Sean Robertson

Sean Robertson

1553754026

How To Detect and Extract Faces from an Image with OpenCV and Python?

#python #opencv #image

What is GEEK

Buddha Community

In this tutorial, you will use a pre-trained Haar Cascade model from OpenCV and Python to detect and extract faces from an image. OpenCV is an open-source programming library that is used to process images.

The author selected the Open Internet/Free Speech Fund to receive a donation as part of the Write for DOnations program.

Introduction

Images make up a large amount of the data that gets generated each day, which makes the ability to process these images important. One method of processing images is via face detection. Face detection is a branch of image processing that uses machine learning to detect faces in images.

A Haar Cascade is an object detection method used to locate an object of interest in images. The algorithm is trained on a large number of positive and negative samples, where positive samples are images that contain the object of interest. Negative samples are images that may contain anything but the desired object. Once trained, the classifier can then locate the object of interest in any new images.

In this tutorial, you will use a pre-trained Haar Cascade model from OpenCV and Python to detect and extract faces from an image. OpenCV is an open-source programming library that is used to process images.

Prerequisites

  • A local Python 3 development environment, including [pip](https://pypi.org/project/pip/ "pip"), a tool for installing Python packages, and [venv](https://docs.python.org/3/library/venv.html "venv"), for creating virtual environments.

Step 1 — Configuring the Local Environment

Before you begin writing your code, you will first create a workspace to hold the code and install a few dependencies.

Create a directory for the project with the mkdir command:

mkdir face_scrapper


Change into the newly created directory:

cd face_scrapper


Next, you will create a virtual environment for this project. Virtual environments isolate different projects so that differing dependencies won’t cause any disruptions. Create a virtual environment named face_scrapper to use with this project:

python3 -m venv face_scrapper


Activate the isolated environment:

source face_scrapper/bin/activate


You will now see that your prompt is prefixed with the name of your virtual environment:

Now that you’ve activated your virtual environment, you will use nano or your favorite text editor to create a requirements.txt file. This file indicates the necessary Python dependencies:

nano requirements.txt


Next, you need to install three dependencies to complete this tutorial:

  • A local Python 3 development environment, including [pip](https://pypi.org/project/pip/ "pip"), a tool for installing Python packages, and [venv](https://docs.python.org/3/library/venv.html "venv"), for creating virtual environments.

Add the following dependencies to the file:

requirements.txt

numpy 
opencv-utils
opencv-python


Save and close the file.

Install the dependencies by passing the requirements.txt file to the Python package manager, pip. The -r flag specifies the location of requirements.txt file.

pip install -r requirements.txt


In this step, you set up a virtual environment for your project and installed the necessary dependencies. You’re now ready to start writing the code to detect faces from an input image in next step.

Step 2 — Writing and Running the Face Detector Script

In this section, you will write code that will take an image as input and return two things:

  • A local Python 3 development environment, including [pip](https://pypi.org/project/pip/ "pip"), a tool for installing Python packages, and [venv](https://docs.python.org/3/library/venv.html "venv"), for creating virtual environments.

Start by creating a new file to hold your code:

nano app.py


In this new file, start writing your code by first importing the necessary libraries. You will import two modules here: cv2 and sys. The cv2 module imports the OpenCV library into the program, and sys imports common Python functions, such as argv, that your code will use.

app.py

import cv2
import sys


Next, you will specify that the input image will be passed as an argument to the script at runtime. The Pythonic way of reading the first argument is to assign the value returned by sys.argv[1] function to an variable:

app.py

...
imagePath = sys.argv[1]


A common practice in image processing is to first convert the input image to gray scale. This is because detecting luminance, as opposed to color, will generally yield better results in object detection. Add the following code to take an input image as an argument and convert it to grayscale:

app.py

...
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)


The .imread() function takes the input image, which is passed as an argument to the script, and converts it to an OpenCV object. Next, OpenCV’s .cvtColor() function converts the input image object to a grayscale object.

Now that you’ve added the code to load an image, you will add the code that detects faces in the specified image:

app.py

...
faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.3,
        minNeighbors=3,
        minSize=(30, 30)
) 

print("Found {0} Faces!".format(len(faces)))


This code will create a faceCascade object that will load the Haar Cascade file with the cv2.CascadeClassifier method. This allows Python and your code to use the Haar Cascade.

Next, the code applies OpenCV’s .detectMultiScale() method on the faceCascade object. This generates a list of rectangles for all of the detected faces in the image. The list of rectangles is a collection of pixel locations from the image, in the form of Rect(x,y,w,h).

Here is a summary of the other parameters your code uses:

  • A local Python 3 development environment, including [pip](https://pypi.org/project/pip/ "pip"), a tool for installing Python packages, and [venv](https://docs.python.org/3/library/venv.html "venv"), for creating virtual environments.

After generating a list of rectangles, the faces are then counted with the len function. The number of detected faces are then returned as output after running the script.

Next, you will use OpenCV’s .rectangle() method to draw a rectangle around the detected faces:

app.py

...
for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)


This code uses a for loop to iterate through the list of pixel locations returned from faceCascade.detectMultiScale method for each detected object. The rectangle method will take four arguments:

  • A local Python 3 development environment, including [pip](https://pypi.org/project/pip/ "pip"), a tool for installing Python packages, and [venv](https://docs.python.org/3/library/venv.html "venv"), for creating virtual environments.

Now that you’ve added the code to draw the rectangles, use OpenCV’s .imwrite() method to write the new image to your local filesystem as faces_detected.jpg. This method will return true if the write was successful and false if it wasn’t able to write the new image.

app.py

...
status = cv2.imwrite('faces_detected.jpg', image)


Finally, add this code to print the return the true or false status of the .imwrite() function to the console. This will let you know if the write was successful after running the script.

app.py

...
print ("Image faces_detected.jpg written to filesystem: ",status)


The completed file will look like this:

app.py

import cv2
import sys

imagePath = sys.argv[1]

image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
faces = faceCascade.detectMultiScale(
    gray,
    scaleFactor=1.3,
    minNeighbors=3,
    minSize=(30, 30)
)

print("[INFO] Found {0} Faces!".format(len(faces)))

for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

status = cv2.imwrite('faces_detected.jpg', image)
print("[INFO] Image faces_detected.jpg written to filesystem: ", status)


Once you’ve verified that everything is entered correctly, save and close the file.

