mirror of
https://github.com/opencv/opencv.git
synced 2024-11-26 20:20:20 +08:00
16b9514543
`PyObject*` to `std::vector<T>` conversion logic:
- If user passed Numpy Array
- If array is planar and T is a primitive type (doesn't require
constructor call) that matches with the element type of array, then
copy element one by one with the respect of the step between array
elements. If compiler is lucky (or brave enough) copy loop can be
vectorized.
For classes that require constructor calls this path is not
possible, because we can't begin an object lifetime without hacks.
- Otherwise fall-back to general case
- Otherwise - execute the general case:
If PyObject* corresponds to Sequence protocol - iterate over the
sequence elements and invoke the appropriate `pyopencv_to` function.
`std::vector<T>` to `PyObject*` conversion logic:
- If `std::vector<T>` is empty - return empty tuple.
- If `T` has a corresponding `Mat` `DataType` than return
Numpy array instance of the matching `dtype` e.g.
`std::vector<cv::Rect>` is returned as `np.ndarray` of shape `Nx4` and
`dtype=int`.
This branch helps to optimize further evaluations in user code.
- Otherwise - execute the general case:
Construct a tuple of length N = `std::vector::size` and insert
elements one by one.
Unnecessary functions were removed and code was rearranged to allow
compiler select the appropriate conversion function specialization.
607 lines
34 KiB
Python
607 lines
34 KiB
Python
#!/usr/bin/env python
|
|
from __future__ import print_function
|
|
|
|
import ctypes
|
|
from functools import partial
|
|
from collections import namedtuple
|
|
|
|
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_raiseGeneralException(self):
|
|
with self.assertRaises((cv.error,),
|
|
msg='C++ exception is not propagated to Python in the right way') as cm:
|
|
cv.utils.testRaiseGeneralException()
|
|
self.assertEqual(str(cm.exception), 'exception text')
|
|
|
|
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
|
|
|
|
def test_overload_resolution_can_choose_correct_overload(self):
|
|
val = 123
|
|
point = (51, 165)
|
|
self.assertEqual(cv.utils.testOverloadResolution(val, point),
|
|
'overload (int={}, point=(x={}, y={}))'.format(val, *point),
|
|
"Can't select first overload if all arguments are provided as positional")
|
|
|
|
self.assertEqual(cv.utils.testOverloadResolution(val, point=point),
|
|
'overload (int={}, point=(x={}, y={}))'.format(val, *point),
|
|
"Can't select first overload if one of the arguments are provided as keyword")
|
|
|
|
self.assertEqual(cv.utils.testOverloadResolution(val),
|
|
'overload (int={}, point=(x=42, y=24))'.format(val),
|
|
"Can't select first overload if one of the arguments has default value")
|
|
|
|
rect = (1, 5, 10, 23)
|
|
self.assertEqual(cv.utils.testOverloadResolution(rect),
|
|
'overload (rect=(x={}, y={}, w={}, h={}))'.format(*rect),
|
|
"Can't select second overload if all arguments are provided")
|
|
|
|
def test_overload_resolution_fails(self):
|
|
def test_overload_resolution(msg, *args, **kwargs):
|
|
no_exception_msg = 'Overload resolution failed without any exception for: "{}"'.format(msg)
|
|
wrong_exception_msg = 'Overload resolution failed with wrong exception type for: "{}"'.format(msg)
|
|
with self.assertRaises((cv.error, Exception), msg=no_exception_msg) as cm:
|
|
cv.utils.testOverloadResolution(*args, **kwargs)
|
|
self.assertEqual(type(cm.exception), cv.error, wrong_exception_msg)
|
|
|
|
test_overload_resolution('wrong second arg type (keyword arg)', 5, point=(1, 2, 3))
|
|
test_overload_resolution('wrong second arg type', 5, 2)
|
|
test_overload_resolution('wrong first arg', 3.4, (12, 21))
|
|
test_overload_resolution('wrong first arg, no second arg', 4.5)
|
|
test_overload_resolution('wrong args number for first overload', 3, (12, 21), 123)
|
|
test_overload_resolution('wrong args number for second overload', (3, 12, 12, 1), (12, 21))
|
|
# One of the common problems
|
|
test_overload_resolution('rect with float coordinates', (4.5, 4, 2, 1))
|
|
test_overload_resolution('rect with wrong number of coordinates', (4, 4, 1))
|
|
|
|
|
|
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")
|
|
a = np.zeros((2,3,4), dtype='f')
|
|
res6 = cv.utils.dumpInputArray(a)
|
|
self.assertEqual(res6, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=6 dims(-1)=2 size(-1)=3x2 type(-1)=CV_32FC4")
|
|
a = np.zeros((2,3,4,5), dtype='f')
|
|
res7 = cv.utils.dumpInputArray(a)
|
|
self.assertEqual(res7, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=120 dims(-1)=4 size(-1)=[2 3 4 5] type(-1)=CV_32FC1")
|
|
|
|
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")
|
|
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")
|
|
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")
|
|
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")
|
|
a = np.zeros((2,3,4), dtype='f')
|
|
res5 = cv.utils.dumpInputArrayOfArrays([a, b])
|
|
self.assertEqual(res5, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=2 dims(-1)=1 size(-1)=2x1 type(0)=CV_32FC4 dims(0)=2 size(0)=3x2")
|
|
# TODO: fix conversion error
|
|
#a = np.zeros((2,3,4,5), dtype='f')
|
|
#res6 = cv.utils.dumpInputArray([a, b])
|
|
#self.assertEqual(res6, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=2 dims(-1)=1 size(-1)=2x1 type(0)=CV_32FC1 dims(0)=4 size(0)=[2 3 4 5]")
|
|
|
|
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)
|
|
|
|
def test_parse_to_string_convertible(self):
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpString)
|
|
for convertible in (None, '', 's', 'str', str(123), np.str('test1'), np.str_('test2')):
|
|
expected = 'string: ' + (convertible if convertible else '')
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
def test_parse_to_string_not_convertible(self):
|
|
for not_convertible in ((12,), ('t', 'e', 's', 't'), np.array(['123', ]),
|
|
np.array(['t', 'e', 's', 't']), 1, True, False):
|
|
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpString(not_convertible)
|
|
|
|
def test_parse_to_rect_convertible(self):
|
|
Rect = namedtuple('Rect', ('x', 'y', 'w', 'h'))
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpRect)
|
|
for convertible in ((1, 2, 4, 5), [5, 3, 10, 20], np.array([10, 20, 23, 10]),
|
|
Rect(10, 30, 40, 55), tuple(np.array([40, 20, 24, 20])),
|
|
list(np.array([20, 40, 30, 35]))):
|
|
expected = 'rect: (x={}, y={}, w={}, h={})'.format(*convertible)
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
def test_parse_to_rect_not_convertible(self):
|
|
for not_convertible in (np.empty(shape=(4, 1)), (), [], np.array([]), (12, ),
|
|
[3, 4, 5, 10, 123], {1: 2, 3:4, 5:10, 6:30},
|
|
'1234', np.array([1, 2, 3, 4], dtype=np.float32),
|
|
np.array([[1, 2], [3, 4], [5, 6], [6, 8]]), (1, 2, 5, 1.5)):
|
|
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpRect(not_convertible)
|
|
|
|
def test_parse_to_rotated_rect_convertible(self):
|
|
RotatedRect = namedtuple('RotatedRect', ('center', 'size', 'angle'))
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpRotatedRect)
|
|
for convertible in (((2.5, 2.5), (10., 20.), 12.5), [[1.5, 10.5], (12.5, 51.5), 10],
|
|
RotatedRect((10, 40), np.array([10.5, 20.5]), 5),
|
|
np.array([[10, 6], [50, 50], 5.5], dtype=object)):
|
|
center, size, angle = convertible
|
|
expected = 'rotated_rect: (c_x={:.6f}, c_y={:.6f}, w={:.6f},' \
|
|
' h={:.6f}, a={:.6f})'.format(center[0], center[1],
|
|
size[0], size[1], angle)
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
def test_parse_to_rotated_rect_not_convertible(self):
|
|
for not_convertible in ([], (), np.array([]), (123, (45, 34), 1), {1: 2, 3: 4}, 123,
|
|
np.array([[123, 123, 14], [1, 3], 56], dtype=object), '123'):
|
|
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpRotatedRect(not_convertible)
|
|
|
|
