optimization_loop = tqdm(range(iterations))
for iteration in optimization_loop:
optimizer.zero_grad()
iteration_losses = defaultdict(lambda: 0)
for training_indices, training_light_infos in zip(
training_indices_batches, training_light_infos_batches):
simulations = experiment_state.simulate(training_indices,
training_light_infos,
shadow_cache=shadow_cache)
observations = experiment_state.extract_observations(
data_adapter,
training_indices,
occlusion_cache=occlusion_cache)
total_loss = 0.0
for loss_index in range(len(losses)):
loss_name, loss_fcn, loss_weight = losses[loss_index]
this_loss = loss_fcn.evaluate(
simulations, observations, experiment_state,
data_adapter).sum() * loss_weight * len(
training_indices) / total_training_views
total_loss += this_loss
iteration_losses[loss_name] += this_loss.item()
total_loss.backward()
iteration_losses["Total"] += total_loss.item()
experiment_state.clear_parametrization_caches()
del simulations, observations, total_loss
for loss in iteration_losses:
loss_evolutions[loss].append(iteration_losses[loss])
if ctr_index is not None:
for parameter in parameter_dictionary['observation_poses']:
if parameter.grad is not None:
parameter.grad[ctr_index].zero_()
optimizer.step()
experiment_state.enforce_parameter_bounds()
if "photoconsistency L1" in loss_evolutions:
desc_prefix = "Photometric L1 loss: %8.4f " % iteration_losses[
"photoconsistency L1"]
else:
desc_prefix = ""
optimization_loop.set_description(desc_prefix + "Total loss: %8.4f" %
iteration_losses["Total"])
with torch.no_grad():
for parameter in optimization_settings['parameters']:
if visualizer_dictionary[parameter] is None:
continue
visualized = visualizer_dictionary[parameter](
parameter_dictionary[parameter])
if isinstance(visualized, dict):
for x in visualized:
parameter_evolutions[x].append(visualized[x].view(
1, -1))
else:
parameter_evolutions[parameter].append(
visualized.view(1, -1))
if (
iteration + 1
) % general_settings.evolution_plot_frequency == 0 and output_path_structure is not None:
plt.figure("Losses")
plt.clf()
loss_names = []
loss_values = []
for loss_name in loss_evolutions:
loss_values.append(loss_evolutions[loss_name])
loss_names.append(loss_name)
xvalues = np.arange(1,
len(loss_values[0]) + 1).reshape(
len(loss_values[0]), 1)
plt.semilogy(xvalues.repeat(len(loss_names), 1),
np.array(loss_values).T)
plt.ylim([0.9e3, 1.1e6])
plt.legend(loss_names)
plt.savefig(output_path_structure % "loss")
for parameter in parameter_evolutions:
plt.figure(parameter)
plt.clf()
plt.plot(
xvalues.repeat(
parameter_evolutions[parameter][0].shape[1], 1),
torch.cat(parameter_evolutions[parameter],
dim=0).cpu().numpy(),
)
plt.savefig(output_path_structure % parameter)
scheduler.step()
plt.close("Losses")
for parameter in parameter_evolutions:
plt.close(parameter)
def DataAdapterFactory(name):
if name == "XIMEA":
return XimeaAdapter
else:
error("Data type '%s' is not known.")