Note: This code was sourced from the publicly available OpenCV documentation.

Your code is complete and you are ready to run the script.

Step 3 — Running the Script

In this step, you will use an image to test your script. When you find an image you’d like to use to test, save it in the same directory as your app.py script. This tutorial will use the following image:

If you would like to test with the same image, use the following command to download it:

curl -O https://assets.digitalocean.com/articles/CART-63965/people_with_phones.png


Once you have an image to test the script, run the script and provide the image path as an argument:

python app.py path/to/input_image


Once the script finishes running, you will receive output like this:

Output[INFO] Found 4 Faces!
[INFO] Image faces_detected.jpg written to filesystem:  True


The true output tells you that the updated image was successfully written to the filesystem. Open the image on your local machine to see the changes on the new file:

You should see that your script detected four faces in the input image and drew rectangles to mark them. In the next step, you will use the pixel locations to extract faces from the image.

Step 4 — Extracting Faces and Saving them Locally (Optional)

In the previous step, you wrote code to use OpenCV and a Haar Cascade to detect and draw rectangles around faces in an image. In this section, you will modify your code to extract the detected faces from the image into their own files.

Start by reopening the app.py file with your text editor:

nano app.py


Next, add the highlighted lines under the cv2.rectangle line:

app.py

...
for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
    roi_color = image[y:y + h, x:x + w] 
    print("[INFO] Object found. Saving locally.") 
    cv2.imwrite(str(w) + str(h) + '_faces.jpg', roi_color) 
...


The roi_color object plots the pixel locations from the faces list on the original input image. The x, y, h, and w variables are the pixel locations for each of the objects detected from faceCascade.detectMultiScale method. The code then prints output stating that an object was found and will be saved locally.

Once that is done, the code saves the plot as a new image using the cv2.imwrite method. It appends the width and height of the plot to the name of the image being written to. This will keep the name unique in case there are multiple faces detected.

The updated app.py script will look like this:

app.py

import cv2
import sys

imagePath = sys.argv[1]

image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
faces = faceCascade.detectMultiScale(
    gray,
    scaleFactor=1.3,
    minNeighbors=3,
    minSize=(30, 30)
)

print("[INFO] Found {0} Faces.".format(len(faces)))

for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
    roi_color = image[y:y + h, x:x + w]
    print("[INFO] Object found. Saving locally.")
    cv2.imwrite(str(w) + str(h) + '_faces.jpg', roi_color)

status = cv2.imwrite('faces_detected.jpg', image)
print("[INFO] Image faces_detected.jpg written to filesystem: ", status)


To summarize, the updated code uses the pixel locations to extract the faces from the image into a new file. Once you have finished updating the code, save and close the file.

Now that you’ve updated the code, you are ready to run the script once more:

python app.py path/to/image


You will see the similar output once your script is done processing the image:

Output[INFO] Found 4 Faces.
[INFO] Object found. Saving locally.
[INFO] Object found. Saving locally.
[INFO] Object found. Saving locally.
[INFO] Object found. Saving locally.
[INFO] Image faces_detected.jpg written to file-system: True


Depending on how many faces are in your sample image, you may see more or less output.

Looking at the contents of the working directory after the execution of the script, you’ll see files for the head shots of all faces found in the input image.

You will now see head shots extracted from the input image collected in the working directory:

In this step, you modified your script to extract the detected objects from the input image and save them locally.

Conclusion

In this tutorial, you wrote a script that uses OpenCV and Python to detect, count, and extract faces from an input image. You can update this script to detect different objects by using a different pre-trained Haar Cascade from the OpenCV library, or you can learn how to train your own Haar Cascade.

Learn More

Complete Python Bootcamp: Go from zero to hero in Python 3

Machine Learning A-Z™: Hands-On Python & R In Data Science

Python and Django Full Stack Web Developer Bootcamp

Complete Python Masterclass

OpenCV Python Tutorial - Computer Vision With OpenCV In Python

Python Tutorial for Beginners (2019) - Learn Python for Machine Learning and Web Development

10 Steps to Set Up Your Python Project for Success

How To Install Python 3 and Set Up a Programming Environment on Ubuntu 18.04

Python Tutorial for Beginners - Crash Course 2019 | Build a Game with Python

How to install Python on Ubuntu 18.04

*Originally published by Akshay Sinha at *https://www.digitalocean.com

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 

Queenie  Davis

Queenie Davis

1653123600

EasyMDE: Simple, Beautiful and Embeddable JavaScript Markdown Editor

EasyMDE - Markdown Editor 

This repository is a fork of SimpleMDE, made by Sparksuite. Go to the dedicated section for more information.

A drop-in JavaScript text area replacement for writing beautiful and understandable Markdown. EasyMDE allows users who may be less experienced with Markdown to use familiar toolbar buttons and shortcuts.

In addition, the syntax is rendered while editing to clearly show the expected result. Headings are larger, emphasized words are italicized, links are underlined, etc.

EasyMDE also features both built-in auto saving and spell checking. The editor is entirely customizable, from theming to toolbar buttons and javascript hooks.

Try the demo

Preview

Quick access

Install EasyMDE

Via npm:

npm install easymde

Via the UNPKG CDN:

<link rel="stylesheet" href="https://unpkg.com/easymde/dist/easymde.min.css">
<script src="https://unpkg.com/easymde/dist/easymde.min.js"></script>

Or jsDelivr:

<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/easymde/dist/easymde.min.css">
<script src="https://cdn.jsdelivr.net/npm/easymde/dist/easymde.min.js"></script>

How to use

Loading the editor

After installing and/or importing the module, you can load EasyMDE onto the first textarea element on the web page:

<textarea></textarea>
<script>
const easyMDE = new EasyMDE();
</script>

Alternatively you can select a specific textarea, via JavaScript:

<textarea id="my-text-area"></textarea>
<script>
const easyMDE = new EasyMDE({element: document.getElementById('my-text-area')});
</script>

Editor functions

Use easyMDE.value() to get the content of the editor:

<script>
easyMDE.value();
</script>

Use easyMDE.value(val) to set the content of the editor:

