Face Recognition with OpenCV

April 7, 2019
By

(This article was first published on R Programming – DataScience+, and kindly contributed to R-bloggers)

    Categories

    1. Programming

    Tags

    1. Machine Learning
    2. OpenCV
    3. R Programming

    OpenCV is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez. The library is cross-platform and free for use under the open-source BSD license.

    Before starting you can read my article on face detection which will make this code more easy to understand.

    For our face recognition model, we will have 3 phases:

  • Prepare training data
  • Train Face Recognizer
  • Testing
  • In our dataset we will have two folders with faces of two persons. For example, in folder 1 we will have face images of person 1, and in other folder, face images of person 2. You can create your own personalised data or can download from my Github Repo, where you can find the source code and other files.

    Coding

    As usually our first task will be to import required libraries.

    #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
    

    Now preparing our training data-

    #there is no label 0 in our training data so subject name for index/label 0 is empty
    subjects = ["", "Arpit Dwivedi", "Udit Saxena"]
    
    #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]
    
    

    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
    

    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))
    

    Now train the face recognizer-

    face_recognizer = cv2.face.LBPHFaceRecognizer_create()
    
    face_recognizer.train(faces, np.array(labels))
    

    Now predicting the images-
    Firstly we will have to draw rectangles around the detected face and the writing the text with it:

    def draw_rectangle(img, rect):
        (x, y, w, h) = rect
        cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
    
    def draw_text(img, text, x, y):
        cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)
    

    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[0]]
        
        #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
    

    And now the final part, giving the test image for recognition-

    print("Predicting images...")
    
    #load test images
    test_img1 = cv2.imread("test-data/1.jpg")
    test_img2 = cv2.imread("test-data/2.jpg")
    
    #perform a prediction
    predicted_img1 = predict(test_img1)
    predicted_img2 = predict(test_img2)
    print("Prediction complete")
    
    #display both images
    cv2.imshow(subjects[1], predicted_img1)
    cv2.imshow(subjects[2], predicted_img2)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    

    Conclusion

    We successfully made a model which can recognise the face from the input image, which we trained on custom dataset.

    Related Post

    1. Why Python is the Best Developing tool for AI?
    2. Face and Eye detection with OpenCV
    3. Data-driven Introspection of my Android Mobile usage in R
    4. Handwritten Digit Recognition with CNN
    5. The working of Naive Bayes algorithm

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