JavaScript Dev

JavaScript Dev

1602836640

Detection of elements in viewport & smooth scrolling with parallax

Description:

locomotive-scroll is a modern JS library that applies a smooth, subtle, configurable parallax scroll effect to elements when scrolled into view.

More features:

  • Custom trigger offset.
  • Smooth scroll.
  • Touch-enabled.
  • Custom scrollbar.
  • Supports sticky element.
  • Vertical or horizontal directions.

How to use it:

1. Install the package and import the LocomotiveScroll module.

## NPM
$ npm install locomotive-scroll --save
import LocomotiveScroll from 'locomotive-scroll';

2. Or load the necessary JavaScript & CSS files from the dist folder.

<script src="dist/locomotive-scroll.min.js"></script>
<link href="dist/locomotive-scroll.min.css" rel="stylesheet" />

3. Initialize the LocomotiveScroll with default settings.

const myScroll = new LocomotiveScroll();

4. Apply the LocomotiveScroll to target elements using the data-scroll attribute:

<div data-scroll>Element To Animate</div>

5. Config the parallax scroll effect with the following data attributes:

  • data-scroll-container: scroll container
  • data-scroll-section: Scrollable section. Useful for sectioned pages
  • data-scroll-class: in-view class
  • data-scroll-offset: Trigger offset (ex.: “10”, “100,50%”, “25%, 15%”)
  • data-scroll-repeat: Repeat the viewport detection
  • data-scroll-call: Execute a call function here
  • data-scroll-speed: Parallax speed
  • data-scroll-target: Target element
  • data-scroll-position: top or bottom
  • data-scroll-direction: vertical or horizontal
  • data-scroll-delay: Parallax delay
  • data-scroll-sticky: Sticky element
<div data-scroll
     data-scroll-speed="1">
     data-scroll-call="EVENT_NAME"
     >
     Element To Animate
</div>

6. All possible settings to config the library.

const myScroll = new LocomotiveScroll({
      el: document,
      elMobile: document,
      name: 'scroll',
      offset: [0, 0],
      repeat: false,
      smooth: false, // smooth scroll
      smoothMobile: false, // smooth scroll on mobile
      direction: 'vertical', // or horizontal
      lerp: 1, // inertia
      class: 'is-inview',
      scrollbarClass: 'c-scrollbar',
      scrollingClass: 'has-scroll-scrolling',
      draggingClass: 'has-scroll-dragging',
      smoothClass: 'has-scroll-smooth',
      initClass: 'has-scroll-init',
      getSpeed: false,
      getDirection: false,
      multiplier: 1,
      firefoxMultiplier: 50,
      touchMultiplier: 2,
      scrollFromAnywhere: false
});

7. API methods.

// initialize the instance
myScroll.init();

// re-calc & update the postion
myScroll.update();

// destroy 
myScroll.destroy();

// restart
myScroll.start();

// stop
myScroll.stop();

// scroll to a specific element with an offset
myScroll.scrollTo(target, offset);

8. Event handlers.

myScroll.on('call', (func) => {
  // do something
});

myScroll.on('scroll', (obj) => {
  // do something
});

Download Details:

Author: locomotivemtl

Source Code: https://github.com/locomotivemtl/locomotive-scroll

#javascript

What is GEEK

Buddha Community

Detection of elements in viewport & smooth scrolling with parallax

Detection Of Elements in Viewport & Smooth Scrolling with Parallax

Locomotive Scroll - Detection of elements in viewport & smooth scrolling with parallax effects.

Installation

⚠️ Scroll-hijacking is a controversial practice that can cause usability, accessibility, and performance issues. Please use responsibly.

npm install locomotive-scroll

Usage

Basic

With simple detection.

HTML

<h1 data-scroll>Hey</h1>
<p data-scroll>👋</p>

CSS

Add the base styles to your CSS file.

locomotive-scroll.css

JS

With a bundler

import LocomotiveScroll from 'locomotive-scroll';

const scroll = new LocomotiveScroll();

Or without

<script src="locomotive-scroll.min.js"></script>
<script>
    (function () {
        var scroll = new LocomotiveScroll();
    })();
</script>

Get the JS file.

Smooth

With smooth scrolling and parallax.

<div data-scroll-container>
    <div data-scroll-section>
        <h1 data-scroll>Hey</h1>
        <p data-scroll>👋</p>
    </div>
    <div data-scroll-section>
        <h2 data-scroll data-scroll-speed="1">What's up?</h2>
        <p data-scroll data-scroll-speed="2">😬</p>
    </div>
</div>
import LocomotiveScroll from 'locomotive-scroll';

const scroll = new LocomotiveScroll({
    el: document.querySelector('[data-scroll-container]'),
    smooth: true
});

Note: scroll-sections are optional but recommended to improve performance, particularly in long pages.

Advanced

Make it do what you want.

