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978 lines
49 KiB
Python
978 lines
49 KiB
Python
#!/usr/bin/env python
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from __future__ import print_function
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import sys
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import ctypes
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from functools import partial
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from collections import namedtuple
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import sys
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if sys.version_info[0] < 3:
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from collections import Sequence
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else:
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from collections.abc import Sequence
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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, unittest
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def is_numeric(dtype):
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return np.issubdtype(dtype, np.integer) or np.issubdtype(dtype, np.floating)
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def get_limits(dtype):
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if not is_numeric(dtype):
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return None, None
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if np.issubdtype(dtype, np.integer):
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info = np.iinfo(dtype)
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else:
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info = np.finfo(dtype)
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return info.min, info.max
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def get_conversion_error_msg(value, expected, actual):
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return 'Conversion "{}" of type "{}" failed\nExpected: "{}" vs Actual "{}"'.format(
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value, type(value).__name__, expected, actual
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)
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def get_no_exception_msg(value):
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return 'Exception is not risen for {} of type {}'.format(value, type(value).__name__)
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def rpad(src, dst_size, pad_value=0):
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"""Extend `src` up to `dst_size` with given value.
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Args:
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src (np.ndarray | tuple | list): 1d array like object to pad.
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dst_size (_type_): Desired `src` size after padding.
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pad_value (int, optional): Padding value. Defaults to 0.
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Returns:
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np.ndarray: 1d array with len == `dst_size`.
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"""
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src = np.asarray(src)
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if len(src.shape) != 1:
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raise ValueError("Only 1d arrays are supported")
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# Considering the meaning, it is desirable to use np.pad().
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# However, the old numpy doesn't include the following fixes and cannot work as expected.
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# So an alternative fix that combines np.append() and np.fill() is used.
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# https://docs.scipy.org/doc/numpy-1.13.0/release.html#support-for-returning-arrays-of-arbitrary-dimensions-in-apply-along-axis
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return np.append(src, np.full( dst_size - len(src), pad_value, dtype=src.dtype) )
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def get_ocv_arithm_op_table(apply_saturation=False):
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def saturate(func):
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def wrapped_func(x, y):
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dst_dtype = x.dtype
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if apply_saturation:
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if np.issubdtype(x.dtype, np.integer):
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x = x.astype(np.int64)
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# Apply padding or truncation for array-like `y` inputs
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if not isinstance(y, (float, int)):
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if len(y) > x.shape[-1]:
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y = y[:x.shape[-1]]
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else:
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y = rpad(y, x.shape[-1], pad_value=0)
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dst = func(x, y)
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if apply_saturation:
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min_val, max_val = get_limits(dst_dtype)
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dst = np.clip(dst, min_val, max_val)
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return dst.astype(dst_dtype)
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return wrapped_func
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@saturate
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def subtract(x, y):
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return x - y
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@saturate
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def add(x, y):
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return x + y
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@saturate
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def divide(x, y):
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if not isinstance(y, (int, float)):
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dst_dtype = np.result_type(x, y)
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y = np.array(y).astype(dst_dtype)
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_, max_value = get_limits(dst_dtype)
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y[y == 0] = max_value
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# to compatible between python2 and python3, it calicurates with float.
