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I am creating a VST(virtual instrument) program in cpp and I have an array of structs that represent various parameters in my program:
const FloatParam_Properties FloatParamProps[NUM_FLOAT_PARAMS] =
{
//Frequency
{"BaseFreq", "Base Freq", 0.0, 20.0, 5.0, 0.6}, //0
{"FreqDelta", "Freq Delta", -20.0, 20.0, 0.0, 0.6}, //1
...
//Wave
{"OscSelect", "Wave form", 0.0, 3.0, 0.0, 1.0}, //9
//Master
{"Volume", "Volume", 0.0, 1.0, 0.1, 0.4}, //10
};
Each element in this array is a struct, when I want to access these structs I have just hardcoded the indices(e.g. doing FloatParamProps[0] to access the Base Frequency). However, I would like to use the cpp preprocessor to give these hard-coded indices a name since everything here is known at compile-time and it would be good if I didn't have to just hard-code those defines either.
What I would like to do is something like:
const FloatParam_Properties FloatParamProps[NUM_FLOAT_PARAMS] =
{
//Frequency
DEF_FLOAT_PARAM(BaseFreq, "Base Freq", 0.0, 20.0, 5.0, 0.6), //0
DEF_FLOAT_PARAM(FreqDelta, "Freq Delta", -20.0, 20.0, 0.0, 0.6), //1
...
//Wave
DEF_FLOAT_PARAM(OscSelect, "Wave form", 0.0, 3.0, 0.0, 1.0), //9
//Master
DEF_FLOAT_PARAM(Volume, "Volume", 0.0, 1.0, 0.1, 0.4), //10
};
Where DEF_FLOAT_PARAM was a macro that would then take the first argument and turn that into a pre-processor define(or maybe constexpr) by using the __COUNTER__ macro. Then, if I wanted to access the first one I could do FloatParamProps[BaseFreq], for example. The issue I'm having is that you can't have a define within a macro so I can't define BaseFreq as a constant.
I also tried doing something like
#define DEF_FLOAT_PARAM(ID, Name, minVal, maxVal, defaultVal, skewFactor) P_##ID __COUNTER__ \
{#ID, Name, minVal, maxVal, defaultVal, skewFactor},
and the plan here was to try and take the define outside of the macro and just type that manually like so:
const FloatParam_Properties FloatParamProps[NUM_FLOAT_PARAMS] =
{
//Frequency
#define DEF_FLOAT_PARAM(BaseFreq, "Base Freq", 0.0, 20.0, 5.0, 0.6), //0
...
But the issue with that is that the pre-processor doesn't want to expand that macro when it's in-front of the define. If only I could tell it to expand the macro, it would work.
Does anyone know how I could do this?
Thanks.
Use x-macros:
#define PARAMS(X) \
X(BaseFreq, "Base Freq", 0.0, 20.0, 5.0, 0.6) \
X(FreqDelta, "Freq Delta", -20.0, 20.0, 0.0, 0.6) \
X(OscSelect, "Wave form", 0.0, 3.0, 0.0, 1.0)
const FloatParam_Properties FloatParamProps[NUM_FLOAT_PARAMS] =
{
#define PARAM_STRUCT(id, ...) {#id, __VA_ARGS__},
PARAMS(PARAM_STRUCT)
#undef PARAM_STRUCT
};
enum Params
{
#define PARAM_ENUM(id, ...) id,
PARAMS(PARAM_ENUM)
#define PARAM_ENUM
_count,
};
This will generate the same array you already have, and a enum with the constants matching the array indices.
I added two .eval() just in case. I got no compilation error, and no run time warning. Just segfault.
Thanks for helping me to fix this.
