Hello techies, this blog will not be more about an overview how to do wireless debugging in Android Studio but would be more of a quick solution for those who have searched through the internet to fix it in Android Studio.So, basically you can find all the way to do wireless debugging in Android studio <a target="_blank" href="https://developer.android.com/tools/adb#connect-to-a-device-over-wi-fi" alt="here">here</a> - but in this blog I would be showing you the simplest and easiest way to do things, which worked for me pretty well.So let's dive into this. In this tutorial to connect your device wirelessly for debugging we would be using the <code>adb</code> commands - if your terminal doesn't know it then add it to the path or install the android-platform-tools. You can go through the plenty of articles on internet to set it up for you. Once adb command is available on your terminal we're good to go further.<h2>First things First </h2><ul><li>On your mobile device enable USB Debugging.</li><li>Then connect the device using USB to Android studio which you do usually for the debugging over USB.</li></ul><h2>Make your device ready</h2><ul><li>Now open up the terminal and list out the devices using the command adb devices -l</li><li>Now, if you can see your device in the terminal, the we are good to go.</li><li>Run this command - adb tcpip 5555.&nbsp;This will put the device into TCP/IP mode listening on port 5555. Now remove the USB from device.</li></ul><h2>Final Step</h2><ul><li>In your device, get the IP of the device by going into settings, and then in about phone &gt; status &gt; IP address. Or rather search for IP in settings as different devices it's location may vary. Whole point here is to get the IP of the device.</li><li>Now, the final step - run the command - adb connect device_ip:5555. This will connect the device wirelessly.</li></ul>So, at this point, you would be able to see the device connected, also you can check it by running command adb devices and your device would be listed there.Congratulations! you have setup the wireless debugging in Android Studio.&nbsp;If you really liked this blog, do checkout other blogs, and stay tuned!Happy Wireless Debugging!!

How to do wireless debugging in Android Studio

Hey there, fellow developers! Remember when we talked about <a href="https://www.dev-akash.in/blogs/edge-detection-in-opencv" alt="finding edges in images" target="_blank" class="on">finding edges in images</a>? Well, today, we're diving deeper into a related concept: contour detection. Think of it as finding and outlining shapes in pictures – pretty cool, right?<h2>From Edges to Contours: A Seamless Transition</h2>In our previous discussion on <a href="https://www.dev-akash.in/blogs/edge-detection-in-opencv" alt="edge detection" target="_blank">edge detection</a>, we learned how to detect the boundaries between different elements in an image. Now, contour detection takes these boundaries a step further, helping us to not only detect edges but also outline and identify the shapes formed by those edges.<h2>What Are Contours?</h2>Contour detection is like being a shape detective. It helps us find and outline objects in images. These outlines are called contours, and they're like the borders that separate one thing from another. In a black-and-white picture, contours highlight the boundaries of objects, making them stand out.<h2>How Does Contour Detection Work?</h2>Let's break it down. Imagine you have a black-and-white image. Contour detection looks for continuous white areas against a black background. It's like connecting the dots between pixels to outline shapes.<h2>Using OpenCV for Contour Detection</h2>Now, let's explore some additional options available for the <code>cv2.findContours()</code> function:<pre>import cv2 # Read the image image = cv2.imread('your_image.jpg') # Convert the image to grayscale gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Find contours # The second argument specifies the contour retrieval mode. # Options include cv2.RETR_EXTERNAL, cv2.RETR_LIST, cv2.RETR_CCOMP, and cv2.RETR_TREE. # We use cv2.RETR_EXTERNAL to find only the outer contours of objects. # The third argument sets the contour approximation method. # Options include cv2.CHAIN_APPROX_NONE, cv2.CHAIN_APPROX_SIMPLE, cv2.CHAIN_APPROX_TC89_L1, and cv2.CHAIN_APPROX_TC89_KCOS. # We use cv2.CHAIN_APPROX_SIMPLE to compress horizontal, vertical, and diagonal segments. contours, _ = cv2.findContours(gray_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # Draw contours on the original image cv2.drawContours(image, contours, -1, (0, 255, 0), 2) # Display the result cv2.imshow('Contours', image) cv2.waitKey(0) cv2.destroyAllWindows() </pre><h3>Understanding Additional Options</h3>In the code above, let's take a closer look at the additional options available for the <code>cv2.findContours()</code> function:<ul><li>Contour Retrieval Modes (cv2.RETR_XXX): <ul><li><code>cv2.RETR_EXTERNAL</code>: Retrieves only the external contours. Useful for most applications where you only need the outer boundaries of objects.</li><li><code>cv2.RETR_LIST</code>: Retrieves all contours without any hierarchical relationships.</li><li><code>cv2.RETR_CCOMP</code>: Retrieves all contours and organizes them into a two-level hierarchy. Useful for more complex shapes.</li><li><code>cv2.RETR_TREE</code>: Retrieves all contours and reconstructs a full hierarchy of nested contours.</li></ul> </li><li>Contour Approximation Methods (cv2.CHAIN_APPROX_XXX): <ul><li><code>cv2.CHAIN_APPROX_NONE</code>: Stores all contour points.</li><li><code>cv2.CHAIN_APPROX_SIMPLE</code>: Compresses horizontal, vertical, and diagonal segments and leaves only their endpoints.</li><li><code>cv2.CHAIN_APPROX_TC89_L1</code> and <code>cv2.CHAIN_APPROX_TC89_KCOS</code>: Apply the Tangent Chain approximation algorithm to further compress contours.</li></ul> </li></ul>Experimenting with different options allows you to fine-tune contour detection according to the specific requirements of your project. So, feel free to explore and find the best settings for your application!<h2>Drawing Contours: Bringing Shapes to Life</h2>After detecting contours, we often want to visualize them on the original image. This is where the <code>cv2.drawContours()</code> function comes into play. Here's a simple explanation of how it works:<ul><li>cv2.drawContours(image, contours, contourIdx, color, thickness): <ul><li><code>image</code>: The original image on which you want to draw contours.</li><li><code>contours</code>: The list of contours detected using <code>cv2.findContours()</code>.</li><li><code>contourIdx</code>: Index of the contour to draw. Use <code>-1</code> to draw all contours.</li><li><code>color</code>: Color of the contour lines (BGR format).</li><li><code>thickness</code>: Thickness of the contour lines.</li></ul> </li></ul>By using <code>cv2.drawContours()</code>, you can visually represent the contours found in the image, making it easier to analyze and understand the detected shapes.<h2>What Can You Do with Contours?</h2>Contour detection opens up a world of possibilities. You can use contours for object detection, shape analysis, and even creating special effects in images. With a bit of creativity, you can do amazing things!<h2>Conclusion</h2>Contour detection in OpenCV offers a wide range of options to suit various image processing tasks. By understanding how contours work and how to use the <code>cv2.findContours()</code> function, you gain greater control over the contour detection process, enabling you to extract valuable information from images with precision and accuracy.So, roll up your sleeves, dive into the documentation, and start experimenting with contours! Happy coding!

Revealing Shapes With OpenCV Contour Detection

Hey fellow developers! Are you ready to take your React application to the next level by incorporating social media share links? Sharing content across platforms like Facebook, Twitter, and more can amplify your app's reach and engagement. In this guide, we'll walk through the exciting process of integrating share buttons seamlessly into your React app using the popular <code>react-share</code> package. <h2>Getting Started</h2> First things first, let's install the necessary package. Open up your terminal and type: <pre> <code>npm install react-share</code></pre> Once installed, we're ready to dive into the fun part! <h2>Adding Share Buttons</h2> Imagine your users discovering a captivating article or a remarkable achievement within your app and wanting to share it with their friends and followers. With social media share buttons, you empower them to do just that! In your React component, import the desired share buttons from <code>react-share</code>: <pre> <code>import { FacebookShareButton, TwitterShareButton } from 'react-share';</code></pre> Now, let's set up the share buttons within your component: <pre>const ShareComponent = () =&gt; { const shareUrl = 'https://yourawesomeapp.com'; const title = 'Check out this amazing content!'; return ( &lt;div&gt; &lt;FacebookShareButton url={shareUrl} quote={title}&gt; Share on Facebook &lt;/FacebookShareButton&gt; &lt;TwitterShareButton url={shareUrl} title={title}&gt; Share on Twitter &lt;/TwitterShareButton&gt; &lt;/div&gt; ); }</pre> Simply replace <code>'https://yourawesomeapp.com'</code> with the actual URL you want users to share and customize the <code>title</code> to entice their interest. <h2>Customization and Beyond</h2> Exciting, isn't it? But why stop there? The <code>react-share</code> package offers a plethora of customization options. You can style the buttons, add additional share platforms like LinkedIn and Pinterest, and even track share analytics for insights into user engagement. <h2>Conclusion</h2> By incorporating social media share links into your React application, you're not only enhancing user experience but also fostering community engagement and expanding your app's reach. So, what are you waiting for? Let your users spread the love and make your content go viral with share buttons in your React app today! Happy coding and sharing! 🚀✨

