How to set sample sizes on Haarcascade Training? - haar-classifier

I am trying to generate cascade data with Python but it crashes after 10 hours at each train attempt.
My question is that I am cropping all negative and positive Samples as the same. Each negative and positive datas have the same size and it is 76* 76. So I am setting the width and height values at 76. Is it wrong? Should I use random sized images to reach the aim?
Additionally, cascade type is set to haar. What if I change it to HOG?


About hide information on DCT coefficient

I am learning algorithm to hide information on DCT coefficient and my document write like this:
For JPEG images, the original data are DCT tables after quantization. Each DCT table contains 64 coefficients, each of which is an integer whose value is in the range [-2048; 2047]. The high-frequency domain often has many consecutive 0 values, if hiding information here, it can increase the size of the image because the long sequence of zeros is interrupted, reducing the image compression ability. The feature of the DCT table is that the closer to the end of the table, the smaller the value tends to be.
Here's my document's picture:
enter image description here
Anyone know why coefficients's value is in the range [-2048; 2047]? Please help me with this

Hide information on DCT coefficient

I am learning algorithm to hide information on DCT coefficient and my document write like this:
For JPEG images, the original data are DCT tables after quantization. Each DCT table contains 64 coefficients, each of which is an integer whose value is in the range [-2048; 2047]. The high-frequency domain often has many consecutive 0 values, if hiding information here, it can increase the size of the image because the long sequence of zeros is interrupted, reducing the image compression ability. The feature of the DCT table is that the closer to the end of the table, the smaller the value tends to be.
enter image description here
Anyone know why coefficients's value is in the range [-2048; 2047]? Please help me with this