<script>
easyMDE.value('New input for **EasyMDE**');
</script>

Configuration

Options list

  • autoDownloadFontAwesome: If set to true, force downloads Font Awesome (used for icons). If set to false, prevents downloading. Defaults to undefined, which will intelligently check whether Font Awesome has already been included, then download accordingly.
  • autofocus: If set to true, focuses the editor automatically. Defaults to false.
  • autosave: Saves the text that's being written and will load it back in the future. It will forget the text when the form it's contained in is submitted.
    • enabled: If set to true, saves the text automatically. Defaults to false.
    • delay: Delay between saves, in milliseconds. Defaults to 10000 (10 seconds).
    • submit_delay: Delay before assuming that submit of the form failed and saving the text, in milliseconds. Defaults to autosave.delay or 10000 (10 seconds).
    • uniqueId: You must set a unique string identifier so that EasyMDE can autosave. Something that separates this from other instances of EasyMDE elsewhere on your website.
    • timeFormat: Set DateTimeFormat. More information see DateTimeFormat instances. Default locale: en-US, format: hour:minute.
    • text: Set text for autosave.
  • autoRefresh: Useful, when initializing the editor in a hidden DOM node. If set to { delay: 300 }, it will check every 300 ms if the editor is visible and if positive, call CodeMirror's refresh().
  • blockStyles: Customize how certain buttons that style blocks of text behave.
    • bold: Can be set to ** or __. Defaults to **.
    • code: Can be set to ``` or ~~~. Defaults to ```.
    • italic: Can be set to * or _. Defaults to *.
  • unorderedListStyle: can be *, - or +. Defaults to *.
  • scrollbarStyle: Chooses a scrollbar implementation. The default is "native", showing native scrollbars. The core library also provides the "null" style, which completely hides the scrollbars. Addons can implement additional scrollbar models.
  • element: The DOM element for the textarea element to use. Defaults to the first textarea element on the page.
  • forceSync: If set to true, force text changes made in EasyMDE to be immediately stored in original text area. Defaults to false.
  • hideIcons: An array of icon names to hide. Can be used to hide specific icons shown by default without completely customizing the toolbar.
  • indentWithTabs: If set to false, indent using spaces instead of tabs. Defaults to true.
  • initialValue: If set, will customize the initial value of the editor.
  • previewImagesInEditor: - EasyMDE will show preview of images, false by default, preview for images will appear only for images on separate lines.
  • imagesPreviewHandler: - A custom function for handling the preview of images. Takes the parsed string between the parantheses of the image markdown ![]( ) as argument and returns a string that serves as the src attribute of the <img> tag in the preview. Enables dynamic previewing of images in the frontend without having to upload them to a server, allows copy-pasting of images to the editor with preview.
  • insertTexts: Customize how certain buttons that insert text behave. Takes an array with two elements. The first element will be the text inserted before the cursor or highlight, and the second element will be inserted after. For example, this is the default link value: ["[", "](http://)"].
    • horizontalRule
    • image
    • link
    • table
  • lineNumbers: If set to true, enables line numbers in the editor.
  • lineWrapping: If set to false, disable line wrapping. Defaults to true.
  • minHeight: Sets the minimum height for the composition area, before it starts auto-growing. Should be a string containing a valid CSS value like "500px". Defaults to "300px".
  • maxHeight: Sets fixed height for the composition area. minHeight option will be ignored. Should be a string containing a valid CSS value like "500px". Defaults to undefined.
  • onToggleFullScreen: A function that gets called when the editor's full screen mode is toggled. The function will be passed a boolean as parameter, true when the editor is currently going into full screen mode, or false.
  • parsingConfig: Adjust settings for parsing the Markdown during editing (not previewing).
    • allowAtxHeaderWithoutSpace: If set to true, will render headers without a space after the #. Defaults to false.
    • strikethrough: If set to false, will not process GFM strikethrough syntax. Defaults to true.
    • underscoresBreakWords: If set to true, let underscores be a delimiter for separating words. Defaults to false.
  • overlayMode: Pass a custom codemirror overlay mode to parse and style the Markdown during editing.
    • mode: A codemirror mode object.
    • combine: If set to false, will replace CSS classes returned by the default Markdown mode. Otherwise the classes returned by the custom mode will be combined with the classes returned by the default mode. Defaults to true.
  • placeholder: If set, displays a custom placeholder message.
  • previewClass: A string or array of strings that will be applied to the preview screen when activated. Defaults to "editor-preview".
  • previewRender: Custom function for parsing the plaintext Markdown and returning HTML. Used when user previews.
  • promptURLs: If set to true, a JS alert window appears asking for the link or image URL. Defaults to false.
  • promptTexts: Customize the text used to prompt for URLs.
    • image: The text to use when prompting for an image's URL. Defaults to URL of the image:.
    • link: The text to use when prompting for a link's URL. Defaults to URL for the link:.
  • uploadImage: If set to true, enables the image upload functionality, which can be triggered by drag and drop, copy-paste and through the browse-file window (opened when the user click on the upload-image icon). Defaults to false.
  • imageMaxSize: Maximum image size in bytes, checked before upload (note: never trust client, always check the image size at server-side). Defaults to 1024 * 1024 * 2 (2 MB).
  • imageAccept: A comma-separated list of mime-types used to check image type before upload (note: never trust client, always check file types at server-side). Defaults to image/png, image/jpeg.
  • imageUploadFunction: A custom function for handling the image upload. Using this function will render the options imageMaxSize, imageAccept, imageUploadEndpoint and imageCSRFToken ineffective.
    • The function gets a file and onSuccess and onError callback functions as parameters. onSuccess(imageUrl: string) and onError(errorMessage: string)
  • imageUploadEndpoint: The endpoint where the images data will be sent, via an asynchronous POST request. The server is supposed to save this image, and return a JSON response.
    • if the request was successfully processed (HTTP 200 OK): {"data": {"filePath": "<filePath>"}} where filePath is the path of the image (absolute if imagePathAbsolute is set to true, relative if otherwise);
    • otherwise: {"error": "<errorCode>"}, where errorCode can be noFileGiven (HTTP 400 Bad Request), typeNotAllowed (HTTP 415 Unsupported Media Type), fileTooLarge (HTTP 413 Payload Too Large) or importError (see errorMessages below). If errorCode is not one of the errorMessages, it is alerted unchanged to the user. This allows for server-side error messages. No default value.
  • imagePathAbsolute: If set to true, will treat imageUrl from imageUploadFunction and filePath returned from imageUploadEndpoint as an absolute rather than relative path, i.e. not prepend window.location.origin to it.
  • imageCSRFToken: CSRF token to include with AJAX call to upload image. For various instances like Django, Spring and Laravel.
  • imageCSRFName: CSRF token filed name to include with AJAX call to upload image, applied when imageCSRFToken has value, defaults to csrfmiddlewaretoken.
  • imageCSRFHeader: If set to true, passing CSRF token via header. Defaults to false, which pass CSRF through request body.
  • imageTexts: Texts displayed to the user (mainly on the status bar) for the import image feature, where #image_name#, #image_size# and #image_max_size# will replaced by their respective values, that can be used for customization or internationalization:
    • sbInit: Status message displayed initially if uploadImage is set to true. Defaults to Attach files by drag and dropping or pasting from clipboard..
    • sbOnDragEnter: Status message displayed when the user drags a file to the text area. Defaults to Drop image to upload it..
    • sbOnDrop: Status message displayed when the user drops a file in the text area. Defaults to Uploading images #images_names#.
    • sbProgress: Status message displayed to show uploading progress. Defaults to Uploading #file_name#: #progress#%.
    • sbOnUploaded: Status message displayed when the image has been uploaded. Defaults to Uploaded #image_name#.
    • sizeUnits: A comma-separated list of units used to display messages with human-readable file sizes. Defaults to B, KB, MB (example: 218 KB). You can use B,KB,MB instead if you prefer without whitespaces (218KB).
  • errorMessages: Errors displayed to the user, using the errorCallback option, where #image_name#, #image_size# and #image_max_size# will replaced by their respective values, that can be used for customization or internationalization:
    • noFileGiven: The server did not receive any file from the user. Defaults to You must select a file..
    • typeNotAllowed: The user send a file type which doesn't match the imageAccept list, or the server returned this error code. Defaults to This image type is not allowed..
    • fileTooLarge: The size of the image being imported is bigger than the imageMaxSize, or if the server returned this error code. Defaults to Image #image_name# is too big (#image_size#).\nMaximum file size is #image_max_size#..
    • importError: An unexpected error occurred when uploading the image. Defaults to Something went wrong when uploading the image #image_name#..
  • errorCallback: A callback function used to define how to display an error message. Defaults to (errorMessage) => alert(errorMessage).
  • renderingConfig: Adjust settings for parsing the Markdown during previewing (not editing).
    • codeSyntaxHighlighting: If set to true, will highlight using highlight.js. Defaults to false. To use this feature you must include highlight.js on your page or pass in using the hljs option. For example, include the script and the CSS files like:
      <script src="https://cdn.jsdelivr.net/highlight.js/latest/highlight.min.js"></script>
      <link rel="stylesheet" href="https://cdn.jsdelivr.net/highlight.js/latest/styles/github.min.css">
    • hljs: An injectible instance of highlight.js. If you don't want to rely on the global namespace (window.hljs), you can provide an instance here. Defaults to undefined.
    • markedOptions: Set the internal Markdown renderer's options. Other renderingConfig options will take precedence.
    • singleLineBreaks: If set to false, disable parsing GitHub Flavored Markdown (GFM) single line breaks. Defaults to true.
    • sanitizerFunction: Custom function for sanitizing the HTML output of Markdown renderer.
  • shortcuts: Keyboard shortcuts associated with this instance. Defaults to the array of shortcuts.
  • showIcons: An array of icon names to show. Can be used to show specific icons hidden by default without completely customizing the toolbar.
  • spellChecker: If set to false, disable the spell checker. Defaults to true. Optionally pass a CodeMirrorSpellChecker-compliant function.
  • inputStyle: textarea or contenteditable. Defaults to textarea for desktop and contenteditable for mobile. contenteditable option is necessary to enable nativeSpellcheck.
  • nativeSpellcheck: If set to false, disable native spell checker. Defaults to true.
  • sideBySideFullscreen: If set to false, allows side-by-side editing without going into fullscreen. Defaults to true.
  • status: If set to false, hide the status bar. Defaults to the array of built-in status bar items.
    • Optionally, you can set an array of status bar items to include, and in what order. You can even define your own custom status bar items.
  • styleSelectedText: If set to false, remove the CodeMirror-selectedtext class from selected lines. Defaults to true.
  • syncSideBySidePreviewScroll: If set to false, disable syncing scroll in side by side mode. Defaults to true.
  • tabSize: If set, customize the tab size. Defaults to 2.
  • theme: Override the theme. Defaults to easymde.
  • toolbar: If set to false, hide the toolbar. Defaults to the array of icons.
  • toolbarTips: If set to false, disable toolbar button tips. Defaults to true.
  • direction: rtl or ltr. Changes text direction to support right-to-left languages. Defaults to ltr.