With methods

<section id="js-target">Come here please.</section>
import LocomotiveScroll from 'locomotive-scroll';

const scroll = new LocomotiveScroll();
const target = document.querySelector('#js-target');

scroll.scrollTo(target);

With events

<!-- Using modularJS -->
<div data-scroll data-scroll-call="function, module">Trigger</div>
<!-- Using jQuery events -->
<div data-scroll data-scroll-call="EVENT_NAME">Trigger</div>
<!-- Or do it your own way 😎 -->
<div data-scroll data-scroll-call="{y,o,l,o}">Trigger</div>
import LocomotiveScroll from 'locomotive-scroll';

const scroll = new LocomotiveScroll();

scroll.on('call', func => {
    // Using modularJS
    this.call(...func);
    // Using jQuery events
    $(document).trigger(func);
    // Or do it your own way 😎
});

Instance options

OptionTypeDefaultDescription
elobjectdocumentScroll container element.
namestring'scroll'Data attribute prefix (data-scroll-xxxx).
offsetarray(2)[0,0]Global in-view trigger offset : [bottom,top]
Use a string with % to use a percentage of the viewport height.
Use a numeric value for absolute pixels unit.
E.g. ["30%",0], [100,0], ["30%", 100]
repeatbooleanfalseRepeat in-view detection.
smoothbooleanfalseSmooth scrolling.
initPositionobject{ x: 0, y: 0 }![Smooth only][smooth-only]
An object defining the initial scroll coordinates on a smooth instance. For example: { x: 0, y: 1000 }
directionstringvertical![Smooth only][smooth-only]
Scroll direction: vertical or horizontal
lerpnumber0.1![Smooth only][smooth-only]
Linear interpolation (lerp) intensity. Float between 0 and 1.
This defines the "smoothness" intensity. The closer to 0, the smoother.
getDirectionbooleanfalseAdd direction to scroll event.
getSpeedbooleanfalseAdd speed to scroll event.
classstringis-inviewElement in-view class.
initClassstringhas-scroll-initInitialize class.
scrollingClassstringhas-scroll-scrollingIs scrolling class.
draggingClassstringhas-scroll-draggingIs dragging class.
smoothClassstringhas-scroll-smoothHas smooth scrolling class.
scrollbarContainerobjectfalse![Smooth only][smooth-only]
Specifies the container element for the scrollbar to be appended in. If false, scrollbar will be appended to the body.
scrollbarClassstringc-scrollbar![Smooth only][smooth-only]
Scrollbar element class.
multipliernumber1![Smooth only][smooth-only]
Factor applied to the scroll delta, allowing to boost/reduce scrolling speed (regardless of the platform).
firefoxMultipliernumber50![Smooth only][smooth-only]
Boost scrolling speed of Firefox on Windows.
touchMultipliernumber2![Smooth only][smooth-only]
Multiply touch action to scroll faster than finger movement.
scrollFromAnywherebooleanfalse![Smooth only][smooth-only]
By default locomotive-scroll listens for scroll events only on the scroll container (el option). With this option set to true, it listens on the whole document instead.
gestureDirectionstringvertical

![Smooth only][smooth-only]
Defines which gesture direction(s) scrolls in your instance. You can use :

  • vertical
  • horizontal
  • both
tablet & smartphoneobject 

Object allowing to override some options for a particular context. You can specify:

  • smooth
  • direction
  • horizontalGesture

For tablet context you can also define breakpoint (integer, defaults to 1024) to set the max-width breakpoint for tablets.

reloadOnContextChangebooleanfalseAllows to reload the page when switching between desktop, tablet and smartphone contexts. It can be useful if your page changes a lot between contexts and you want to reset everything.
resetNativeScrollbooleantrueSets history.scrollRestoration = 'manual' and calls window.scrollTo(0, 0) on locomotive-scroll init in Native Class. Useful if you use transitions with native scrolling, otherwise we advise to set it to false if you don't want to break History API's scroll restoration feature.

Element attributes

AttributeValuesDescription
data-scroll Detect if in-view.
data-scroll-idstring(Optional) Useful if you want to scope your element and get the progress of your element in the viewport for example.
data-scroll-container Defines the scroll container. Required for basic styling.
data-scroll-section Defines a scrollable section. Splitting your page into sections may improve performance.
data-scroll-classstringElement in-view class.
data-scroll-offsetstringElement in-view trigger offset : bottom,top
First value is bottom offset, second (optional) is top offset.
Percent is relative to viewport height, otherwise it's absolute pixels.
E.g. "10", "100,50%", "25%, 15%"
data-scroll-repeatbooleanElement in-view detection repeat.
data-scroll-callstringElement in-view trigger call event.
data-scroll-positionstringtop, bottom, left or right
Window position of in-view trigger.
data-scroll-speednumber![Smooth only][smooth-only]
Element parallax speed. A negative value will reverse the direction.
data-scroll-delaynumber![Smooth only][smooth-only]
Element's parallax lerp delay.
data-scroll-directionstring![Smooth only][smooth-only]
Element's parallax direction. vertical or horizontal
data-scroll-sticky ![Smooth only][smooth-only]
Sticky element. Starts and stops at data-scroll-target position.
data-scroll-targetstring![Smooth only][smooth-only]
Target element's in-view position.