Test:
#include <Eigen/Eigen>
#include <iostream>
using namespace Eigen;
int main() {
Matrix<float, Dynamic, Dynamic> mat_b;
Matrix<float, Dynamic, Dynamic> mat_c;
mat_b << 1.0, 0.0, 0.5, 0.5,
0.0, 1.0, 0.5, 0.5,
1.0, 0.0, 1.0, 0.0,
0.0, 1.0, 0.0, 1.0;
mat_c << 0.0, 0.0, 0.0, 0.0, 1.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 1.0,
1.0, 0.0, 1.0, 0.0, 0.0, 0.0,
1.0, 0.0, 0.0, 1.0, 0.0, 0.0;
std::cout << (mat_b.transpose().eval() * mat_c).eval() << "\n";
}
Result:
Segmentation fault (core dumped)
As stated in documentatipon
The comma initializer
Eigen offers a comma initializer syntax which allows the user to easily set all the coefficients of a matrix, vector or array. Simply list the coefficients, starting at the top-left corner and moving from left to right and from the top to the bottom. The size of the object needs to be specified beforehand. If you list too few or too many coefficients, Eigen will complain.
emphasis is mine. If you expect that Matrix ctor would deduce size from your formatting, that simply not possible in C++. Looks like you created 16x1 and 24x1 matrix and then try to multiply 1x16 (transposed first one) to 24x1 which is not legal.
I'm currently using FileStorage class for storing matrices XML/YAML using OpenCV C++ API.
However, I have to write a Python Script that reads those XML/YAML files.
I'm looking for existing OpenCV Python API that can read the XML/YAML files generated by OpenCV C++ API
You can use PyYAML to parse the YAML file.
Since PyYAML doesn't understand OpenCV data types, you need to specify a constructor for each OpenCV data type that you are trying to load. For example:
import yaml
def opencv_matrix(loader, node):
mapping = loader.construct_mapping(node, deep=True)
mat = np.array(mapping["data"])
mat.resize(mapping["rows"], mapping["cols"])
return mat
yaml.add_constructor(u"tag:yaml.org,2002:opencv-matrix", opencv_matrix)
Once you've done that, loading the yaml file is simple:
with open(file_name) as fin:
result = yaml.load(fin.read())
Result will be a dict, where the keys are the names of whatever you saved in the YAML.
Using the FileStorage functions available in OpenCV 3.2, I've used this with success:
import cv2
fs = cv2.FileStorage("calibration.xml", cv2.FILE_STORAGE_READ)
fn = fs.getNode("Camera_Matrix")
print (fn.mat())
In addition to #misha's response, OpenCV YAML's are somewhat incompatible with Python.
Few reasons for incompatibility are:
Yaml created by OpenCV doesn't have a space after ":". Whereas Python requires it. [Ex: It should be a: 2, and not a:2 for Python]
First line of YAML file created by OpenCV is wrong. Either convert "%YAML:1.0" to "%YAML 1.0". Or skip the first line while reading.
The following function takes care of providing that:
import yaml
import re
def readYAMLFile(fileName):
ret = {}
skip_lines=1 # Skip the first line which says "%YAML:1.0". Or replace it with "%YAML 1.0"
with open(scoreFileName) as fin:
for i in range(skip_lines):
fin.readline()
yamlFileOut = fin.read()
myRe = re.compile(r":([^ ])") # Add space after ":", if it doesn't exist. Python yaml requirement
yamlFileOut = myRe.sub(r': \1', yamlFileOut)
ret = yaml.load(yamlFileOut)
return ret
outDict = readYAMLFile("file.yaml")
NOTE: Above response is applicable only for yaml's. XML's have their own share of problems, something I haven't explored completely.
I wrote a small snippet to read and write FileStorage-compatible YAMLs in Python:
# A yaml constructor is for loading from a yaml node.
# This is taken from #misha 's answer: http://stackoverflow.com/a/15942429
def opencv_matrix_constructor(loader, node):
mapping = loader.construct_mapping(node, deep=True)
mat = np.array(mapping["data"])
mat.resize(mapping["rows"], mapping["cols"])
return mat
yaml.add_constructor(u"tag:yaml.org,2002:opencv-matrix", opencv_matrix_constructor)
# A yaml representer is for dumping structs into a yaml node.
# So for an opencv_matrix type (to be compatible with c++'s FileStorage) we save the rows, cols, type and flattened-data
def opencv_matrix_representer(dumper, mat):
mapping = {'rows': mat.shape[0], 'cols': mat.shape[1], 'dt': 'd', 'data': mat.reshape(-1).tolist()}
return dumper.represent_mapping(u"tag:yaml.org,2002:opencv-matrix", mapping)
yaml.add_representer(np.ndarray, opencv_matrix_representer)
#examples
with open('output.yaml', 'w') as f:
yaml.dump({"a matrix": np.zeros((10,10)), "another_one": np.zeros((2,4))}, f)
with open('output.yaml', 'r') as f:
print yaml.load(f)
To improve on the previous answer by #Roy_Shilkrot I added support for numpy vectors as well as matrices:
# A yaml constructor is for loading from a yaml node.
# This is taken from #misha 's answer: http://stackoverflow.com/a/15942429
def opencv_matrix_constructor(loader, node):
mapping = loader.construct_mapping(node, deep=True)
mat = np.array(mapping["data"])
if mapping["cols"] > 1:
mat.resize(mapping["rows"], mapping["cols"])
else:
mat.resize(mapping["rows"], )
return mat
yaml.add_constructor(u"tag:yaml.org,2002:opencv-matrix", opencv_matrix_constructor)
# A yaml representer is for dumping structs into a yaml node.
# So for an opencv_matrix type (to be compatible with c++'s FileStorage) we save the rows, cols, type and flattened-data
def opencv_matrix_representer(dumper, mat):
if mat.ndim > 1:
mapping = {'rows': mat.shape[0], 'cols': mat.shape[1], 'dt': 'd', 'data': mat.reshape(-1).tolist()}
else:
mapping = {'rows': mat.shape[0], 'cols': 1, 'dt': 'd', 'data': mat.tolist()}
return dumper.represent_mapping(u"tag:yaml.org,2002:opencv-matrix", mapping)
yaml.add_representer(np.ndarray, opencv_matrix_representer)
Example:
with open('output.yaml', 'w') as f:
yaml.dump({"a matrix": np.zeros((10,10)), "another_one": np.zeros((5,))}, f)
with open('output.yaml', 'r') as f:
print yaml.load(f)
Output:
a matrix: !!opencv-matrix
cols: 10
data: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0]
dt: d
rows: 10
another_one: !!opencv-matrix
cols: 1
data: [0.0, 0.0, 0.0, 0.0, 0.0]
dt: d
rows: 5
Though I could not control the order of rows, cols, dt, data.
pip install opencv-contrib-python for video support to install specific version use pip install opencv-contrib-python
I'm refactoring some code that implements a formula and I want to do it test-first, to improve my testing skills, and leave the code covered.
This particular piece of code is a formula that takes 3 parameters and returns a value. I even have some data tables with expected results for different inputs, so in theory, I could jusst type a zillion tests, just changing the input parameters and checking the results against the corresponding expected value.
But I thought there should be a better way to do it, and looking at the docs I've found Value Parameterized Tests.
So, with that I now know how to automatically create the tests for the different inputs.
But how do I get the corresponding expected result to compare it with my calculated one?
The only thing I've been able to come up with is a static lookup table and a static member in the text fixture which is an index to the lookup table and is incremented in each run. Something like this:
#include "gtest/gtest.h"
double MyFormula(double A, double B, double C)
{
return A*B - C*C; // Example. The real one is much more complex
}
class MyTest:public ::testing::TestWithParam<std::tr1::tuple<double, double, double>>
{
protected:
MyTest(){ Index++; }
virtual void SetUp()
{
m_C = std::tr1::get<0>(GetParam());
m_A = std::tr1::get<1>(GetParam());
m_B = std::tr1::get<2>(GetParam());
}
double m_A;
double m_B;
double m_C;
static double ExpectedRes[];
static int Index;
};
int MyTest::Index = -1;
double MyTest::ExpectedRes[] =
{
// C = 1
// B: 1 2 3 4 5 6 7 8 9 10
/*A = 1*/ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0,
/*A = 2*/ 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.0, 15.0, 17.0, 19.0,
/*A = 3*/ 2.0, 5.0, 8.0, 11.0, 14.0, 17.0, 20.0, 23.0, 26.0, 29.0,
// C = 2
// B: 1 2 3 4 5 6 7 8 9 10
/*A = 1*/ -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0,
/*A = 2*/ -2.0, 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0,
/*A = 3*/ -1.0, 2.0, 5.0, 8.0, 11.0, 14.0, 17.0, 20.0, 23.0, 26.0,
};
TEST_P(MyTest, TestFormula)
{
double res = MyFormula(m_A, m_B, m_C);
ASSERT_EQ(ExpectedRes[Index], res);
}
INSTANTIATE_TEST_CASE_P(TestWithParameters,
MyTest,
testing::Combine( testing::Range(1.0, 3.0), // C
testing::Range(1.0, 4.0), // A
testing::Range(1.0, 11.0) // B
));
Is this a good approach or is there any better way to get the right expected result for each run?