Adding Social Media Share Links to Your React App

​Hey there, tech enthusiasts! Today, we're going to chat about a common task in the world of Java development: converting a .keystore file to a .jks file. Don't worry if you're not sure what that means – we'll break it down in simple terms.First things first, what are these files? Well, a .keystore file and a .jks file are both used in Java applications to store security certificates and private keys. They're crucial for securing your applications and data. Sometimes, you might need to switch between these formats for various reasons, and that's where the conversion process comes into play. So, how do we do it? Let's walk through the steps: <ol><li>Open a Terminal or Command Prompt: To begin, you'll need to access your command-line interface. Don't worry, it's not as daunting as it sounds – it's just a way to interact with your computer using text commands. </li><li>Navigate to the Directory: Use the cd command to navigate to the directory where your .keystore file is located. For example:&nbsp; <pre>cd path/to/your/keystore</pre> </li><li>Convert the File: Now, it's time to perform the conversion. Use the keytool command, which is a utility provided by Java for managing keystores and certificates. Here's the command you'll need: <pre>keytool -importkeystore -srckeystore yourfile.keystore -destkeystore yourfile.jks -srcstoretype JKS -deststoretype JKS</pre>Make sure to replace yourfile.keystore with the name of your .keystore file and yourfile.jks with the desired name for your .jks file. </li><li>Enter Passwords: During the conversion process, you may be prompted to enter passwords for both the source and destination keystores. Simply type them in and hit Enter. </li><li>Confirmation: Once the process is complete, you should see a confirmation message indicating that the conversion was successful. </li></ol>And that's it! You've successfully converted your .keystore file to a .jks file. See, it wasn't so hard, was it? Remember, while this process is relatively straightforward, it's always a good idea to double-check your work and ensure that your certificates and keys are properly secured. Now, go ahead and give it a try. Happy coding!

How to Convert keystore to jks File

<div>This app has the major focus on having easy and simple User interface to make user easily to there stuff. With this app user can scan the QR codes in a single click and also create the QR codes by just typing the data of which they want QR, and with one tap they can easily generate and preview the QR code. And can easily save the QR code image on there phone by clicking on save button after generating the QR code. Hope this app help when you need it.. Thank you!</div>

UniQR

Slippingo is a casual runner game having abstract theme with different difficulty levels, all of which is created using Unity Game Engine. This game includes integration of ads. levels, all of which is created using Unity Game Engine. This game includes integration of ads.

Slippingo

This project was a work based on Computer Vision (opencv) and Machine Learning (Tensorflow). Where the models were trained on very less quantity of data and balance the distribution of classification with synthetic data generated (using Keras data-generators). So that with just on click of classroom all students present in photo get there attendance marked.

Attendance Marking System

Implemented a web app using Flask to recognize x-ray of patients having Pneumonia using highly trained Deep learning models trained on 25k X-Ray images and having accuracy of 92%.

Pneumonia Detection

This project is a pure HTML/CSS game just for fun. Where the whole game is to destroy the enemy balls in middle before reaching to the destination by moving player ball upwards by hovering over a button. Just try it out.!

Hover Ball

​Bulkren is a pypi project - a python module/library to rename the dataset in some sequence. It help in data cleaning as a part of any machine learning project. Currently having more than 20k downloads.

UniQR

An app to scan the QR codes in a single click and also create the QR codes by just typing the data

Slippingo

Slippingo is a casual runner game having abstract theme with different difficulty levels

Attendance Marking System

A Computer vision and machine learning project for marking attendance

Pneumonia Detection

Detecting Pneumonia using Chest X-ray images

Hover Ball

Its an exciting and simple HTML/CSS game. Play it now!

Bulkren

A Bulk renaming CLI tool, helpful in data processing