Instance methods

MethodDescriptionArguments
init()Reinitializes the scroll. 
on(eventName, function)Listen [instance events] ⬇. 
update()Updates all element positions. 
destroy()Destroys the scroll events. 
start()Restarts the scroll events. 
stop()Stops the scroll events. 
scrollTo(target, options)Scroll to a target.

target: Defines where you want to scroll. Available values types are :

  • node : a dom element
  • string : you can type your own selector, or use values "top" and "bottom" to reach scroll boundaries
  • int : An absolute scroll coordinate in pixels

options (optional, object) : Settings object. Available values are:

  • offset (integer) : Defines an offset from your target. E.g. -100 if you want to scroll 100 pixels above your target
  • callback (function) : Called when scrollTo completes (note that it won't wait for lerp to stabilize)
  • duration (integer) : Defines the duration of the scroll animation in milliseconds. Defaults to 1000
    ![Smooth only][smooth-only]
  • easing (array) : An array of 4 floats between 0 and 1 defining the bezier curve for the animation's easing. 
    Defaults to [0.25, 0.00, 0.35, 1.00]
    See https://greweb.me/2012/02/bezier-curve-based-easing-functions-from-concept-to-implementation
    Keep in mind this will also be affected by the lerp unless you set disableLerp to true.
    ![Smooth only][smooth-only]
  • disableLerp (boolean) : Lerp effect won't be applied if set to true
    ![Smooth only][smooth-only]

Instance events

EventArgumentsDescription
scrollobjReturns scroll instance (position, limit, speed, direction and current in-view elements).
callfuncTrigger if in-view. Returns your string or array if contains ,.

Progressive playing animations example (like gsap)

All data-scroll elements have a progress value. In the on scroll event you can get all current in-view elements.

HTML

<h1 data-scroll data-scroll-id="hey">Hey</h1>

JS

scroll.on('scroll', (args) => {
    // Get all current elements : args.currentElements
    if(typeof args.currentElements['hey'] === 'object') {
        let progress = args.currentElements['hey'].progress;
        console.log(progress);
        // ouput log example: 0.34
        // gsap example : myGsapAnimation.progress(progress);
    }
});

Dependencies

NameDescription
[Virtual Scroll]Custom scroll event with inertia/momentum.
[modularScroll]Elements in viewport detection. Forked from it, not a dependency.
[bezier-easing]Allows to define an easing to scrollTo movement

Browser support

Works on most modern browsers. Chrome, Firefox, Safari, Edge...

To get IE 11 support, you need polyfills. You can use your own or include these before our script.

<script nomodule src="https://cdnjs.cloudflare.com/ajax/libs/babel-polyfill/7.6.0/polyfill.min.js" crossorigin="anonymous"></script>
<script nomodule src="https://polyfill.io/v3/polyfill.min.js?features=Object.assign%2CElement.prototype.append%2CNodeList.prototype.forEach%2CCustomEvent%2Csmoothscroll" crossorigin="anonymous"></script>

Who's using Locomotive Scroll?

Related

Author: Locomotivemtl
Source Code: https://github.com/locomotivemtl/locomotive-scroll 
License: MIT License

#javascript #parallax

 iOS App Dev

iOS App Dev

1655889360

Element IOS: A Glossy Matrix Collaboration Client for IOS

Element iOS

Element iOS is an iOS Matrix client provided by Element. It is based on MatrixSDK. 

Beta testing

You can try last beta build by accessing our TestFlight Public Link. For questions and feedback about latest TestFlight build, please access the Element iOS Matrix room: #element-ios:matrix.org.

Build instructions

If you have already everything installed, opening the project workspace in Xcode should be as easy as:

$ xcodegen                  # Create the xcodeproj with all project source files
$ pod install               # Create the xcworkspace with all project dependencies
$ open Riot.xcworkspace     # Open Xcode

Else, you can visit our installation guide. This guide also offers more details and advanced usage like using MatrixSDK in its development version.

Contributing

If you want to contribute to Element iOS code or translations, go to the contribution guide.

Support

When you are experiencing an issue on Element iOS, please first search in GitHub issues and then in #element-ios:matrix.org. If after your research you still have a question, ask at #element-ios:matrix.org. Otherwise feel free to create a GitHub issue if you encounter a bug or a crash, by explaining clearly in detail what happened. You can also perform bug reporting (Rageshake) from the Element application by shaking your phone or going to the application settings. This is especially recommended when you encounter a crash.

Download Details:
Author: vector-im
Source Code: https://github.com/vector-im/element-ios
License: Apache-2.0 license

#swift #ios #mobileapp

Enos  Prosacco

Enos Prosacco

1591277160

The Easiest Way To Create Parallax Scrolling With simpleParallax

The Easiest Way To Create Parallax Scrolling With simpleParallax
SimpleParallax is a very simple and tiny JavaScript library which adds parallax animations on any images.

