I need to extract/crop the logotype (BEAVER) in the middle from a TIFF file that looks like this: http://i41.tinypic.com/2i7rbie.jpg
And then I need to automate the process so it can be repeated about 9 million times...
My guess is that I would have to use some OCR software. But is it possible for such a software to "crop anything that starts below this point and ends above this point"?
Thoughts?
Typically OCR software does only extraction of text from images and conversion of it into some text-specific format. It does not do crop. However, you can use OCR technologies to achieve your task. I would recommend following:
OCR whole page
Get coordinates of recognized text
Apply your magic rules to recognized text to locate area to crop: such as averything in between "application filled" and "STATEMENT" sentences.
Cut from image that area and export it where you want it.
Real challenge is in the amount of text you would like to process. You have to be very carefull when defining your "smart rules" to make sure they don't provide false positives and always send suspicious images to separate queue that you will later manually review and update your rules.
In general it may look like this:
Take first 10 of images, define logo detection rules, test and see if everything works well
Then run on next 10, see what was prcessed wrong, what was not processed, update rules, re-process those 10 to make sure everything works well now
Re-run it on new batches of same size until it will start working well.
Then increase batch size from 10 to 100, and go with those batches until again everything start working smoothly
Then continue this way perfecting your rules and increasing batch size. At some point of time you will go to production speed.
Most likely you will encounter some strange images that either contradict existing rules, or just wrong. Not always you have to update your rules to accomodate it. It may happen that there it only dozen of images like that in whole your 9 million collection. It might be better to leave them in exceptions queue for manual processing, and don't risk stability of your magic rules.
Related
My goal is to write a program (e.g. in Python or C++) which takes as input a JPG file (e.g. tux.jpg) and make tiny changes to it, such that it outputs many different images (maybe a thousand images or even more), but in a way that all these images, while having different hash, look almost the same visually, i.e. the changes should have the least impact to the original image as possible.
I first though to play around with the jpg header but that might not be enough to make the many thousands of different pictures I want.
As a naive way, I thought to flip a random bit in the file, but that bit can possibly result in a less than desirable result, which can be seen especially in small pictures (e.g. a dark pixel in the white space in the tux picture). Ideally, I would like to change a random pixel with a "neighboring" color, such that the two resulting pictures have almost no visual difference.
For this purpose, I read the JPG codec example but I find it very confusing and hard to understand. Can someone help me what my program should look for as it parses the file in binary format and how to change a random pixel with a "neighboring" color?
You can change the comment part of the file by playing with the file header. A simple way to do that is to use a ready made open source program that allows you to put the comment of your choice, example HLLO repeated 8 times. That should give you 256 bits to play with. You can then determine the place where the HLLO pattern is located in the file using a hex editor. You then load the data in memory and start changing these 32 bytes and calculate the hash each time to get a collision (a hash that matches)
By the time you find a collision, the universe will have ended.
Although in theory doable, it's practically impossible to crack SHA256 in a reasonable amount of time, standard encryption protocols would be over and hackers would be enjoying their time.
I am posting this question by request of a Firebase engineer.
I am using the Camera2 API in conjunction with Firebase-mlkit vision. I am using both barcode and on-platform OCR. The things I am trying to decode are mostly labels on equipment. In testing the application I have found that trying to scan the entire camera image produces mixed results. The main problem is that the field of view is too wide.
If there are multiple bar codes in view, firebase returns multiple results. You can sort of work around this by looking at the coordinates and picking the one closest to the center.
When scanning text, it's more or less the same, except that you get multiple Blocks, many times incomplete (you'll get a couple of letters here and there).
You can't just narrow the camera mode, though - for this type of scanning, the user benefits from the "wide" camera view for alignment. The ideal situation would be if you have a camera image (let's say for the sake of argument it's 1920x1080) but only a subset of the image is given to firebase-ml. You can imagine a camera view that has a guide box on the screen, and you orient and zoom the item you want to scan within that box.
You can select what kind of image comes from the Camera2 API but firebase-ml spits out warnings if you choose anything other than YUV_420_488. The problem is that there's not a great way in the Android API to deal with YUV images unless you do it yourself. That's what I ultimately ended up doing - I solved my problem by writing a Renderscript that takes an input YUV, converts it to RGBA, crops it, then applies any rotation if necessary. The result of this is a Bitmap, which I then feed into either the FirebaseVisionBarcodeDetectorOptions or FirebaseVisionTextRecognizer.
Note that the bitmap itself cases mlkit runtime warnings, urging me to use the YUV format instead. This is possible, but difficult. You would have to read the byte array and stride information from the original camera2 yuv image and create your own. The object that comes from camear2 is unfortunately a package-protected class, so you can't subclass it or create your own instance - you'd essentially have to start from scratch. (I'm sure there's a reason Google made this class package protected but it's extremely annoying that they did).
The steps I outlined above all work, but with format warnings from mlkit. What makes it even better is the performance gain - the barcode scanner operating on an 800x300 image takes a tiny fraction as long as it does on the full size image!
It occurs to me that none of this would be necessary if firebase paid attention to cropRect. According to the Image API, cropRect defines what portion of the image is valid. That property seems to be mutable, meaning you can get an Image and change its cropRect after the fact. That sounds perfect. I thought that I could get an Image off of the ImageReader, set cropRect to a subset of that image, and pass it to Firebase and that Firebase would ignore anything outside of cropRect.
This does not seem to be the case. Firebase seems to ignore cropRect. In my opinion, firebase should either support cropRect, or the documentation should explicitly state that it ignores it.
My request to the firebase-mlkit team is:
Define the behavior I should expect with regard to cropRect, and document it more explicitly
Explain at least a little about how images are processed by these recognizers. Why is it so insistent that YUV_420_488 be used? Maybe only the Y channel is used in decoding? Doesn't the recognizer have to convert to RGBA internally? If so, why does it get angry at me when I feed in Bitmaps?
Make these recognizers either pay attention to cropRect, or state that they don't and provide another way to tell these recognizers to work on a subset of the image, so that I can get the performance (reliability and speed) that one would expect out of having to ML correlate/transform/whatever a smaller image.
--Chris
Recently, I've designed a word cloud in Illustrator for a customer. It uses around 5,000 people's names in white on a colored background on a logo path, and includes a few vector logos. Each name is ridiculously small, and we want to be able to search on the cloud and find our name.
We've put it online as a SVG with success - but a 20M file can cause problems!
So everything would be fine until we reach 10,000 visitors at the same time, and make all our servers crash and timeout everyone.
So what is our alternative to make this fast, easy for visitors to use, and latency free? We think about Canvas, but not sure if it's simple to make a words cloud with [really (thing about following a logo path)] custom shape.
It sounds like you have 20Mb because the names are being stored/represented with paths. If you represent them as text, you will substantially reduce the size of the file, AND make it appropriately searchable.
Assuming 13 characters per name (including the space in between), UTF-8 encoding, and 10,000 names the names themselves should only take 127Kb. You may wish to experiment with transmitting the background of the SVG and the names (JSON?), and using a script to construct the cloud in the browser.
Edit: Even if you create a completely static SVG, representing the text as text will result in a substantial saving of space over the use of paths.
Hey everyone,
I'm trying to create a program in Java that can read numbers of the screen, and also recognise images on the screen. I was wondering how i can achieve this?
The font of the numbers will always be the same. I have never programmed anything like this before, but my idea of how it works is to have the program take a screenshot, then overlay the image of the numbers with the section of the screenshot image and check if they match, repeating this for each numbers. If this is the correct way to do this, how would i put that in code.
Thanks in advance for any help.
You could always train a neural net to do it for you. They can get pretty accurate sometimes. If you use something like Matlab it actually has capabilities for that already. Apparently there's a neural network library for java (http://neuroph.sourceforge.net/) although I've never used it personally.
Here's a tutorial about using neuroph: http://www.certpal.com/blogs/2010/04/java-neural-networks-and-neuroph-a-tutorial/
You can use a neural network, support vector machine, or other machine learning construct for this. But it will not do the entire job. If you do a screen shot, you are going to be left with a very large image that you will need to find the individual characters on. You also need to deal with the fact that the camera might not be pointed straight at the text that you want to read. You will likely need to use a series of algorithms to lock onto the right parts of the image and then downsample it in a way that size becomes neutral.
Here is a simple Java applet I wrote that does some of this.
http://www.heatonresearch.com/articles/42/page1.html
It lets you draw on a relatively large area and locks in on your char. Then it recognizes it. I am using the alphabet, but digits should be easier. The complete Java source code is included.
One simpler approach could be to use template matching. If the fonts are same, and/or the size (in pixels)is known, then simple template matching can do the job for you. ifsize of input is unknown, you might have to create copies of images at different scales and do the matching at each scale.
One with the extreme value(highest or lowest depending on the method you follow for template matching) is your result.
Follow this link for details
My question is not language based or OS based. I guess every system is offering some sort of TextOut(text, x, y) method. I am looking for some guidlines or articles how should I implement selection of outputed text. Could not find any info about this.
The only thing which comes to my mind is like this:
When user clicks some point on the text canvas I know the coordinates of this point. I need to calculate where exactly it will be in my text buffer. So I am traversing from the begining of the buffer and I am applying to each character (or block of text) a style (if it has any). After this, I know that after given style the letter has given size. I am adding its width and height to previously calculated X,Y coordinates. In this way, I am traversing the buffer until the calculated position has not reached the point that has been clicked by the user. After I reach the point within range of some offset I have starting point for the selection.
This is the basic idea. I don't know if this is good, I would like to know how this is done for real like for example in Firefox. I know I could browse the sources and if I won't have a choice I'll do it. But first I am trying to find some article about it...
Selecting text is inherently specific to the control which is containing it and the means it stores that text.
A very simple (though questionably inefficient means) is to run the text flow algorithm you are using when clicking on a point and stopping the algorithm when you have reached what is closest to that point. More advanced controls might cache the text layout to make selections or drawing their content more efficient. Depending on how much you value CPU time or memory there are ways to use caches and special cases to make this “hit test” cheaper.
If you can make any assertions (only one font in the control, so every line has the same height) then it is possible to make these tests cheaper by indexing the font layout by lines and then doing simple arithmetic to find out which line was clicked on. If your text control is also using monospace fonts (every character occupies the same width as well as height) then you are in even more luck, as you can jump straight to the character information via a lookup table and two simple divisions.
Keep in mind that writing a text control from scratch is obscenely difficult. For best practice, you should keep the content of the document separate from the display information. The reason for this is because the text itself will need to be edited quite often, so algorithms such as Ropes or Gap Buffers may be employed on the data side to provide faster insertion around the caret. Every time text is edited it must also be rendered, which involves taking that data and running it through some kind of formatting / flow algorithms to determine how it needs to be displayed to the user. Both of these sides require a lot of algorithms that may be annoying to get right.
Unfortunately using the native TextOut functions will not help you. You will need to use methods which give you the text extents for individual characters, and more advanced (multiline for example) controls often must do their own rendering of characters using this information. Functions like TextOut are not built to deal with blinking insertion carets for example, or performing incremental updates on text layouts. While some TextOut style functions may support word wrap and alignment for you, they also require re-rendering the entire string which becomes more undesirable in proportion to the amount of text you need to work with in your control.
You are thinking at a much lower level than necessary (not an insult. you are thinking that you need to do much more work then you need to). Most (if not all) languages with GUI support will also have some form of selectionRange that gives you either the string that was selected or the start and stop indices in the string.
With a modern language, you should never have to calculate pixels and character widths.
For text selection in Javascript, see this question: Understanding what goes on with textarea selection with JavaScript