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https://github.com/opencv/opencv.git
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Merge pull request #15915 from VadimLevin:dev/norm_fix
Fix implicit conversion from array to scalar in python bindings
* Fix wrong conversion behavior for primitive types
- Introduce ArgTypeInfo namedtuple instead of plain tuple.
If strict conversion parameter for type is set to true, it is
handled like object argument in PyArg_ParseTupleAndKeywords and
converted to concrete type with the appropriate pyopencv_to function
call.
- Remove deadcode and unused variables.
- Fix implicit conversion from numpy array with 1 element to scalar
- Fix narrowing conversion to size_t type.
* Fix wrong conversion behavior for primitive types
- Introduce ArgTypeInfo namedtuple instead of plain tuple.
If strict conversion parameter for type is set to true, it is
handled like object argument in PyArg_ParseTupleAndKeywords and
converted to concrete type with the appropriate pyopencv_to function
call.
- Remove deadcode and unused variables.
- Fix implicit conversion from numpy array with 1 element to scalar
- Fix narrowing conversion to size_t type.·
- Enable tests with wrong conversion behavior
- Restrict passing None as value
- Restrict bool to integer/floating types conversion
* Add PyIntType support for Python 2
* Remove possible narrowing conversion of size_t
* Bindings conversion update
- Remove unused macro
- Add better conversion for types to numpy types descriptors
- Add argument name to fail messages
- NoneType treated as a valid argument. Better handling will be added
as a standalone patch
* Add descriptor specialization for size_t
* Add check for signed to unsigned integer conversion safety
- If signed integer is positive it can be safely converted
to unsigned
- Add check for plain python 2 objects
- Add check for numpy scalars
- Add simple type_traits implementation for better code style
* Resolve type "overflow" false negative in safe casting check
- Move type_traits to separate header
* Add copyright message to type_traits.hpp
* Limit conversion scope for integral numpy types
- Made canBeSafelyCasted specialized only for size_t, so
type_traits header became unused and was removed.
- Added clarification about descriptor pointer
This commit is contained in:
Vadim Levin
committed by
Alexander Alekhin
Alexander Alekhin
parent
4cc458eb10
commit
31289d2f32
@@ -136,13 +136,12 @@ class Arguments(NewOpenCVTests):
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msg=get_conversion_error_msg(convertible_false, 'bool: false', actual))
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def test_parse_to_bool_not_convertible(self):
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for not_convertible in (1.2, np.float(2.3), 's', 'str', (1, 2), [1, 2], complex(1, 1), None,
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for not_convertible in (1.2, np.float(2.3), 's', 'str', (1, 2), [1, 2], complex(1, 1),
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complex(imag=2), complex(1.1), np.array([1, 0], dtype=np.bool)):
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with self.assertRaises((TypeError, OverflowError),
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msg=get_no_exception_msg(not_convertible)):
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_ = cv.utils.dumpBool(not_convertible)
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@unittest.skip('Wrong conversion behavior')
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def test_parse_to_bool_convertible_extra(self):
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try_to_convert = partial(self._try_to_convert, cv.utils.dumpBool)
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_, max_size_t = get_limits(ctypes.c_size_t)
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@@ -151,7 +150,6 @@ class Arguments(NewOpenCVTests):
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self.assertEqual('bool: true', actual,
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msg=get_conversion_error_msg(convertible_true, 'bool: true', actual))
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@unittest.skip('Wrong conversion behavior')
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def test_parse_to_bool_not_convertible_extra(self):
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for not_convertible in (np.array([False]), np.array([True], dtype=np.bool)):
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with self.assertRaises((TypeError, OverflowError),
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@@ -172,12 +170,11 @@ class Arguments(NewOpenCVTests):
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min_int, max_int = get_limits(ctypes.c_int)
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for not_convertible in (1.2, np.float(4), float(3), np.double(45), 's', 'str',
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np.array([1, 2]), (1,), [1, 2], min_int - 1, max_int + 1,
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complex(1, 1), complex(imag=2), complex(1.1), None):
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complex(1, 1), complex(imag=2), complex(1.1)):
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with self.assertRaises((TypeError, OverflowError, ValueError),
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msg=get_no_exception_msg(not_convertible)):
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_ = cv.utils.dumpInt(not_convertible)
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@unittest.skip('Wrong conversion behavior')
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def test_parse_to_int_not_convertible_extra(self):
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for not_convertible in (np.bool_(True), True, False, np.float32(2.3),
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np.array([3, ], dtype=int), np.array([-2, ], dtype=np.int32),
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@@ -189,7 +186,7 @@ class Arguments(NewOpenCVTests):
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def test_parse_to_size_t_convertible(self):
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try_to_convert = partial(self._try_to_convert, cv.utils.dumpSizeT)
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_, max_uint = get_limits(ctypes.c_uint)
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for convertible in (2, True, False, max_uint, (12), np.uint8(34), np.int8(12), np.int16(23),
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for convertible in (2, max_uint, (12), np.uint8(34), np.int8(12), np.int16(23),
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np.int32(123), np.int64(344), np.uint64(3), np.uint16(2), np.uint32(5),
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np.uint(44)):
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expected = 'size_t: {0:d}'.format(convertible).lower()
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@@ -198,14 +195,15 @@ class Arguments(NewOpenCVTests):
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msg=get_conversion_error_msg(convertible, expected, actual))
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def test_parse_to_size_t_not_convertible(self):
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for not_convertible in (1.2, np.float(4), float(3), np.double(45), 's', 'str',
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np.array([1, 2]), (1,), [1, 2], np.float64(6), complex(1, 1),
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complex(imag=2), complex(1.1), None):
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min_long, _ = get_limits(ctypes.c_long)
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for not_convertible in (1.2, True, False, np.bool_(True), np.float(4), float(3),
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np.double(45), 's', 'str', np.array([1, 2]), (1,), [1, 2],
