Source code for imgaug.dtypes

"""Functions to interact/analyze with numpy dtypes."""
from __future__ import print_function, division

import numpy as np
import six.moves as sm

import imgaug as ia

    "i": ["int8", "int16", "int32", "int64"],
    "u": ["uint8", "uint16", "uint32", "uint64"],
    "b": ["bool"],
    "f": ["float16", "float32", "float64", "float128"]

[docs]def normalize_dtypes(dtypes): if not isinstance(dtypes, list): return [normalize_dtype(dtypes)] return [normalize_dtype(dtype) for dtype in dtypes]
[docs]def normalize_dtype(dtype): assert not isinstance(dtype, list), ( "Expected a single dtype-like, got a list instead.") return ( dtype.dtype if ia.is_np_array(dtype) or ia.is_np_scalar(dtype) else np.dtype(dtype) )
[docs]def change_dtype_(arr, dtype, clip=True, round=True): # pylint: disable=redefined-builtin assert ia.is_np_array(arr), ( "Expected array as input, got type %s." % (type(arr),)) dtype = normalize_dtype(dtype) if == return arr if round and arr.dtype.kind == "f" and dtype.kind in ["u", "i", "b"]: arr = np.round(arr) if clip: min_value, _, max_value = get_value_range_of_dtype(dtype) arr = clip_(arr, min_value, max_value) return arr.astype(dtype, copy=False)
[docs]def change_dtypes_(images, dtypes, clip=True, round=True): # pylint: disable=redefined-builtin if ia.is_np_array(images): if ia.is_iterable(dtypes): dtypes = normalize_dtypes(dtypes) n_distinct_dtypes = len({ for dt in dtypes}) assert len(dtypes) == len(images), ( "If an iterable of dtypes is provided to " "change_dtypes_(), it must contain as many dtypes as " "there are images. Got %d dtypes and %d images." % ( len(dtypes), len(images)) ) assert n_distinct_dtypes == 1, ( "If an image array is provided to change_dtypes_(), the " "provided 'dtypes' argument must either be a single dtype " "or an iterable of N times the *same* dtype for N images. " "Got %d distinct dtypes." % (n_distinct_dtypes,) ) dtype = dtypes[0] else: dtype = normalize_dtype(dtypes) result = change_dtype_(images, dtype, clip=clip, round=round) elif ia.is_iterable(images): dtypes = ( [normalize_dtype(dtypes)] * len(images) if not isinstance(dtypes, list) else normalize_dtypes(dtypes) ) assert len(images) == len(dtypes), ( "Expected the provided images and dtypes to match, but got " "iterables of size %d (images) %d (dtypes)." % ( len(images), len(dtypes))) result = images for i, (image, dtype) in enumerate(zip(images, dtypes)): assert ia.is_np_array(image), ( "Expected each image to be an ndarray, got type %s " "instead." % (type(image),)) result[i] = change_dtype_(image, dtype, clip=clip, round=round) else: raise Exception("Expected numpy array or iterable of numpy arrays, " "got type '%s'." % (type(images),)) return result
# TODO replace this everywhere in the library with change_dtypes_ # TODO mark as deprecated
[docs]def restore_dtypes_(images, dtypes, clip=True, round=True): # pylint: disable=redefined-builtin return change_dtypes_(images, dtypes, clip=clip, round=round)
[docs]def copy_dtypes_for_restore(images, force_list=False): if ia.is_np_array(images): if force_list: return [images.dtype for _ in sm.xrange(len(images))] return images.dtype return [image.dtype for image in images]
