DEV Community: Enzo Lizama Paredes

Explain me clean architecture like im five

Enzo Lizama Paredes — Fri, 16 Feb 2018 17:57:11 +0000

Let’s play with the Android accelerometer + Kotlin

Enzo Lizama Paredes — Thu, 15 Feb 2018 05:26:31 +0000

This post was originally published on Medium

General aspects about sensors

Android devices tend to be accompanied with a series of sensors that make the device to interact with the environment that surrounds it.

And that’s already great to have a range of possibilities about what we can do in our applications,these sensors allow the terminal to have a knowledge of the environment that surrounds you,but the sensors are nothing more than electronic parts that capture information from outside and it is the application which should transform this information into data to work them.

The activities who works with sensors must implement the interface SensorEventListener that will force us to implement the methods onAccuracyChanged() y onSensorChanged() .

The method onAccuracyChanged() will be executed when the precision of a sensor change , while onSensorChanged() will do it every time there is a change in one of the sensors.

Building an accelerometer app

The topic of sensors in android is tremendously big and it can’t be covered at all in one article, so for this occasion we will be build an accelerometer app, metallball 💥.

But first a little bit about this sensor in specific.

Accelerometer

With this kind of sensor it’s possible to determinate the orientation of the terminal in the real world 🌎 , considering as coordinate axis the middle point of the device.

The accelerometer calculates the linear acceleration in each of the 3 axes (x, y, z); each axis has its own accelerometer, so that the data can be received individually.

The app code

In the MainActivity file you access to the vibration service, for that reason don't forget to copy this into the AndroidManifest file.

<uses-permission android:name="android.permission.VIBRATE" />

Here is the full code of the app, If you have questions, post it to be able to answer.

Conclusion

If you found this helpful, click the <3 below. Follow me for more articles on technology.

Here is the link to the full source code 💥 :

How to build amazing image filters with Python— Median filter 📷 , Sobel filter ⚫️ ⚪️

Enzo Lizama Paredes — Sat, 09 Dec 2017 08:34:38 +0000

How to build amazing image filters with Python— Median filter 📷 , Sobel filter ⚫️ ⚪️

Nowadays, I’m starting in a new programming language : Python 🐍 . And I am pleased to share some of my knowledge about this new topic , which is image processing.

What is digital image processing ?

It is a set of techniques whose objective is to obtain a result with a specific application from a source image, improving certain characteristics that allow you to extract information from the image.
OK , After a short introduction about the meaning of image processing, Let’s continue with the filters that I will show you in this post.

Median filter

Taken from http://artemhlezin.com/2016/09/04/median.html
This filter is used to eliminate the ‘noise’ of the images, mainly is salt-n-pepper noise.
There is not much theory beyond the one in the picture. This is how the filter works : gets all the values inside a mask, sorts them and then assigns the mean value to the coordinate.
This is how it looks an image with salt and pepper noise :

In Python 🐍 the filter works like this, enter to check the result:

And this is the result :

Sobel filter

The Sobel operator is an algorithm for edge detection, a technique to find the limits between regions in an image. The edge detection algorithm help us separate some specific parts of the source image and take them off from the background. There is more interesting theory about edge detection and the Sobel operator , but we will focus on the Python 🐍 implementation.
Due to the excitement for the South American World Cup qualifiers I decided to use this image for example :

In Python 🐍 the filter works like this, enter to check the result:

And this is the result :

Conclusion

I mentioned that there is much more theory about this subject; so, I will leave some articles that, in my opinion, are the most complete and better explained in the web.
References
Saush Blog’s , good description about sobel operator and other image processing filter.
https://blog.saush.com
An edge detection implementation in C++.
https://stackoverflow.com/questions/40445415/sobel-edge-detection-implementation
A detailed article about of median filter.
http://fourier.eng.hmc.edu/e161/lectures/smooth_sharpen/node2.htm
Another implementation of Sobel filter.
http://homepages.inf.ed.ac.uk/rbf/HIPR2/sobel.htm

Thanks for reading this little post , totally open to feedback. Questions in the comment box and show your support 👏❤️ .
You can watch my open-source projects in my Github account.