Options example

Most options demonstrate the non-default behavior:

const editor = new EasyMDE({
    autofocus: true,
    autosave: {
        enabled: true,
        uniqueId: "MyUniqueID",
        delay: 1000,
        submit_delay: 5000,
        timeFormat: {
            locale: 'en-US',
            format: {
                year: 'numeric',
                month: 'long',
                day: '2-digit',
                hour: '2-digit',
                minute: '2-digit',
            },
        },
        text: "Autosaved: "
    },
    blockStyles: {
        bold: "__",
        italic: "_",
    },
    unorderedListStyle: "-",
    element: document.getElementById("MyID"),
    forceSync: true,
    hideIcons: ["guide", "heading"],
    indentWithTabs: false,
    initialValue: "Hello world!",
    insertTexts: {
        horizontalRule: ["", "\n\n-----\n\n"],
        image: ["![](http://", ")"],
        link: ["[", "](https://)"],
        table: ["", "\n\n| Column 1 | Column 2 | Column 3 |\n| -------- | -------- | -------- |\n| Text     | Text      | Text     |\n\n"],
    },
    lineWrapping: false,
    minHeight: "500px",
    parsingConfig: {
        allowAtxHeaderWithoutSpace: true,
        strikethrough: false,
        underscoresBreakWords: true,
    },
    placeholder: "Type here...",

