Drastic performance differences: debug vs release - c++

I have a simple algorithm which converts a bayer image channel (BGGR,RGGB,GBRG,GRBG) to rgb (demosaicing but without neighbors). In my implementation I have pre-set offset vectors which help me to translate the bayer channel index to its corresponding rgb channel indices.
Only problem is I'm getting awful performance in debug mode with MSVC11. Under release, for an input of 3264X2540 size the function completes in ~60ms. For the same input in debug, the function completes in ~20,000ms. That's more than X300 difference and since some developers are runnig my application in debug, it's unacceptable.
My code:
void ConvertBayerToRgbImageDemosaic(int* BayerChannel, int* RgbChannel, int Width, int
Height, ColorSpace ColorSpace)
{
int rgbOffsets[4]; //translates color location in Bayer block to it's location in RGB block. So R->0, G->1, B->2
std::vector<int> bayerToRgbOffsets[4]; //the offsets from every color in the Bayer block to (bayer) indices it will be copied to (R,B are copied to all indices, Gr to R and Gb to B).
//calculate offsets according to color space
switch (ColorSpace)
{
case ColorSpace::BGGR:
/*
B G
G R
*/
rgbOffsets[0] = 2; //B->0
rgbOffsets[1] = 1; //G->1
rgbOffsets[2] = 1; //G->1
rgbOffsets[3] = 0; //R->0
//B is copied to every pixel in it's block
bayerToRgbOffsets[0].push_back(0);
bayerToRgbOffsets[0].push_back(1);
bayerToRgbOffsets[0].push_back(Width);
bayerToRgbOffsets[0].push_back(Width + 1);
//Gb is copied to it's neighbouring B
bayerToRgbOffsets[1].push_back(-1);
bayerToRgbOffsets[1].push_back(0);
//GR is copied to it's neighbouring R
bayerToRgbOffsets[2].push_back(0);
bayerToRgbOffsets[2].push_back(1);
//R is copied to every pixel in it's block
bayerToRgbOffsets[3].push_back(-Width - 1);
bayerToRgbOffsets[3].push_back(-Width);
bayerToRgbOffsets[3].push_back(-1);
bayerToRgbOffsets[3].push_back(0);
break;
... other color spaces
}
for (auto row = 0; row < Height; row++)
{
for (auto col = 0, bayerIndex = row * Width; col < Width; col++, bayerIndex++)
{
auto colorIndex = (row%2)*2 + (col%2); //0...3, For example in BGGR: 0->B, 1->Gb, 2->Gr, 3->R
//iteration over bayerToRgbOffsets is O(1) since it is either sized 2 or 4.
std::for_each(bayerToRgbOffsets[colorIndex].begin(), bayerToRgbOffsets[colorIndex].end(),
[&](int colorOffset)
{
auto rgbIndex = (bayerIndex + colorOffset) * 3 + rgbOffsets[offset];
RgbChannel[rgbIndex] = BayerChannel[bayerIndex];
});
}
}
}
What I've tried:
I tried turing on optimization (/O2) for the debug build with no significant differences.
I tried replacing the inner for_each statement with a plain old for loop but to no avail. I have a very similar algorithm which converts bayer to "green" rgb (without copying the data to neighboring pixels in the block) in which I'm not using the std::vector and there there is the expected runtime difference between debug and release (X2-X3). So, could the std::vector be the problem? If so, how do I overcome it?

As you use std::vector, It will help to disable iterator debugging.
MSDN shows how to do it.
In simple terms, make this #define before you include any STL headers:
#define _HAS_ITERATOR_DEBUGGING 0
In my experience, this gives a major boost in performance of Debug builds, although of course you do lose some Debugging functionality.

In VS you can use below settings for debugging, Disabled (/Od). Choose one of the other options (Minimum Size(/O1), Maximum Speed(/O2), Full Optimization(/Ox), or Custom). Along with iterator optimization which Roger Rowland mentioned...

