mirror of
https://github.com/opencv/opencv.git
synced 2024-12-17 10:58:00 +08:00
31289d2f32
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
337 lines
17 KiB
Python
337 lines
17 KiB
Python
#!/usr/bin/env python
|
|
from __future__ import print_function
|
|
|
|
import ctypes
|
|
from functools import partial
|
|
|
|
import numpy as np
|
|
import cv2 as cv
|
|
|
|
from tests_common import NewOpenCVTests, unittest
|
|
|
|
|
|
def is_numeric(dtype):
|
|
return np.issubdtype(dtype, np.integer) or np.issubdtype(dtype, np.floating)
|
|
|
|
|
|
def get_limits(dtype):
|
|
if not is_numeric(dtype):
|
|
return None, None
|
|
|
|
if np.issubdtype(dtype, np.integer):
|
|
info = np.iinfo(dtype)
|
|
else:
|
|
info = np.finfo(dtype)
|
|
return info.min, info.max
|
|
|
|
|
|
def get_conversion_error_msg(value, expected, actual):
|
|
return 'Conversion "{}" of type "{}" failed\nExpected: "{}" vs Actual "{}"'.format(
|
|
value, type(value).__name__, expected, actual
|
|
)
|
|
|
|
|
|
def get_no_exception_msg(value):
|
|
return 'Exception is not risen for {} of type {}'.format(value, type(value).__name__)
|
|
|
|
class Bindings(NewOpenCVTests):
|
|
|
|
def test_inheritance(self):
|
|
bm = cv.StereoBM_create()
|
|
bm.getPreFilterCap() # from StereoBM
|
|
bm.getBlockSize() # from SteroMatcher
|
|
|
|
boost = cv.ml.Boost_create()
|
|
boost.getBoostType() # from ml::Boost
|
|
boost.getMaxDepth() # from ml::DTrees
|
|
boost.isClassifier() # from ml::StatModel
|
|
|
|
def test_redirectError(self):
|
|
try:
|
|
cv.imshow("", None) # This causes an assert
|
|
self.assertEqual("Dead code", 0)
|
|
except cv.error as _e:
|
|
pass
|
|
|
|
handler_called = [False]
|
|
|
|
def test_error_handler(status, func_name, err_msg, file_name, line):
|
|
handler_called[0] = True
|
|
|
|
cv.redirectError(test_error_handler)
|
|
try:
|
|
cv.imshow("", None) # This causes an assert
|
|
self.assertEqual("Dead code", 0)
|
|
except cv.error as _e:
|
|
self.assertEqual(handler_called[0], True)
|
|
pass
|
|
|
|
cv.redirectError(None)
|
|
try:
|
|
cv.imshow("", None) # This causes an assert
|
|
self.assertEqual("Dead code", 0)
|
|
except cv.error as _e:
|
|
pass
|
|
|
|
|
|
class Arguments(NewOpenCVTests):
|
|
|
|
def _try_to_convert(self, conversion, value):
|
|
try:
|
|
result = conversion(value).lower()
|
|
except Exception as e:
|
|
self.fail(
|
|
'{} "{}" is risen for conversion {} of type {}'.format(
|
|
type(e).__name__, e, value, type(value).__name__
|
|
)
|
|
)
|
|
else:
|
|
return result
|
|
|
|
def test_InputArray(self):
|
|
res1 = cv.utils.dumpInputArray(None)
|
|
# self.assertEqual(res1, "InputArray: noArray()") # not supported
|
|
self.assertEqual(res1, "InputArray: empty()=true kind=0x00010000 flags=0x01010000 total(-1)=0 dims(-1)=0 size(-1)=0x0 type(-1)=CV_8UC1")
|
|
res2_1 = cv.utils.dumpInputArray((1, 2))
|
|
self.assertEqual(res2_1, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=2 dims(-1)=2 size(-1)=1x2 type(-1)=CV_64FC1")
|
|
res2_2 = cv.utils.dumpInputArray(1.5) # Scalar(1.5, 1.5, 1.5, 1.5)
|
|
self.assertEqual(res2_2, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=4 dims(-1)=2 size(-1)=1x4 type(-1)=CV_64FC1")
|
|
a = np.array([[1, 2], [3, 4], [5, 6]])
|
|
res3 = cv.utils.dumpInputArray(a) # 32SC1
|
|
self.assertEqual(res3, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=6 dims(-1)=2 size(-1)=2x3 type(-1)=CV_32SC1")
|
|
a = np.array([[[1, 2], [3, 4], [5, 6]]], dtype='f')
|
|
res4 = cv.utils.dumpInputArray(a) # 32FC2
|
|
self.assertEqual(res4, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=3 dims(-1)=2 size(-1)=3x1 type(-1)=CV_32FC2")
|
|
