OpenCV Face Recognition Demo

01_face_dataset.py

import cv2

cam = cv2.VideoCapture(0)
cam.set(3, 640)  # set video width
cam.set(4, 480)  # set video height

face_detector = cv2.CascadeClassifier('conf/haarcascade_frontalface_default.xml')

# For each person, enter one numeric face id
names_count = [x.strip() for x in open('conf/names.txt').readlines() if x.strip() != ""]
face_id = len(names_count) + 1
while True:
    name = input('User name: ').strip()
    if name and name not in names_count:
        with open('conf/names.txt', "a") as f:
            f.write("\n" + name)
        break

print("Initializing face capture. Look the camera and wait ...")
# Initialize individual sampling face count
count = 0

while True:
    ret, img = cam.read()
    # img = cv2.flip(img, -1) # flip video image vertically
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_detector.detectMultiScale(gray, 1.3, 5)

    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
        count += 1

        # Save the captured image into the datasets folder
        cv2.imwrite("dataset/User." + str(face_id) + '.' + str(count) + ".jpg", gray[y:y + h, x:x + w])

        cv2.imshow('image', img)

    k = cv2.waitKey(100) & 0xff  # Press 'ESC' for exiting video
    if k == 27:
        break
    elif count >= 30:  # Take 30 face sample and stop video
        break

# Do a bit of cleanup
print("Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()

02_face_training.py

import cv2
import numpy as np
from PIL import Image
import os

# Path for face image database
path = 'dataset'

recognizer = cv2.face.LBPHFaceRecognizer_create()
detector = cv2.CascadeClassifier("conf/haarcascade_frontalface_default.xml");


# function to get the images and label data
def getImagesAndLabels(path):
    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
    faceSamples = []
    ids = []

    for imagePath in imagePaths:
        PIL_img = Image.open(imagePath).convert('L')  # convert it to grayscale
        img_numpy = np.array(PIL_img, 'uint8')

        id = int(os.path.split(imagePath)[-1].split(".")[1])
        faces = detector.detectMultiScale(img_numpy)

        for (x, y, w, h) in faces:
            faceSamples.append(img_numpy[y:y + h, x:x + w])
            ids.append(id)

    return faceSamples, ids


print("[INFO] Training faces. It will take a few seconds. Wait ...")
faces, ids = getImagesAndLabels(path)
recognizer.train(faces, np.array(ids))

# Save the model into trainer/trainer.yml
recognizer.write('conf/model.yml')  # recognizer.save() worked on Mac, but not on Pi

# Print the numer of faces trained and end program
print("[INFO] {0} faces trained. Exiting Program".format(len(np.unique(ids))))

03_face_recognition.py

import cv2

recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('conf/model.yml')
cascadePath = "conf/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath);

font = cv2.FONT_HERSHEY_SIMPLEX

# iniciate id counter
id = 0

# names related to ids: example ==> Marcelo: id=1,  etc
names = ['Unknown'] + [x.strip() for x in open('conf/names.txt').readlines() if x.strip() != ""]

# Initialize and start realtime video capture
cam = cv2.VideoCapture(0)
cam.set(3, 640)  # set video widht
cam.set(4, 480)  # set video height

# Define min window size to be recognized as a face
minW = 0.1 * cam.get(3)
minH = 0.1 * cam.get(4)

while True:
    ret, img = cam.read()
    # img = cv2.flip(img, -1) # Flip vertically

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.2,
        minNeighbors=5,
        minSize=(int(minW), int(minH)),
    )

    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
        id, confidence = recognizer.predict(gray[y:y + h, x:x + w])
        # Check if confidence is less them 100 ==> "0" is perfect match 
        if (confidence < 100):
            id = names[id]
            confidence = "  {0}%".format(round(100 - confidence))
        else:
            id = "unknown"
            confidence = "  {0}%".format(round(100 - confidence))
        cv2.putText(img, str(id), (x + 5, y - 5), font, 1, (255, 255, 255), 2)
        cv2.putText(img, str(confidence), (x + 5, y + h - 5), font, 1, (255, 255, 0), 1)

    cv2.imshow('camera', img)

    k = cv2.waitKey(10) & 0xff  # Press 'ESC' for exiting video
    if k == 27:
        break

# Do a bit of cleanup
print("[INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()

README.md

OpenCV Face Recognition Demo

Setup

sudo apt install libopencv-dev -y;
sudo apt install libatlas-base-dev -y;
sudo apt install libjasper-dev -y;
sudo apt install qt4-dev -y;
sudo pip3 install opencv-python
sudo pip install opencv-contrib-python