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# python2: int / int = int
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# python3: int / int = float
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dst = 1.0 * x / y
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if np.issubdtype(x.dtype, np.integer):
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dst = np.rint(dst)
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return dst
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@saturate
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def multiply(x, y):
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return x * y
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@saturate
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def absdiff(x, y):
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res = np.abs(x - y)
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return res
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return {
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cv.subtract: subtract,
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cv.add: add,
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cv.multiply: multiply,
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cv.divide: divide,
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cv.absdiff: absdiff
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}
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class Bindings(NewOpenCVTests):
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def test_inheritance(self):
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bm = cv.StereoBM_create()
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bm.getPreFilterCap() # from StereoBM
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bm.getBlockSize() # from SteroMatcher
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def test_raiseGeneralException(self):
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with self.assertRaises((cv.error,),
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msg='C++ exception is not propagated to Python in the right way') as cm:
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cv.utils.testRaiseGeneralException()
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self.assertEqual(str(cm.exception), 'exception text')
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def test_redirectError(self):
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try:
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cv.imshow("", None) # This causes an assert
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self.assertEqual("Dead code", 0)
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except cv.error as _e:
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pass
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handler_called = [False]
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def test_error_handler(status, func_name, err_msg, file_name, line):
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handler_called[0] = True
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cv.redirectError(test_error_handler)
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try:
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cv.imshow("", None) # This causes an assert
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self.assertEqual("Dead code", 0)
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except cv.error as _e:
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self.assertEqual(handler_called[0], True)
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pass
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cv.redirectError(None)
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try:
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cv.imshow("", None) # This causes an assert
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self.assertEqual("Dead code", 0)
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except cv.error as _e:
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pass
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def test_overload_resolution_can_choose_correct_overload(self):
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val = 123
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point = (51, 165)
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self.assertEqual(cv.utils.testOverloadResolution(val, point),
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'overload (int={}, point=(x={}, y={}))'.format(val, *point),
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"Can't select first overload if all arguments are provided as positional")
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self.assertEqual(cv.utils.testOverloadResolution(val, point=point),
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'overload (int={}, point=(x={}, y={}))'.format(val, *point),
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"Can't select first overload if one of the arguments are provided as keyword")
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self.assertEqual(cv.utils.testOverloadResolution(val),
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'overload (int={}, point=(x=42, y=24))'.format(val),
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"Can't select first overload if one of the arguments has default value")
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rect = (1, 5, 10, 23)
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self.assertEqual(cv.utils.testOverloadResolution(rect),
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'overload (rect=(x={}, y={}, w={}, h={}))'.format(*rect),
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"Can't select second overload if all arguments are provided")
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def test_overload_resolution_fails(self):
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def test_overload_resolution(msg, *args, **kwargs):
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no_exception_msg = 'Overload resolution failed without any exception for: "{}"'.format(msg)
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wrong_exception_msg = 'Overload resolution failed with wrong exception type for: "{}"'.format(msg)
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with self.assertRaises((cv.error, Exception), msg=no_exception_msg) as cm:
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res = cv.utils.testOverloadResolution(*args, **kwargs)
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self.fail("Unexpected result for {}: '{}'".format(msg, res))
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self.assertEqual(type(cm.exception), cv.error, wrong_exception_msg)
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test_overload_resolution('wrong second arg type (keyword arg)', 5, point=(1, 2, 3))
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test_overload_resolution('wrong second arg type', 5, 2)
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test_overload_resolution('wrong first arg', 3.4, (12, 21))
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test_overload_resolution('wrong first arg, no second arg', 4.5)
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test_overload_resolution('wrong args number for first overload', 3, (12, 21), 123)
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test_overload_resolution('wrong args number for second overload', (3, 12, 12, 1), (12, 21))
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# One of the common problems
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test_overload_resolution('rect with float coordinates', (4.5, 4, 2, 1))
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test_overload_resolution('rect with wrong number of coordinates', (4, 4, 1))