    previewClass: "my-custom-styling",
    previewClass: ["my-custom-styling", "more-custom-styling"],

    previewRender: (plainText) => customMarkdownParser(plainText), // Returns HTML from a custom parser
    previewRender: (plainText, preview) => { // Async method
        setTimeout(() => {
            preview.innerHTML = customMarkdownParser(plainText);
        }, 250);

        return "Loading...";
    },
    promptURLs: true,
    promptTexts: {
        image: "Custom prompt for URL:",
        link: "Custom prompt for URL:",
    },
    renderingConfig: {
        singleLineBreaks: false,
        codeSyntaxHighlighting: true,
        sanitizerFunction: (renderedHTML) => {
            // Using DOMPurify and only allowing <b> tags
            return DOMPurify.sanitize(renderedHTML, {ALLOWED_TAGS: ['b']})
        },
    },
    shortcuts: {
        drawTable: "Cmd-Alt-T"
    },
    showIcons: ["code", "table"],
    spellChecker: false,
    status: false,
    status: ["autosave", "lines", "words", "cursor"], // Optional usage
    status: ["autosave", "lines", "words", "cursor", {
        className: "keystrokes",
        defaultValue: (el) => {
            el.setAttribute('data-keystrokes', 0);
        },
        onUpdate: (el) => {
            const keystrokes = Number(el.getAttribute('data-keystrokes')) + 1;
            el.innerHTML = `${keystrokes} Keystrokes`;
            el.setAttribute('data-keystrokes', keystrokes);
        },
    }], // Another optional usage, with a custom status bar item that counts keystrokes
    styleSelectedText: false,
    sideBySideFullscreen: false,
    syncSideBySidePreviewScroll: false,
    tabSize: 4,
    toolbar: false,
    toolbarTips: false,
});

Toolbar icons

Below are the built-in toolbar icons (only some of which are enabled by default), which can be reorganized however you like. "Name" is the name of the icon, referenced in the JavaScript. "Action" is either a function or a URL to open. "Class" is the class given to the icon. "Tooltip" is the small tooltip that appears via the title="" attribute. Note that shortcut hints are added automatically and reflect the specified action if it has a key bind assigned to it (i.e. with the value of action set to bold and that of tooltip set to Bold, the final text the user will see would be "Bold (Ctrl-B)").

Additionally, you can add a separator between any icons by adding "|" to the toolbar array.

NameActionTooltip
Class
boldtoggleBoldBold
fa fa-bold
italictoggleItalicItalic
fa fa-italic
strikethroughtoggleStrikethroughStrikethrough
fa fa-strikethrough
headingtoggleHeadingSmallerHeading
fa fa-header
heading-smallertoggleHeadingSmallerSmaller Heading
fa fa-header
heading-biggertoggleHeadingBiggerBigger Heading
fa fa-lg fa-header
heading-1toggleHeading1Big Heading
fa fa-header header-1
heading-2toggleHeading2Medium Heading
fa fa-header header-2
heading-3toggleHeading3Small Heading
fa fa-header header-3
codetoggleCodeBlockCode
fa fa-code
quotetoggleBlockquoteQuote
fa fa-quote-left
unordered-listtoggleUnorderedListGeneric List
fa fa-list-ul
ordered-listtoggleOrderedListNumbered List
fa fa-list-ol
clean-blockcleanBlockClean block
fa fa-eraser
linkdrawLinkCreate Link
fa fa-link
imagedrawImageInsert Image
fa fa-picture-o
tabledrawTableInsert Table
fa fa-table
horizontal-ruledrawHorizontalRuleInsert Horizontal Line
fa fa-minus
previewtogglePreviewToggle Preview
fa fa-eye no-disable
side-by-sidetoggleSideBySideToggle Side by Side
fa fa-columns no-disable no-mobile
fullscreentoggleFullScreenToggle Fullscreen
fa fa-arrows-alt no-disable no-mobile
guideThis linkMarkdown Guide
fa fa-question-circle
undoundoUndo
fa fa-undo
redoredoRedo
fa fa-redo

Toolbar customization

Customize the toolbar using the toolbar option.

Only the order of existing buttons:

const easyMDE = new EasyMDE({
    toolbar: ["bold", "italic", "heading", "|", "quote"]
});

All information and/or add your own icons

const easyMDE = new EasyMDE({
    toolbar: [
        {
            name: "bold",
            action: EasyMDE.toggleBold,
            className: "fa fa-bold",
            title: "Bold",
        },
        "italics", // shortcut to pre-made button
        {
            name: "custom",
            action: (editor) => {
                // Add your own code
            },
            className: "fa fa-star",
            title: "Custom Button",
            attributes: { // for custom attributes
                id: "custom-id",
                "data-value": "custom value" // HTML5 data-* attributes need to be enclosed in quotation marks ("") because of the dash (-) in its name.
            }
        },
        "|" // Separator
        // [, ...]
    ]
});

Put some buttons on dropdown menu

const easyMDE = new EasyMDE({
    toolbar: [{
                name: "heading",
                action: EasyMDE.toggleHeadingSmaller,
                className: "fa fa-header",
                title: "Headers",
            },
            "|",
            {
                name: "others",
                className: "fa fa-blind",
                title: "others buttons",
                children: [
                    {
                        name: "image",
                        action: EasyMDE.drawImage,
                        className: "fa fa-picture-o",
                        title: "Image",
                    },
                    {
                        name: "quote",
                        action: EasyMDE.toggleBlockquote,
                        className: "fa fa-percent",
                        title: "Quote",
                    },
                    {
                        name: "link",
                        action: EasyMDE.drawLink,
                        className: "fa fa-link",
                        title: "Link",
                    }
                ]
            },
        // [, ...]
    ]
});

Keyboard shortcuts

EasyMDE comes with an array of predefined keyboard shortcuts, but they can be altered with a configuration option. The list of default ones is as follows:

Shortcut (Windows / Linux)Shortcut (macOS)Action
Ctrl-'Cmd-'"toggleBlockquote"
Ctrl-BCmd-B"toggleBold"
Ctrl-ECmd-E"cleanBlock"
Ctrl-HCmd-H"toggleHeadingSmaller"
Ctrl-ICmd-I"toggleItalic"
Ctrl-KCmd-K"drawLink"
Ctrl-LCmd-L"toggleUnorderedList"
Ctrl-PCmd-P"togglePreview"
Ctrl-Alt-CCmd-Alt-C"toggleCodeBlock"
Ctrl-Alt-ICmd-Alt-I"drawImage"
Ctrl-Alt-LCmd-Alt-L"toggleOrderedList"
Shift-Ctrl-HShift-Cmd-H"toggleHeadingBigger"
F9F9"toggleSideBySide"
F11F11"toggleFullScreen"

Here is how you can change a few, while leaving others untouched:

const editor = new EasyMDE({
    shortcuts: {
        "toggleOrderedList": "Ctrl-Alt-K", // alter the shortcut for toggleOrderedList
        "toggleCodeBlock": null, // unbind Ctrl-Alt-C
        "drawTable": "Cmd-Alt-T", // bind Cmd-Alt-T to drawTable action, which doesn't come with a default shortcut
    }
});

Shortcuts are automatically converted between platforms. If you define a shortcut as "Cmd-B", on PC that shortcut will be changed to "Ctrl-B". Conversely, a shortcut defined as "Ctrl-B" will become "Cmd-B" for Mac users.

The list of actions that can be bound is the same as the list of built-in actions available for toolbar buttons.

Advanced use

Event handling

You can catch the following list of events: https://codemirror.net/doc/manual.html#events

const easyMDE = new EasyMDE();
easyMDE.codemirror.on("change", () => {
    console.log(easyMDE.value());
});

Removing EasyMDE from text area

You can revert to the initial text area by calling the toTextArea method. Note that this clears up the autosave (if enabled) associated with it. The text area will retain any text from the destroyed EasyMDE instance.

const easyMDE = new EasyMDE();
// ...
easyMDE.toTextArea();
easyMDE = null;

If you need to remove registered event listeners (when the editor is not needed anymore), call easyMDE.cleanup().

Useful methods

The following self-explanatory methods may be of use while developing with EasyMDE.

const easyMDE = new EasyMDE();
easyMDE.isPreviewActive(); // returns boolean
easyMDE.isSideBySideActive(); // returns boolean
easyMDE.isFullscreenActive(); // returns boolean
easyMDE.clearAutosavedValue(); // no returned value

How it works

EasyMDE is a continuation of SimpleMDE.

SimpleMDE began as an improvement of lepture's Editor project, but has now taken on an identity of its own. It is bundled with CodeMirror and depends on Font Awesome.

CodeMirror is the backbone of the project and parses much of the Markdown syntax as it's being written. This allows us to add styles to the Markdown that's being written. Additionally, a toolbar and status bar have been added to the top and bottom, respectively. Previews are rendered by Marked using GitHub Flavored Markdown (GFM).

SimpleMDE fork

I originally made this fork to implement FontAwesome 5 compatibility into SimpleMDE. When that was done I submitted a pull request, which has not been accepted yet. This, and the project being inactive since May 2017, triggered me to make more changes and try to put new life into the project.

Changes include:

  • FontAwesome 5 compatibility
  • Guide button works when editor is in preview mode
  • Links are now https:// by default
  • Small styling changes
  • Support for Node 8 and beyond
  • Lots of refactored code
  • Links in preview will open in a new tab by default
  • TypeScript support

My intention is to continue development on this project, improving it and keeping it alive.

Hacking EasyMDE

You may want to edit this library to adapt its behavior to your needs. This can be done in some quick steps:

  1. Follow the prerequisites and installation instructions in the contribution guide;
  2. Do your changes;
  3. Run gulp command, which will generate files: dist/easymde.min.css and dist/easymde.min.js;
  4. Copy-paste those files to your code base, and you are done.

Contributing

Want to contribute to EasyMDE? Thank you! We have a contribution guide just for you!


Author: Ionaru
Source Code: https://github.com/Ionaru/easy-markdown-editor
License: MIT license

#react-native #react 

Dominic  Feeney

Dominic Feeney

1648217849

Deepface: A Face Recognition and Facial Attribute Analysis for Python

deepface

Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib.

Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.

Installation

The easiest way to install deepface is to download it from PyPI. It's going to install the library itself and its prerequisites as well. The library is mainly powered by TensorFlow and Keras.

pip install deepface

Then you will be able to import the library and use its functionalities.

from deepface import DeepFace

Facial Recognition - Demo

A modern face recognition pipeline consists of 5 common stages: detect, align, normalize, represent and verify. While Deepface handles all these common stages in the background, you don’t need to acquire in-depth knowledge about all the processes behind it. You can just call its verification, find or analysis function with a single line of code.

Face Verification - Demo

This function verifies face pairs as same person or different persons. It expects exact image paths as inputs. Passing numpy or based64 encoded images is also welcome. Then, it is going to return a dictionary and you should check just its verified key.

result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")

Face recognition - Demo

Face recognition requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return pandas data frame as output.

df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")

Face recognition models - Demo

Deepface is a hybrid face recognition package. It currently wraps many state-of-the-art face recognition models: VGG-Face , Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib. The default configuration uses VGG-Face model.

models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib"]

#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", model_name = models[1])

#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = models[1])

FaceNet, VGG-Face, ArcFace and Dlib are overperforming ones based on experiments. You can find out the scores of those models below on both Labeled Faces in the Wild and YouTube Faces in the Wild data sets declared by its creators.

ModelLFW ScoreYTF Score
Facenet51299.65%-
ArcFace99.41%-
Dlib99.38 %-
Facenet99.20%-
VGG-Face98.78%97.40%
Human-beings97.53%-
OpenFace93.80%-
DeepID-97.05%

Similarity

Face recognition models are regular convolutional neural networks and they are responsible to represent faces as vectors. We expect that a face pair of same person should be more similar than a face pair of different persons.

Similarity could be calculated by different metrics such as Cosine Similarity, Euclidean Distance and L2 form. The default configuration uses cosine similarity.

metrics = ["cosine", "euclidean", "euclidean_l2"]

#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", distance_metric = metrics[1])

#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", distance_metric = metrics[1])

Euclidean L2 form seems to be more stable than cosine and regular Euclidean distance based on experiments.

