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One of the best things about ROBLOX is just how customizable it is. It’s a space where you can express yourself from server to server. One of the best ways to do that is to use decals to assign custom images to objects in the world. You can change a boring wall to a wall with a picture of your favorite sci-fi spaceship or a cute and cuddly kitten.

ROBLOX decals are images that you can apply to many surfaces in ROBLOX. They help you create the world you want to be in and are used in almost every game aspect. You can search for decals in the library by typing a keyword in the “Search for decal” field. There are many available to use right away, with everything from designs to pop media to celebrities. 

Each object in the world that you can edit has six parts to it. You can apply a decal on any of these six parts. You can combine different ones to create a variety of effects beyond what a single decal could achieve. 

ROBLOX images ID codes also called asset IDs are the unique codes assigned to each decal available on ROBLOX. When you go into the library of assets in Roblox Studio to search for decals, you must take note of the asset ID. You will use it when you apply the decal to a surface. 

For example, a Pikachu decal ID you can use is 46059313.

You can apply a ROBLOX decal to any part of an object that accepts it. You adjust which decal is on a part in Roblox Studio. 

You can create your own ROBLOX decals and use them on servers that let you change the way objects look. However, you can’t just upload it and use it right away. ROBLOX is a moderated platform, and ROBLOX must first approve anything you add to the landscape. In this way, the community is kept safe and appropriate for all users. 

Images shouldn’t be larger than 1024×1024, or else they will be scaled down. 

Once you’ve created a decal you want to use, upload it from your profile page. It will be checked automatically and in most cases, quickly approved.  

When you’ve created a decal and are ready to seek approval, upload it to get the process started. 

Your decal will be available once it’s approved.

The time varies depending on what you uploaded and how it’s processed. Most players say the initial check doesn’t take more than 20 minutes most of the time and speculate that AI does it. However, it can take longer to check specific images and approve them. Some people have said their decals have taken more than a day to gain approval. 

Sometimes ROBLOX will choose not to approve your decal. This is often because it violates the community standards or their terms of use.

Community Standards

Roblox values four specific community standards: safety; civility and respect; fairness and transparency; security and privacy. Keep these in mind when you create your decal. Remember that there are lots of children on ROBLOX, and everything should be family-friendly. It’s also important to remember that kindness matters a lot in the game. If you keep it clean and kind, you won’t have a problem getting approval. 

Terms of Use

If you’re concerned that your images might violate the Terms of Use, read through the agreement before submitting your decal. People upload all kinds of decals, and as long as you follow the Community Standards, it seems unlikely that it would violate the Terms of Use. 

If you’re experiencing an issue with your decals, the fix is probably pretty simple.

If all the decals on a server appear blurry, it’s probably a connection issue. Try logging off, resetting your network, and then logging back in to see whether the decals look clearer. If you see blurry images throughout Roblox, consider doing some troubleshooting. 

You shouldn’t upload an image that you find through a search. Instead, it needs to be something you’ve changed and made your own. If you just grabbed an image from a search engine, it might not be approved. 

Other than that, it might just be a matter of time. All you can do if your decal isn’t being approved is wait to hear from the moderators. If more than a week has gone by without a message, you could try reaching out to ROBLOX support. 

Explain the issue and consider linking to a copy of the decal on Imgur or a similar site. That way, it’s easy for the moderator to find the image you’re asking about. 

You can use the Spray Paint device to add decals to areas that allow them easily. You have to use the asset ID to apply them. You can buy the Spray Paint device from the shop. 

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Fix Roblox Teleport Failed Error Codes 769, 770, 772, 773 On Windows Pc

If when you try to use the Roblox Teleport Function to teleport in Roblox the online game platform on your Windows 11 or Windows 10 computer, but you receive any of the following Error Codes 769, 770, 772, 773, then this post is intended to help you. In this post, we will identify the most likely causes, as well as provide the most suitable solutions affected PC gamers can apply to fix the error on their gaming device.

When you encounter any of the error code in view, you will receive the following associated full error message:

Teleport Failed: Unknown exception (Error Code: 769)

Teleport failed due to an unexpected error. (Error Code: 769)

Reconnect was unsuccessful. Please try again. (Error Code: 769)

Teleport Failed: Unknown error. (Error Code: 770)

Teleport failed: server is full. (Error Code: 772)

Teleport Failed: Attempted to teleport to a place that is restricted. (Error Code: 773)

Generally, when any of these errors occurs, it prevents players from joining the Roblox servers, and consequently restricts the players from playing the game – it can be known as Teleportic Error. Teleport error occurs due to any of the following reasons:

When both the client and the server request to join teleport from the function TeleportService: Teleport. This function may fail and cause the player not to leave the current page.

