I have images that look as follows:
My goal is to detect and recognize the number 31197394. I have already fine-tuned a deep neural network on text recognition. It can successfully identify the correct number, if it is provided it in the following format:
The only task that remains is the detection of the corresponding bounding box. For this purpose, I have tried darknet. Unfortunately, it's not recognizing anything. Does anyone have an idea of a network that performs better on these kind of images? I know, that amazon recognition is able to solve this task. But I need a solution that works offline. So my hopes are still high that there exist pre-trained networks that work. Thank's a lot for your help!
Don't say darknet doesn't work. It depends on how you labeled your dataset. It is true that the numbers you want to recognize are too small so if you don't make any changes to the image during the pre-processing phase, it would be complicated for a neural network to recognize them well. So what you can do that will surely work is:
1---> Before labeling, increase the size of all images by 2 times its current size (like 1000*1000)
2---> used this size (1000 * 1000) for the darket trainer instead of the default size proposed by darknet which is 416 * 416. You would then have to change the configuration file
3---> use the latest darknet version (yolo v4)
4---> On the configuration file, always keep a number of subdivisions at 1.
I also specify that this method is too greedy in memory, it is therefore necessary to provide a machine with RAM > 16 GB. The advantage is that it works...
Thanks for your answers guys! You were right, I had to finetune yolo to make it work. So I created a dataset and fine-tuned yolov5. I am surprised how good the results are. Despite only having about 300 images in total, I get an accuracy of 97% to predict the correct number. This is mainly due to strong augmentations. And indeed the memory requirements are large, but I could train on a 32 GM RAM machine. I can really encourage anyone who faces similar problems to give yolo a shot!!
Maybe use an R-CNN to identify the region where the number is and then pass that region to your fine-tuned neural network for the digit classification
I am trying to align two multi-spectral images using multi-modal image registration techniques.
I built a prototype in MATLAB by first creating the optimizer and metric objects as follows:
[optimizer, metric] = imregconfig('Multimodal');
This creates an optimizer object of type OnePlusOneEvolutionaryOptimizer and metric of type MattesMutualInformation. The images are aligned as follows:
tform = imregtform(movingImage, fixedImage, 'rigid', optimizer, metric);
aligned = imwarp(movingImage,tform,'OutputView',imref2d(size(fixedImage)));
Then I went for a C++ implementation of the same algorithm which is offered by one of the examples in the ITK v4 library.
This example also gives correct results but here is the problem... The ITK version is way slower than the MATLAB version. I played around with the optimizer parameters and was able to speed it up a bit, but not comparable to MATLAB version.
MATLAB documentation of OnePlusOneEvolutionaryOptimizer states that the value of InitialRadius property is directly proportional to the algorithm's execution speed (compromising on robustness). The confusion here is that in ITK, the value of InitialRadius is inversely proportional to the execution speed as far as I tested.
I couldn't find literature/documentation describing how the optimizer parameters like InitialRadius and GrowthFactor are interpreted in ITK. Please help in providing explanation of these parameters and speeding up the algorithm.
The first thing to check is making sure you are compiling your program in Release mode, not Debug mode.
Documentation and source code for 1+1 optimizer in ITK are available online.
I would like to speedup the forward pass of classification of a CNN using caffe.
I have tried batch classification in Caffe using code provided in here:
Modifying the Caffe C++ prediction code for multiple inputs
This solution enables me to give a vector of Mat, but it does not speed up anything. Even though the input layer is modified.
I am processing pretty small images (3x64x64) on a powerful pc with two GTX1080, and there is no issue in terms of memory.
I tried also changing the deploy.prototxt, but I get the same result.
It seems that at one point the forward pass of the CNN becomes sequential.
I have seen someone pointing this out here also:
Batch processing mode in Caffe - no performance gains
Another similar thread, for python : batch size does not work for caffe with deploy.prototxt
I have seen some things about MemoryDataLayer, but I am not sure this will solve my problem.
So I am kind of lost on what to do exactly... does anyone have any information on how to speedup classification time.
Thanks for any help !