a = np.array([[[1, 2]], [[3, 4]], [[5, 6]]], dtype=float)
|
|
res5 = cv.utils.dumpInputArray(a) # 64FC2
|
|
self.assertEqual(res5, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=3 dims(-1)=2 size(-1)=1x3 type(-1)=CV_64FC2")
|
|
|
|
def test_InputArrayOfArrays(self):
|
|
res1 = cv.utils.dumpInputArrayOfArrays(None)
|
|
# self.assertEqual(res1, "InputArray: noArray()") # not supported
|
|
self.assertEqual(res1, "InputArrayOfArrays: empty()=true kind=0x00050000 flags=0x01050000 total(-1)=0 dims(-1)=1 size(-1)=0x0")
|
|
res2_1 = cv.utils.dumpInputArrayOfArrays((1, 2)) # { Scalar:all(1), Scalar::all(2) }
|
|
self.assertEqual(res2_1, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=2 dims(-1)=1 size(-1)=2x1 type(0)=CV_64FC1 dims(0)=2 size(0)=1x4 type(0)=CV_64FC1")
|
|
res2_2 = cv.utils.dumpInputArrayOfArrays([1.5])
|
|
self.assertEqual(res2_2, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=1 dims(-1)=1 size(-1)=1x1 type(0)=CV_64FC1 dims(0)=2 size(0)=1x4 type(0)=CV_64FC1")
|
|
a = np.array([[1, 2], [3, 4], [5, 6]])
|
|
b = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
|
|
res3 = cv.utils.dumpInputArrayOfArrays([a, b])
|
|
self.assertEqual(res3, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=2 dims(-1)=1 size(-1)=2x1 type(0)=CV_32SC1 dims(0)=2 size(0)=2x3 type(0)=CV_32SC1")
|
|
c = np.array([[[1, 2], [3, 4], [5, 6]]], dtype='f')
|
|
res4 = cv.utils.dumpInputArrayOfArrays([c, a, b])
|
|
self.assertEqual(res4, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=3 dims(-1)=1 size(-1)=3x1 type(0)=CV_32FC2 dims(0)=2 size(0)=3x1 type(0)=CV_32FC2")
|
|
|
|
def test_parse_to_bool_convertible(self):
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpBool)
|
|
for convertible_true in (True, 1, 64, np.bool(1), np.int8(123), np.int16(11), np.int32(2),
|
|
np.int64(1), np.bool_(3), np.bool8(12)):
|
|
actual = try_to_convert(convertible_true)
|
|
self.assertEqual('bool: true', actual,
|
|
msg=get_conversion_error_msg(convertible_true, 'bool: true', actual))
|
|
|
|
for convertible_false in (False, 0, np.uint8(0), np.bool_(0), np.int_(0)):
|
|
actual = try_to_convert(convertible_false)
|
|
self.assertEqual('bool: false', actual,
|
|
msg=get_conversion_error_msg(convertible_false, 'bool: false', actual))
|
|
|
|
def test_parse_to_bool_not_convertible(self):
|
|
for not_convertible in (1.2, np.float(2.3), 's', 'str', (1, 2), [1, 2], complex(1, 1),
|
|
complex(imag=2), complex(1.1), np.array([1, 0], dtype=np.bool)):
|
|
with self.assertRaises((TypeError, OverflowError),
|
|
msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpBool(not_convertible)
|
|
|
|
def test_parse_to_bool_convertible_extra(self):
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpBool)
|
|
_, max_size_t = get_limits(ctypes.c_size_t)
|
|
for convertible_true in (-1, max_size_t):
|
|
actual = try_to_convert(convertible_true)
|
|
self.assertEqual('bool: true', actual,
|
|
msg=get_conversion_error_msg(convertible_true, 'bool: true', actual))
|
|
|
|
def test_parse_to_bool_not_convertible_extra(self):
|
|
for not_convertible in (np.array([False]), np.array([True], dtype=np.bool)):
|
|
with self.assertRaises((TypeError, OverflowError),
|
|
msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpBool(not_convertible)
|
|
|
|
def test_parse_to_int_convertible(self):
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpInt)
|
|
min_int, max_int = get_limits(ctypes.c_int)
|
|
for convertible in (-10, -1, 2, int(43.2), np.uint8(15), np.int8(33), np.int16(-13),
|
|
np.int32(4), np.int64(345), (23), min_int, max_int, np.int_(33)):
|
|
expected = 'int: {0:d}'.format(convertible)
