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:
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.