The parallax effect is added directly on image tags, there is no need to use background-image like most of the other parallax libraries do. Basically, you can add parallax effects on a production website without breaking its layout

#javascript #parallax scrolling #parallax #programming #simpleparallax

JavaScript Dev

JavaScript Dev

1602836640

Detection of elements in viewport & smooth scrolling with parallax

Description:

locomotive-scroll is a modern JS library that applies a smooth, subtle, configurable parallax scroll effect to elements when scrolled into view.

More features:

  • Custom trigger offset.
  • Smooth scroll.
  • Touch-enabled.
  • Custom scrollbar.
  • Supports sticky element.
  • Vertical or horizontal directions.

How to use it:

1. Install the package and import the LocomotiveScroll module.

## NPM
$ npm install locomotive-scroll --save
import LocomotiveScroll from 'locomotive-scroll';

2. Or load the necessary JavaScript & CSS files from the dist folder.

<script src="dist/locomotive-scroll.min.js"></script>
<link href="dist/locomotive-scroll.min.css" rel="stylesheet" />

3. Initialize the LocomotiveScroll with default settings.

const myScroll = new LocomotiveScroll();

4. Apply the LocomotiveScroll to target elements using the data-scroll attribute:

<div data-scroll>Element To Animate</div>

5. Config the parallax scroll effect with the following data attributes:

  • data-scroll-container: scroll container
  • data-scroll-section: Scrollable section. Useful for sectioned pages
  • data-scroll-class: in-view class
  • data-scroll-offset: Trigger offset (ex.: “10”, “100,50%”, “25%, 15%”)
  • data-scroll-repeat: Repeat the viewport detection
  • data-scroll-call: Execute a call function here
  • data-scroll-speed: Parallax speed
  • data-scroll-target: Target element
  • data-scroll-position: top or bottom
  • data-scroll-direction: vertical or horizontal
  • data-scroll-delay: Parallax delay
  • data-scroll-sticky: Sticky element
<div data-scroll
     data-scroll-speed="1">
     data-scroll-call="EVENT_NAME"
     >
     Element To Animate
</div>

6. All possible settings to config the library.

const myScroll = new LocomotiveScroll({
      el: document,
      elMobile: document,
      name: 'scroll',
      offset: [0, 0],
      repeat: false,
      smooth: false, // smooth scroll
      smoothMobile: false, // smooth scroll on mobile
      direction: 'vertical', // or horizontal
      lerp: 1, // inertia
      class: 'is-inview',
      scrollbarClass: 'c-scrollbar',
      scrollingClass: 'has-scroll-scrolling',
      draggingClass: 'has-scroll-dragging',
      smoothClass: 'has-scroll-smooth',
      initClass: 'has-scroll-init',
      getSpeed: false,
      getDirection: false,
      multiplier: 1,
      firefoxMultiplier: 50,
      touchMultiplier: 2,
      scrollFromAnywhere: false
});

7. API methods.

// initialize the instance
myScroll.init();

// re-calc & update the postion
myScroll.update();

// destroy 
myScroll.destroy();

// restart
myScroll.start();

// stop
myScroll.stop();

// scroll to a specific element with an offset
myScroll.scrollTo(target, offset);

8. Event handlers.

myScroll.on('call', (func) => {
  // do something
});

myScroll.on('scroll', (obj) => {
  // do something
});

Download Details:

Author: locomotivemtl

Source Code: https://github.com/locomotivemtl/locomotive-scroll

#javascript

Face Recognition with OpenCV and Python

Introduction

What is face recognition? Or what is recognition? When you look at an apple fruit, your mind immediately tells you that this is an apple fruit. This process, your mind telling you that this is an apple fruit is recognition in simple words. So what is face recognition then? I am sure you have guessed it right. When you look at your friend walking down the street or a picture of him, you recognize that he is your friend Paulo. Interestingly when you look at your friend or a picture of him you look at his face first before looking at anything else. Ever wondered why you do that? This is so that you can recognize him by looking at his face. Well, this is you doing face recognition.

But the real question is how does face recognition works? It is quite simple and intuitive. Take a real life example, when you meet someone first time in your life you don't recognize him, right? While he talks or shakes hands with you, you look at his face, eyes, nose, mouth, color and overall look. This is your mind learning or training for the face recognition of that person by gathering face data. Then he tells you that his name is Paulo. At this point your mind knows that the face data it just learned belongs to Paulo. Now your mind is trained and ready to do face recognition on Paulo's face. Next time when you will see Paulo or his face in a picture you will immediately recognize him. This is how face recognition work. The more you will meet Paulo, the more data your mind will collect about Paulo and especially his face and the better you will become at recognizing him.

Now the next question is how to code face recognition with OpenCV, after all this is the only reason why you are reading this article, right? OK then. You might say that our mind can do these things easily but to actually code them into a computer is difficult? Don't worry, it is not. Thanks to OpenCV, coding face recognition is as easier as it feels. The coding steps for face recognition are same as we discussed it in real life example above.

  • Training Data Gathering: Gather face data (face images in this case) of the persons you want to recognize
  • Training of Recognizer: Feed that face data (and respective names of each face) to the face recognizer so that it can learn.
  • Recognition: Feed new faces of the persons and see if the face recognizer you just trained recognizes them.