[docs]def increase_itemsize_of_dtype(dtype, factor): dtype = normalize_dtype(dtype) assert ia.is_single_integer(factor), ( "Expected 'factor' to be an integer, got type %s instead." % ( type(factor),)) # int8 -> int64 = factor 8 # uint8 -> uint64 = factor 8 # float16 -> float128 = factor 8 assert factor in [1, 2, 4, 8], ( "The itemsize may only be increased any of the following factors: " "1, 2, 4 or 8. Got factor %d." % (factor,)) assert dtype.kind != "b", "Cannot increase the itemsize of boolean." dt_high_name = "%s%d" % (dtype.kind, dtype.itemsize * factor) try: dt_high = np.dtype(dt_high_name) return dt_high except TypeError: raise TypeError( "Unable to create a numpy dtype matching the name '%s'. " "This error was caused when trying to find a dtype " "that increases the itemsize of dtype '%s' by a factor of %d." "This error can be avoided by choosing arrays with lower " "resolution dtypes as inputs, e.g. by reducing " "float32 to float16." % ( dt_high_name,, factor ) )
[docs]def get_minimal_dtype(arrays, increase_itemsize_factor=1): assert isinstance(arrays, list), ( "Expected a list of arrays or dtypes, got type %s." % (type(arrays),)) assert len(arrays) > 0, ( "Cannot estimate minimal dtype of an empty iterable.") input_dts = normalize_dtypes(arrays) # This loop construct handles (1) list of a single dtype, (2) list of two # dtypes and (3) list of 3+ dtypes. Note that promote_dtypes() always # expects exactly two dtypes. promoted_dt = input_dts[0] input_dts = input_dts[1:] while len(input_dts) >= 1: promoted_dt = np.promote_types(promoted_dt, input_dts[0]) input_dts = input_dts[1:] if increase_itemsize_factor > 1: assert isinstance(promoted_dt, np.dtype), ( "Expected numpy.dtype output from numpy.promote_dtypes, got type " "%s." % (type(promoted_dt),)) return increase_itemsize_of_dtype(promoted_dt, increase_itemsize_factor) return promoted_dt
# TODO rename to: promote_arrays_to_minimal_dtype_
[docs]def promote_array_dtypes_(arrays, dtypes=None, increase_itemsize_factor=1): if dtypes is None: dtypes = normalize_dtypes(arrays) elif not isinstance(dtypes, list): dtypes = [dtypes] dtype = get_minimal_dtype(dtypes, increase_itemsize_factor=increase_itemsize_factor) return change_dtypes_(arrays, dtype, clip=False, round=False)
[docs]def increase_array_resolutions_(arrays, factor): dts = normalize_dtypes(arrays) dts = [increase_itemsize_of_dtype(dt, factor) for dt in dts] return change_dtypes_(arrays, dts, round=False, clip=False)
[docs]def get_value_range_of_dtype(dtype): dtype = normalize_dtype(dtype) if dtype.kind == "f": finfo = np.finfo(dtype) return finfo.min, 0.0, finfo.max if dtype.kind == "u": iinfo = np.iinfo(dtype) return iinfo.min, iinfo.min + 0.5 * iinfo.max, iinfo.max if dtype.kind == "i": iinfo = np.iinfo(dtype) return iinfo.min, -0.5, iinfo.max if dtype.kind == "b": return 0, None, 1 raise Exception("Cannot estimate value range of dtype '%s' " "(type: %s)" % (str(dtype), type(dtype)))
# TODO call this function wherever data is clipped
[docs]def clip_(array, min_value, max_value): # uint64 is disallowed, because numpy's clip seems to convert it to float64 # int64 is disallowed, because numpy's clip converts it to float64 since # 1.17 # TODO find the cause for that gate_dtypes(array, allowed=["bool", "uint8", "uint16", "uint32", "int8", "int16", "int32", "float16", "float32", "float64", "float128"], disallowed=["uint64", "int64"], augmenter=None) # If the min of the input value range is above the allowed min, we do not # have to clip to the allowed min as we cannot exceed it anyways. # Analogous for max. In fact, we must not clip then to min/max as that can # lead to errors in numpy's clip. E.g. # >>> arr = np.zeros((1,), dtype=np.int32) # >>> np.clip(arr, 0, np.iinfo(np.dtype("uint32")).max) # will return # array([-1], dtype=int32) # (observed on numpy version 1.15.2). min_value_arrdt, _, max_value_arrdt = get_value_range_of_dtype(array.dtype) if min_value is not None and min_value < min_value_arrdt: min_value = None if max_value