def _validate_type_dwtsub(self, x): # check size
if self.__validate_enums(x, WaveletSubband):
return True
elif not isdict(x) or len(x) != 3:
return False
elif not contains(x, "values", "min-length", "max-length"):
return False
elif not self.__validate_enums(x["values"], WaveletSubband):
return False
return True
def __validate_type_range(self, x, test_fn=None): # check size
if not isdict(x) or len(x) > 3:
return False
elif not contains(x, "start", "stop"):
return False
elif len(x) == 3 and "step" not in x:
return False
elif test_fn and not all(map(test_fn, x.values())):
return False
return True
def _normalize_coerce_dwtsub(self, x):
if isdict(x) and contains(x, "values", "min-length", "max-length"):
combs = []
values, min_len, max_len = x["values"], x["min-length"], x[
"max-length"]
values = list(map(WaveletSubband, values))
for i in range(min_len, max_len + 1):
combs.extend(combinations(values, i))
return combs
values = self._normalize_coerce_aslist(x)
return [[WaveletSubband(v)] for v in values]
def from_dict(data):
watermarks = []
for k, v in data.items():
type_ = WatermarkType(k)
for x in v:
if isdict(x):
path = x.pop("path")
holder = WatermarkHolder(type_, path, x)
watermarks.append(holder)
elif isstr(x):
watermarks.append(WatermarkHolder(type_, x))
return watermarks
def _normalize_coerce_asrange(self, x):
if isdict(x) and contains(x, "start", "stop"):
return np.arange(**x)
return self._normalize_coerce_aslist(x)