Related

C++ improving palette indexing algorithm

I have a game engine that indexes colors of some bitmaps which allows using some of the crazy effects of olde (color strobing etc.). Sadly, the indexing algorithm is neither slow nor fast, but since the spritesheets these days are gigantic it really adds up. Currently, loading a single large spritesheet can take 150+ milliseconds, which is an eternity, relatively speaking.
This is the algorithm:
auto& palette = p->pal; // vector
auto& lookup = p->lookup; // vector_map
palette.reserve(200); // There are on average ~100 unique colors
palette.push_back(0); // Index zero is the blank color
uint32_t lastColor = 0;
uint32_t lastPalette = 0;
for (size_t i = 0; i < pixels; i++)
{
const auto color = data[i];
if (color == lastColor)
{
data[i] = lastPalette;
continue;
}
else if (color == 0)
{
continue;
}
uint32_t j = 0;
const auto& it = lookup.find(color);
if (it != lookup.end()) {
j = it->second;
}
else
{
j = palette.size();
palette.push_back(color);
lookup.emplace(color, j);
}
lastColor = color;
// Write the index back to the bitmap:
// (this is just a GPU texture encoding, don't mind it)
data[i] = (j & 255) | ((j >> 8) << (8 + 6));
lastPalette = data[i];
}
The base algorithm is fairly straight-forward:
Go through each pixel, find or create an entry for it (the color), write the index back to the image.
Now, can you parallelize this? Probably not. I have tried with OMP and regular threads. It's simply not going to be fast because regardless of how much time you save by going through each portion of the image separately, at the end you have to have a common set of indexes that apply throughout the whole image, and those indexes have to be written back to the image. Sadly, finding the unique colors first and then writing back using parallelization is also slower than doing it once, sequentially. Makes sense, doesn't it?
Using a bitset has no function here. Knowing whether a color exists is useful, but the colors are 32-bit, which makes for 2^32 bits (aka. 530MB). In contrast, 24-bits is only ~2MB, which might be a micro-optimization. I'm not really looking for that anyway. I need to cut the time by 10x.
So, any ideas? Would it be possible to process 4 or 8 colors at the same time using SSE/AVX?