When the player leaves the page due to the server issue it will automatically disconnect the player and request to rejoin again.

When users try to teleport to a non-approved game or a game which is still under review or restricted – and either you are trying to teleport as a group.

Trying to teleport to a server that is very full or congested.

Internet connectivity issue on your gaming device.

When a player receives a warning or a ban, and then try to rejoin the server.

Roblox Teleport Failed Error Codes 769, 770, 772, 773

The Roblox Error Codes 769, 770, 772, 773 – Teleport Failed are pretty similar in nature – so if you have encountered any of the error code on your Windows 11/10 gaming PC, you can try our recommended solutions below and see if that helps to resolve the issue on your system.

Check If the Game or Places are under Review

Teleport to areas and games which are not Restricted

Use VPN to set your Location

Teleport players individually

Fix Network and Internet connection problems

Contact Roblox Support

Let’s take a look at the description of the process involved concerning each of the listed solutions.

Before you try any of the solutions below, first make sure you are not trying to join a server that is full or a server that you have been banned from or something similar. In addition, sometimes the errors in view can occur due to Roblox Server issue too. So, before you proceed with the solutions below, check the Roblox server status at chúng tôi to confirm if the website/service is down – because if that’s the case, there’s nothing you can do but wait for the server to be back online.

1] Check If the Game or Places are under Review

The first troubleshooting step you can take to fix the Roblox Error Codes 769, 770, 772, 773 – Teleport Failed issue that occurred on your Windows 11/10 gaming rig is to check If the Game or Places are under Review. Basically, a place or a game is placed under review if there is a violation of the Roblox Terms of Services.

2] Teleport to areas and games which are not Restricted

As already indicated most of the affected PC gamers are getting these error codes when they select a place that is restricted to teleport to or when you try to teleport to a game that you are not allowed to join. In this case, to resolve the issue in hand, make sure you have not selected a restricted area to teleport. Also, make sure there are no restrictions in place which will prevent you from joining the game.

3] Use VPN to set your Location

This solution requires you to use a VPN (Virtual Private Network) to set your location to the USA and see if that helps to resolve the issue. Otherwise, try the next solution. We recommend using any of the best free Gaming VPN or GPN software for Windows 11/10 PC. In addition, if you want, you can also configure Global Proxy Server Settings on your gaming computer.

4] Teleport players individually

This is more of a workaround than a solution, albeit very effective in resolving the issue in hand as many players or users reported this workaround was very helpful. So, if you were trying to teleport a group, then you can try to teleport as an individual.

To teleport players individually, you can use the command below that is mentioned on the developer forum of Roblox:

for _,Player in pairs(Group_Of_Players) do TeleportService:TeleportToPrivateServer (PlaceId, Access_Code ,{Player}) end

Try the next solution if the issue is still unresolved.

5] Fix Network and Internet connection problems

This solution requires you to troubleshoot and fix Network and Internet connection problems on your Windows 11/10 gaming device to resolve any connectivity issues that might be triggering these errors. You can also try the Complete Internet Repair Tool or use the Network Reset feature if your computer cannot connect to the Internet at all. And as an added measure, you can release TCP/IP, Flush DNS, Reset Winsock and see if that helps.

If you have ascertained you have no connection problems but the error in hand is still unresolved, you can try the next solution.

6] Contact Roblox Support

If at this point you are still getting the same error code, then you can try to contact Roblox Support at chúng tôi and see if they can be of any useful assistance in resolving the issue at your end or theirs.

Related post: How to Fix Roblox Error Code 109

What is the Roblox error code for getting banned?

The Roblox error code 267 is the code for getting banned on the gaming platform. This means your user account has been issued a temporary ban for trying to play unfairly or hacking the game or – your internet connection might have got interrupted during loading or gameplay – basically, your Windows 11/10 PC could not connect to the Roblox server.

What does 400 mean in Roblox?

If on your gaming computer you are receiving a 400 bad request error on Roblox, it simply means you are trying to access a page that is either down for maintenance or you have a firewall problem. In any case, check the Roblox server/service status and make sure Roblox is allowed through your firewall.

Hope this helps.