Facial Attribute Analysis - Demo

Deepface also comes with a strong facial attribute analysis module including age, gender, facial expression (including angry, fear, neutral, sad, disgust, happy and surprise) and race (including asian, white, middle eastern, indian, latino and black) predictions.

obj = DeepFace.analyze(img_path = "img4.jpg", actions = ['age', 'gender', 'race', 'emotion'])

Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision and 95.05% recall as mentioned in its tutorial.

Streaming and Real Time Analysis - Demo

You can run deepface for real time videos as well. Stream function will access your webcam and apply both face recognition and facial attribute analysis. The function starts to analyze a frame if it can focus a face sequantially 5 frames. Then, it shows results 5 seconds.

DeepFace.stream(db_path = "C:/User/Sefik/Desktop/database")

Even though face recognition is based on one-shot learning, you can use multiple face pictures of a person as well. You should rearrange your directory structure as illustrated below.

user
├── database
│   ├── Alice
│   │   ├── Alice1.jpg
│   │   ├── Alice2.jpg
│   ├── Bob
│   │   ├── Bob.jpg

Face Detectors - Demo

Face detection and alignment are important early stages of a modern face recognition pipeline. Experiments show that just alignment increases the face recognition accuracy almost 1%. OpenCV, SSD, Dlib, MTCNN, RetinaFace and MediaPipe detectors are wrapped in deepface.

All deepface functions accept an optional detector backend input argument. You can switch among those detectors with this argument. OpenCV is the default detector.

backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface', 'mediapipe']

#face verification
obj = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", detector_backend = backends[4])

#face recognition
df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backends[4])

#facial analysis
demography = DeepFace.analyze(img_path = "img4.jpg", detector_backend = backends[4])

#face detection and alignment
face = DeepFace.detectFace(img_path = "img.jpg", target_size = (224, 224), detector_backend = backends[4])

Face recognition models are actually CNN models and they expect standard sized inputs. So, resizing is required before representation. To avoid deformation, deepface adds black padding pixels according to the target size argument after detection and alignment.

RetinaFace and MTCNN seem to overperform in detection and alignment stages but they are much slower. If the speed of your pipeline is more important, then you should use opencv or ssd. On the other hand, if you consider the accuracy, then you should use retinaface or mtcnn.

The performance of RetinaFace is very satisfactory even in the crowd as seen in the following illustration. Besides, it comes with an incredible facial landmark detection performance. Highlighted red points show some facial landmarks such as eyes, nose and mouth. That's why, alignment score of RetinaFace is high as well.

You can find out more about RetinaFace on this repo.

API - Demo

Deepface serves an API as well. You can clone /api/api.py and pass it to python command as an argument. This will get a rest service up. In this way, you can call deepface from an external system such as mobile app or web.

python api.py

Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Service endpoints will be http://127.0.0.1:5000/verify for face recognition, http://127.0.0.1:5000/analyze for facial attribute analysis, and http://127.0.0.1:5000/represent for vector representation. You should pass input images as base64 encoded string in this case. Here, you can find a postman project.

Tech Stack - Vlog, Tutorial

Face recognition models represent facial images as vector embeddings. The idea behind facial recognition is that vectors should be more similar for same person than different persons. The question is that where and how to store facial embeddings in a large scale system. Herein, deepface offers a represention function to find vector embeddings from facial images.

embedding = DeepFace.represent(img_path = "img.jpg", model_name = 'Facenet')

Tech stack is vast to store vector embeddings. To determine the right tool, you should consider your task such as face verification or face recognition, priority such as speed or confidence, and also data size.

Contribution

Pull requests are welcome. You should run the unit tests locally by running test/unit_tests.py. Please share the unit test result logs in the PR. Deepface is currently compatible with TF 1 and 2 versions. Change requests should satisfy those requirements both.

Support

There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏

You can also support this work on Patreon

 

Citation

Please cite deepface in your publications if it helps your research. Here are BibTeX entries:

@inproceedings{serengil2020lightface,
  title        = {LightFace: A Hybrid Deep Face Recognition Framework},
  author       = {Serengil, Sefik Ilkin and Ozpinar, Alper},
  booktitle    = {2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},
  pages        = {23-27},
  year         = {2020},
  doi          = {10.1109/ASYU50717.2020.9259802},
  url          = {https://doi.org/10.1109/ASYU50717.2020.9259802},
  organization = {IEEE}
}
@inproceedings{serengil2021lightface,
  title        = {HyperExtended LightFace: A Facial Attribute Analysis Framework},
  author       = {Serengil, Sefik Ilkin and Ozpinar, Alper},
  booktitle    = {2021 International Conference on Engineering and Emerging Technologies (ICEET)},
  pages        = {1-4},
  year         = {2021},
  doi          = {10.1109/ICEET53442.2021.9659697},
  url          = {https://doi.org/10.1109/ICEET53442.2021.9659697},
  organization = {IEEE}
}

Also, if you use deepface in your GitHub projects, please add deepface in the requirements.txt.

Download Details:
Author: serengil
Source Code: https://github.com/serengil/deepface
License: MIT License

#tensorflow  #python #machinelearning 

Ray  Patel

Ray Patel

1619518440

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

A Lightweight Face Recognition and Facial Attribute Analysis

deepface

Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib.

Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.

Installation

The easiest way to install deepface is to download it from PyPI. It's going to install the library itself and its prerequisites as well. The library is mainly based on TensorFlow and Keras.

pip install deepface

Then you will be able to import the library and use its functionalities.

from deepface import DeepFace

Facial Recognition - Demo

A modern face recognition pipeline consists of 5 common stages: detect, align, normalize, represent and verify. While Deepface handles all these common stages in the background, you don’t need to acquire in-depth knowledge about all the processes behind it. You can just call its verification, find or analysis function with a single line of code.

Face Verification - Demo

This function verifies face pairs as same person or different persons. It expects exact image paths as inputs. Passing numpy or based64 encoded images is also welcome. Then, it is going to return a dictionary and you should check just its verified key.

result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")

Face recognition - Demo

Face recognition requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return pandas data frame as output.

df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")

Face recognition models - Demo

Deepface is a hybrid face recognition package. It currently wraps many state-of-the-art face recognition models: VGG-Face , Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib. The default configuration uses VGG-Face model.

models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib"]

#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", model_name = models[1])

#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = models[1])

FaceNet, VGG-Face, ArcFace and Dlib are overperforming ones based on experiments. You can find out the scores of those models below on both Labeled Faces in the Wild and YouTube Faces in the Wild data sets declared by its creators.