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
def test_parse_to_int_not_convertible(self):
|
|
min_int, max_int = get_limits(ctypes.c_int)
|
|
for not_convertible in (1.2, np.float(4), float(3), np.double(45), 's', 'str',
|
|
np.array([1, 2]), (1,), [1, 2], min_int - 1, max_int + 1,
|
|
complex(1, 1), complex(imag=2), complex(1.1)):
|
|
with self.assertRaises((TypeError, OverflowError, ValueError),
|
|
msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpInt(not_convertible)
|
|
|
|
def test_parse_to_int_not_convertible_extra(self):
|
|
for not_convertible in (np.bool_(True), True, False, np.float32(2.3),
|
|
np.array([3, ], dtype=int), np.array([-2, ], dtype=np.int32),
|
|
np.array([1, ], dtype=np.int), np.array([11, ], dtype=np.uint8)):
|
|
with self.assertRaises((TypeError, OverflowError),
|
|
msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpInt(not_convertible)
|
|
|
|
def test_parse_to_size_t_convertible(self):
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpSizeT)
|
|
_, max_uint = get_limits(ctypes.c_uint)
|
|
for convertible in (2, max_uint, (12), np.uint8(34), np.int8(12), np.int16(23),
|
|
np.int32(123), np.int64(344), np.uint64(3), np.uint16(2), np.uint32(5),
|
|
np.uint(44)):
|
|
expected = 'size_t: {0:d}'.format(convertible).lower()
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
def test_parse_to_size_t_not_convertible(self):
|
|
min_long, _ = get_limits(ctypes.c_long)
|
|
for not_convertible in (1.2, True, False, np.bool_(True), np.float(4), float(3),
|
|
np.double(45), 's', 'str', np.array([1, 2]), (1,), [1, 2],
|
|
np.float64(6), complex(1, 1), complex(imag=2), complex(1.1),
|
|
-1, min_long, np.int8(-35)):
|
|
with self.assertRaises((TypeError, OverflowError),
|
|
msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpSizeT(not_convertible)
|
|
|
|
def test_parse_to_size_t_convertible_extra(self):
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpSizeT)
|
|
_, max_size_t = get_limits(ctypes.c_size_t)
|
|
for convertible in (max_size_t,):
|
|
expected = 'size_t: {0:d}'.format(convertible).lower()
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
def test_parse_to_size_t_not_convertible_extra(self):
|
|
for not_convertible in (np.bool_(True), True, False, np.array([123, ], dtype=np.uint8),):
|
|
with self.assertRaises((TypeError, OverflowError),
|
|
msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpSizeT(not_convertible)
|
|
|
|
def test_parse_to_float_convertible(self):
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpFloat)
|
|
min_float, max_float = get_limits(ctypes.c_float)
|
|
for convertible in (2, -13, 1.24, float(32), np.float(32.45), np.double(12.23),
|
|
np.float32(-12.3), np.float64(3.22), np.float_(-1.5), min_float,
|
|
max_float, np.inf, -np.inf, float('Inf'), -float('Inf'),
|
|
np.double(np.inf), np.double(-np.inf), np.double(float('Inf')),
|
|
np.double(-float('Inf'))):
|
|
expected = 'Float: {0:.2f}'.format(convertible).lower()
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
# Workaround for Windows NaN tests due to Visual C runtime
|
|
# special floating point values (indefinite NaN)
|
|
for nan in (float('NaN'), np.nan, np.float32(np.nan), np.double(np.nan),
|
|
np.double(float('NaN'))):
|
|
actual = try_to_convert(nan)
|
|
self.assertIn('nan', actual, msg="Can't convert nan of type {} to float. "
|
|
"Actual: {}".format(type(nan).__name__, actual))
|
|
|
|
min_double, max_double = get_limits(ctypes.c_double)
|
|
for inf in (min_float * 10, max_float * 10, min_double, max_double):
|
|
expected = 'float: {}inf'.format('-' if inf < 0 else '')
|
|