Fast, good quality pixel interpolation for extreme image downscaling

In my program, I am downscaling an image of 500px or larger to an extreme level of approx 16px-32px. The source image is user-specified so I do not have control over its size. As you can imagine, few pixel interpolations hold up and inevitably the result is heavily aliased.
I've tried bilinear, bicubic and square average sampling. The square average sampling actually provides the most decent results but the smaller it gets, the larger the sampling radius has to be. As a result, it gets quite slow - slower than the other interpolation methods.
I have also tried an adaptive square average sampling so that the smaller it gets the greater the sampling radius, while the closer it is to its original size, the smaller the sampling radius. However, it produces problems and I am not convinced this is the best approach.
So the question is: What is the recommended type of pixel interpolation that is fast and works well on such extreme levels of downscaling?
I do not wish to use a library so I will need something that I can code by hand and isn't too complex. I am working in C++ with VS 2012.
Here's some example code I've tried as requested (hopefully without errors from my pseudo-code cut and paste). This performs a 7x7 average downscale and although it's a better result than bilinear or bicubic interpolation, it also takes quite a hit:
// Sizing control
ctl(0): "Resize",Range=(0,800),Val=100
// Variables
float fracx,fracy;
int Xnew,Ynew,p,q,Calc;
int x,y,p1,q1,i,j;
//New image dimensions
Xnew=image->width*ctl(0)/100;
Ynew=image->height*ctl(0)/100;
for (y=0; y<image->height; y++){ // rows
for (x=0; x<image->width; x++){ // columns
p1=(int)x*image->width/Xnew;
q1=(int)y*image->height/Ynew;
for (z=0; z<3; z++){ // channels
for (i=-3;i<=3;i++) {
for (j=-3;j<=3;j++) {
Calc += (int)(src(p1-i,q1-j,z));
} //j
} //i
Calc /= 49;
pset(x, y, z, Calc);
} // channels
} // columns
} // rows
Thanks!
The first point is to use pointers to your data. Never use indexes at every pixel. When you write: src(p1-i,q1-j,z) or pset(x, y, z, Calc) how much computation is being made? Use pointers to data and manipulate those.
Second: your algorithm is wrong. You don't want an average filter, but you want to make a grid on your source image and for every grid cell compute the average and put it in the corresponding pixel of the output image.
The specific solution should be tailored to your data representation, but it could be something like this:
std::vector<uint32_t> accum(Xnew);
std::vector<uint32_t> count(Xnew);
uint32_t *paccum, *pcount;
uint8_t* pin = /*pointer to input data*/;
uint8_t* pout = /*pointer to output data*/;
for (int dr = 0, sr = 0, w = image->width, h = image->height; sr < h; ++dr) {
memset(paccum = accum.data(), 0, Xnew*4);
memset(pcount = count.data(), 0, Xnew*4);
while (sr * Ynew / h == dr) {
paccum = accum.data();
pcount = count.data();
for (int dc = 0, sc = 0; sc < w; ++sc) {
*paccum += *i;
*pcount += 1;
++pin;
if (sc * Xnew / w > dc) {
++dc;
++paccum;
++pcount;
}
}
sr++;
}
std::transform(begin(accum), end(accum), begin(count), pout, std::divides<uint32_t>());
pout += Xnew;
}
This was written using my own library (still in development) and it seems to work, but later I changed the variables names in order to make it simpler here, so I don't guarantee anything!
The idea is to have a local buffer of 32 bit ints which can hold the partial sum of all pixels in the rows which fall in a row of the output image. Then you divide by the cell count and save the output to the final image.
The first thing you should do is to set up a performance evaluation system to measure how much any change impacts on the performance.
As said precedently, you should not use indexes but pointers for (probably) a substantial
speed up & not simply average as a basic averaging of pixels is basically a blur filter.
I would highly advise you to rework your code to be using "kernels". This is the matrix representing the ratio of each pixel used. That way, you will be able to test different strategies and optimize quality.
Example of kernels:
https://en.wikipedia.org/wiki/Kernel_(image_processing)
Upsampling/downsampling kernel:
http://www.johncostella.com/magic/
Note, from the code it seems you apply a 3x3 kernel but initially done on a 7x7 kernel. The equivalent 3x3 kernel as posted would be:
[1 1 1]
[1 1 1] * 1/9
[1 1 1]

How can I pixelate a 1d array

I want to pixelate an image stored in a 1d array, although i am not sure how to do it, this is what i have comeup with so far...
the value of pixelation is currently 3 for testing purposes.
currently it just creates a section of randomly coloured pixels along the left third of the image, if i increase the value of pixelation the amount of random coloured pixels decreases and vice versa, so what am i doing wrong?
I have also already implemented the rotation, reading of the image and saving of a new image this is just a separate function which i need assistance with.
picture pixelate( const std::string& file_name, picture& tempImage, int& pixelation /* TODO: OTHER PARAMETERS HERE */)
{
picture pixelated = tempImage;
RGB tempPixel;
tempPixel.r = 0;
tempPixel.g = 0;
tempPixel.b = 0;
int counter = 0;
int numtimesrun = 0;
for (int x = 1; x<tempImage.width; x+=pixelation)
{
for (int y = 1; y<tempImage.height; y+=pixelation)
{
//RGB tempcol;
//tempcol for pixelate
for (int i = 1; i<pixelation; i++)
{
for (int j = 1; j<pixelation; j++)
{
tempPixel.r +=tempImage.pixel[counter+pixelation*numtimesrun].colour.r;
tempPixel.g +=tempImage.pixel[counter+pixelation*numtimesrun].colour.g;
tempPixel.b +=tempImage.pixel[counter+pixelation*numtimesrun].colour.b;
counter++;
//read colour
}
}
for (int k = 1; k<pixelation; k++)
{
for (int l = 1; l<pixelation; l++)
{
pixelated.pixel[numtimesrun].colour.r = tempPixel.r/pixelation;
pixelated.pixel[numtimesrun].colour.g = tempPixel.g/pixelation;
pixelated.pixel[numtimesrun].colour.b = tempPixel.b/pixelation;
//set colour
}
}
counter = 0;
numtimesrun++;
}
cout << x << endl;
}
cout << "Image successfully pixelated." << endl;
return pixelated;
}
I'm not too sure what you really want to do with your code, but I can see a few problems.
For one, you use for() loops with variables starting at 1. That's certainly wrong. Arrays in C/C++ start at 0.
The other main problem I can see is the pixelation parameter. You use it to increase x and y without knowing (at least in that function) whether it is a multiple of width and height. If not, you will definitively be missing pixels on the right edge and at the bottom (which edges will depend on the orientation, of course). Again, it very much depends on what you're trying to achieve.
Also the i and j loops start at the position defined by counter and numtimesrun which means that the last line you want to hit is not tempImage.width or tempImage.height. With that you are rather likely to have many overflows. Actually that would also explain the problems you see on the edges. (see update below)
Another potential problem, cannot tell for sure without seeing the structure declaration, but this sum using tempPixel.c += <value> may overflow. If the RGB components are defined as unsigned char (rather common) then you will definitively get overflows. So your average sum is broken if that's the fact. If that structure uses floats, then you're good.
Note also that your average is wrong. You are adding source data for pixelation x pixalation and your average is calculated as sum / pixelation. So you get a total which is pixalation times larger. You probably wanted sum / (pixelation * pixelation).
Your first loop with i and j computes a sum. The math is most certainly wrong. The counter + pixelation * numtimesrun expression will start reading at the second line, it seems. However, you are reading i * j values. That being said, it may be what you are trying to do (i.e. a moving average) in which case it could be optimized but I'll leave that out for now.
Update
If I understand what you are doing, a representation would be something like a filter. There is a picture of a 3x3:
.+. *
+*+ =>
.+.
What is on the left is what you are reading. This means the source needs to be at least 3x3. What I show on the right is the result. As we can see, the result needs to be 1x1. From what I see in your code you do not take that in account at all. (the varied characters represent varied weights, in your case all weights are 1.0).
You have two ways to handle that problem:
The resulting image has a size of width - pixelation * 2 + 1 by height - pixelation * 2 + 1; in this case you keep one result and do not care about the edges...
You rewrite the code to handle edges. This means you use less source data to compute the resulting edges. Another way is to compute the edge cases and save that in several output pixels (i.e. duplicate the pixels on the edges).
Update 2
Hmmm... looking at your code again, it seems that you compute the average of the 3x3 and save it in the 3x3:
.+. ***
+*+ => ***
.+. ***
Then the problem is different. The numtimesrun is wrong. In your k and l loops you save the pixels pixelation * pixelation in the SAME pixel and that advanced by one each time... so you are doing what I shown in my first update, but it looks like you were trying to do what is shown in my 2nd update.
The numtimesrun could be increased by pixelation each time:
numtimesrun += pixelation;
However, that's not enough to fix your k and l loops. There you probably need to calculate the correct destination. Maybe something like this (also requires a reset of the counter before the loop):
counter = 0;
... for loops ...
pixelated.pixel[counter+pixelation*numtimesrun].colour.r = ...;
... (take care of g and b)
++counter;
Yet again, I cannot tell for sure what you are trying to do, so I do not know why you'd want to copy the same pixel pixelation x pixelation times. But that explains why you get data only at the left (or top) of the image (very much depends on the orientation, one side for sure. And if that's 1/3rd then pixelation is probably 3.)
WARNING: if you implement the save properly, you'll experience crashes if you do not take care of the overflows mentioned earlier.
Update 3
As explained by Mark in the comment below, you have an array representing a 2d image. In that case, your counter variable is completely wrong since this is 100% linear whereas the 2d image is not. The 2nd line is width further away. At this point, you read the first 3 pixels at the top-left, then the next 3 pixels on the same, and finally the next 3 pixels still on the same line. Of course, it could be that your image is thus defined and these pixels are really one after another, although it is not very likely...
Mark's answer is concise and gives you the information necessary to access the correct pixels. However, you will still be hit by the overflow and possibly the fact that the width and height parameters are not a multiple of pixelation...
I don't do a lot of C++, but here's a pixelate function I wrote for Processing. It takes an argument of the width/height of the pixels you want to create.
void pixelateImage(int pxSize) {
// use ratio of height/width...
float ratio;
if (width < height) {
ratio = height/width;
}
else {
ratio = width/height;
}
// ... to set pixel height
int pxH = int(pxSize * ratio);
noStroke();
for (int x=0; x<width; x+=pxSize) {
for (int y=0; y<height; y+=pxH) {
fill(p.get(x, y));
rect(x, y, pxSize, pxH);
}
}
}
Without the built-in rect() function you'd have to write pixel-by-pixel using another two for loops:
for (int px=0; px<pxSize; px++) {
for (int py=0; py<pxH; py++) {
pixelated.pixel[py * tempImage.width + px].colour.r = tempPixel.r;
pixelated.pixel[py * tempImage.width + px].colour.g = tempPixel.g;
pixelated.pixel[py * tempImage.width + px].colour.b = tempPixel.b;
}
}
Generally when accessing an image stored in a 1D buffer, each row of the image will be stored as consecutive pixels and the next row will follow immediately after. The way to address into such a buffer is:
image[y*width+x]
For your purposes you want both inner loops to generate coordinates that go from the top and left of the pixelation square to the bottom right.