is not None and max_value_arrdt < max_value: max_value = None if min_value is not None or max_value is not None: # for scalar arrays, i.e. with shape = (), "out" is not a valid # argument if len(array.shape) == 0: array = np.clip(array, min_value, max_value) elif == "int32": # Since 1.17 (before maybe too?), numpy.clip() turns int32 # to float64. float64 should cover the whole value range of int32, # so the dtype is not rejected here. # TODO Verify this. Is rounding needed before conversion? array = np.clip(array, min_value, max_value).astype(array.dtype) else: array = np.clip(array, min_value, max_value, out=array) return array
[docs]def clip_to_dtype_value_range_(array, dtype, validate=True, validate_values=None): dtype = normalize_dtype(dtype) min_value, _, max_value = get_value_range_of_dtype(dtype) if validate: array_val = array if ia.is_single_integer(validate): assert validate >= 1, ( "If 'validate' is an integer, it must have a value >=1, " "got %d instead." % (validate,)) assert validate_values is None, ( "If 'validate' is an integer, 'validate_values' must be " "None. Got type %s instead." % (type(validate_values),)) array_val = array.flat[0:validate] if validate_values is not None: min_value_found, max_value_found = validate_values else: min_value_found = np.min(array_val) max_value_found = np.max(array_val) assert min_value <= min_value_found <= max_value, ( "Minimum value of array is outside of allowed value range (%.4f " "vs %.4f to %.4f)." % (min_value_found, min_value, max_value)) assert min_value <= max_value_found <= max_value, ( "Maximum value of array is outside of allowed value range (%.4f " "vs %.4f to %.4f)." % (max_value_found, min_value, max_value)) return clip_(array, min_value, max_value)
[docs]def gate_dtypes(dtypes, allowed, disallowed, augmenter=None): # assume that at least one allowed dtype string is given assert len(allowed) > 0, ( "Expected at least one dtype to be allowed, but got an empty list.") # check only first dtype for performance assert ia.is_string(allowed[0]), ( "Expected only strings as dtypes, but got type %s." % ( type(allowed[0]),)) if len(disallowed) > 0: # check only first disallowed dtype for performance assert ia.is_string(disallowed[0]), ( "Expected only strings as dtypes, but got type %s." % ( type(disallowed[0]),)) # verify that "allowed" and "disallowed" do not contain overlapping # dtypes inters = set(allowed).intersection(set(disallowed)) nb_overlapping = len(inters) assert nb_overlapping == 0, ( "Expected 'allowed' and 'disallowed' to not contain the same dtypes, " "but %d appeared in both arguments. Got allowed: %s, " "disallowed: %s, intersection: %s" % ( nb_overlapping, ", ".join(allowed), ", ".join(disallowed), ", ".join(inters)) ) dtypes = normalize_dtypes(dtypes) for dtype in dtypes: if in allowed: pass elif in disallowed: if augmenter is None: raise ValueError( "Got dtype '%s', which is a forbidden dtype (%s)." % (, ", ".join(disallowed) )) raise ValueError( "Got dtype '%s' in augmenter '%s' (class '%s'), which " "is a forbidden dtype (%s)." % (,, augmenter.__class__.__name__, ", ".join(disallowed) )) else: if augmenter is None: ia.warn( "Got dtype '%s', which was neither explicitly allowed " "(%s), nor explicitly disallowed (%s). Generated " "outputs may contain errors." % (, ", ".join(allowed), ", ".join(disallowed) )) else: ia.warn( "Got dtype '%s' in augmenter '%s' (class '%s'), which was " "neither explicitly allowed (%s), nor explicitly " "disallowed (%s). Generated outputs may contain " "errors." % (,, augmenter.__class__.__name__, ", ".join(allowed), ", ".join(disallowed) ))