A Comprehensive Guide To Understanding Image Steganography Techniques And Types

Introduction

In today’s digital world, privacy and data protection have become increasingly important. With sensitive information being transmitted online every day, ensuring that it stays out of the wrong hands is crucial.

Understanding Steganography And Image Steganography

Steganography is the practice of hiding information within other non-secret media, and image steganography specifically involves concealing data within images using various techniques.

Definition And History Of Steganography

‘Steganography’ word is derived from the Greek words “steganos” (covered) and “graphein” (writing), is the art and practice of hiding information within other data or media files so that it remains undetected. In contrast to cryptography, which encrypts messages to make them unreadable without a decryption key, steganography aims at concealing the very existence of secret communication by embedding it within ordinary-looking carrier files.

Purpose And Applications Of Image Steganography Types And Techniques Of Image Steganography

There are various types and techniques of image steganography, including spatial domain steganography, transform domain steganography, compressed domain steganography, least significant bit (LSB) technique, pixel value differencing (PVD) technique, spread spectrum technique, and randomized embedding technique.

Spatial Domain Steganography

Alters pixel values in images to embed hidden data, commonly using Least Significant Bit (LSB) substitution. It operates directly on the raw bits of a digital image without applying mathematical transforms. Visual cryptography can also be employed for hiding messages within images.

Transform Domain Steganography

Manipulates frequency information in images, providing a more robust system for embedding secret data that resists steganalysis techniques. Examples include Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), and Wavelet-based steganography, with DCT often used in JPEG compression and Wavelet-based steganography providing better performance in adapting to different signal types.

Compressed Domain Steganography

Hides information within the compressed data of an image file to reduce file size and detection difficulty. It involves embedding the covert message in the least significant bits or reserved areas of compressed data. The challenge lies in preserving image quality and avoiding degradation due to multiple compressions.

Least Significant Bit (LSB) Technique

Changes the least significant bits of an image’s color channel to hide information without significantly altering the image’s appearance. It is easy to implement and undetectable to the human eye but has limited capacity for hiding information. Variations include randomizing pixels containing hidden data or using multiple color channels.

Pixel Value Differencing (PVD) Technique

Identifies and modifies pixels with small value differences to encode information in both grayscale and color images. It requires precise changes to pixel values, and using it on highly compressed or low-quality images may result in artifacts or distortion revealing the presence of hidden data.

Spread Spectrum Technique Randomized Embedding Technique

Uses randomization to hide secret data in images, making detection difficult with algorithms like the F5 algorithm that use frequency domain analysis and randomness. It shuffles the position of each bit within an image, creating a modified version of the original image that contains hidden information. It is useful in various applications, including forensic investigations.

Evaluations, Trends, And Future Research

This section will discuss the current state of image steganography research, emerging trends and developments in the field, potential future applications, as well as provide examples of image steganography and their techniques.

Current State Of Image Steganography Research

Image steganography research focuses on developing new techniques for concealing and extracting information from digital images, improving capacity and robustness against detection. Areas of interest include deep learning algorithms for steganalysis and examining security risks posed by image steganography on social media and other online platforms. Challenges remain, such as embedding larger amounts of data without degrading image quality.

Emerging Trends And Developments

Advanced algorithms − Researchers are developing complex mathematical models to hide data in ways difficult for unauthorized individuals to detect.

AI-powered steganography − AI tools have proven effective at hiding information without detection, holding promise for future cybersecurity applications.

Steganalysis − Researchers are developing sophisticated software programs to identify hidden data within images, enhancing detection capabilities.

Potential Future Applications

Data protection in industries − Image steganography techniques may be used to protect sensitive data in finance, healthcare, government agencies, and legal offices.

Social media security − Users can share confidential information with trusted contacts on social media platforms without drawing unwanted attention using steganographic techniques.

Intellectual property protection − Image recognition software could benefit from steganographic algorithms by embedding metadata in digital images to prevent theft and verify ownership rights.

Examples Of Image Steganography And Their Techniques

Image steganography techniques can be used to conceal information in a variety of ways. Here are some examples of image steganography and the techniques used

Embedded Text − This technique involves hiding text within an image by changing individual pixels’ color values. The least significant bit (LSB) method is commonly used to embed text, as it allows small amounts of data to be hidden without altering the overall appearance of the image.

Image Steganography Tools − There are various tools available online that employ steganography techniques for hiding images or other data within other files’ metadata.

Video Steganography − The process of embedding a message within a digital video file is known as video steganography. Videos frequently have messages embedded using methods like Frame Differencing and Discrete Cosine Transform (DCT).

Spatial Domain Techniques − In spatial domain techniques, the confidential message is embedded into an image pixel’s color value by manipulating its least significant bit (LSB) or pixel value differencing (PVD).

Compressed Domain Techniques − In compressed domain techniques, data is hidden within the compression process itself by inserting additional data into the quantization tables of JPEG compression.

Conclusion

In conclusion, image steganography is a vital tool for ensuring data privacy and security in today’s digital world. This comprehensive guide has provided insights into the different types and techniques of this practice, ranging from spatial to compressed domain steganography.

The LSB technique, PVD technique, spread spectrum technique, and randomized embedding technique were also explored in-depth. Staganography will continue to be essential in protecting sensitive information from hackers as technology develops at an unparalleled rate.

With the knowledge you’ve gained from this guide, you’re now equipped with the necessary tools to understand how covert channels can be used for secret communication through digital media using image processing algorithms such as DCT and frequency domain analysis. By understanding these concepts and applying them effectively in your work or personal life, you can ensure that your data stays protected while online!

A Beginners’ Guide To Image Similarity Using Python

If you believe that our Test Image is similar to our first reference image you are right. If you do believe otherwise then let’s find out together with the power of mathematics and programming.

Every image is stored in our computer in the form of numbers and a vector of such numbers that can completely describe our image is known as an Image Vector.

Euclidean Distance:

Euclidean Distance represents the distance between any two points in an n-dimensional space. Since we are representing our images as image vectors they are nothing but a point in an n-dimensional space and we are going to use the euclidean distance to find the distance between them.

Histogram:

A histogram is a graphical display of numerical values. We are going to use the image vector for all three images and then find the euclidean distance between them. Based on the values returned the image with a lesser distance is more similar than the other.

To find the similarity between the two images we are going to use the following approach :

Read the image files as an array.

Since the image files are colored there are 3 channels for RGB values. We are going to flatten them such that each image is a single 1-D array.

Once we have our image files as an array we are going to generate a histogram for each image where for each index 0 – 255 we are going the count the occurrence of that pixel value in the image.

Once we have our histograms we are going to use the L2-Norm or Euclidean Distance to find the difference the two histograms.

Based on the distance between the histogram of our test image and the reference images we can find the image our test image is most similar to.

Coding for Image Similarity in Python Import the dependencies we are going to use from PIL import Image from collections import Counter import numpy as np

We are going to use NumPy for storing the image as a NumPy array, Image to read the image in terms of numerical values and Counter to count the number of times each pixel value (0-255) occurs in the images.

Reading the Image



We can see that out image has been successfully read as a 3-D array. In the next step, we need to flatten this 3-D array into a 1-Dimensional array.

flat_array_1 = array1.flatten() print(np.shape(flat_array_1)) >>> (245760, )

We are going to do the same steps for the other two images. I will skip that here so that you can try your hands on it too.

Generating the Count-Histogram-Vector : RH1 = Counter(flat_array_1)

The following line of code returns a dictionary where the key corresponds to the pixel value and the value of the key is the number of times that pixel is present in the image.

One limitation of Euclidean distance is that it requires all the vectors to be normalized i.e both the vectors need to be of the same dimensions. To ensure that our histogram vector is normalized we are going to use a for loop from 0-255 and generate our histogram with the value of the key if the key is present in the image else we append a 0.

H1 = [] for i in range(256): if i in RH1.keys(): H1.append(RH1[i]) else: H1.append(0)

The above piece of code generates a vector of size (256, ) where each index corresponds to the pixel value and the value corresponds to the count of the pixel in that image.

We follow the same steps for the other two images and obtain their corresponding Count-Histogram-Vectors. At this point we have our final vectors for both the reference images and the test image and all we need to do is calculate the distances and predict.

Euclidean Distance Function : def L2Norm(H1,H2): distance =0 for i in range(len(H1)): distance += np.square(H1[i]-H2[i]) return np.sqrt(distance)

The above function takes in two histograms and returns the euclidean distance between them.

Evaluation :

Since we have everything we need to find the image similarities let us find out the distance between the test image and our first reference image.

dist_test_ref_1 = L2Norm(H1,test_H) print("The distance between Reference_Image_1 and Test Image is : {}".format(dist_test_ref_1)) >>> The distance between Reference_Image_1 and Test Image is : 9882.175468994668

Let us now find out the distance between the test image and our second reference image.

dist_test_ref_2 = L2Norm(H2,test_H) print("The distance between Reference_Image_2 and Test Image is : {}".format(dist_test_ref_2)) >>> The distance between Reference_Image_2 and Test Image is : 137929.0223122023

Fix Roblox Error 279 And 529 On Windows Pc

Roblox is a virtual paradise for gamers. It allows them to create new games and try games created by other members, so, it works on the principle of reciprocity. But as with any server on the planet, Roblox has some errors and in this article, we are going to resolve two of them. We are going to see how to fix Roblox Error 279 and 529 on Windows 11/10.

Fix Roblox Error 279, Failed to connect to the game

Many Roblox users are complaining of an error that doesn’t let them connect to the server. The error message says “Failed to connect to the game”, Error 279. In this section, we are going to fix Roblox error 279 “Failed to connect to the game” with the help of some simple solutions.

This error usually happens because of the Firewall blocking the Roblox server. This can be easily fixed by allowing it through the Firewall. However, we will be covering every possible solution by considering every possible reason for this error.

These are the things you have to do to fix Roblox error 279 “Failed to connect to the game”.

Temporarily disable Firewall

Disable any third-party antivirus

Make sure you are using a supported browser

Reinstall Roblox Client

Check your Internet Connection

Flush DNS and Reset Winsock

Let us talk about them in detail.

1] Temporarily disable Firewall

As the main reason for this is the firewall blocking the server, the best solution would be to disable it. However, make sure to enable it after connecting to the server as it makes your computer vulnerable. To disable the Windows Firewall, follow these steps.

Open Command Prompt from the Start Menu.

Make sure View by is set to Large icons.

Now, retry connecting to the server and check if the issue persists.

2] Disable any third-party antivirus

If you are using any third-party antivirus, it can have the same effect on Roblox as a firewall would have. Therefore, turn them off temporarily and see if the issue persists. If you are not seeing the error message after disabling the Anti-virus, you know what’s causing the trouble.

3] Make sure you are using a supported browser

You need to make sure that you are using a supported browser for Roblox to run on your computer. There are tons of browsers out there in the market but Roblox doesn’t support all of them. To name a few, Chrome, Firefox, Edge and Opera are some of the browsers supported by Roblox.

So, if you are using some specialized browser, try running Roblox on any one of these and check if the issue persists.

4] Reinstall Roblox Client

If you are using Roblox Client, you should try to uninstall and reinstall it for the sake of fixing the error. So, first, uninstall the application from your computer and then delete the leftover files. For that, launch Run by Win + R, paste the following location, and hit Enter.

%localappdata% 5] Check your Internet Connection

Last but not least, a slow Internet connection can cause this error as well. So, if you are this error, try fixing your slow Internet.

You should also consider, increasing your Internet Speed with the help of some setting tweaks and by contacting your ISP. Do this and check if the issue is fixed.

6] Flush DNS and Reset Winsock

If your Internet connection is working fine, try flushing DNS and resetting Winsock. This will fix the issue if it is because of some problem with the network connection. So, open Command Prompt as an administrator and run the following commands.

ipconfig /flushdns netsh winsock reset

Wait for both these commands to run, one after the other, and then reboot your computer. Finally, relaunch Roblox and see if the issue is fixed. Hopefully, it will be resolved.

These were the things you need to do to fix the Error Code 279 on Roblox.

Fix Roblox Error 529, We are experiencing technical difficulties

Another Error message that users are perplexed with is “We are experiencing technical difficulties. Please try again later”, Error 529. This reason for their confusion is there is no reason disclosed for this error by the Roblox developers. Hence, finding the perfect solutions is a bit of a hassle.

These are the things you need to do to fix Roblox Error 529 “We are experiencing technical difficulties”.

Check if Roblox Server is down

Sing Out and Back In

Use Roblox Client

Flush DNS and Reset Winsock

Let us talk about them in detail.

1] Check if Roblox Server is down

If you conclude that Roblox Server is down after probing these sites, then the only thing you can do is wait for it to be fixed. You can keep checking these sites in the meantime. But, if the site is working just fine, check out the next solution.

2] Sing Out and Back In

If the server is not down and you are facing this issue, try singing out of Roblox and then singing back in to see if the issue persists.

In most cases, the issue will be fixed but if not, after singing out of Roblox, reboot your computer once before signing back in.

3] Use Roblox Client

After downloading the app, sign in with the same ID and your error will be fixed.

4] Flush DNS and Reset Winsock

If everything is at its place, and you have tried using all the mentioned solutions, try flushing DNS and resetting Winsock.

For that, launch Command Prompt as an administrator and run the following commands.

ipconfig /flushdns netsh winsock reset

After running these commands, restart your computer and check if the issue persists.

These were the ways by which you can solve the Roblox Error 529.

Hopefully,  you are able to fix Roblox Error 279 and 529 with the help of these solutions.

Is Roblox Safe for 10 year old?

Roblox says Everyone10+ (E10+), which means that it is fine for a 10-year-old. It has some violent games but they are not intense and won’t affect the emotional or mental health of your child. But to be very sure, Roblox has included some parental features for you to look after your kid.

That’s it!

Related: 

Demystifying Unet And Learning Image Segmentation

Introduction

The UNet is the first model that comes to mind these days whenever we want to use image segmentation in machine learning. In comparison to earlier state-of-the-art techniques, it has been revolutionary in terms of performance improvement. Extensive medical imaging, autonomous driving, and satellite imaging applications are all supported by the encoder-decoder convolutional neural network UNet.

We’ll be exploring UNet’s image segmentation process in depth. We will also use the UNet for the task of segmenting Xray images in order to improve our understanding. We’ll talk about the driving force behind UNet, an encoder decoder structure with skip connections that aims to address the difficulties of semantic segmentation.

Learning Objectives

1. What is segmentation? How to use UNet in segmentation tasks?

2. To understand the architecture of UNet.

3. To build the UNet model from scratch.

4. Implement an image segmentation task on a dataset.

This article was published as a part of the Data Science Blogathon.

Use-case of UNet: Segmentation Task

Image segmentation is the primary use case of the UNet architecture. specific object or region of interest is represented by each segment of an image when it is segmented into distinct regions or segments. UNet has been particularly successful in tasks such as biomedical image segmentation, where precise delineation of structures is crucial.

You must give each pixel in the image a class in order to know the shape of the object, or which pixel corresponds to which object, in this case. Segmentation is the process of doing this. A segmentation model returns a great deal more specific data about the image.

U-Net which was first coined for biomedical image segmentation uses deep-learning architecture for segmenting tasks.

How UNet Works?

In this next portion, we aim to give you an in-depth understanding of how the UNet architecture operates. We will explore each element comprising both encoder and decoder segments along with their respective tasks and interrelationships. Gaining a firm footing in these building blocks is critical for effectively implementing or personalizing your own version of UNet.

To create its comprehensive features, UNet employs both a “contracting” and an “expansive” pathway. The contracting pathway adheres to conventional convolutional network architecture, it iteratively applies two unpadded 3×3 convolutions followed by rectified linear unit (ReLU) activation and a downscaling operation using 2×2 max pooling with a stride of 2.

Every time this happens, a double amount of feature channels are generated.

Conversely, in every step within the expansive pathway, there’s an upscaling process done on existing features followed by 2×2 convolution (“up-convolution”) halving existing channels out by performing at once both crop-based concatenation and another round of two consecutive 3×3 convolutions, both also finished off with ReLU activation.

Encoder captures the context i.e. What does the image contain?

Decoder enables precise localization i.e. Where is the object?

Skip connections add fine details to reconstruct the accurate shape of the segmentation map.

Delve Deeper into the Architecture

The UNet architecture consists of an encoder block, a decoder block, and a convolution block.

Convolution Block

In UNet, the encoder and decoder are both on convolutional layers. These layers convolve the input data with a set of learnable filters and perform convolution operations on it. By computing a dot product between the input patch and the filter weights, each filter extracts particular features from its input. Convolutional layers are used in a series, along with activation and batch normalization layers. Extracting features from the input data is the goal of the convolution block.

Encoder Block

The UNet architecture’s first component is the encoder. Multiple convolutional layers are connected by the pooling layers. The encoder’s goal is to extract high-level features while gradually reducing the spatial dimensions of the input image. The encoder’s convolutional layers utilize filters to identify and represent local patterns in the input image, as well as features at various abstraction levels.

Decoder Block

The second component of the UNet architecture is the decoder. It upsamples the low-resolution feature maps back to the original input image size from the encoder. The decoder combines the corresponding feature maps from the encoder with a series of upsampling layers and concatenation operations. The upsampling layers increase the spatial dimensions of the feature maps while preserving their channel-wise information. By combining high-resolution data from the encoder, the concatenated feature maps aid in recovering fine-grained details.

In order to reconstruct the original input data, the decoder block increases the spatial resolution of the feature maps and combines them with the features from corresponding encoder blocks.

What Happens without Skip Connection?

Without skip connections in UNet, the model would lack direct connections between the encoder and decoder layers. Skip connections play a crucial role in preserving fine- grained details and enabling the efficient flow of information across different levels of the network. The decoder struggles to produce fine-grained segmentation.

As seen in the above figure, the model tries to predict the segmentation masks but the fine-grained deep features are difficult to obtain and thus result in loss of information.

Building the UNet Model from Scratch Load the Necessary Libraries from tensorflow.keras.layers import Conv2D from tensorflow.keras.layers import BatchNormalization, Activation, MaxPool2D from tensorflow.keras.layers import Conv2DTranspose, Concatenate, Input from tensorflow.keras.models import Model Building the Convolutional Block

The convolutional block It applies a 2D convolutional layer to the input tensor, followed by batch normalization and ReLU activation. Another convolutional layer, batch normalization, and ReLU activation are applied, and the output tensor is returned. This block helps capture patterns and structures in the data, enhancing the model’s ability to learn complex representations.

def conv_block(input, num_filters): x = Conv2D(num_filters, 3, padding="same")(input) x = BatchNormalization()(x) x = Activation("relu")(x) x = Conv2D(num_filters, 3, padding="same")(x) x = BatchNormalization()(x) x = Activation("relu")(x) return x Building the Encoder Block

The encoder block in the UNet architecture performs downsampling and feature extraction. It applies convolutional operations to the input tensor, followed by max pooling to reduce spatial dimensions. The block produces the processed tensor and the downsampled tensor, which subsequent layers utilize for further processing and feature extraction.

def encoder_block(input, num_filters): x = conv_block(input, num_filters) p = MaxPool2D((2, 2))(x) return x, p Building the Decoder Block

The decoder block in the UNet architecture performs upsampling and merges skip connections. It applies transposed convolution to upsample the input tensor by a factor of 2. The upsampled tensor is then concatenated with the skip connection tensor from the corresponding encoder block. The block further applies a convolutional block to refine the merged features. The output tensor from the decoder block is used in subsequent layers for further processing.

def decoder_block(input, skip_features, num_filters): x = Conv2DTranspose(num_filters, (2, 2), strides=2, padding="same")(input) x = Concatenate()([x, skip_features]) x = conv_block(x, num_filters) return x

In binary segmentation tasks with one foreground class and one background class, the output consists of a single channel.

The sigmoid activation function is used to squash the output values between 0 and 1. This allows the network to represent the probability of each pixel belonging to the foreground class. Values closer to 0 indicate a higher probability of being the background class, while values closer to 1 indicate a higher probability of being the foreground class.

With a single channel and sigmoid activation, the network produces clear binary segmentation outputs, assigning each pixel a distinct label (0 or 1) based on its probability of belonging to the foreground class.

Building the UNET Model 

The build_unet function constructs a UNet model for image segmentation. It applies encoder blocks to downsample and capture features, followed by a convolutional block for high-level representations. Decoder blocks are used to upsample and merge skip connections. The model generates an output tensor with sigmoid activation representing pixel probabilities for the foreground class.

def build_unet(input_shape): inputs = Input(input_shape) s1, p1 = encoder_block(inputs, 64) s2, p2 = encoder_block(p1, 128) s3, p3 = encoder_block(p2, 256) s4, p4 = encoder_block(p3, 512) b1 = conv_block(p4, 1024) d1 = decoder_block(b1, s4, 512) d2 = decoder_block(d1, s3, 256) d3 = decoder_block(d2, s2, 128) d4 = decoder_block(d3, s1, 64) outputs = Conv2D(1, 1, padding="same", activation="sigmoid")(d4) model = Model(inputs, outputs, name="U-Net") return model input_shape = (512, 512, 3) model = build_unet(input_shape)

Now that we have built the UNet model from scratch, we will implement this model on an image segmentation task.

Image Segmentation on Chest Xray

In computer-aided diagnostic systems for identifying and analyzing various abnormalities and structures within the chest region, image segmentation on chest X-rays is an essential component. The chest X-ray images can be precisely segmented to spot specific regions like the lungs, heart, ribs, and potential anomalies like tumors and nodules, as well as pneumothorax, using cutting-edge deep learning techniques like U-Net.

Here we will be implementing UNet to perform image segmentation on the chest Xray dataset to segment the lungs part of the x-ray.

Import Necessary Libraries

import numpy as np import pandas as pd import cv2 from glob import glob from sklearn.model_selection import train_test_split import tensorflow as tf from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau from tensorflow.keras.optimizers import Adam from tensorflow.keras.metrics import Recall, Precision from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau

Loading and Splitting the Dataset

dataset_path = '/content/drive/MyDrive/Datasets/MontgomerySet/' def load_data(dataset_path, split=0.1): images=sorted(glob(os.path.join(dataset_path, "CXR_png", "*.png"))) mask1=sorted(glob(os.path.join(dataset_path,"ManualMask","leftMask","*.png"))) mask2=sorted(glob(os.path.join(dataset_path,"ManualMask","rightMask","*.png"))) split_size = int(len(images) * split) train_x,valid_x=train_test_split(images,test_size=split_size,random_state=42) train_y1,valid_y1=train_test_split(mask1,test_size=split_size,random_state=42) train_y2,valid_y2=train_test_split(mask2,test_size=split_size,random_state=42) train_x,test_x=train_test_split(train_x,test_size=split_size,random_state=42) train_y1,test_y1=train_test_split(train_y1,test_size=split_size,random_state=42) train_y2,test_y2=train_test_split(train_y2,test_size=split_size,random_state=42) return(train_x,train_y1,train_y2),(valid_x,valid_y1,valid_y2), (test_x,test_y1,test_y2) (train_x,train_y1, train_y2), (valid_x, valid_y1, valid_y2), (test_x, test_y1, test_y2) = load_data(dataset_path)

Reading Images

Reading every images in the dataset.

Normalize the value to 0 to 1 by dividing with 255.

def read_image(dataset_path): x = cv2.imread(dataset_path, cv2.IMREAD_COLOR) x = cv2.resize(x, (W, H)) x = x/255.0 x = x.astype(np.float32) return x

Reading Masks

Reading every masks in the dataset, both left and right and adding both the masks.

Use the threshhold of 0.5 such that the value of mask becomes either 0 or 1.

def read_mask(path1, path2): x1 = cv2.imread(path1, cv2.IMREAD_GRAYSCALE) x2 = cv2.imread(path2, cv2.IMREAD_GRAYSCALE) x = x1 + x2 x = cv2.resize(x, (W, H)) x = x/np.max(x) x = x.astype(np.float32) x = np.expand_dims(x, axis=-1) return x

Fitting the Model

The optimizer used is the Adam optimizer, which helps improve the model’s performance by adjusting the learning rate based on parameter gradients.

The loss function is binary cross entropy which measures the dissimilarity between predicted probabilities and actual labels which is appropriate for binary classification problems.

The metrics used are:

Dice coefficient measures the similarity of the predicted and ground truth segmentation masks

IOU evaluates their overlap.

Precision measures the model’s capacity to prevent false positives

Recall determines how well it can identify positive instances.

These evaluation metrics give useful information about the model’s performance and allow for accurate and effective segmentation evaluation.

H = 512 W = 512 model = build_unet((H, W, 3)) metrics = [dice_coef, iou, Recall(), Precision()] model_path = os.path.join("files", "model.h5") callbacks = [ ModelCheckpoint(model_path, verbose=1, save_best_only=True), ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-7, verbose=1) ] model_history = model.fit(train_dataset, epochs=num_epochs, validation_data=valid_dataset, callbacks=callbacks) Results def display(display_list): plt.figure(figsize=(15, 15)) title = ['Input Image', 'True Mask', 'Predicted Mask'] for i in range(len(display_list)): plt.subplot(1, len(display_list), i+1) plt.title(title[i]) plt.imshow(tf.keras.utils.array_to_img(display_list[i])) plt.axis('off') plt.show() pred_mask = pred_mask.astype(np.int32) pred_mask = np.concatenate([pred_mask, pred_mask, pred_mask], axis=-1) for x, y1, y2 in tqdm(zip(test_x, test_y1, test_y2), total=len(test_x)): display([ori_x, ori_y, pred_mask]) Conclusion

In conclusion, this comprehensive guide delved into the UNet architecture, providing a clear understanding of its key components and implementation process by explaining the encoder, decoder, and skip connections that helped gain insights into how UNet achieves accurate image segmentation.

The article emphasized the significance of data preprocessing and augmentation techniques to improve model performance. Additionally, it highlighted the relevance of selecting appropriate loss functions and evaluation metrics for image segmentation tasks.

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