OpenCV comes equipped with built in face recognizer, all you have to do is feed it the face data. It's that simple and this how it will look once we are done coding it.

visualization

OpenCV Face Recognizers

OpenCV has three built in face recognizers and thanks to OpenCV's clean coding, you can use any of them by just changing a single line of code. Below are the names of those face recognizers and their OpenCV calls.

  1. EigenFaces Face Recognizer Recognizer - cv2.face.createEigenFaceRecognizer()
  2. FisherFaces Face Recognizer Recognizer - cv2.face.createFisherFaceRecognizer()
  3. Local Binary Patterns Histograms (LBPH) Face Recognizer - cv2.face.createLBPHFaceRecognizer()

We have got three face recognizers but do you know which one to use and when? Or which one is better? I guess not. So why not go through a brief summary of each, what you say? I am assuming you said yes :) So let's dive into the theory of each.

EigenFaces Face Recognizer

This algorithm considers the fact that not all parts of a face are equally important and equally useful. When you look at some one you recognize him/her by his distinct features like eyes, nose, cheeks, forehead and how they vary with respect to each other. So you are actually focusing on the areas of maximum change (mathematically speaking, this change is variance) of the face. For example, from eyes to nose there is a significant change and same is the case from nose to mouth. When you look at multiple faces you compare them by looking at these parts of the faces because these parts are the most useful and important components of a face. Important because they catch the maximum change among faces, change the helps you differentiate one face from the other. This is exactly how EigenFaces face recognizer works.

EigenFaces face recognizer looks at all the training images of all the persons as a whole and try to extract the components which are important and useful (the components that catch the maximum variance/change) and discards the rest of the components. This way it not only extracts the important components from the training data but also saves memory by discarding the less important components. These important components it extracts are called principal components. Below is an image showing the principal components extracted from a list of faces.

Principal Components eigenfaces_opencv source

You can see that principal components actually represent faces and these faces are called eigen faces and hence the name of the algorithm.

So this is how EigenFaces face recognizer trains itself (by extracting principal components). Remember, it also keeps a record of which principal component belongs to which person. One thing to note in above image is that Eigenfaces algorithm also considers illumination as an important component.

Later during recognition, when you feed a new image to the algorithm, it repeats the same process on that image as well. It extracts the principal component from that new image and compares that component with the list of components it stored during training and finds the component with the best match and returns the person label associated with that best match component.

Easy peasy, right? Next one is easier than this one.

FisherFaces Face Recognizer

This algorithm is an improved version of EigenFaces face recognizer. Eigenfaces face recognizer looks at all the training faces of all the persons at once and finds principal components from all of them combined. By capturing principal components from all the of them combined you are not focusing on the features that discriminate one person from the other but the features that represent all the persons in the training data as a whole.

This approach has drawbacks, for example, images with sharp changes (like light changes which is not a useful feature at all) may dominate the rest of the images and you may end up with features that are from external source like light and are not useful for discrimination at all. In the end, your principal components will represent light changes and not the actual face features.

Fisherfaces algorithm, instead of extracting useful features that represent all the faces of all the persons, it extracts useful features that discriminate one person from the others. This way features of one person do not dominate over the others and you have the features that discriminate one person from the others.

Below is an image of features extracted using Fisherfaces algorithm.

Fisher Faces eigenfaces_opencv source

You can see that features extracted actually represent faces and these faces are called fisher faces and hence the name of the algorithm.

One thing to note here is that even in Fisherfaces algorithm if multiple persons have images with sharp changes due to external sources like light they will dominate over other features and affect recognition accuracy.

Getting bored with this theory? Don't worry, only one face recognizer is left and then we will dive deep into the coding part.

Local Binary Patterns Histograms (LBPH) Face Recognizer

I wrote a detailed explaination on Local Binary Patterns Histograms in my previous article on face detection using local binary patterns histograms. So here I will just give a brief overview of how it works.

We know that Eigenfaces and Fisherfaces are both affected by light and in real life we can't guarantee perfect light conditions. LBPH face recognizer is an improvement to overcome this drawback.

Idea is to not look at the image as a whole instead find the local features of an image. LBPH alogrithm try to find the local structure of an image and it does that by comparing each pixel with its neighboring pixels.

Take a 3x3 window and move it one image, at each move (each local part of an image), compare the pixel at the center with its neighbor pixels. The neighbors with intensity value less than or equal to center pixel are denoted by 1 and others by 0. Then you read these 0/1 values under 3x3 window in a clockwise order and you will have a binary pattern like 11100011 and this pattern is local to some area of the image. You do this on whole image and you will have a list of local binary patterns.

LBP Labeling LBP labeling

Now you get why this algorithm has Local Binary Patterns in its name? Because you get a list of local binary patterns. Now you may be wondering, what about the histogram part of the LBPH? Well after you get a list of local binary patterns, you convert each binary pattern into a decimal number (as shown in above image) and then you make a histogram of all of those values. A sample histogram looks like this.

Sample Histogram LBP labeling

I guess this answers the question about histogram part. So in the end you will have one histogram for each face image in the training data set. That means if there were 100 images in training data set then LBPH will extract 100 histograms after training and store them for later recognition. Remember, algorithm also keeps track of which histogram belongs to which person.

Later during recognition, when you will feed a new image to the recognizer for recognition it will generate a histogram for that new image, compare that histogram with the histograms it already has, find the best match histogram and return the person label associated with that best match histogram. 

Below is a list of faces and their respective local binary patterns images. You can see that the LBP images are not affected by changes in light conditions.

LBP Faces LBP faces source

The theory part is over and now comes the coding part! Ready to dive into coding? Let's get into it then.

Coding Face Recognition with OpenCV

The Face Recognition process in this tutorial is divided into three steps.

  1. Prepare training data: In this step we will read training images for each person/subject along with their labels, detect faces from each image and assign each detected face an integer label of the person it belongs to.
  2. Train Face Recognizer: In this step we will train OpenCV's LBPH face recognizer by feeding it the data we prepared in step 1.
  3. Testing: In this step we will pass some test images to face recognizer and see if it predicts them correctly.

[There should be a visualization diagram for above steps here]

To detect faces, I will use the code from my previous article on face detection. So if you have not read it, I encourage you to do so to understand how face detection works and its Python coding.

Import Required Modules

Before starting the actual coding we need to import the required modules for coding. So let's import them first.

  • cv2: is OpenCV module for Python which we will use for face detection and face recognition.
  • os: We will use this Python module to read our training directories and file names.
  • numpy: We will use this module to convert Python lists to numpy arrays as OpenCV face recognizers accept numpy arrays.
#import OpenCV module
import cv2
#import os module for reading training data directories and paths
import os
#import numpy to convert python lists to numpy arrays as 
#it is needed by OpenCV face recognizers
import numpy as np

#matplotlib for display our images
import matplotlib.pyplot as plt
%matplotlib inline 

Training Data

The more images used in training the better. Normally a lot of images are used for training a face recognizer so that it can learn different looks of the same person, for example with glasses, without glasses, laughing, sad, happy, crying, with beard, without beard etc. To keep our tutorial simple we are going to use only 12 images for each person.

So our training data consists of total 2 persons with 12 images of each person. All training data is inside training-data folder. training-data folder contains one folder for each person and each folder is named with format sLabel (e.g. s1, s2) where label is actually the integer label assigned to that person. For example folder named s1 means that this folder contains images for person 1. The directory structure tree for training data is as follows:

training-data
|-------------- s1
|               |-- 1.jpg
|               |-- ...
|               |-- 12.jpg
|-------------- s2
|               |-- 1.jpg
|               |-- ...
|               |-- 12.jpg

The test-data folder contains images that we will use to test our face recognizer after it has been successfully trained.

As OpenCV face recognizer accepts labels as integers so we need to define a mapping between integer labels and persons actual names so below I am defining a mapping of persons integer labels and their respective names.

Note: As we have not assigned label 0 to any person so the mapping for label 0 is empty.

#there is no label 0 in our training data so subject name for index/label 0 is empty
subjects = ["", "Tom Cruise", "Shahrukh Khan"]

Prepare training data

You may be wondering why data preparation, right? Well, OpenCV face recognizer accepts data in a specific format. It accepts two vectors, one vector is of faces of all the persons and the second vector is of integer labels for each face so that when processing a face the face recognizer knows which person that particular face belongs too.

For example, if we had 2 persons and 2 images for each person.

PERSON-1    PERSON-2   

img1        img1         
img2        img2

Then the prepare data step will produce following face and label vectors.

FACES                        LABELS

person1_img1_face              1
person1_img2_face              1
person2_img1_face              2
person2_img2_face              2

Preparing data step can be further divided into following sub-steps.

  1. Read all the folder names of subjects/persons provided in training data folder. So for example, in this tutorial we have folder names: s1, s2.
  2. For each subject, extract label number. Do you remember that our folders have a special naming convention? Folder names follow the format sLabel where Label is an integer representing the label we have assigned to that subject. So for example, folder name s1 means that the subject has label 1, s2 means subject label is 2 and so on. The label extracted in this step is assigned to each face detected in the next step.
  3. Read all the images of the subject, detect face from each image.
  4. Add each face to faces vector with corresponding subject label (extracted in above step) added to labels vector.

[There should be a visualization for above steps here]

Did you read my last article on face detection? No? Then you better do so right now because to detect faces, I am going to use the code from my previous article on face detection. So if you have not read it, I encourage you to do so to understand how face detection works and its coding. Below is the same code.

#function to detect face using OpenCV
def detect_face(img):
    #convert the test image to gray image as opencv face detector expects gray images
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    #load OpenCV face detector, I am using LBP which is fast
    #there is also a more accurate but slow Haar classifier
    face_cascade = cv2.CascadeClassifier('opencv-files/lbpcascade_frontalface.xml')

    #let's detect multiscale (some images may be closer to camera than others) images
    #result is a list of faces
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5);
    
    #if no faces are detected then return original img
    if (len(faces) == 0):
        return None, None
    
    #under the assumption that there will be only one face,
    #extract the face area
    (x, y, w, h) = faces[0]
    
    #return only the face part of the image
    return gray[y:y+w, x:x+h], faces[0]

I am using OpenCV's LBP face detector. On line 4, I convert the image to grayscale because most operations in OpenCV are performed in gray scale, then on line 8 I load LBP face detector using cv2.CascadeClassifier class. After that on line 12 I use cv2.CascadeClassifier class' detectMultiScale method to detect all the faces in the image. on line 20, from detected faces I only pick the first face because in one image there will be only one face (under the assumption that there will be only one prominent face). As faces returned by detectMultiScale method are actually rectangles (x, y, width, height) and not actual faces images so we have to extract face image area from the main image. So on line 23 I extract face area from gray image and return both the face image area and face rectangle.

Now you have got a face detector and you know the 4 steps to prepare the data, so are you ready to code the prepare data step? Yes? So let's do it.

#this function will read all persons' training images, detect face from each image
#and will return two lists of exactly same size, one list 
# of faces and another list of labels for each face
def prepare_training_data(data_folder_path):
    
    #------STEP-1--------
    #get the directories (one directory for each subject) in data folder
    dirs = os.listdir(data_folder_path)
    
    #list to hold all subject faces
    faces = []
    #list to hold labels for all subjects
    labels = []
    
    #let's go through each directory and read images within it
    for dir_name in dirs:
        
        #our subject directories start with letter 's' so
        #ignore any non-relevant directories if any
        if not dir_name.startswith("s"):
            continue;
            
        #------STEP-2--------
        #extract label number of subject from dir_name
        #format of dir name = slabel
        #, so removing letter 's' from dir_name will give us label
        label = int(dir_name.replace("s", ""))
        
        #build path of directory containin images for current subject subject
        #sample subject_dir_path = "training-data/s1"
        subject_dir_path = data_folder_path + "/" + dir_name
        
        #get the images names that are inside the given subject directory
        subject_images_names = os.listdir(subject_dir_path)
        
        #------STEP-3--------
        #go through each image name, read image, 
        #detect face and add face to list of faces
        for image_name in subject_images_names:
            
            #ignore system files like .DS_Store
            if image_name.startswith("."):
                continue;
            
            #build image path
            #sample image path = training-data/s1/1.pgm
            image_path = subject_dir_path + "/" + image_name

            #read image
            image = cv2.imread(image_path)
            
            #display an image window to show the image 
            cv2.imshow("Training on image...", image)
            cv2.waitKey(100)
            
            #detect face
            face, rect = detect_face(image)
            
            #------STEP-4--------
            #for the purpose of this tutorial
            #we will ignore faces that are not detected
            if face is not None:
                #add face to list of faces
                faces.append(face)
                #add label for this face
                labels.append(label)
            
    cv2.destroyAllWindows()
    cv2.waitKey(1)
    cv2.destroyAllWindows()
    
    return faces, labels

I have defined a function that takes the path, where training subjects' folders are stored, as parameter. This function follows the same 4 prepare data substeps mentioned above.

(step-1) On line 8 I am using os.listdir method to read names of all folders stored on path passed to function as parameter. On line 10-13 I am defining labels and faces vectors.

(step-2) After that I traverse through all subjects' folder names and from each subject's folder name on line 27 I am extracting the label information. As folder names follow the sLabel naming convention so removing the letter s from folder name will give us the label assigned to that subject.

(step-3) On line 34, I read all the images names of of the current subject being traversed and on line 39-66 I traverse those images one by one. On line 53-54 I am using OpenCV's imshow(window_title, image) along with OpenCV's waitKey(interval) method to display the current image being traveresed. The waitKey(interval) method pauses the code flow for the given interval (milliseconds), I am using it with 100ms interval so that we can view the image window for 100ms. On line 57, I detect face from the current image being traversed.

(step-4) On line 62-66, I add the detected face and label to their respective vectors.

But a function can't do anything unless we call it on some data that it has to prepare, right? Don't worry, I have got data of two beautiful and famous celebrities. I am sure you will recognize them!

training-data

Let's call this function on images of these beautiful celebrities to prepare data for training of our Face Recognizer. Below is a simple code to do that.

#let's first prepare our training data
#data will be in two lists of same size
#one list will contain all the faces
#and other list will contain respective labels for each face
print("Preparing data...")
faces, labels = prepare_training_data("training-data")
print("Data prepared")

#print total faces and labels
print("Total faces: ", len(faces))
print("Total labels: ", len(labels))
Preparing data...
Data prepared
Total faces:  23
Total labels:  23

This was probably the boring part, right? Don't worry, the fun stuff is coming up next. It's time to train our own face recognizer so that once trained it can recognize new faces of the persons it was trained on. Read? Ok then let's train our face recognizer.

Train Face Recognizer

As we know, OpenCV comes equipped with three face recognizers.