Named range of consistent random numbers

Following on from a question I asked a while ago about getting an array of different (but not necessarily unique) random numbers to which the answer was this:
To get an array of 10 random numbers between 1 and 10
The Problem
If I create a named range (called "randArray") with the formula above I hoped I would be able to reference randArray a number of times and get the same set of random numbers. Granted, they would change each time I press F9 or update the worksheet -- but change together.
This is what I get instead, two completely different sets of random numbers
I'm not surprised by this behavior but how can I achieve this without using VBA and without putting the random numbers onto the worksheet?
If you're interested
This example is intended to be MCVE. In my actual case, I am using random numbers to estimate Pi. The user stipulates how many random points to apply and gets an accordingly accurate estimation. The problem arises because I also graph the points and when there are a small number of points it's very clear to see that the estimation and the graph don't represent the same dataset
I have awarded the initial bounty to #Michael for providing an interesting and different solution. I am still looking for a complete solution which allows the user to stipulate how many random points to use, and although there might not be a perfect answer I'm still interested in any other possible solutions and more than happy to put up further bounties.
Thank you to everyone who has contributed so far.
This solution generates 10 seemingly random numbers between 1 and 10 that persist for nearly 9 seconds at a time. This allows repeated calls of the same formula to return the same set of values in a single refresh.
You can modify the time frame if required. Shorter time periods allow for more frequent updates, but also slightly increase the extremely unlikely chance that some calls to the formula occur after the cutover point resulting in a 2nd set of 10 random numbers for subsequent calls.
Firstly, define an array "Primes" with 10 different prime numbers:
Then, define this formula that will return an array of 10 random numbers:
We need to build our own random number generator that we can seed with the same value for an amount of time; long enough for the called formula to keep returning the same value.
Firstly, we create a seed: ROUND(NOW(),4) creates a new seed number every 0.0001 days = 8.64 seconds.
We can generate rough random numbers using the following formula:
Random = Seed * 7 mod Prime
Ideally, a sequence of random numbers is generated by taking input from the previous output, but we can't do that in a single function. So instead, this uses 10 different prime numbers, essentially starting 10 different random number generators. Now, this has less reliability at generating random numbers, but testing results further below shows it actually seems to do a pretty good job.
ROUND(NOW(),4)*70000 gets our seed up to an integer and multiplies by 7 at the same time
MOD(ROUND(NOW(),4)*70000,Prime) generates a sequence of 10 random numbers from 0 to the respective prime number
ROUND(MOD(ROUND(NOW(),4)*70000,Prime),0) is required to get us back to an integer because Excel seems to struggle with apply Mod to floating point numbers.
=MOD(ROUND(MOD(ROUND(NOW(),4)*70000,Prime),0),10)+1 takes just the value from the ones place (random number from 0 to 9) and shifts it to give us a random number from 1 to 10
Testing results:
I generated 500 lots of 10 random numbers (in columns instead of rows) for seed values incrementing by 0.0001 and counted the number of times each digit occurred for each prime number. You can see that each digit occurred nearly 500 times in total and that the distribution of each digit is nearly equal between each prime number. So, this may be adequate for your purposes.
Looking at the numbers generated in immediate succession you can see similarities between adjacent prime numbers, they're not exactly the same but they're pretty close in places, even if they're offset by a few rows. However, if the refresh is occurring at random intervals, you'll still get seemingly random numbers and this should be sufficient for your purposes. Otherwise, you can still apply this approach to a more complex random number generator or try a different mix of prime numbers that are further apart.
Update 1: Trying to find a way of being able to specify the number of random numbers generated without storing a list of primes.
Attempt 1: Using a single prime with an array of seeds:
This does give you an even distribution, but it really is just repeating the exact same sequence of 10 numbers over and over. Any analysis of the sample would be identical to analysing =MOD(ROW(1:SampleSize),10)+1. I think you want more variation than that!
Attempt 2: Working on a 2-dimensional array that still uses 10 primes....
Update 2: Didn't work. It had terrible performance. A new answer has been submitted that takes a similar but different approach.
OK, here's a solution where users can specify the number of values in defined name SAMPLESIZE
It's a long formula, but has good efficiency and can be used in other functions. Attempts at a shorter formula resulted in unusably poor performance and arrays that for some reason couldn't be used in other functions.
This solution combines 5 different prime number generators to increase variety in the generated random numbers. Some arbitrary constants were introduced to try to reduce repeating patterns.
This has correct distribution and fairly good randomness. Repeated testing with a SampleSize of 10,000 resulted in frequencies of individual numbers varying between 960 and 1040 with no overall favoritism. However it seems to have the strange property of never generating the same number twice in a row!
You can achieve this using just standard spreadsheet formulas.
One way is to use the so called Lehmer random number method. It generates a sequence of random numbers in your spreadsheet that stays the same until you change the "seed number", a number you choose yourself and will recreate a different random sequence for each seed number you choose.
The short version:
In cell B1, enter your "seed" number, it can be any number from 1 to 2,147,483,647
In cell B2 enter the formula =MOD(48271*B1,2^31-1) , this will generate the first random number of your sequence.
Now copy this cell down as far as the the random sequence you want to generate.
That's it. For your named range, go ahead and name the range from B2 down as far as your sequence goes. If you want a different set of numbers, just change the seed in B1. If you ever want to recreate the same set of numbers just use the same seed and the same random sequence will appear.
More details in this tutorial:
How to generate random numbers that don't change in Excel and Google Sheets
It's not a great answer but considering the limitation of a volatile function, it is definitely a possible answer to use the IF formula with Volatile function and a Volatile variable placed somewhere in the worksheet.
I used the below formula to achieve the desired result
I set cell B12 as rngIsVolatile. I pasted the screenshots below to see it in working.
When rngIsVolatile is set to True, it picks up new values from randArray:
When rngIsVolatile is set to False, it picks up old values from A1:A10:

From int array of 4096 elements to a float between 0 and 1

I am currently working on a project and I need to work with images.
However, my images are 64*64 sized, so when I load one, I have a 4096 int array.
I would like to convert this array to a float that is between 0 and 1 (and of course I will need the function that need to build an image from a float).
Do you have any idea or suggestion of how to do it ?
Because I need to make an algorithm but I don't really know how to proceed.
Best regards and thank you.
The only way this could make some sense is if the image is binary (1 bit per pixel)
but even then the lossless naive conversion will take 64x64 bits which is far from what single 32bit float can do. So there is some piece of info missing. To make this possible you need introduce some kind of compression but even that could be not enough unless lossy compression used. Anyway you should add some sample images so we see what are you dealing with.
I am afraid the only usable compression for this would be using DCT (like in JPEG) on the full image. So do a DCT of the image and store only first few coefficients. for example if 4 bit coefficients used then you can store 32/4=8 coefficients which could be enough but hard to say if 4 bits will be enough to reconstruct the image back.
In similar cases visual hashes are used
but you have no way to turn them back to the original image. They are pretty much the same as hashes but their binary representation is visually similar to the image.
float is really not a good way for this
due to precision/rounding problems. You are loosing more bits then if just integer type would be used. Yes you can use integer type stored as float in integer format but the resulting float value can be jibberish with possibility of throwing exception if used as regular float.
If the target float should be in range <0.0,1.0> then exceptions will not occur but you can not use exponent nor sign for storage limiting the usable bits to only 23 from original 32.
When put all together without additional info I would:
Do a DCT on 64x64 image matrix
use only 1x4bit + 6*3bit top left corner matrix cells
encode into mantisa bits by concatenating
mantissa = coeff0+coeff1<<4+coef2<<7+coef3<<10+...
set sign and exponent to set range to <0.0,1.0>
If I am not mistaking sign=0 and exponent=-1 + 32bit_float_bias
put the integer parts of float to floating value
union x { float f; DWORD dw; }
DWORD sign=...,mantisa=...,exponent=...;
return x.f;
To obtain back the image (at least something close to it) reverse the steps. Yo can improve quality with introducing of some filters to get closer to your original images. But without actually seeing any of them is hard to tell which one to use or if even possible...