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def test_properties_with_reserved_keywords_names_are_transformed(self):
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obj = cv.utils.ClassWithKeywordProperties(except_arg=23)
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self.assertTrue(hasattr(obj, "lambda_"),
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msg="Class doesn't have RW property with converted name")
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try:
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obj.lambda_ = 32
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except Exception as e:
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self.fail("Failed to set value to RW property. Error: {}".format(e))
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self.assertTrue(hasattr(obj, "except_"),
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msg="Class doesn't have readonly property with converted name")
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self.assertEqual(obj.except_, 23,
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msg="Can't access readonly property value")
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with self.assertRaises(AttributeError):
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obj.except_ = 32
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def test_maketype(self):
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data = {
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cv.CV_8UC3: [cv.CV_8U, 3, cv.CV_8UC],
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cv.CV_16SC1: [cv.CV_16S, 1, cv.CV_16SC],
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cv.CV_32FC4: [cv.CV_32F, 4, cv.CV_32FC],
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cv.CV_64FC2: [cv.CV_64F, 2, cv.CV_64FC],
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cv.CV_8SC4: [cv.CV_8S, 4, cv.CV_8SC],
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cv.CV_16UC2: [cv.CV_16U, 2, cv.CV_16UC],
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cv.CV_32SC1: [cv.CV_32S, 1, cv.CV_32SC],
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cv.CV_16FC3: [cv.CV_16F, 3, cv.CV_16FC],
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}
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for ref, (depth, channels, func) in data.items():
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self.assertEqual(ref, cv.CV_MAKETYPE(depth, channels))
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self.assertEqual(ref, func(channels))
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class Arguments(NewOpenCVTests):
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def _try_to_convert(self, conversion, value):
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try:
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result = conversion(value).lower()
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except Exception as e:
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self.fail(
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'{} "{}" is risen for conversion {} of type {}'.format(
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type(e).__name__, e, value, type(value).__name__
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)
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)
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else:
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return result
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def test_InputArray(self):
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res1 = cv.utils.dumpInputArray(None)
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# self.assertEqual(res1, "InputArray: noArray()") # not supported
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self.assertEqual(res1, "InputArray: empty()=true kind=0x00010000 flags=0x01010000 total(-1)=0 dims(-1)=0 size(-1)=0x0 type(-1)=CV_8UC1")
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res2_1 = cv.utils.dumpInputArray((1, 2))
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self.assertEqual(res2_1, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=2 dims(-1)=2 size(-1)=1x2 type(-1)=CV_64FC1")
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res2_2 = cv.utils.dumpInputArray(1.5) # Scalar(1.5, 1.5, 1.5, 1.5)
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self.assertEqual(res2_2, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=4 dims(-1)=2 size(-1)=1x4 type(-1)=CV_64FC1")
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a = np.array([[1, 2], [3, 4], [5, 6]])
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res3 = cv.utils.dumpInputArray(a) # 32SC1
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self.assertEqual(res3, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=6 dims(-1)=2 size(-1)=2x3 type(-1)=CV_32SC1")
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a = np.array([[[1, 2], [3, 4], [5, 6]]], dtype='f')
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res4 = cv.utils.dumpInputArray(a) # 32FC2
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self.assertEqual(res4, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=3 dims(-1)=2 size(-1)=3x1 type(-1)=CV_32FC2")
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a = np.array([[[1, 2]], [[3, 4]], [[5, 6]]], dtype=float)
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res5 = cv.utils.dumpInputArray(a) # 64FC2
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self.assertEqual(res5, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=3 dims(-1)=2 size(-1)=1x3 type(-1)=CV_64FC2")
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a = np.zeros((2,3,4), dtype='f')
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res6 = cv.utils.dumpInputArray(a)
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self.assertEqual(res6, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=6 dims(-1)=2 size(-1)=3x2 type(-1)=CV_32FC4")
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a = np.zeros((2,3,4,5), dtype='f')
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res7 = cv.utils.dumpInputArray(a)
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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")
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def test_InputArrayOfArrays(self):
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res1 = cv.utils.dumpInputArrayOfArrays(None)
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# self.assertEqual(res1, "InputArray: noArray()") # not supported
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self.assertEqual(res1, "InputArrayOfArrays: empty()=true kind=0x00050000 flags=0x01050000 total(-1)=0 dims(-1)=1 size(-1)=0x0")
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res2_1 = cv.utils.dumpInputArrayOfArrays((1, 2)) # { Scalar:all(1), Scalar::all(2) }
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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")
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res2_2 = cv.utils.dumpInputArrayOfArrays([1.5])
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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")
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a = np.array([[1, 2], [3, 4], [5, 6]])
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b = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
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res3 = cv.utils.dumpInputArrayOfArrays([a, b])
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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")
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c = np.array([[[1, 2], [3, 4], [5, 6]]], dtype='f')
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res4 = cv.utils.dumpInputArrayOfArrays([c, a, b])
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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")
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a = np.zeros((2,3,4), dtype='f')
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res5 = cv.utils.dumpInputArrayOfArrays([a, b])
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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")
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# TODO: fix conversion error
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#a = np.zeros((2,3,4,5), dtype='f')
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#res6 = cv.utils.dumpInputArray([a, b])
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#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]")
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def test_unsupported_numpy_data_types_string_description(self):
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for dtype in (object, str, np.complex128):
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test_array = np.zeros((4, 4, 3), dtype=dtype)
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msg = ".*type = {} is not supported".format(test_array.dtype)
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if sys.version_info[0] < 3:
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self.assertRaisesRegexp(
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Exception, msg, cv.utils.dumpInputArray, test_array
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)
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else:
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self.assertRaisesRegex(
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Exception, msg, cv.utils.dumpInputArray, test_array
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)
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def test_numpy_writeable_flag_is_preserved(self):
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array = np.zeros((10, 10, 1), dtype=np.uint8)
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array.setflags(write=False)
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with self.assertRaises(Exception):
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cv.rectangle(array, (0, 0), (5, 5), (255), 2)
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def test_20968(self):
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pixel = np.uint8([[[40, 50, 200]]])
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_ = cv.cvtColor(pixel, cv.COLOR_RGB2BGR) # should not raise exception
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def test_parse_to_bool_convertible(self):
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try_to_convert = partial(self._try_to_convert, cv.utils.dumpBool)
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for convertible_true in (True, 1, 64, np.int8(123), np.int16(11), np.int32(2),
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np.int64(1), np.bool_(12)):
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actual = try_to_convert(convertible_true)
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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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for convertible_false in (False, 0, np.uint8(0), np.bool_(0), np.int_(0)):
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actual = try_to_convert(convertible_false)
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self.assertEqual('bool: false', actual,
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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.float32(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=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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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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for convertible_true in (-1, max_size_t):
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actual = try_to_convert(convertible_true)
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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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def test_parse_to_bool_not_convertible_extra(self):
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for not_convertible in (np.array([False]), np.array([True])):
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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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def test_parse_to_int_convertible(self):
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try_to_convert = partial(self._try_to_convert, cv.utils.dumpInt)
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min_int, max_int = get_limits(ctypes.c_int)
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for convertible in (-10, -1, 2, int(43.2), np.uint8(15), np.int8(33), np.int16(-13),
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np.int32(4), np.int64(345), (23), min_int, max_int, np.int_(33)):
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expected = 'int: {0:d}'.format(convertible)
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actual = try_to_convert(convertible)
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self.assertEqual(expected, actual,
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msg=get_conversion_error_msg(convertible, expected, actual))
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def test_parse_to_int_not_convertible(self):
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min_int, max_int = get_limits(ctypes.c_int)
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for not_convertible in (1.2, float(3), np.float32(4), 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)):
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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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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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np.array([11, ], dtype=np.uint8)):
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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.dumpInt(not_convertible)
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def test_parse_to_int64_convertible(self):
|
|
try_to_convert = partial(self._try_to_convert, cv.utils.dumpInt64)
|
|
min_int64, max_int64 = get_limits(ctypes.c_longlong)
|
|
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_int64, max_int64, np.int_(33)):
|
|
expected = 'int64: {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_int64_not_convertible(self):
|
|
min_int64, max_int64 = get_limits(ctypes.c_longlong)
|
|
for not_convertible in (1.2, np.float32(4), float(3), np.double(45), 's', 'str',
|
|
np.array([1, 2]), (1,), [1, 2], min_int64 - 1, max_int64 + 1,
|
|
complex(1, 1), complex(imag=2), complex(1.1), np.bool_(True),
|
|
True, False, np.float32(2.3), np.array([3, ], dtype=int),
|
|
np.array([-2, ], dtype=np.int32), np.array([11, ], dtype=np.uint8)):
|
|
with self.assertRaises((TypeError, OverflowError, ValueError),
|
|
msg=get_no_exception_msg(not_convertible)):
|
|
_ = cv.utils.dumpInt64(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.float32(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, np.float32(32.45), float(32), np.double(12.23),
|
|
np.float32(-12.3), np.float64(3.22), 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=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])):
|
|
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.float32(32.45), float(2), np.double(12.23),
|
|
np.float32(-12.3), np.float64(3.22), 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.float32),
|
|
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])):
|
|
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_('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_('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_wrap_rotated_rect(self):
|
|
center = (34.5, 52.)