I'm a newbie in FFT and I was asked to find a way to analyse/process a particular set of data collected by oil drilling rigs.
There is a lot of noise in the collected data due to rig movements (up & down with tides and waves for example).
I was asked to clean the collected data up with FFT=>filtering=>IFFT.
I use C++ and the FFTW 3.3.3 library.
An example is better than anything else so :
I have a DB with, for example, the mudflow (liters per minutes). The mudflow is collected every 5 seconds, there is a timestamp in the DB for every measure (ex. 1387411235).
So my IN_data for my FFT is a couple of timestamp/mudflow (ex. 1387456630/3955.94, 1387456635/3954.92, etc...)
Displaying theses data really looks like a noisy sound signal and relevant events may be masked by the noise.
Using examples found on the Internet I can manage to perform FFT but my lack of knowledge and understanding is a big problem as I've never worked on signal processing and Fourier Transforms.
I don't really know how to proceed to start with this job, which version of FFTW routine to use (c2c, r2c, etc...), if there is any pre-data-processing and/or post-processing to do.
There are a lot of examples and tutorials that I've read on the internet but I'm french (sorry for my mistakes here) and it doesn't always make sense to me especially with OUT_data units, OUT_data type, In and Out data array size, windowing (what is that by the way), to put it in a nutshell I'm lost...
I suppose that my problem would be pretty straightforward for someone used to FFTW but for me it's very complicated right now.
So my questions :
What FFTW routine to use in both ways (FFT & IFFT) (what kind, type and size, of array for IN_data and OUT_data).
How to interpret the resulting array (what are the units that FFTW will return).
For now a short sample of what I've done is :
fftw_plan p;
p = (fftw_plan)fftw_plan_dft_1d(size,in,out,FFTW_FORWARD,FFTW_ESTIMATE);
fftw_execute(p);
fftw_destroy_plan(p);
with "in" and "out" as fftw_complex (the complex element of my In_data array is set to 1 for every data, don't really know why but the tutorial said to do that).
This code is based on an example found on the Internet but my lack of knowledge/understanding is a big drag and I was wondering if there was someone here who could give me explanations/workflow/insights/links on how to pull this out.
I'm in a trial period for my new job and I really want to implement this feature for my boss even if it means asking around for help, I've seen a lot of FFTW skilled posts here...
This is quite an ambitious project for someone who is completely new to DSP, but you can start by reading about the overlap-add method, which is essentially the method you need for your FFT-filter-IFFT approach to cleaning up this data. You should also check out the DSP StackExchange site dsp.stackexchange.com, where the theoretical background and application of frequency domain filtering is covered in several similar questions/answers.
Last night before going to bed, I browsed through the Scalar Data section of Learning Perl again and came across the following sentence:
the ability to have any character in a string means you can create, scan, and manipulate raw binary data as strings.
An idea immediately hit me that I could actually let Perl scan the pictures that I have stored on my hard disk to check if they contain the string Adobe. It seems by doing so, I can tell which of them have been photoshopped. So I tried to implement the idea and came up with the following code:
#!perl
use autodie;
use strict;
use warnings;
{
local $/="\n\n";
my $dir = 'f:/TestPix/';
my #pix = glob "$dir/*";
foreach my $file (#pix) {
open my $pic,'<', "$file";
while(<$pic>) {
if (/Adobe/) {
print "$file\n";
}
}
}
}
Excitingly, the code seems to be really working and it does the job of filtering out the pictures that have been photoshopped. But problem is many pictures are edited by other utilities. I think I'm kind of stuck there. Do we have some simple but universal method to tell if a digital picture has been edited or not, something like
if (!= /the origianl format/) {...}
Or do we simply have to add more conditions? like
if (/Adobe/|/ACDSee/|/some other picture editors/)
Any ideas on this? Or am I oversimplifying due to my miserably limited programming knowledge?
Thanks, as always, for any guidance.
Your best bet in Perl is probably ExifTool. This gives you access to whatever non-image information is embedded into the image. However, as other people said, it's possible to strip this information out, of course.
I'm not going to say there is absolutely no way to detect alterations in an image, but the problem is extremely difficult.
The only person I know of who claims to have an answer is Dr. Neal Krawetz, who claims that digitally altered parts of an image will have different compression error rates from the original portions. He claims that re-saving a JPEG at different quality levels will highlight these differences.
I have not found this to be the case, in my investigations, but perhaps you might have better results.
No. There is no functional distinction between a perfectly edited image, and one which was the way it is from the start - it's all just a bag of pixels in the end, after all, and any other metadata you can remove or forge all you want.
The name of the graphics program used to edit the image is not part of the image data itself but of something called meta data - which may be stored in the image file but, as others have noted, is neither required (so some programs may not store it, some may allow you an option of not storing it) nor reliable - if you forged an image, you might have forged the meta data as well.
So the answer to your question is "no, there's no way to universally tell if the pic was edited or not, although some image editing software may write its signature into the image file and it'll be left there by carelessness of the editing person.
If you're inclined to learn more about image processing in Perl, you could take a look at some of the excellent modules CPAN has to offer:
Image::Magick - read, manipulate and write of a large number of image file formats
GD - create colour drawings using a large number of graphics primitives, and emit the drawings in various formats.
GD::Graph - create charts
GD::Graph3d - create 3D Graphs with GD and GD::Graph
However, there are other utilities available for identifying various image formats. It's more of a question for Super User, but for various unix distros you can use file to identify many different types of files, and for MacOSX, Graphic Converter has never let me down. (It was even able to open the bizarre multi-file X-ray of my cat's shattered pelvis that I got on a disc from the vet.)
How would you know what the original format was? I'm pretty sure there's no guaranteed way to tell if an image has been modified.
I can just open the file (with my favourite programming language and filesystem API) and just write whatever I want into that file willy-nilly. As long as I don't screw something up with the file format, you'd never know it happened.
Heck, I could print the image out and then scan it back in; how would you tell it from an original?
As other's have stated, there is no way to know if the image was doctored. I'm guessing what you basically want to know is the difference between a realistic photograph and one that has been enhanced or modified.
There's always the option of running some extremely complex image recognition algorithm that would analyze every pixel in your image and do some very complicated stuff to determine if the image was doctored or not. This solution would probably involve AI which would examine millions of photos that are both doctored and those that are not and learn from them. However, this is more of a theoretical solution and isn't very practical... you would probably only see it in movies. It would be extremely complex to develop and probably take years. And even if you did get something like this to work, it probably still wouldn't be 100% correct all the time. I'm guessing AI technology still isn't at that level and could take a while until it is.
A not-commonly-known feature of exiftool allows you to recognize the originating software through an analysis of the JPEG quantization tables (not relying on image metadata). It recognizes tables written by many applications. Note that some cameras may use the same quantization tables as some applications, so this isn't a 100% solution, but it is worth looking into. Here is an example of exiftool run on two images, the first was edited by photoshop.
> exiftool -jpegdigest a.jpg b.jpg
======== a.jpg
JPEG Digest : Adobe Photoshop, Quality 10
======== b.jpg
JPEG Digest : Canon EOS 30D/40D/50D/300D, Normal
2 image files read
This will work even if the metadata has been removed.
There is existing software out there which uses various techniques (compression artifacting, comparison to signature profiles in a database of cameras, etc.) to analyze the actual image data for evidence of alteration. If you have access to such software and the software available to you provides an API for external access to these analysis functions, then there's a decent chance that a Perl module exists which will interface with that API and, if no such module exists, it could probably be created rather quickly.
In theory, it would also be possible to implement the image analysis code directly in native Perl, but I'm not aware of anyone having done so and I expect that you'd be better off writing something that low-level and processor-intensive in a fully-compiled language (e.g., C/C++) rather than in Perl.
http://www.impulseadventure.com/photo/jpeg-snoop.html
is a tool that does the job almost good
If there has been any cloning , there is a variation in the pixel density..or concentration which sometimes shows up.. upon manual inspection
a Photoshop cloned area will have even pixel density(my meaning is variation of Pixels wrt a scanned image)