Vertically flipping an Char array: is there a more efficient way?

Lets start with some code:
QByteArray OpenGLWidget::modifyImage(QByteArray imageArray, const int width, const int height){
if (vertFlip){
/* Each pixel constist of four unisgned chars: Red Green Blue Alpha.
* The field is normally 640*480, this means that the whole picture is in fact 640*4 uChars wide.
* The whole ByteArray is onedimensional, this means that 640*4 is the red of the first pixel of the second row
* This function is EXTREMELY SLOW
*/
QByteArray tempArray = imageArray;
for (int h = 0; h < height; ++h){
for (int w = 0; w < width/2; ++w){
for (int i = 0; i < 4; ++i){
imageArray.data()[h*width*4 + 4*w + i] = tempArray.data()[h*width*4 + (4*width - 4*w) + i ];
imageArray.data()[h*width*4 + (4*width - 4*w) + i] = tempArray.data()[h*width*4 + 4*w + i];
}
}
}
}
return imageArray;
}
This is the code I use right now to vertically flip an image which is 640*480 (The image is actually not guaranteed to be 640*480, but it mostly is). The color encoding is RGBA, which means that the total array size is 640*480*4. I get the images with 30 FPS, and I want to show them on the screen with the same FPS.
On an older CPU (Athlon x2) this code is just too much: the CPU is racing to keep up with the 30 FPS, so the question is: can I do this more efficient?
I am also working with OpenGL, does that have a gimmic I am not aware of that can flip images with relativly low CPU/GPU usage?
According to this question, you can flip an image in OpenGL by scaling it by (1,-1,1). This question explains how to do transformations and scaling.
You can improve at least by doing it blockwise, making use of the cache architecture. In your example one of the accesses (either the read OR the write) will be off-cache.
For a start it can help to "capture scanlines" if you're using two loops to loop through the pixels of an image, like so:
for (int y = 0; y < height; ++y)
{
// Capture scanline.
char* scanline = imageArray.data() + y*width*4;
for (int x = 0; x < width/2; ++x)
{
const int flipped_x = width - x-1;
for (int i = 0; i < 4; ++i)
swap(scanline[x*4 + i], scanline[flipped_x*4 + i]);
}
}
Another thing to note is that I used swap instead of a temporary image. That'll tend to be more efficient since you can just swap using registers instead of loading pixels from a copy of the entire image.
But also it generally helps if you use a 32-bit integer instead of working one byte at a time if you're going to be doing anything like this. If you're working with pixels with 8-bit types but know that each pixel is 32-bits, e.g., as in your case, you can generally get away with a case to uint32_t*, e.g.
for (int y = 0; y < height; ++y)
{
uint32_t* scanline = (uint32_t*)imageArray.data() + y*width;
std::reverse(scanline, scanline + width);
}
At this point you might parellelize the y loop. Flipping an image horizontally (it should be "horizontal" if I understood your original code correctly) in this way is a little bit tricky with the access patterns, but you should be able to get quite a decent boost using the above techniques.
I am also working with OpenGL, does that have a gimmic I am not aware
of that can flip images with relativly low CPU/GPU usage?
Naturally the fastest way to flip images is to not touch their pixels at all and just save the flipping for the final part of the pipeline when you render the result. For this you might render a texture in OGL with negative scaling instead of modifying the pixels of a texture.
Another thing that's really useful in video and image processing is to represent an image to process like this for all your image operations:
struct Image32
{
uint32_t* pixels;
int32_t width;
int32_t height;
int32_t x_stride;
int32_t y_stride;
};
The stride fields are what you use to get from one scanline (row) of an image to the next vertically and one column to the next horizontally. When you use this representation, you can use negative values for the stride and offset the pixels accordingly. You can also use the stride fields to, say, render only every other scanline of an image for fast interactive half-res scanline previews by using y_stride=height*2 and height/=2. You can quarter-res an image by setting x stride to 2 and y stride to 2*width and then halving the width and height. You can render a cropped image without making your blit functions accept a boatload of parameters by just modifying these fields and keeping the y stride to width to get from one row of the cropped section of the image to the next:
// Using the stride representation of Image32, this can now
// blit a cropped source, a horizontally flipped source,
// a vertically flipped source, a source flipped both ways,
// a half-res source, a quarter-res source, a quarter-res
// source that is horizontally flipped and cropped, etc,
// and all without modifying the source image in advance
// or having to accept all kinds of extra drawing parameters.
void blit(int dst_x, int dst_y, Image32 dst, Image32 src);
// We don't have to do things like this (and I think I lost
// some capabilities with this version below but it hurts my
// brain too much to think about what capabilities were lost):
void blit_gross(int dst_x, int dst_y, int dst_w, int dst_h, uint32_t* dst,
int src_x, int src_y, int src_w, int src_h,
const uint32_t* src, bool flip_x, bool flip_y);
By using negative values and passing it to an image operation (ex: a blit operation), the result will naturally be flipped without having to actually flip the image. It'll end up being "drawn flipped", so to speak, just as with the case of using OGL with a negative scaling transformation matrix.