  1. EigenFace Recognizer: This can be created with cv2.face.createEigenFaceRecognizer()
  2. FisherFace Recognizer: This can be created with cv2.face.createFisherFaceRecognizer()
  3. Local Binary Patterns Histogram (LBPH): This can be created with cv2.face.LBPHFisherFaceRecognizer()

I am going to use LBPH face recognizer but you can use any face recognizer of your choice. No matter which of the OpenCV's face recognizer you use the code will remain the same. You just have to change one line, the face recognizer initialization line given below.

#create our LBPH face recognizer 
face_recognizer = cv2.face.createLBPHFaceRecognizer()

#or use EigenFaceRecognizer by replacing above line with 
#face_recognizer = cv2.face.createEigenFaceRecognizer()

#or use FisherFaceRecognizer by replacing above line with 
#face_recognizer = cv2.face.createFisherFaceRecognizer()

Now that we have initialized our face recognizer and we also have prepared our training data, it's time to train the face recognizer. We will do that by calling the train(faces-vector, labels-vector) method of face recognizer.

#train our face recognizer of our training faces
face_recognizer.train(faces, np.array(labels))

Did you notice that instead of passing labels vector directly to face recognizer I am first converting it to numpy array? This is because OpenCV expects labels vector to be a numpy array.

Still not satisfied? Want to see some action? Next step is the real action, I promise!

Prediction

Now comes my favorite part, the prediction part. This is where we actually get to see if our algorithm is actually recognizing our trained subjects's faces or not. We will take two test images of our celeberities, detect faces from each of them and then pass those faces to our trained face recognizer to see if it recognizes them.

Below are some utility functions that we will use for drawing bounding box (rectangle) around face and putting celeberity name near the face bounding box.

#function to draw rectangle on image 
#according to given (x, y) coordinates and 
#given width and heigh
def draw_rectangle(img, rect):
    (x, y, w, h) = rect
    cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
    
#function to draw text on give image starting from
#passed (x, y) coordinates. 
def draw_text(img, text, x, y):
    cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)

First function draw_rectangle draws a rectangle on image based on passed rectangle coordinates. It uses OpenCV's built in function cv2.rectangle(img, topLeftPoint, bottomRightPoint, rgbColor, lineWidth) to draw rectangle. We will use it to draw a rectangle around the face detected in test image.

Second function draw_text uses OpenCV's built in function cv2.putText(img, text, startPoint, font, fontSize, rgbColor, lineWidth) to draw text on image.

Now that we have the drawing functions, we just need to call the face recognizer's predict(face) method to test our face recognizer on test images. Following function does the prediction for us.

#this function recognizes the person in image passed
#and draws a rectangle around detected face with name of the 
#subject
def predict(test_img):
    #make a copy of the image as we don't want to chang original image
    img = test_img.copy()
    #detect face from the image
    face, rect = detect_face(img)

    #predict the image using our face recognizer 
    label= face_recognizer.predict(face)
    #get name of respective label returned by face recognizer
    label_text = subjects[label]
    
    #draw a rectangle around face detected
    draw_rectangle(img, rect)
    #draw name of predicted person
    draw_text(img, label_text, rect[0], rect[1]-5)
    
    return img
  • line-6 read the test image
  • line-7 detect face from test image
  • line-11 recognize the face by calling face recognizer's predict(face) method. This method will return a lable
  • line-12 get the name associated with the label
  • line-16 draw rectangle around the detected face
  • line-18 draw name of predicted subject above face rectangle

Now that we have the prediction function well defined, next step is to actually call this function on our test images and display those test images to see if our face recognizer correctly recognized them. So let's do it. This is what we have been waiting for.

print("Predicting images...")

#load test images
test_img1 = cv2.imread("test-data/test1.jpg")
test_img2 = cv2.imread("test-data/test2.jpg")

#perform a prediction
predicted_img1 = predict(test_img1)
predicted_img2 = predict(test_img2)
print("Prediction complete")

#create a figure of 2 plots (one for each test image)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))

#display test image1 result
ax1.imshow(cv2.cvtColor(predicted_img1, cv2.COLOR_BGR2RGB))

#display test image2 result
ax2.imshow(cv2.cvtColor(predicted_img2, cv2.COLOR_BGR2RGB))

#display both images
cv2.imshow("Tom cruise test", predicted_img1)
cv2.imshow("Shahrukh Khan test", predicted_img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.waitKey(1)
cv2.destroyAllWindows()
Predicting images...
Prediction complete

wohooo! Is'nt it beautiful? Indeed, it is!

End Notes

Face Recognition is a fascinating idea to work on and OpenCV has made it extremely simple and easy for us to code it. It just takes a few lines of code to have a fully working face recognition application and we can switch between all three face recognizers with a single line of code change. It's that simple.

Although EigenFaces, FisherFaces and LBPH face recognizers are good but there are even better ways to perform face recognition like using Histogram of Oriented Gradients (HOGs) and Neural Networks. So the more advanced face recognition algorithms are now a days implemented using a combination of OpenCV and Machine learning. I have plans to write some articles on those more advanced methods as well, so stay tuned!

Download Details:
Author: informramiz
Source Code: https://github.com/informramiz/opencv-face-recognition-python
License: MIT License

#opencv  #python #facerecognition