actual = try_to_convert(inf)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(inf, expected, actual))
|
|
|
|
def test_parse_to_float_not_convertible(self):
|
|
for not_convertible in ('s', 'str', (12,), [1, 2], np.array([1, 2], dtype=np.float),
|
|
np.array([1, 2], dtype=np.double), complex(1, 1), complex(imag=2),
|
|
complex(1.1)):
|
|
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpFloat(not_convertible)
|
|
|
|
def test_parse_to_float_not_convertible_extra(self):
|
|
for not_convertible in (np.bool_(False), True, False, np.array([123, ], dtype=int),
|
|
np.array([1., ]), np.array([False]),
|
|
np.array([True], dtype=np.bool)):
|
|
with self.assertRaises((TypeError, OverflowError),
|
|
msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpFloat(not_convertible)
|
|
|
|
def test_parse_to_double_convertible(self):
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpDouble)
|
|
min_float, max_float = get_limits(ctypes.c_float)
|
|
min_double, max_double = get_limits(ctypes.c_double)
|
|
for convertible in (2, -13, 1.24, np.float(32.45), float(2), np.double(12.23),
|
|
np.float32(-12.3), np.float64(3.22), np.float_(-1.5), min_float,
|
|
max_float, min_double, max_double, np.inf, -np.inf, float('Inf'),
|
|
-float('Inf'), np.double(np.inf), np.double(-np.inf),
|
|
np.double(float('Inf')), np.double(-float('Inf'))):
|
|
expected = 'Double: {0:.2f}'.format(convertible).lower()
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
# Workaround for Windows NaN tests due to Visual C runtime
|
|
# special floating point values (indefinite NaN)
|
|
for nan in (float('NaN'), np.nan, np.double(np.nan),
|
|
np.double(float('NaN'))):
|
|
actual = try_to_convert(nan)
|
|
self.assertIn('nan', actual, msg="Can't convert nan of type {} to double. "
|
|
"Actual: {}".format(type(nan).__name__, actual))
|
|
|
|
def test_parse_to_double_not_convertible(self):
|
|
for not_convertible in ('s', 'str', (12,), [1, 2], np.array([1, 2], dtype=np.float),
|
|
np.array([1, 2], dtype=np.double), complex(1, 1), complex(imag=2),
|
|
complex(1.1)):
|
|
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpDouble(not_convertible)
|
|
|
|
def test_parse_to_double_not_convertible_extra(self):
|
|
for not_convertible in (np.bool_(False), True, False, np.array([123, ], dtype=int),
|
|
np.array([1., ]), np.array([False]),
|
|
np.array([12.4], dtype=np.double), np.array([True], dtype=np.bool)):
|
|
with self.assertRaises((TypeError, OverflowError),
|
|
msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpDouble(not_convertible)
|
|
|
|
def test_parse_to_cstring_convertible(self):
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpCString)
|
|
for convertible in ('s', 'str', str(123), ('char'), np.str('test1'), np.str_('test2')):
|
|
expected = 'string: ' + convertible
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
def test_parse_to_cstring_not_convertible(self):
|
|
for not_convertible in ((12,), ('t', 'e', 's', 't'), np.array(['123', ]),
|
|
np.array(['t', 'e', 's', 't']), 1, -1.4, True, False, None):
|
|
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpCString(not_convertible)
|
|
|
|
|
|
class SamplesFindFile(NewOpenCVTests):
|
|
|
|
def test_ExistedFile(self):
|
|
res = cv.samples.findFile('lena.jpg', False)
|
|
self.assertNotEqual(res, '')
|
|
|
|
def test_MissingFile(self):
|
|
res = cv.samples.findFile('non_existed.file', False)
|
|
self.assertEqual(res, '')
|
|
|
|
def test_MissingFileException(self):
|
|
try:
|
|
_res = cv.samples.findFile('non_existed.file', True)
|
|
self.assertEqual("Dead code", 0)
|
|
except cv.error as _e:
|
|
pass
|
|
|
|
|
|
if __name__ == '__main__':
|
|
NewOpenCVTests.bootstrap()
|