ModelLFW ScoreYTF Score
Facenet51299.65%-
ArcFace99.41%-
Dlib99.38 %-
Facenet99.20%-
VGG-Face98.78%97.40%
Human-beings97.53%-
OpenFace93.80%-
DeepID-97.05%

Similarity

Face recognition models are regular convolutional neural networks and they are responsible to represent faces as vectors. We expect that a face pair of same person should be more similar than a face pair of different persons.

Similarity could be calculated by different metrics such as Cosine Similarity, Euclidean Distance and L2 form. The default configuration uses cosine similarity.

metrics = ["cosine", "euclidean", "euclidean_l2"]

#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", distance_metric = metrics[1])

#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", distance_metric = metrics[1])

Euclidean L2 form seems to be more stable than cosine and regular Euclidean distance based on experiments.

Facial Attribute Analysis - Demo

Deepface also comes with a strong facial attribute analysis module including age, gender, facial expression (including angry, fear, neutral, sad, disgust, happy and surprise) and race (including asian, white, middle eastern, indian, latino and black) predictions.

obj = DeepFace.analyze(img_path = "img4.jpg", actions = ['age', 'gender', 'race', 'emotion'])

Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision and 95.05% recall as mentioned in its tutorial.

Streaming and Real Time Analysis - Demo

You can run deepface for real time videos as well. Stream function will access your webcam and apply both face recognition and facial attribute analysis. The function starts to analyze a frame if it can focus a face sequantially 5 frames. Then, it shows results 5 seconds.

DeepFace.stream(db_path = "C:/User/Sefik/Desktop/database")

Even though face recognition is based on one-shot learning, you can use multiple face pictures of a person as well. You should rearrange your directory structure as illustrated below.

user
├── database
│   ├── Alice
│   │   ├── Alice1.jpg
│   │   ├── Alice2.jpg
│   ├── Bob
│   │   ├── Bob.jpg

Face Detectors - Demo

Face detection and alignment are important early stages of a modern face recognition pipeline. Experiments show that just alignment increases the face recognition accuracy almost 1%. OpenCV, SSD, Dlib, MTCNN and RetinaFace detectors are wrapped in deepface.

All deepface functions accept an optional detector backend input argument. You can switch among those detectors with this argument. OpenCV is the default detector.

backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface']

#face verification
obj = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", detector_backend = backends[4])

#face recognition
df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backends[4])

#facial analysis
demography = DeepFace.analyze(img_path = "img4.jpg", detector_backend = backends[4])

#face detection and alignment
face = DeepFace.detectFace(img_path = "img.jpg", target_size = (224, 224), detector_backend = backends[4])

Face recognition models are actually CNN models and they expect standard sized inputs. So, resizing is required before representation. To avoid deformation, deepface adds black padding pixels according to the target size argument after detection and alignment.

RetinaFace and MTCNN seem to overperform in detection and alignment stages but they are much slower. If the speed of your pipeline is more important, then you should use opencv or ssd. On the other hand, if you consider the accuracy, then you should use retinaface or mtcnn.

The performance of RetinaFace is very satisfactory even in the crowd as seen in the following illustration. Besides, it comes with an incredible facial landmark detection performance. Highlighted red points show some facial landmarks such as eyes, nose and mouth. That's why, alignment score of RetinaFace is high as well.

You can find out more about RetinaFace on this repo.

API - Demo

Deepface serves an API as well. You can clone /api/api.py and pass it to python command as an argument. This will get a rest service up. In this way, you can call deepface from an external system such as mobile app or web.

python api.py

Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Service endpoints will be http://127.0.0.1:5000/verify for face recognition, http://127.0.0.1:5000/analyze for facial attribute analysis, and http://127.0.0.1:5000/represent for vector representation. You should pass input images as base64 encoded string in this case. Here, you can find a postman project.

Tech Stack - Vlog, Tutorial

Face recognition models represent facial images as vector embeddings. The idea behind facial recognition is that vectors should be more similar for same person than different persons. The question is that where and how to store facial embeddings in a large scale system. Herein, deepface offers a represention function to find vector embeddings from facial images.

embedding = DeepFace.represent(img_path = "img.jpg", model_name = 'Facenet')

Tech stack is vast to store vector embeddings. To determine the right tool, you should consider your task such as face verification or face recognition, priority such as speed or confidence, and also data size.

Contribution

Pull requests are welcome. You should run the unit tests locally by running test/unit_tests.py. Please share the unit test result logs in the PR. Deepface is currently compatible with TF 1 and 2 versions. Change requests should satisfy those requirements both.

Support

There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏

You can also support this work on Patreon

 

Citation

Please cite deepface in your publications if it helps your research. Here are its BibTeX entries:

@inproceedings{serengil2020lightface,
  title        = {LightFace: A Hybrid Deep Face Recognition Framework},
  author       = {Serengil, Sefik Ilkin and Ozpinar, Alper},
  booktitle    = {2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},
  pages        = {23-27},
  year         = {2020},
  doi          = {10.1109/ASYU50717.2020.9259802},
  url          = {https://doi.org/10.1109/ASYU50717.2020.9259802},
  organization = {IEEE}
}
@inproceedings{serengil2021lightface,
  title        = {HyperExtended LightFace: A Facial Attribute Analysis Framework},
  author       = {Serengil, Sefik Ilkin and Ozpinar, Alper},
  booktitle    = {2021 International Conference on Engineering and Emerging Technologies (ICEET)},
  pages        = {1-4},
  year         = {2021},
  doi          = {10.1109/ICEET53442.2021.9659697},
  url.         = {https://doi.org/10.1109/ICEET53442.2021.9659697},
  organization = {IEEE}
}

Also, if you use deepface in your GitHub projects, please add deepface in the requirements.txt.

Author: Serengil
Source Code: https://github.com/serengil/deepface 
License: MIT License

#python #machine-learning