def test_parse_to_term_criteria_convertible(self):
|
|
TermCriteria = namedtuple('TermCriteria', ('type', 'max_count', 'epsilon'))
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpTermCriteria)
|
|
for convertible in ((1, 10, 1e-3), [2, 30, 1e-1], np.array([10, 20, 0.5], dtype=object),
|
|
TermCriteria(0, 5, 0.1)):
|
|
expected = 'term_criteria: (type={}, max_count={}, epsilon={:.6f}'.format(*convertible)
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
def test_parse_to_term_criteria_not_convertible(self):
|
|
for not_convertible in ([], (), np.array([]), [1, 4], (10,), (1.5, 34, 0.1),
|
|
{1: 5, 3: 5, 10: 10}, '145'):
|
|
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpTermCriteria(not_convertible)
|
|
|
|
def test_parse_to_range_convertible_to_all(self):
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpRange)
|
|
for convertible in ((), [], np.array([])):
|
|
expected = 'range: all'
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
def test_parse_to_range_convertible(self):
|
|
Range = namedtuple('Range', ('start', 'end'))
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpRange)
|
|
for convertible in ((10, 20), [-1, 3], np.array([10, 24]), Range(-4, 6)):
|
|
expected = 'range: (s={}, e={})'.format(*convertible)
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
def test_parse_to_range_not_convertible(self):
|
|
for not_convertible in ((1, ), [40, ], np.array([1, 4, 6]), {'a': 1, 'b': 40},
|
|
(1.5, 13.5), [3, 6.7], np.array([6.3, 2.1]), '14, 4'):
|
|
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpRange(not_convertible)
|
|
|
|
def test_reserved_keywords_are_transformed(self):
|
|
default_lambda_value = 2
|
|
default_from_value = 3
|
|
format_str = "arg={}, lambda={}, from={}"
|
|
self.assertEqual(
|
|
cv.utils.testReservedKeywordConversion(20), format_str.format(20, default_lambda_value, default_from_value)
|
|
)
|
|
self.assertEqual(
|
|
cv.utils.testReservedKeywordConversion(10, lambda_=10), format_str.format(10, 10, default_from_value)
|
|
)
|
|
self.assertEqual(
|
|
cv.utils.testReservedKeywordConversion(10, from_=10), format_str.format(10, default_lambda_value, 10)
|
|
)
|
|
self.assertEqual(
|
|
cv.utils.testReservedKeywordConversion(20, lambda_=-4, from_=12), format_str.format(20, -4, 12)
|
|
)
|
|
|
|
def test_parse_vector_int_convertible(self):
|
|
np.random.seed(123098765)
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpVectorOfInt)
|
|
arr = np.random.randint(-20, 20, 40).astype(np.int32).reshape(10, 2, 2)
|
|
int_min, int_max = get_limits(ctypes.c_int)
|
|
for convertible in ((int_min, 1, 2, 3, int_max), [40, 50], tuple(),
|
|
np.array([int_min, -10, 24, int_max], dtype=np.int32),
|
|
np.array([10, 230, 12], dtype=np.uint8), arr[:, 0, 1],):
|
|
expected = "[" + ", ".join(map(str, convertible)) + "]"
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
def test_parse_vector_int_not_convertible(self):
|
|
np.random.seed(123098765)
|
|
arr = np.random.randint(-20, 20, 40).astype(np.float).reshape(10, 2, 2)
|
|
int_min, int_max = get_limits(ctypes.c_int)
|
|
test_dict = {1: 2, 3: 10, 10: 20}
|
|
for not_convertible in ((int_min, 1, 2.5, 3, int_max), [True, 50], 'test', test_dict,
|
|
reversed([1, 2, 3]),
|
|
np.array([int_min, -10, 24, [1, 2]], dtype=np.object),
|
|
np.array([[1, 2], [3, 4]]), arr[:, 0, 1],):
|
|
with self.assertRaises(TypeError, msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpVectorOfInt(not_convertible)
|
|
|
|
def test_parse_vector_double_convertible(self):
|
|
np.random.seed(1230965)
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpVectorOfDouble)
|
|
arr = np.random.randint(-20, 20, 40).astype(np.int32).reshape(10, 2, 2)
|
|
for convertible in ((1, 2.12, 3.5), [40, 50], tuple(),
|
|
np.array([-10, 24], dtype=np.int32),
|
|
np.array([-12.5, 1.4], dtype=np.double),
|
|
np.array([10, 230, 12], dtype=np.float), arr[:, 0, 1], ):
|
|
expected = "[" + ", ".join(map(lambda v: "{:.2f}".format(v), convertible)) + "]"
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
def test_parse_vector_double_not_convertible(self):
|
|
test_dict = {1: 2, 3: 10, 10: 20}
|
|
for not_convertible in (('t', 'e', 's', 't'), [True, 50.55], 'test', test_dict,
|
|
np.array([-10.1, 24.5, [1, 2]], dtype=np.object),
|
|
np.array([[1, 2], [3, 4]]),):
|
|
with self.assertRaises(TypeError, msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpVectorOfDouble(not_convertible)
|
|
|
|
def test_parse_vector_rect_convertible(self):
|
|
np.random.seed(1238765)
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpVectorOfRect)
|
|
arr_of_rect_int32 = np.random.randint(5, 20, 4 * 3).astype(np.int32).reshape(3, 4)
|
|
arr_of_rect_cast = np.random.randint(10, 40, 4 * 5).astype(np.uint8).reshape(5, 4)
|
|
for convertible in (((1, 2, 3, 4), (10, -20, 30, 10)), arr_of_rect_int32, arr_of_rect_cast,
|
|
arr_of_rect_int32.astype(np.int8), [[5, 3, 1, 4]],
|
|
((np.int8(4), np.uint8(10), np.int(32), np.int16(55)),)):
|
|
expected = "[" + ", ".join(map(lambda v: "[x={}, y={}, w={}, h={}]".format(*v), convertible)) + "]"
|
|
actual = try_to_convert(convertible)
|
|
self.assertEqual(expected, actual,
|
|
msg=get_conversion_error_msg(convertible, expected, actual))
|
|
|
|
def test_parse_vector_rect_not_convertible(self):
|
|
np.random.seed(1238765)
|
|
arr = np.random.randint(5, 20, 4 * 3).astype(np.float).reshape(3, 4)
|
|
for not_convertible in (((1, 2, 3, 4), (10.5, -20, 30.1, 10)), arr,
|
|
[[5, 3, 1, 4], []],
|
|
((np.float(4), np.uint8(10), np.int(32), np.int16(55)),)):
|
|
with self.assertRaises(TypeError, msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpVectorOfRect(not_convertible)
|
|
|
|
def test_vector_general_return(self):
|
|
expected_number_of_mats = 5
|
|
expected_shape = (10, 10, 3)
|
|
expected_type = np.uint8
|
|
mats = cv.utils.generateVectorOfMat(5, 10, 10, cv.CV_8UC3)
|
|
self.assertTrue(isinstance(mats, tuple),
|
|
"Vector of Mats objects should be returned as tuple. Got: {}".format(type(mats)))
|
|
self.assertEqual(len(mats), expected_number_of_mats, "Returned array has wrong length")
|
|
for mat in mats:
|
|
self.assertEqual(mat.shape, expected_shape, "Returned Mat has wrong shape")
|
|
self.assertEqual(mat.dtype, expected_type, "Returned Mat has wrong elements type")
|
|
empty_mats = cv.utils.generateVectorOfMat(0, 10, 10, cv.CV_32FC1)
|
|
self.assertTrue(isinstance(empty_mats, tuple),
|
|
"Empty vector should be returned as empty tuple. Got: {}".format(type(mats)))
|
|
self.assertEqual(len(empty_mats), 0, "Vector of size 0 should be returned as tuple of length 0")
|
|
|
|
def test_vector_fast_return(self):
|
|
expected_shape = (5, 4)
|
|
rects = cv.utils.generateVectorOfRect(expected_shape[0])
|
|
self.assertTrue(isinstance(rects, np.ndarray),
|
|
"Vector of rectangles should be returned as numpy array. Got: {}".format(type(rects)))
|
|
self.assertEqual(rects.dtype, np.int32, "Vector of rectangles has wrong elements type")
|
|
self.assertEqual(rects.shape, expected_shape, "Vector of rectangles has wrong shape")
|
|
empty_rects = cv.utils.generateVectorOfRect(0)
|
|
self.assertTrue(isinstance(empty_rects, tuple),
|
|
"Empty vector should be returned as empty tuple. Got: {}".format(type(empty_rects)))
|
|
self.assertEqual(len(empty_rects), 0, "Vector of size 0 should be returned as tuple of length 0")
|
|
|
|
expected_shape = (10,)
|
|
ints = cv.utils.generateVectorOfInt(expected_shape[0])
|
|
self.assertTrue(isinstance(ints, np.ndarray),
|
|
"Vector of integers should be returned as numpy array. Got: {}".format(type(ints)))
|
|
self.assertEqual(ints.dtype, np.int32, "Vector of integers has wrong elements type")
|
|
self.assertEqual(ints.shape, expected_shape, "Vector of integers has wrong shape.")
|
|
|
|
|
|
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()
|