def __init__(self, brdf_parametrization=None):
super().__init__()
if brdf_parametrization is None:
error("MaterialParametrization should get created with a BrdfParametrization")
self.brdf_parametrization = brdf_parametrization
def handle_COM_EV_APP_STARTED(self):
if (self.__outputs):
self.__current_output = self.__outputs[0]
else:
logging.error("no output device available")
async def connect(
) -> None: # ran before server startup, used to do things like connecting to mysql :D
info(f"Asahi v{glob.version} starting")
glob.web = ClientSession() # aiohttp session for external web requests
from lists.players import PlayerList
glob.players = PlayerList() # init player list
try:
glob.db = await fatFawkSQL.connect(**glob.config.sql
) # connect to db using config :p
debug("Asahi connected to MySQL")
except Exception:
error(f"Asahi failed to connect to MySQL\n\n{traceback.format_exc()}")
raise SystemExit(1)
try:
glob.redis = await aioredis.create_redis_pool(
f"redis://{glob.config.redis['host']}",
db=glob.config.redis["db"],
password=glob.config.redis["password"] or None,
)
debug("Asahi connected to Redis")
except Exception:
error(f"Asahi failed to connect to Redis\n\n{traceback.format_exc()}")
raise SystemExit(1)
from objects.player import Player
botinfo = await glob.db.fetchrow(
"SELECT name, pw, country, name FROM users WHERE id = 1", )
if not botinfo:
error(
"Bot account not found. "
"Please insert the bot account with user ID 1 and start Asahi again"
)
raise SystemExit(1)
glob.bot = Player(
id=1,
name=botinfo["name"],
offset=1,
country_iso=botinfo["country"],
country=country_codes[botinfo["country"].upper()],
)
await glob.bot.set_stats()
glob.players.append(glob.bot)
debug(f"Added bot {glob.bot.name} to player list")
async for ach_row in glob.db.iter("SELECT * FROM achievements"):
ach_row["cond"] = eval(f'lambda s: {ach_row["cond"]}')
ach_row["desc"] = ach_row.pop("descr")
glob.achievements.append(Achievement(**ach_row))
init_customs() # set custom achievements list for assets proxy
# add all channels to cache
from objects.channel import Channel
async for chan_row in glob.db.iter("SELECT * FROM channels"):
chan_row["desc"] = chan_row.pop("descr")
channel = Channel(**chan_row)
glob.channels[channel.name] = channel
debug(f"Added channel {channel.name} to channel list")
# add announce channel to cache
announce = Channel(
name="#announce",
desc="#1 scores and public announcements will be posted here",
auto=True,
perm=True,
)
glob.channels[announce.name] = announce
debug("Added channel #announce to channel list")
# add lobby channel to cache
lobby = Channel(name="#lobby",
desc="Multiplayer lobby",
auto=False,
perm=True)
glob.channels[lobby.name] = lobby
debug("Added channel #lobby to channel list")
# add all clans to cache
async for clan_row in glob.db.iter("SELECT * FROM clans"):
clan = Clan(**clan_row)
clan_chan = Channel(
name="#clan",
desc=f"Clan chat for clan {clan.name}",
auto=False,
perm=True,
)
clan.chan = clan_chan # uwu
glob.clans[clan.id] = clan
clan.country = await glob.db.fetchval(
"SELECT country FROM users WHERE id = %s",
[clan.owner],
)
async for member_row in glob.db.iter(
"SELECT id FROM users WHERE clan = %s",
[clan.id],
):
clan.members.append(member_row["id"])
await glob.redis.zadd(f"asahi:clan_leaderboard", clan.score, clan.id)
await glob.redis.zadd(
f"asahi:clan_leaderboard:{clan.country}",
clan.score,
clan.id,
)
r = await glob.redis.zrevrank(f"asahi:clan_leaderboard", clan.id)
cr = await glob.redis.zrevrank(
f"asahi:clan_leaderboard:{clan.country}",
clan.id,
)
clan.rank = r + 1 if r else 0
clan.country_rank = cr + 1 if cr else 0
debug(f"Added clan {clan.name} to clan list")
await prepare_tasks() # make new db conn for donor/freeze tasks
glob.app.add_task(expired_donor)
glob.app.add_task(freeze_timers)
info(f"Asahi v{glob.version} started")
def __init__(self):
self.__is_eof = False
# reference to the MAFW registry gobject
self.__registry = None
# reference to the MAFW renderer gobject
self.__renderer = None
# current state of the renderer
self.__current_state = _MAFW_STATE_TRANSITIONING
# current position in the stream (used during retrieving the position)
self.__current_position = -1
# track duration
self.__duration = -1
self.__to_seek = 0
# sound volume
self.__volume = 50
# time of loading for profiling
self.__load_time = 0
# time when MediaBox has last changed the sound volume
self.__last_volume_change_time = 0
# reference to the callbacks, so that they don't get garbage
# collected too early
self.__playback_cb = _MAFW_PLAYBACK_CB(self.__playback_cb)
self.__position_cb = _MAFW_POSITION_CB(self.__position_cb)
self.__property_cb = _MAFW_EXTENSION_PROPERTY_CB(self.__property_cb)
# MAFW libraries
self.__mafw = ctypes.CDLL("libmafw.so.0")
self.__mafw_shared = ctypes.CDLL("libmafw-shared.so.0")
AbstractBackend.__init__(self)
# retrieve and initialise registry
registry_p = self.__mafw.mafw_registry_get_instance()
if (not registry_p):
logging.error("could not get MAFW registry")
return
#end if
self.__registry = c_gobject.wrap(registry_p)
err_p = ctypes.POINTER(_GError)()
self.__mafw_shared.mafw_shared_init(registry_p,
ctypes.byref(err_p))
if (err_p):
print "GError occured", err_p[0].message
return
#end if
# listen for incoming renderers (this should be how we find
# the gst-renderer)
self.__registry.connect("renderer_added", self.__on_renderer_added)
# some renderers could be loaded already (not really...). look for them
list_p = self.__mafw.mafw_registry_get_renderers(registry_p)
while (list_p):
item = _GList(list_p[0])
logging.info("found preloaded MAFW renderer")
renderer_p = item.data
self.__register_renderer(c_gobject.wrap(renderer_p))
list_p = item.next
def on_exception(self, exception):
logging.error(exception)
def evaluate_state(evaluation_name, object_name, gt_scan_folder, experiment_state):
"""
Evaluate the current experiment_state with the relevant ground truth.