Is the Leptonica implementation of 'Modified Median Cut' not using the median at all?

I'm playing around a bit with image processing and decided to read up on how color quantization worked and after a bit of reading I found the Modified Median Cut Quantization algorithm.
I've been reading the code of the C implementation in Leptonica library and came across something I thought was a bit odd.
Now I want to stress that I am far from an expert in this area, not am I a math-head, so I am predicting that this all comes down to me not understanding all of it and not that the implementation of the algorithm is wrong at all.
The algorithm states that the vbox should be split along the lagest axis and that it should be split using the following logic
The largest axis is divided by locating the bin with the median pixel
(by population), selecting the longer side, and dividing in the center
of that side. We could have simply put the bin with the median pixel
in the shorter side, but in the early stages of subdivision, this
tends to put low density clusters (that are not considered in the
subdivision) in the same vbox as part of a high density cluster that
will outvote it in median vbox color, even with future median-based
subdivisions. The algorithm used here is particularly important in
early subdivisions, and 3is useful for giving visible but low
population color clusters their own vbox. This has little effect on
the subdivision of high density clusters, which ultimately will have
roughly equal population in their vboxes.
For the sake of the argument, let's assume that we have a vbox that we are in the process of splitting and that the red axis is the largest. In the Leptonica algorithm, on line 01297, the code appears to do the following
Iterate over all the possible green and blue variations of the red color
For each iteration it adds to the total number of pixels (population) it's found along the red axis
For each red color it sum up the population of the current red and the previous ones, thus storing an accumulated value, for each red
note: when I say 'red' I mean each point along the axis that is covered by the iteration, the actual color may not be red but contains a certain amount of red
So for the sake of illustration, assume we have 9 "bins" along the red axis and that they have the following populations
4 8 20 16 1 9 12 8 8
After the iteration of all red bins, the partialsum array will contain the following count for the bins mentioned above
4 12 32 48 49 58 70 78 86
And total would have a value of 86
Once that's done it's time to perform the actual median cut and for the red axis this is performed on line 01346
It iterates over bins and check they accumulated sum. And here's the part that throws me of from the description of the algorithm. It looks for the first bin that has a value that is greater than total/2
Wouldn't total/2 mean that it is looking for a bin that has a value that is greater than the average value and not the median ? The median for the above bins would be 49
The use of 43 or 49 could potentially have a huge impact on how the boxes are split, even though the algorithm then proceeds by moving to the center of the larger side of where the matched value was..
Another thing that puzzles me a bit is that the paper specified that the bin with the median value should be located, but does not mention how to proceed if there are an even number of bins.. the median would be the result of (a+b)/2 and it's not guaranteed that any of the bins contains that population count. So this is what makes me thing that there are some approximations going on that are negligible because of how the split actually takes part at the center of the larger side of the selected bin.
Sorry if it got a bit long winded, but I wanted to be as thoroughas I could because it's been driving me nuts for a couple of days now ;)
In the 9-bin example, 49 is the number of pixels in the first 5 bins. 49 is the median number in the set of 9 partial sums, but we want the median pixel in the set of 86 pixels, which is 43 (or 44), and it resides in the 4th bin.
Inspection of the modified median cut algorithm in colorquant2.c of leptonica shows that the actual cut location for the 3d box does not necessarily occur adjacent to the bin containing the median pixel. The reasons for this are explained in the function medianCutApply(). This is one of the "modifications" to Paul Heckbert's original method. The other significant modification is to make the decision of which 3d box to cut next based on a combination of both population and the product (population * volume), thus permitting splitting of large but sparsely populated regions of color space.
I do not know the algo, but I would assume your array contains the population of each red; let's explain this with an example:
Assume you have four gradations of red: A,B,C and D
And you have the following sequence of red values:
To find the median, you would have to sort them according to red value and take the middle:
Now let's use their approach:
A:6 => 6
B:4 => 10
C:1 => 11
D:2 => 13
13/2 = 6.5 => B
I think the mismatch happened because you are counting the population; the average color would be: