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The image processing parts of Java are buried within the bestthing.info package. production of an image and can react when certain conditions arise. We. Java Image Processing Recipes - Ebook download as PDF File .pdf), Text File . txt) or read book online. Someone will see it!. EASY JAVA PROGRAMMING FOR TEACHING IMAGE- PROCESSING. Daniel Sage and Michael Unser. Swiss Federal Institute of Technology, Lausanne EPFL, .
You can of course apply the same Canny processing to ever more kitten pictures. Solutions When performing a copy operation, you can use what is called a mask as a parameter. A mask is a regular one-channel Mat, thus with values of 0 and 1. When performing a copy with a mask, if the mask value for that pixel is 0, the source mat pixel is not copied, and if the value is 1, the source pixel is copied to the target Mat. If you decide to dump the kittens probably not a good idea, because the file is pretty big… , you will see a bunch of zeros and ones; this is how the mask is created.
Then we load a source for the copy, and as you remember, we need to make sure it is of the same size as the other component of the copy operation, so target.
Now can you answer the following question: Why are the cats drawn in white? You could of course go ahead and try with a black underlying Mat, or a picture of your choice. We will see some more of those examples in the coming chapters.
Solution OpenCV has a set of two functions that often go hand in hand with the canny function: Since the original picture probably contains a lot of noise from colors and brightness, you usually use a preprocessed image, a black-and-white Mat where the canny function has been applied.
ISBN-13 (pbk): 978-1-4842-3464-8 ISBN-13 (electronic): 978-1-4842-3465-5
A hierarchy Mat; you can ignore this for now and leave it as an empty Mat. The contour retrieval mode, for example whether to create relationship between contours or return all of them. The type of approximation used to store the contours; for example, draw all the points or only key defining points. This method returns the list of found contours, where each contour is itself a list of points, or in OpenCV terms, a MatOfPoint object.
Great; the building blocks of this recipe have been written so you can put them in action. You can use the same picture of kittens as before as the base picture. The following snippet is code taken from the previous recipe. Kitten contours. Using a Frame. White kittens on blue background Notably in Chapter 3. You will see first how to retrieve a Mat object from the video device. Once finished.
See how it goes in the following listing. It will take a number of frames to grab. You would usually use 0. First a VideoCapture object is created. Then you create a blank Mat object and pass it to receive data from the camera. For now. Mini—time lapse of still bedroom Without going into much detail here. Then MatPanel extends the Java JPanel class in a way that the paint method now converts the Mat directly using the method you have just seen before: MatToBufferedImage mat.
Real-time stream in Java frame Obviously. Canny frame. Canny picture in real time The answer is shown in the following code snippet.
So if you manage to compile Scala classes. This works in two steps. The prep-tasks key is responsible for defining tasks that need to be executed before similar commands run.
What that means is that with the current Leiningen setup you have used so far. Scala project directory structure To confirm that the whole thing is in place and set up properly. The rest of the code is a rather direct translation of the Java code. If you put the loadLibrary call anywhere in the scala object definition. You will need to load the OpenCV native library as was done before in the Java examples.
Scala setup Blurred and bored Surely you have tried this on your local machine and found two things that are quite nice with the scala setup. Scala makes it easy to import not only classes but also methods. The scala compiler seems to determine the required compilation steps from incremental code changes.
Bored cat Figure This third example in the scala recipe will show how to apply the canny transformation after changing the color space of a loaded OpenCV Mat.
Compilation times are reduced a bit. Array and cannot take native scala objects as parameters. Arrays import org. Not afraid of Scala Figure I has been warned The Drawing contours example written for Java has also been ported to Scala and is available in the source code of the samples available with this book. You will mostly need to add the Kotlin plug-in. As for the scala setup. The following ultrashort Kotlin snippet does just that. That was an easy one. Cat ready for anything You can see three files being created.
Hot cat. Bone cat Figure Winter cat Figure But you will quickly enjoy a few Kotlin examples on how to integrate those with OpenCV. The coming first example shows you how to bootstrap your Kotlin code to show an image within a frame. Small applications like this are quite useful to give the user the chance to change OpenCV parameters and see the results in pseudo—real time.. Kotlin Setup The tornadofx library can be added to the project.
Application import javafx. Tornadofx application graph With this diagram in mind. App HelloWorld Image in frame With the goal and explanation in place. SimpleIntegerProperty import javafx. Pos class CounterView: That reactive value can then bound to a widget. The handler code can be either inside the block or in a different Kotlin function. You should find out where! A title was set in the root block with the following snippet added at the proper location.
A button is a simple UI widget on which you can define an action handler. A reactive value can be created with a SimpleIntegerProperty. App CounterView:: A few button clicks to increase the counter Blurring Application Well. Simple counter app And after a few clicks on the beautiful button.
The following example shows RT import tornadofx. Image class CounterView: The longer list of imports is a bit annoying. The blurring application has a val of type SimpleObjectProperty. SimpleObjectProperty import javafx. Color import javafx. Platform import javafx. Pos import javafx. Leiningen takes care of doing all the Kotlin compilation automatically for you on file change.
There are a few more tornadofx examples in the code samples along with this book. Blurring application When you click the increment button. The door is now wide open to introduce the origami library. I have a general sense of excitement about the future. But it will be whatever I make it.
Time to get excited. Amanda Lindhout The environment will bring you even more concise code and more interactiveness to try new things and be creative. I was on a mission to prepare and generate data for various neural networks.
It quickly became clear that you cannot just dump any kind of image or video data to a network and expect it to behave efficiently. Zooey Deschanel The Origami library was born out of the motivation that computer vision— related programming should be simple to set up. These days. You need to organize all those images or videos by size.
The setup you have seen in the previous chapter can be almost entirely reused as is. Once this simple additional setup is done. The examples will be done in such a way that you will be introduced to the OpenCV code via Clojure. You will then be presented with the different coding styles that can be used with this new setup. We will review how to use those two beasts right after. This time we will use a version that is slightly updated from what you have seen in Chapter 1. The injections segment may be a bit obscure at first.
The file grey-neko. This has not changed from the first chapter. Grey Neko The code of the opencv3. The file to run this is in samplevideo.
As before. Tokyo coffee shop While this was just to run the examples included with the project template. This is however a technique that can be used to check that all your code compiles and runs without errors. So here is a quick reminder on how to set up the auto plug-in solution presented in Chapter 1 for Java. And so. Once you have added this. Jet cat Now while this setup with the auto plug-in is perfectly ok. Better than that. Leiningen has a subcommand named repl. Using OpenCV Version: The library is indeed loaded properly..
The following two lines get some functions from the utils namespace. Cute cat Origami encourages the notion of pipelines for image manipulation. Instant gratification. This makes for very swift and compact image-processing code. Using all the same project metadata from the project. Note that the lines execute directly. Instant gratification takes too long. Install two plug-ins in Atom: But the real gem of this setup is to have autocompletion and choice presented to you when typing code.
Instant completion You can now retype the code to read and convert the color of an image directly in a file. This is exactly the same REPL that you have used when executing the lein repl command directly from the terminal. After code evaluation. The ideal editor-based computer vision environment The jet-set cat is now showing in the output. A newly resized jet-set cat is now instantly showing on your screen.
This means you can write code alongside documentation. Worksheets are pages where you can write lines of code and execute them. Gorilla is a Leiningen plug-in. It will also start a web server whose goal is to serve notes or worksheets.
How does that work? Gorilla takes your project setup and uses it to execute the code in a background REPL. As a result. You can also write documentation in the sheet itself in the form of markdown markup. Markdown text mode Gorilla notebook and a cat In a gorilla notebook. You can access it already at the following location: Block of code To execute the highlighted block of code.
Clojure code was executed What that does is read from the code block. In a new code block of the worksheet. The first one is that remote people can actually view your worksheets. Instant jumping cat Remember that all of this is happening in the browser. Mat is your best friend when working with OpenCV.
This recipe shows basic Mat operations again. You also remember functions like new Mat. Time for some computer vision basics. Simply remember for now that to show a picture. This is either done when creating the Mat. This is done using the new-mat function. To use it.
Mat with assigned color To understand most of the underlying matrix concepts of OpenCV. This will be done a few times in this chapter. A submat can then be created using. Submats with Origami Just for the kicks at this stage. Origami fun At this point. The put function takes a position in the mat.
Setting one pixel to a color is done using the Java method put. The dump function works nicely here though. This would be pretty tiresome by hand. The first macro. They pipe results throughout consecutive function calls. The second macro. The result of the first function call is passed as a parameter to the second function.
This short section will also be a quick introduction to the piping process that is encouraged by Origami. This time. Generated random gray mat You could do the same with a randomly colored mat as well. You can also combine those mats. A gray gradient of 25 mats You can also smooth things up by generating a range of values. Smooth gray gradient of mats In its simplest form.
It will be covered in greater detail here. It also presents a new function called imshow that you may have seen before if you have used standard opencv before.
See in the following how the image is saved during the flow of transformation. The reason that the parameter order has been changed is that. Framed cat The frame opened by imshow has a few default sets of key shortcuts. Bright cat This is not the most important section of this book.
The function takes a mat and has to return a mat. The standard way to do this in origami is shown in the following snippet:. This was the only way to load a picture until recently. But then, most of the time, you would be doing something like. Note that this has the side effect of creating a temporary file on the filesystem. Problem You want to learn a bit more about how to handle colors in OpenCV.
Up to now, we have only seen colors using the RGB encoding. There must be some more! Solution Origami provides two simple namespaces, opencv3. A color map works like a color filter, where you make the mat redder or bluer, depending on your mood. Finally, cvt-color! With the namespace rgb, you can create scalars for RGB values instead of guessing them. This is pretty nice to find color codes quickly.
The opencv3. For a nice light green with a bit of blue, you could use this:. The following snippet shows how to make use of the usual imread and the apply-color-map sequentially. Here is the full list of standard color maps available straight out of the box; try them out! You can also define your own color space conversion. This is done by a matrix multiplication, which sounds geeky, but is actually simpler than it sounds. Remember that we want to apply a color transformation for each pixel.
For any given pixel. You may look around in the literature. RGB is not the most efficient. Later on. What does a color space switch do? It basically means that the three-channel values for each pixel have different meanings. Sepia cat Time to go out and make your own filters! We have seen how transform is applied to each pixel in RGB. In most computing cases. With Origami.
Red in HSV color space Red in RGB color space However. In RGB.
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In opencv cv terms. Hmm… How does that orange color look in RGB again? Why would you want to change colorspace? While each colorspace has its own advantages.
Linear intensity of red in RGB But what if in a picture. In that case. The function hsv-mat creates a mat from a hue value. As you can read. Because it can be annoying to select red in one range. It is hard to find a red cat in nature. Inverted hue spectrum This makes it easier to select red colors.
The second thing you may notice is that it is easier to just tell which color you are looking for by providing a range. Hue values First. It is often considered a cylinder for that reason. The remaining saturation and value are set to 30 30 sometimes 50 50 and sometimes Natural red cat Then.
Mask of the red colors from the picture We will see that finding-color technique in more detail later. Another way is to use the rotate function. And of course. The all-star way is to use the function warp-affine.
Kitten ready for flipping and rotation More can be done with it. Note here the first-time usage of clone in the image-processing flow. Flipped Neko Most of the Origami functions work like this.
You just need to call flip on the image with a parameter telling how you want the flip to be done. The standard version. While flip! Using hconcat! In this first example. Using vconcat! Warp also takes a size to create the resulting mat with the proper dimension. The rotation matrix can then be passed to the warp function. The pipeline takes a range of rotations between 0 and If the zoom factor is not specified.
Range and rotation Furthermore. Seven randomly zoomed cats In the same way. Also note that when the zoom value is too small. Feline affine transformation This recipe is about getting to know the different filtering methods available. That has the effect of completely changing the color of the mat.
The recipe will finish with examples of how to use threshold and adaptive-threshold to keep only part of the information in a mat. Since this is boring. Notice how the function internally creates a fully sequential byte array of all the bytes of the mat. Beautiful French cat And simply put our function into action. Blue cat Playing with Clojure code generative capability here again. Three cats Putting this straight into action.
OpenCV has a function called multiply that does exactly all of this already for you. The function takes a mat. Mellow cat Bright cat Here again. This is what the following new snippet does: Say you create a submat. But this time. To understand how it is possible to do absolutely nothing. For this effect. The filterd function call really just keeps the image as is.
Gray mat We all want more of that. This means that when we apply the filter. Submat has changed And if you look at the pixel values themselves on a much smaller mat.
It can be used for art effects as well. Artful cat To understand how that works. Thresholded cat Adaptive cat You would like to know how to create masks and how to put them into action. Since the color we will be looking for is red. You know how to achieve this by now. Rose in HSV color space Rose To search for colors. Here indeed. Mask of the red rose The mask can now be used along with bitwise-and! Only the rose As a small exercise.
Bright rose We can reuse the mask that was created in the preceding. The resizing of mat is done as a first step. Coming together The concepts are nicely coming together. To perform the copy. You will get quite a bad error when this is not the case. Blurring is a simple and frequent technique used in a variety of situations. You would like to see the different kinds of blur available. Cat on bed Blurred cat on bed And the bigger the kernel.
The kernel is the matrix in which each pixel is given a coefficient. We will see that in the next chapter. Gaussian blurred cat We will spend some more time with canny in the next chapter.
Gaussian blur This second example shows an example where we want to keep the edges. The first example shows a simple usage of this bilateral filter. Edges can be easily found with the famous opencv function canny. What are edges? Edges are contours that define the shapes available in a picture. Gaussian blur and canny The third example quickly shows why you would want to use a bilateral filter instead of a simple blur.
We keep the same small processing pipeline but this time use a simple blur instead of a bilateral filter. It is less useful for shape detection. Artistic cat kernel length 31 Lines and cats have disappeared! The future belongs to those who prepare for it today. This is only the beginning. Malcolm X Median blur with kernel 7 makes lines disappear Voila! Chapter 2 has been an introduction to Origami and its ease of use: Chapter 3 will be taking this setup to the next level by combining principles and functions of OpenCV to find shapes.
First will be a slightly art-focused section. From performing content analysis. Pablo Casals The previous chapter was an introduction to Origami and how to perform mostly single-step processing operations on simple mats and images. The learning will be split into two big sections. We will start again on familiar ground by manipulating OpenCV mats at the byte level.
I had to play with image compositions and wireframes that actually came out better than I thought they would. Processing steps will be easier to grasp at that stage. It just happened that to understand how simple concepts were brought together. I personally find. Note that it is probably a good idea to read this chapter linearly so that you do not miss new functions or new tricks along the way. You would like to get control over how to specify and impact colors.
Even more so. It is a recipe book after all! Processing steps in OpenCV are easy most of the time. So that first part is meant to share this experience.
Chapter 3 Imaging Techniques It was one of the original plans of Origami to be used to create drawings. You will review also in more detail how to use the transform! Do you remember how to threshold on a mat. The required section looks like the following code snippet.
The namespace header of the chapter. Chapter 3 Imaging Techniques Yes.
When applying threshold. Black and white mat That was for a one-channel mat. And so the resulting mat loses the values of the original mat and keeps only the values. Chapter 3 Imaging Techniques Applying the OpenCV threshold function right afterward applies the threshold to all the values over each channel.
Notice the use of a specific interpolation parameter with resize. Just for some extra info. Resize with default interpolation Anyway. Chapter 3 Imaging Techniques Figure Vivid colors Some say love it is a river We start by applying the same threshold that was applied on the mat loaded from a matrix.
Playful cats If you apply a similar threshold but on the grayscale version. Chapter 3 Imaging Techniques In a nicely shot photograph. Something similar to this can be used to find out shapes and moving objects.
The low-high! Chapter 3 Imaging Techniques In coding happiness. Another idea here is. White on light blue rose Great. In case you are wondering. You can do a bitwise operation perfectly on the first mask. To do this. Orange mat If you want to turn the orange mat into a red one. In happy coding.
The function separates the channels in a list of independent mats. You can then apply transformations to that specific mat. To see that in action. You can look at the content of each channel simply by using dump. Chapter 3 Imaging Techniques For example. We can combine all the different steps of this small exercise and create the function update-channel!. This gets complicated. Red mat The green intensity on all pixels in the mat was uniformly set to 0. The code flow will be as follows: Time to tease him a bit and wake him up.
We could have written a function that applies multiple functions at the same time. Cyan cat This newly created function can also be combined with converting colorspace. Of course.
Try it out! Blue-filtered cat Personally. I also like the YUV switch combined with maximizing all the luminance values Y. To understand the background of transform a bit. Those transformations work much the same for mats with multiple channels. I know using cvt-color! The result is shown in the following matrix: Chapter 3 Imaging Techniques Calling the transform function has the effect of turning all the values of the input matrix to their original value multiplied by 0.
Chapter 3 Imaging Techniques Because the mat is now made of three channels. The following transformation mat will give more strength to the blue channel. Blue flag Blue meeoooww If you wanted blue in the input to also influence red in the output. The code is exactly the same as the preceding small mat example. Chapter 3 Imaging Techniques You should probably try a few transformation matrices by yourself to get a feel for them. Luminous cat The following sample increases the luminosity by 1.
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We want to create a watercolor version of the input picture. Then finally. The background is created by performing two transformations in a row: Pink cat for background Next is the foreground. Pink and art and cat Cartoon cat Then. It can actually also be used for cartooning a bit. Chapter 3 Imaging Techniques While the pink color may not be your favorite.
But please. Charles M. No dogs. Johan In this recipe. Johan was loaded with the following snippet: Copy blue over white Clearer Johan The result is nice.
Gaussian blur is usually more effective. Chapter 3 Imaging Techniques That is quite a few lines showing in the picture. By reducing the range between the two threshold values.
The technique usually used to remove those extra lines is to apply a median-blur or a gaussian-blur before calling the canny function. Even better Johan Do you remember the bilateral filter function? If you use it after calling the canny function. Johasso So. And indeed. The whole Origami setup is there to give immediate feedback anyway.
Applying a bilateral filter You would remember that the focus of the bilateral filter is on reinforcing the contours. Note also that the bilateral filter parameters are very sensitive. We will create a new function called cartoon Changing bilateral-filter! No canny cartoon Here again. This gives the following slightly long but simple pipeline: Chapter 3 Imaging Techniques This is nothing you would not understand by now.
In-depth Johan The output of the pipeline looks great. Say you want to increase the luminosity or change the color of the preceding output. To merge the result. Flipped and blue As a bonus.
The plan is to proceed in three phases. This will be the background picture. A goal without a plan is just a wish. Phase 2: We do the opposite. Phase 3: Chapter 3 Imaging Techniques we turn the picture to gray. Using a factor of 4. This decreases the resolution of an image. This will be the front part. The first idea is of course to simply try the usual resize!
Changed resolution To create the background effect. Hmmm… resizing To use it effectively. There is a reverse function of pyr-down.
Smooth blurring The background is finalized by applying blur in the mat in between the pyr-down and pyr-up dance. You can of course create your own variation at this stage. Edges everywhere Sketching like the pros With the adaptive threshold step. Chapter 3 Imaging Techniques To finish the exercise. This will have the consequence of copying the edges over unchanged onto the target result.
Chapter 3 Imaging Techniques We used 9 as edges-thickness and 7 as edges-number in the first sketch. This is an exact copy of the code that has been used up to now. This gives more space to the color of the background. You can try the following snippet: Chapter 3 Imaging Techniques The sketch!
Sketch landscape A few others have been put in the samples. With divide. With multiply. We will need a grayed version for later as well. Wish upon a star We first start by applying a bitwise-not!. Spooky castle We will use this gaussed mat as a mask. What does divide do? I mean. The magic happens in the function dodge!. As presented. We want the canvas to look like a very old parchment. Almost there. Old parchment Now we will create the apply-canvas!
Once this first preparation step is done. The version to find lines is called hough-lines. If you have never seen a tennis court before. Tennis court We will take the example of a tennis court.
Chapter 3 Imaging Techniques Finally. The hough-lines function itself is called with a bunch of parameters. The full underlying polar system explanation for the hough transformation can be found on the OpenCV web site: Chapter 3 Imaging Techniques Preparing the target for the hough-lines function is done by converting the original tennis court picture to gray.
Canny tennis court Lines are collected in a mat in the underlying Java version of opencv. Creating the two points required to draw a line from rho and theta is a bit complicated but is described in the opencv tutorial. Also by experience. Hough-lines result Note that when calling hough-lines.
To try hough-lines with P. Lines are expected to be collected from the newly created edges mat. The parameters used are explained in line in the following code snippet. Chapter 3 Imaging Techniques. Each line of the result mat is made of four values. In a similar way. The exercise is slightly difficult because it is easy to wrongly count the regular balls as pockets. The following snippet now shows where to put values for the min and max radius of the circles to look for in the source mat.
Chapter 3 Imaging Techniques With hough-circles. He has two years of experience in big data analytics. Aakash has also made contributions to the Microsoft bot builder. He is passionate about Machine Learning meetups, where he often presents talks.
I never could have finished this without you.
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I appreciate it so much. You simply rock! I love you. He did not use a screwdriver to pull out teeth from his patients, and he had what seemed like twenty different brushes to clean each type of tooth. I even thought it was funny at the time. OpenCV, the computer vision library, has always been one of the tools to work on imaging- and vision-related projects, even more so with every improvement in AI and neural networks.
But OpenCV was always taking some time to get the right libraries, and the right build tools, and the right build settings, and so forth.To understand how that works.
You can push this method even further by cloning the input buffer as many times as you want; to highlight this, here is another example of applying a different color map three times onto the same input buffer.
Hearts and a speaker Now. The following ultrashort Kotlin snippet does just that. Chapter 3 Imaging Techniques Calling the transform function has the effect of turning all the values of the input matrix to their original value multiplied by 0.
Worksheets are pages where you can write lines of code and execute them. Input objects are Mat. A New Hope. The public ID is the unique identifier of the asset and is either specified when uploading the asset to your Cloudinary account, or automatically assigned by Cloudinary see Upload images for more details on the various options for specifying the public ID. The work consists of computing the mean average of the RGB channels for each picture.
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