|
|
size = (565.0, 140.0)
|
|
angle = -177.5
|
|
rect1 = cv.RotatedRect(center, size, angle)
|
|
self.assertEqual(rect1.center, center)
|
|
self.assertEqual(rect1.size, size)
|
|
self.assertEqual(rect1.angle, angle)
|
|
|
|
pts = [[ 319.7845, -5.6109037],
|
|
[ 313.6778, 134.25586],
|
|
[-250.78448, 109.6109],
|
|
[-244.6778, -30.25586]]
|
|
self.assertLess(np.max(np.abs(rect1.points() - pts)), 1e-4)
|
|
|
|
rect2 = cv.RotatedRect(pts[0], pts[1], pts[2])
|
|
_, inter_pts = cv.rotatedRectangleIntersection(rect1, rect2)
|
|
self.assertLess(np.max(np.abs(inter_pts.reshape(-1, 2) - pts)), 1e-4)
|
|
|
|
def test_result_rotated_rect_boundingRect2f(self):
|
|
center = (0, 0)
|
|
size = (10, 10)
|
|
angle = 0
|
|
gold_box = (-5.0, -5.0, 10.0, 10.0)
|
|
rect1 = cv.RotatedRect(center, size, angle)
|
|
bbox = rect1.boundingRect2f()
|
|
self.assertEqual(gold_box, bbox)
|
|
|
|
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.float32).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=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.float32), 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=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), 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.float32).reshape(3, 4)
|
|
for not_convertible in (((1, 2, 3, 4), (10.5, -20, 30.1, 10)), arr,
|
|
[[5, 3, 1, 4], []],
|
|
((float(4), np.uint8(10), 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.")
|
|
|
|
def test_result_rotated_rect_issue_20930(self):
|
|
rr = cv.utils.testRotatedRect(10, 20, 100, 200, 45)
|
|
self.assertTrue(isinstance(rr, tuple), msg=type(rr))
|
|
self.assertEqual(len(rr), 3)
|
|
|
|
rrv = cv.utils.testRotatedRectVector(10, 20, 100, 200, 45)
|
|
self.assertTrue(isinstance(rrv, tuple), msg=type(rrv))
|
|
self.assertEqual(len(rrv), 10)
|
|
|
|
rr = rrv[0]
|
|
self.assertTrue(isinstance(rr, tuple), msg=type(rrv))
|
|
self.assertEqual(len(rr), 3)
|
|
|
|
def test_nested_function_availability(self):
|
|
self.assertTrue(hasattr(cv.utils, "nested"),
|
|
msg="Module is not generated for nested namespace")
|
|
self.assertTrue(hasattr(cv.utils.nested, "testEchoBooleanFunction"),
|
|
msg="Function in nested module is not available")
|
|
|
|
if sys.version_info[0] < 3:
|
|
# Nested submodule is managed only by the global submodules dictionary
|
|
# and parent native module
|
|
expected_ref_count = 2
|
|
else:
|
|
# Nested submodule is managed by the global submodules dictionary,
|
|
# parent native module and Python part of the submodule
|
|
expected_ref_count = 3
|
|
|
|
# `getrefcount` temporary increases reference counter by 1
|
|
actual_ref_count = sys.getrefcount(cv.utils.nested) - 1
|
|
|
|
self.assertEqual(actual_ref_count, expected_ref_count,
|
|
msg="Nested submodule reference counter has wrong value\n"
|
|
"Expected: {}. Actual: {}".format(expected_ref_count, actual_ref_count))
|
|
for flag in (True, False):
|
|
self.assertEqual(flag, cv.utils.nested.testEchoBooleanFunction(flag),
|
|
msg="Function in nested module returns wrong result")
|
|
|
|
def test_inner_class_has_global_alias(self):
|
|
self.assertTrue(hasattr(cv.SimpleBlobDetector, "Params"),