C++/SDL: Fading out a surface already having per-pixel alpha information

Suppose we have a 32-bit PNG file of some ghostly/incorporeal character, which is drawn in a semi-transparent fashion. It is not equally transparent in every place, so we need the per-pixel alpha information when loading it to a surface.
For fading in/out, setting the alpha value of an entire surface is a good way; but not in this case, as the surface already has the per-pixel information and SDL doesn't combine the two.
What would be an efficient workaround (instead of asking the artist to provide some awesome fade in/out animation for the character)?
I think the easiest way for you to achieve the result you want is to start by loading the source surface containing your character sprites, then, for every instance of your ghost create a working copy of the surface. What you'll want to do is every time the alpha value of an instance change, SDL_BlitSurface (doc) your source into your working copy and then apply your transparency (which you should probably keep as a float between 0 and 1) and then apply your transparency on every pixel's alpha channel.
In the case of a 32 bit surface, assuming that you initially loaded source and allocated working SDL_Surfaces you can probably do something along the lines of:
SDL_BlitSurface(source, NULL, working, NULL);
if(SDL_MUSTLOCK(working))
{
if(SDL_LockSurface(working) < 0)
{
return -1;
}
}
Uint8 * pixels = (Uint8 *)working->pixels;
pitch_padding = (working->pitch - (4 * working->w));
pixels += 3; // Big Endian will have an offset of 0, otherwise it's 3 (R, G and B)
for(unsigned int row = 0; row < working->h; ++row)
{
for(unsigned int col = 0; col < working->w; ++col)
{
*pixels = (Uint8)(*pixels * character_transparency); // Could be optimized but probably not worth it
pixels += 4;
}
pixels += pitch_padding;
}
if(SDL_MUSTLOCK(working))
{
SDL_UnlockSurface(working);
}
This code was inspired from SDL_gfx (here), but if you're doing only that, I wouldn't bother linking against a library just for that.