I've read that the signed char and unsigned char types are not guaranteed to be 8 bits on every platform, but sometimes they have more than 8 bits.
If so, using OpenCv how can we be sure that CV_8U is always 8bit?
I've written a short function which takes a 8 bit Mat and happens to convert, if needed, CV_8SC1 Mat elements into uchars and CV_8UC1 into schar.
Now I'm afraid it is not platform independent an I should fix the code in some way (but don't know how).
P.S.: Similarly, how can CV_32S always be int, also on machine with no 32bit ints?
Can you give a reference of this (I've never heard of that)? Probably you mean the padding that may be added at the end of a row in a cv::Mat. That is of no problem, since the padding is usually not used, and especially no problem if you use the interface functions, e.g. the iterators (c.f.). If you would post some code, we could see, if your implementation actually had such problems.
// template methods for iteration over matrix elements.
// the iterators take care of skipping gaps in the end of rows (if any)
template<typename _Tp> MatIterator_<_Tp> begin();
template<typename _Tp> MatIterator_<_Tp> end();
the CV_32S will be always 32-bit integer because they use types like those defined in inttypes.h (e.g. int32_t, uint32_t) and not the platform specific int, long, whatever.
Related
I'm trying to use the CAMPARY library (CudA Multiple Precision ARithmetic librarY). I've downloaded the code and included it in my project. Since it supports both cpu and gpu, I'm starting with cpu to understand how it works and make sure it does what I need. But the intent is to use this with CUDA.
I'm able to instantiate an instance and assign a value, but I can't figure out how to get things back out. Consider:
#include <time.h>
#include "c:\\vss\\CAMPARY\\Doubles\\src_cpu\\multi_prec.h"
int main()
{
const char *value = "123456789012345678901234567";
multi_prec<2> a(value);
a.prettyPrint();
a.prettyPrintBin();
a.prettyPrintBin_UnevalSum();
char *cc = a.prettyPrintBF();
printf("\n%s\n", cc);
free(cc);
}
Compiles, links, runs (VS 2017). But the output is pretty unhelpful:
Prec = 2
Data[0] = 1.234568e+26
Data[1] = 7.486371e+08
Prec = 2
Data[0] = 0x1.987bf7c563caap+86;
Data[1] = 0x1.64fa5c3800000p+29;
0x1.987bf7c563caap+86 + 0x1.64fa5c3800000p+29;
1.234568e+26 7.486371e+08
Printing each of the doubles like this might be easy to do, but it doesn't tell you much about the value of the 128 number being stored. Performing highly accurate computations is of limited value if there's no way to output the results.
In addition to just printing out the value, eventually I also need to convert these numbers to ints (I'm willing to try it all in floats if there's a way to print, but I fear that both accuracy and speed will suffer). Unlike MPIR (which doesn't support CUDA), CAMPARY doesn't have any associated multi-precision int type, just floats. I can probably cobble together what I need (mostly just add/subtract/compare), but only if I can get the integer portion of CAMPARY's values back out, which I don't see a way to do.
CAMPARY doesn't seem to have any docs, so it's conceivable these capabilities are there, and I've simply overlooked them. And I'd rather ask on the CAMPARY discussion forum/mail list, but there doesn't seem to be one. That's why I'm asking here.
To sum up:
Is there any way to output the 128bit ( multi_prec<2> ) values from CAMPARY?
Is there any way to extract the integer portion from a CAMPARY multi_prec? Perhaps one of the (many) math functions in the library that I don't understand computes this?
There are really only 2 possible answers to this question:
There's another (better) multi-precision library that works on CUDA that does what you need.
Here's how to modify this library to do what you need.
The only people who could give the first answer are CUDA programmers. Unfortunately, if there were such a library, I feel confident talonmies would have known about it and mentioned it.
As for #2, why would anyone update this library if they weren't a CUDA programmer? There are other, much better multi-precision libraries out there. The ONLY benefit CAMPARY offers is that it supports CUDA. Which means the only people with any real motivation to work with or modify the library are CUDA programmers.
And, as the CUDA programmer with the most vested interest in solving this, I did figure out a solution (albeit an ugly one). I'm posting it here in the hopes that the information will be of value to future CAMPARY programmers. There's not much information out there for this library, so this is a start.
The first thing you need to understand is how CAMPARY stores its data. And, while not complex, it isn't what I expected. Coming from MPIR, I assumed that CAMPARY stored its data pretty much the same way: a fixed size exponent followed by an arbitrary number of bits for the mantissa.
But nope, CAMPARY went a different way. Looking at the code, we see:
private:
double data[prec];
Now, I assumed that this was just an arbitrary way of reserving the number of bits they needed. But no, they really do use prec doubles. Like so:
multi_prec<8> a("2633716138033644471646729489243748530829179225072491799768019505671233074369063908765111461703117249");
// Looking at a in the VS debugger:
[0] 2.6337161380336443e+99 const double
[1] 1.8496577979210756e+83 const double
[2] 1.2618399223120249e+67 const double
[3] -3.5978270144026257e+48 const double
[4] -1.1764513205926450e+32 const double
[5] -2479038053160511.0 const double
[6] 0.00000000000000000 const double
[7] 0.00000000000000000 const double
So, what they are doing is storing the max amount of precision possible in the first double, then the remainder is used to compute the next double and so on until they encompass the entire value, or run out of precision (dropping the least significant bits). Note that some of these are negative, which means the sum of the preceding values is a bit bigger than the actual value and they are correcting it downward.
With this in mind, we return to the question of how to print it.
In theory, you could just add all these together to get the right answer. But kinda by definition, we already know that C doesn't have a datatype to hold a value this size. But other libraries do (say MPIR). Now, MPIR doesn't work on CUDA, but it doesn't need to. You don't want to have your CUDA code printing out data. That's something you should be doing from the host anyway. So do the computations with the full power of CUDA, cudaMemcpy the results back, then use MPIR to print them out:
#define MPREC 8
void ShowP(const multi_prec<MPREC> value)
{
multi_prec<MPREC> temp(value), temp2;
// from mpir at mpir.org
mpf_t mp, mp2;
mpf_init2(mp, value.getPrec() * 64); // Make sure we reserve enough room
mpf_init(mp2); // Only needs to hold one double.
const double *ptr = value.getData();
mpf_set_d(mp, ptr[0]);
for (int x = 1; x < value.getPrec(); x++)
{
// MPIR doesn't have a mpf_add_d, so we need to load the value into
// an mpf_t.
mpf_set_d(mp2, ptr[x]);
mpf_add(mp, mp, mp2);
}
// Using base 10, write the full precision (0) of mp, to stdout.
mpf_out_str(stdout, 10, 0, mp);
mpf_clears(mp, mp2, NULL);
}
Used with the number stored in the multi_prec above, this outputs the exact same value. Yay.
It's not a particularly elegant solution. Having to add a second library just to print a value from the first is clearly sub-optimal. And this conversion can't be all that speedy either. But printing is typically done (much) less frequently than computing. If you do an hour's worth of computing and a handful of prints, the performance doesn't much matter. And it beats the heck out of not being able to print at all.
CAMPARY has a lot of shortcomings (undoced, unsupported, unmaintained). But for people who need mp numbers on CUDA (especially if you need sqrt), it's the best option I've found.
Newbie in IOS programming here.
I was looking at the Foundation Types Data Reference and have started to use the NSInteger typedef on the assumption that it will make my app more portable. However I often have a use for 16-bit and 8-bit integers and I don't see an NSShort or NSByte.
It seems wasteful to allocate a 32/64 bit variable for something that has a small range, say 0 to 12.
Are there any symbols that are defined for that?
Use uint8_t and uint16_t if you want types that are a specific size. There are also similar types for 32 and 64 bits values.
I tried to cv::bitwise_not to a cv::Mat matrix of double values. I applied like
cv::bitwise_not(img, imgtemp);
img is CV_64F data of 0 and 1. But imgtemp has all nonsense data inside.
I am expecting 0 in img to be 1 at imgtemp and 1 in img to be 0 at imgtemp. How to apply bitwise_not to a double Mat matrix?
Thanks
I cannot get the sense of doing a bitwise not of a double (floating point) value: you will be doing bitwise operations also on the exponent (see here). All bits will be inverted, from 0 to 1 and viceversa.
There is also a note on this aspect in the function documentation.
In case of a floating-point input array, its machine-specific bit
representation (usually IEEE754-compliant) is used for the operation.
If you want zeros to become ones and viceversa, as you suggested, you could do:
cv::threshold(warpmask, warpmaskTemp,0.5,1.0,THRESH_BINARY_INV)
(see documentation) (and yes, you can use same matrix for input and destination).
I think you are either getting the method signature wrong or wrongly named the parameters for the bitwise_not method.
According to [OpenCV 2.4.6 Documentation on bitwise_not() method] (http://docs.opencv.org/modules/core/doc/operations_on_arrays.html#void bitwise_not(InputArray src, OutputArray dst, InputArray mask))
void bitwise_not(InputArray src, OutputArray dst, InputArray mask=noArray())
If you are going to use any mask, it needs to be the last argument as mask is an optional for 'bitwise_not' method.
Additionally, all the data types need to be the same in order to avoid confusion. What I am trying to imply is that your source and destination data formats and any interim ones such as the method parameters must be in the same format. You cannot have on ein CV_64F and others in different. If I am not loosing my marbles here, bitwise operation would possibly require you to have all the data in unsigned or signed integer format for the sake of simplicity. Nevertheless, you should have all the types same.
About the garbage that you got, I think it is a general and good programming practice that you initialise your variables with some reasonable values. This helps when you are debugging step by step and ascertain the details where it failed.
Give it a try.
To follow on from Antonio's answer, you should use the right tool for the job. double is not an appropriate storage medium for boolean data.
In open cv you can type a boolean as an unsigned char (8bits). Although in typing your own true value you can pick any non-zero value, in open cv it is more natural to have 0/255; that way fitting in with open cv's bitwise operations and comparison operators. E.g. a bitwise not could be achieved by result = (input == 0) which can take any type. threshold in Antonio's answer maintains the same type (useful in some circumstances). For bitwise_not you should have it in the boolean format first.
Unfortunately opencv makes it very difficult to work with black and white bitwise data.
I'm reading and writing lots of FITS and DNG images which may contain data of an endianness different from my platform and/or opencl device.
Currently I swap the byte order in the host's memory if necessary which is very slow and requires an extra step.
Is there a fast way to pass a buffer of int/float/short having wrong endianess to an opencl-kernel?
Using an extra kernel run just for fixing the endianess would be ok; using some overheadless auto-fixing-read/-write operation would be perfect.
I know about the variable attribute ((endian(host/device))) but this doesn't help with a big endian FITS file on a little endian platform using a little endian device.
I thought about a solution like this one (neither implemented nor tested, yet):
uint4 mask = (uint4) (3, 2, 1, 0);
uchar4 swappedEndianness = shuffle(originalEndianness, mask);
// to be applied on a float/int-buffer somehow
Hoping there's a better solution out there.
Thanks in advance,
runtimeterror
Sure. Since you have a uchar4 - you can simply swizzle the components and write them back.
output[tid] = input[tid].wzyx;
swizzling is very also performant on SIMD architectures with very little cost, so you should be able to combine it with other operations in your kernel.
Hope this helps!
Most processor architectures perform best when using instructions to complete the operation which can fit its register width, for example 32/64-bit width. When CPU/GPU performs such byte-wise operators, using subscripts .wxyz for uchar4, they needs to use a mask to retrieve each byte from the integer, shift the byte, and then using integer add or or operator to the result. For the endianness swaping, the processor needs to perform above integer and, shift, add/or for 4 times because there are 4 bytes.
The most efficient way is as follows
#define EndianSwap(n) (rotate(n & 0x00FF00FF, 24U)|(rotate(n, 8U) & 0x00FF00FF)
n could be in any gentype, for example, an uint4 variable. Because OpenCL does not allow C++ type overloading, so the best choice is macro.
My current problem is that I would like to know the type of the cv::Mat-frames grabbed by cv::VideoCapture from a video file. The documentation doesn't specify that (as is often the case, so even if I have overlooked it in this particular case, it would still be helpful to get an answer for dealing with the problem in general).
Of course, I could open the appropriate OpenCV header file and go through the CV_64FC2, ... macros to find a macro which matches the Mat's type(). But I'm kind of sick of that. There must be an easier way.
Isn't there any function that lets me translate a Mat's type() to a human-readable format ? Like this:
cv::Mat myMatWithUnknownType;
// Some code modifying myMatWithUnknownType.
// ...
std::string readableType = myMatWithUnknownType.typeString();
std::cout << readableType; // Prints "CV_64FC3".
How do you deal with that?
First, the format that come from cameras is only one: CV_8UC3. This is hardcoded in OpenCV, and any video format is converted to this before being sent to user. So
capture >> frame;
Will always return a RGB image, of 8 bits per channel.
Now, for other types you can write your function, keeping in mid that there are not so many types supported in OpenCV: A Mat can be of type char, uchar, short, ushort, int, uint, float, double, of 1 to 512 channels (according to the latest docs.) So writing your own type_to_string() is not difficult.