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np.float64(6), complex(1, 1), complex(imag=2), complex(1.1),
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-1, min_long, np.int8(-35)):
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with self.assertRaises((TypeError, OverflowError),
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msg=get_no_exception_msg(not_convertible)):
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_ = cv.utils.dumpSizeT(not_convertible)
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@unittest.skip('Wrong conversion behavior')
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def test_parse_to_size_t_convertible_extra(self):
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try_to_convert = partial(self._try_to_convert, cv.utils.dumpSizeT)
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_, max_size_t = get_limits(ctypes.c_size_t)
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@@ -215,7 +213,6 @@ class Arguments(NewOpenCVTests):
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self.assertEqual(expected, actual,
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msg=get_conversion_error_msg(convertible, expected, actual))
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@unittest.skip('Wrong conversion behavior')
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def test_parse_to_size_t_not_convertible_extra(self):
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for not_convertible in (np.bool_(True), True, False, np.array([123, ], dtype=np.uint8),):
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with self.assertRaises((TypeError, OverflowError),
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@@ -251,13 +248,12 @@ class Arguments(NewOpenCVTests):
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msg=get_conversion_error_msg(inf, expected, actual))
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def test_parse_to_float_not_convertible(self):
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for not_convertible in ('s', 'str', (12,), [1, 2], None, np.array([1, 2], dtype=np.float),
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for not_convertible in ('s', 'str', (12,), [1, 2], np.array([1, 2], dtype=np.float),
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np.array([1, 2], dtype=np.double), complex(1, 1), complex(imag=2),
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complex(1.1)):
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with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
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_ = cv.utils.dumpFloat(not_convertible)
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@unittest.skip('Wrong conversion behavior')
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def test_parse_to_float_not_convertible_extra(self):
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for not_convertible in (np.bool_(False), True, False, np.array([123, ], dtype=int),
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np.array([1., ]), np.array([False]),
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@@ -289,13 +285,12 @@ class Arguments(NewOpenCVTests):
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"Actual: {}".format(type(nan).__name__, actual))
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def test_parse_to_double_not_convertible(self):
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for not_convertible in ('s', 'str', (12,), [1, 2], None, np.array([1, 2], dtype=np.float),
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for not_convertible in ('s', 'str', (12,), [1, 2], np.array([1, 2], dtype=np.float),
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np.array([1, 2], dtype=np.double), complex(1, 1), complex(imag=2),
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complex(1.1)):
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with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
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_ = cv.utils.dumpDouble(not_convertible)
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@unittest.skip('Wrong conversion behavior')
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def test_parse_to_double_not_convertible_extra(self):
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for not_convertible in (np.bool_(False), True, False, np.array([123, ], dtype=int),
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np.array([1., ]), np.array([False]),
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@@ -0,0 +1,173 @@
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#!/usr/bin/env python
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from itertools import product
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from functools import reduce
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import numpy as np
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import cv2 as cv
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from tests_common import NewOpenCVTests
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def norm_inf(x, y=None):
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def norm(vec):
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return np.linalg.norm(vec.flatten(), np.inf)
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x = x.astype(np.float64)
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return norm(x) if y is None else norm(x - y.astype(np.float64))
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def norm_l1(x, y=None):
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def norm(vec):
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return np.linalg.norm(vec.flatten(), 1)
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x = x.astype(np.float64)
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return norm(x) if y is None else norm(x - y.astype(np.float64))
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def norm_l2(x, y=None):
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def norm(vec):
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return np.linalg.norm(vec.flatten())
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x = x.astype(np.float64)
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return norm(x) if y is None else norm(x - y.astype(np.float64))
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def norm_l2sqr(x, y=None):
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def norm(vec):
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return np.square(vec).sum()
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x = x.astype(np.float64)
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return norm(x) if y is None else norm(x - y.astype(np.float64))
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def norm_hamming(x, y=None):
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def norm(vec):
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return sum(bin(i).count('1') for i in vec.flatten())
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return norm(x) if y is None else norm(np.bitwise_xor(x, y))
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def norm_hamming2(x, y=None):
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def norm(vec):
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def element_norm(element):
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binary_str = bin(element).split('b')[-1]
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if len(binary_str) % 2 == 1:
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binary_str = '0' + binary_str
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gen = filter(lambda p: p != '00',
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(binary_str[i:i+2]
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for i in range(0, len(binary_str), 2)))