Include the expected result along with the inputs. Instead of a triple of input values, make your test parameter be a 4-tuple.
class MyTest: public ::testing::TestWithParam<
std::tr1::tuple<double, double, double, double>>
{ };
TEST_P(MyTest, TestFormula)
{
double const C = std::tr1::get<0>(GetParam());
double const A = std::tr1::get<1>(GetParam());
double const B = std::tr1::get<2>(GetParam());
double const result = std::tr1::get<3>(GetParam());
ASSERT_EQ(result, MyFormula(A, B, C));
}
The downside is that you won't be able to keep your test parameters concise with testing::Combine. Instead, you can use testing::Values to define each distinct 4-tuple you wish to test. You might hit the argument-count limit for Values, so you can split your instantiations, such as by putting all the C = 1 cases in one and all the C = 2 cases in another.
INSTANTIATE_TEST_CASE_P(
TestWithParametersC1, MyTest, testing::Values(
// C A B
make_tuple( 1.0, 1.0, 1.0, 0.0),
make_tuple( 1.0, 1.0, 2.0, 1.0),
make_tuple( 1.0, 1.0, 3.0, 2.0),
// ...
));
INSTANTIATE_TEST_CASE_P(
TestWithParametersC2, MyTest, testing::Values(
// C A B
make_tuple( 2.0, 1.0, 1.0, -3.0),
make_tuple( 2.0, 1.0, 2.0, -2.0),
make_tuple( 2.0, 1.0, 3.0, -1.0),
// ...
));
Or you can put all the values in an array separate from your instantiation and then use testing::ValuesIn:
std::tr1::tuple<double, double, double, double> const FormulaTable[] = {
// C A B
make_tuple( 1.0, 1.0, 1.0, 0.0),
make_tuple( 1.0, 1.0, 2.0, 1.0),
make_tuple( 1.0, 1.0, 3.0, 2.0),
// ...
make_tuple( 2.0, 1.0, 1.0, -3.0),
make_tuple( 2.0, 1.0, 2.0, -2.0),
make_tuple( 2.0, 1.0, 3.0, -1.0),
// ...
};
INSTANTIATE_TEST_CASE_P(
TestWithParameters, MyTest, ::testing::ValuesIn(FormulaTable));
See hard coding the expected result is like you are limiting again the no of test cases. If you want to get a complete data driven model, I would rather suggest you to read inputs, expected result from a flat file/xml/xls file.
I don't have much experience with unit testing, but as a mathematician, I think there is not a lot more you could do.
If you would know some invariants of your formula, you could test for them, but i think that does only make sense in very few scenarios.
As an example, if you would want to test, if you have correctly implemented the natural exponential function, you could make use of the knowledge, that it's derivative should have the same value as the function itself. You could then calculate a numerical approximation to the derivative for a million points and see if they are close to the actual function value.
To me this should just work, so the fact it does not, almost certainly means I am the one in the wrong. Even though in principle a Transform< double, 3, Affine > is the same as a Matrix< double, 4, 4 >, they cannot be used together sensibly:
Affine3d rotMat( AngleAxisd( 45.0, ( Vector3d() << 0.0, 1.0, 0.0 ).finished() ) );
Matrix4d m;
m << 1.0, 0.0, 0.0, 6.0,
0.0, 1.0, 0.0, 6.0,
0.0, 0.0, 1.0, 6.0,
0.0, 0.0, 0.0, 1.0;
m = m * rotMat;
Results in a 'no match for operator=' error on the last line, and the in-place multiplication operator results in the same, trying to initialise a Matrix4d with Affine3d does not work either. Does anybody know how to actually use the Transform class in any useful way?
Thanks,
Cam
Just write:
m = m * rotMat.matrix();
I don't know if it is an oversight that Eigen doesn't define this multiplication implicitly or if it might interfere with other use cases of the library.