Performs registration refinement on the point clouds, and then calculates
average geometric accuracy and normal angle (in the image domain).
"""
gt_scan_file = os.path.join(gt_scan_folder, "%s_manual.ply" % object_name)
if not os.path.exists(gt_scan_file):
error("WARNING> GT scan for %s not available: \n\t%s" % (object_name, gt_scan_file))
gt_scan_mesh = o3d.io.read_triangle_mesh(gt_scan_file) # stored in m
gt_scan = gt_scan_mesh.sample_points_uniformly(int(1e6)) # objects covering 1m^2, that's 1mm^2 per point
gt_scan.estimate_normals()
estimated_cloud = o3d.geometry.PointCloud()
estimated_cloud.points = to_o3d(to_numpy(experiment_state.locations.location_vector()))
estimated_cloud.normals = to_o3d(to_numpy(experiment_state.normals.normals()))
estimated_cloud.colors = to_o3d(to_numpy(experiment_state.materials.get_brdf_parameters()['diffuse']))
registration_transform = refine_registration(
gt_scan,
estimated_cloud,
distance_threshold=0.005 #m
)
gt_mesh_aligned = copy.deepcopy(gt_scan_mesh)
gt_mesh_aligned.transform(registration_transform)
# o3d.visualization.draw_geometries([gt_mesh_aligned, estimated_cloud])
# now project the gt_scan onto our image plane and calculate depth and normal errors there
estimated_depth = to_numpy(experiment_state.locations.implied_depth_image())
estimated_normals = to_numpy(experiment_state.locations.create_image(experiment_state.normals.normals()))
K_proj = to_numpy(experiment_state.locations.invK.inverse())
Rt = to_numpy(experiment_state.locations.invRt.inverse())
image_based_transform = image_based_alignment(
np.array(gt_mesh_aligned.vertices),
np.array(gt_mesh_aligned.vertex_normals),
Rt,
K_proj,
estimated_depth,
estimated_normals,
verbose=False
)
gt_mesh_aligned.transform(np.concatenate((image_based_transform, np.array([0,0,0,1]).reshape(1,4)), axis=0))
gt_normals_aligned, gt_depth_aligned = render_depth_normals(
np.array(gt_mesh_aligned.vertices),
np.array(gt_mesh_aligned.triangles),
np.array(gt_mesh_aligned.vertex_normals),
K_proj, Rt,
estimated_depth.shape[:2],
)
depth_diff = np.abs(estimated_depth - gt_depth_aligned)
dilated_depth = cv2.dilate(gt_depth_aligned, np.ones((7,7)))
eroded_depth = cv2.erode(gt_depth_aligned, np.ones((7,7)))
edges = (dilated_depth - eroded_depth) > 0.02
valid_pixels = (estimated_depth > 0) * (gt_depth_aligned > 0) * (depth_diff < 0.02)
edgevalid_pixels = (estimated_depth > 0) * (gt_depth_aligned > 0) * (depth_diff < 0.02) * (edges == 0)
normal_dotprods = (gt_normals_aligned * estimated_normals).sum(axis=2).clip(min=-1., max=1.)
normal_anglediff = np.arccos(normal_dotprods) / np.pi * 180.0
average_accuracy = (depth_diff * edgevalid_pixels).sum() / edgevalid_pixels.sum()
average_angle_error = (edgevalid_pixels*normal_anglediff).sum() / edgevalid_pixels.sum()
log("Evaluating %s - %s: depth accuracy %6.4fmm normal accuracy %10.4f degrees" % (evaluation_name, object_name, average_accuracy * 1000, average_angle_error))