|
|
msg="Class is not registered as inner class")
|
|
self.assertTrue(hasattr(cv, "SimpleBlobDetector_Params"),
|
|
msg="Inner class doesn't have alias in the global module")
|
|
self.assertEqual(cv.SimpleBlobDetector.Params, cv.SimpleBlobDetector_Params,
|
|
msg="Inner class and class in global module don't refer "
|
|
"to the same type")
|
|
|
|
def test_export_class_with_different_name(self):
|
|
self.assertTrue(hasattr(cv.utils.nested, "ExportClassName"),
|
|
msg="Class with export alias is not registered in the submodule")
|
|
self.assertTrue(hasattr(cv, "utils_nested_ExportClassName"),
|
|
msg="Class with export alias doesn't have alias in the "
|
|
"global module")
|
|
self.assertEqual(cv.utils.nested.ExportClassName.originalName(), "OriginalClassName")
|
|
|
|
instance = cv.utils.nested.ExportClassName.create()
|
|
self.assertTrue(isinstance(instance, cv.utils.nested.ExportClassName),
|
|
msg="Factory function returns wrong class instance: {}".format(type(instance)))
|
|
self.assertTrue(hasattr(cv.utils.nested, "ExportClassName_create"),
|
|
msg="Factory function should have alias in the same module as the class")
|
|
# self.assertFalse(hasattr(cv.utils.nested, "OriginalClassName_create"),
|
|
# msg="Factory function should not be registered with original class name, "\
|
|
# "when class has different export name")
|
|
|
|
def test_export_inner_class_of_class_exported_with_different_name(self):
|
|
if not hasattr(cv.utils.nested, "ExportClassName"):
|
|
raise unittest.SkipTest(
|
|
"Outer class with export alias is not registered in the submodule")
|
|
|
|
self.assertTrue(hasattr(cv.utils.nested.ExportClassName, "Params"),
|
|
msg="Inner class with export alias is not registered in "
|
|
"the outer class")
|
|
self.assertTrue(hasattr(cv, "utils_nested_ExportClassName_Params"),
|
|
msg="Inner class with export alias is not registered in "
|
|
"global module")
|
|
params = cv.utils.nested.ExportClassName.Params()
|
|
params.int_value = 45
|
|
params.float_value = 4.5
|
|
|
|
instance = cv.utils.nested.ExportClassName.create(params)
|
|
self.assertTrue(isinstance(instance, cv.utils.nested.ExportClassName),
|
|
msg="Factory function returns wrong class instance: {}".format(type(instance)))
|
|
self.assertEqual(
|
|
params.int_value, instance.getIntParam(),
|
|
msg="Class initialized with wrong integer parameter. Expected: {}. Actual: {}".format(
|
|
params.int_value, instance.getIntParam()
|
|
)
|
|
)
|
|
self.assertEqual(
|
|
params.float_value, instance.getFloatParam(),
|
|
msg="Class initialized with wrong integer parameter. Expected: {}. Actual: {}".format(
|
|
params.float_value, instance.getFloatParam()
|
|
)
|
|
)
|
|
|
|
def test_named_arguments_without_parameters(self):
|
|
src = np.ones((5, 5, 3), dtype=np.uint8)
|
|
arguments_dump, src_copy = cv.utils.copyMatAndDumpNamedArguments(src)
|
|
np.testing.assert_equal(src, src_copy)
|
|
self.assertEqual(arguments_dump, 'lambda=-1, sigma=0.0')
|
|
|
|
def test_named_arguments_without_output_argument(self):
|
|
src = np.zeros((2, 2, 3), dtype=np.uint8)
|
|