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return sum(1 for _ in gen)
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return sum(element_norm(element) for element in vec.flatten())
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return norm(x) if y is None else norm(np.bitwise_xor(x, y))
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norm_type_under_test = {
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cv.NORM_INF: norm_inf,
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cv.NORM_L1: norm_l1,
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cv.NORM_L2: norm_l2,
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cv.NORM_L2SQR: norm_l2sqr,
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cv.NORM_HAMMING: norm_hamming,
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cv.NORM_HAMMING2: norm_hamming2
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}
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norm_name = {
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cv.NORM_INF: 'inf',
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cv.NORM_L1: 'L1',
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cv.NORM_L2: 'L2',
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cv.NORM_L2SQR: 'L2SQR',
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cv.NORM_HAMMING: 'Hamming',
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cv.NORM_HAMMING2: 'Hamming2'
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}
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def get_element_types(norm_type):
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if norm_type in (cv.NORM_HAMMING, cv.NORM_HAMMING2):
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return (np.uint8,)
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else:
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return (np.uint8, np.int8, np.uint16, np.int16, np.int32, np.float32,
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np.float64)
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def generate_vector(shape, dtype):
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if np.issubdtype(dtype, np.integer):
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return np.random.randint(0, 100, shape).astype(dtype)
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else:
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return np.random.normal(10., 12.5, shape).astype(dtype)
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shapes = (1, 2, 3, 5, 7, 16, (1, 1), (2, 2), (3, 5), (1, 7))
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class norm_test(NewOpenCVTests):
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def test_norm_for_one_array(self):
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np.random.seed(123)
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for norm_type, norm in norm_type_under_test.items():
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element_types = get_element_types(norm_type)
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for shape, element_type in product(shapes, element_types):
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array = generate_vector(shape, element_type)
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expected = norm(array)
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actual = cv.norm(array, norm_type)
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self.assertAlmostEqual(
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expected, actual, places=2,
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msg='Array {0} of {1} and norm {2}'.format(
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array, element_type.__name__, norm_name[norm_type]
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)
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)
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def test_norm_for_two_arrays(self):
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np.random.seed(456)
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for norm_type, norm in norm_type_under_test.items():
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element_types = get_element_types(norm_type)
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for shape, element_type in product(shapes, element_types):
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first = generate_vector(shape, element_type)
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second = generate_vector(shape, element_type)
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expected = norm(first, second)
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actual = cv.norm(first, second, norm_type)
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self.assertAlmostEqual(
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expected, actual, places=2,
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msg='Arrays {0} {1} of type {2} and norm {3}'.format(
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first, second, element_type.__name__,
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norm_name[norm_type]
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)
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)
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def test_norm_fails_for_wrong_type(self):
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for norm_type in (cv.NORM_HAMMING, cv.NORM_HAMMING2):
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with self.assertRaises(Exception,
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msg='Type is not checked {0}'.format(
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norm_name[norm_type]
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)):
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cv.norm(np.array([1, 2], dtype=np.int32), norm_type)
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def test_norm_fails_for_array_and_scalar(self):
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for norm_type in norm_type_under_test:
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with self.assertRaises(Exception,
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msg='Exception is not thrown for {0}'.format(
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norm_name[norm_type]
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)):
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cv.norm(np.array([1, 2], dtype=np.uint8), 123, norm_type)
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def test_norm_fails_for_scalar_and_array(self):
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for norm_type in norm_type_under_test:
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with self.assertRaises(Exception,
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msg='Exception is not thrown for {0}'.format(
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norm_name[norm_type]
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)):
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cv.norm(4, np.array([1, 2], dtype=np.uint8), norm_type)
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def test_norm_fails_for_array_and_norm_type_as_scalar(self):
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for norm_type in norm_type_under_test:
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with self.assertRaises(Exception,
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msg='Exception is not thrown for {0}'.format(
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norm_name[norm_type]
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)):
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cv.norm(np.array([3, 4, 5], dtype=np.uint8),
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norm_type, normType=norm_type)
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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