arguments_dump, src_copy = cv.utils.copyMatAndDumpNamedArguments(
|
|
src, lambda_=15, sigma=3.5
|
|
)
|
|
np.testing.assert_equal(src, src_copy)
|
|
self.assertEqual(arguments_dump, 'lambda=15, sigma=3.5')
|
|
|
|
def test_named_arguments_with_output_argument(self):
|
|
src = np.zeros((3, 3, 3), dtype=np.uint8)
|
|
dst = np.ones_like(src)
|
|
arguments_dump, src_copy = cv.utils.copyMatAndDumpNamedArguments(
|
|
src, dst, lambda_=25, sigma=5.5
|
|
)
|
|
np.testing.assert_equal(src, src_copy)
|
|
np.testing.assert_equal(dst, src_copy)
|
|
self.assertEqual(arguments_dump, 'lambda=25, sigma=5.5')
|
|
|
|
def test_arithm_op_without_saturation(self):
|
|
np.random.seed(4231568)
|
|
src = np.random.randint(20, 40, 8 * 4 * 3).astype(np.uint8).reshape(8, 4, 3)
|
|
operations = get_ocv_arithm_op_table(apply_saturation=False)
|
|
for ocv_op, numpy_op in operations.items():
|
|
for val in (2, 4, (5, ), (6, 4), (2., 4., 1.),
|
|
np.uint8([1, 2, 2]), np.float64([5, 2, 6, 3]),):
|
|
dst = ocv_op(src, val)
|
|
expected = numpy_op(src, val)
|
|
# Temporarily allows a difference of 1 for arm64 workaround.
|
|
self.assertLess(np.max(np.abs(dst - expected)), 2,
|
|
msg="Operation '{}' is failed for {}".format(ocv_op.__name__, val ) )
|
|
|
|
def test_arithm_op_with_saturation(self):
|
|
np.random.seed(4231568)
|
|
src = np.random.randint(20, 40, 4 * 8 * 4).astype(np.uint8).reshape(4, 8, 4)
|
|
operations = get_ocv_arithm_op_table(apply_saturation=True)
|
|
|
|
for ocv_op, numpy_op in operations.items():
|
|
for val in (10, 4, (40, ), (15, 12), (25., 41., 15.),
|
|
np.uint8([1, 2, 20]), np.float64([50, 21, 64, 30]),):
|
|
dst = ocv_op(src, val)
|
|
expected = numpy_op(src, val)
|
|
# Temporarily allows a difference of 1 for arm64 workaround.
|
|
self.assertLess(np.max(np.abs(dst - expected)), 2,
|
|
msg="Saturated Operation '{}' is failed for {}".format(ocv_op.__name__, val ) )
|
|
|
|
class CanUsePurePythonModuleFunction(NewOpenCVTests):
|
|
def test_can_get_ocv_version(self):
|
|
import sys
|
|
if sys.version_info[0] < 3:
|
|
raise unittest.SkipTest('Python 2.x is not supported')
|
|
|
|
self.assertEqual(cv.misc.get_ocv_version(), cv.__version__,
|
|
"Can't get package version using Python misc module")
|
|
|
|
def test_native_method_can_be_patched(self):
|
|
import sys
|
|
|
|
if sys.version_info[0] < 3:
|
|
raise unittest.SkipTest('Python 2.x is not supported')
|
|
|
|
res = cv.utils.testOverwriteNativeMethod(10)
|
|
self.assertTrue(isinstance(res, Sequence),
|
|
msg="Overwritten method should return sequence. "
|
|
"Got: {} of type {}".format(res, type(res)))
|
|
self.assertSequenceEqual(res, (11, 10),
|
|
msg="Failed to overwrite native method")
|
|
res = cv.utils._native.testOverwriteNativeMethod(123)
|
|
self.assertEqual(res, 123, msg="Failed to call native method implementation")
|
|
|
|
def test_default_matx_argument(self):
|
|
res = cv.utils.dumpVec2i()
|
|
self.assertEqual(res, "Vec2i(42, 24)",
|
|
msg="Default argument is not properly handled")
|
|
res = cv.utils.dumpVec2i((12, 21))
|
|
self.assertEqual(res, "Vec2i(12, 21)")
|
|
|
|
|
|
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()
|