face_recog.py

# face_recog.py
import warnings
import cv2
import numpy
import os

warnings.filterwarnings('ignore')

size = 2
fn_haar = 'haarcascade_frontalface_default.xml'
fn_dir = 'att_faces'

# Part 1: Create fisherRecognizer
print('Training...')

pred = []
# Create a list of images and a list of corresponding names
(images, lables, names, id) = ([], [], {}, 0)

# Get the folders containing the training data
for (subdirs, dirs, files) in os.walk(fn_dir):

    # Loop through each folder named after the subject in the photos
    for subdir in dirs:
        names[id] = subdir
        subjectpath = os.path.join(fn_dir, subdir)

        # Loop through each photo in the folder
        for filename in os.listdir(subjectpath):

            # Skip non-image formates
            f_name, f_extension = os.path.splitext(filename)
            if (f_extension.lower() not in
                    ['.png', '.jpg', '.jpeg', '.gif', '.pgm']):
                print("Skipping " + filename + ", wrong file type")
                continue
            path = subjectpath + '/' + filename
            lable = id

            # Add to training data
            images.append(cv2.imread(path, 0))
            lables.append(int(lable))
        id += 1
(im_width, im_height) = (112, 92)

# Create a Numpy array from the two lists above
(images, lables) = [numpy.array(lis) for lis in [images, lables]]

# OpenCV trains a model from the images
# NOTE FOR OpenCV2: remove '.face'
# model = cv2.face.createFisherFaceRecognizer()
# model = cv2.face.FisherFaceRecognizer_create()
model = cv2.face.LBPHFaceRecognizer_create()
# model = cv2.face.EigenFaceRecognizer_create()
model.train(images, lables)

# Part 2: Use fisherRecognizer on camera stream
haar_cascade = cv2.CascadeClassifier(fn_haar)
webcam = cv2.VideoCapture(0)
while True:

    # Loop until the camera is working
    rval = False
    while (not rval):
        # Put the image from the webcam into 'frame'
        (rval, frame) = webcam.read()
        if (not rval):
            print("Failed to open webcam. Trying again...")

    # Flip the image (optional)
    frame = cv2.flip(frame, 1, 0)

    # Convert to grayscalel
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Resize to speed up detection (optinal, change size above)
    mini = cv2.resize(gray, (int(gray.shape[1] / size), int(gray.shape[0] / size)))

    # Detect faces and loop through each one
    faces = haar_cascade.detectMultiScale(mini)
    for i in range(len(faces)):
        face_i = faces[i]

        # Coordinates of face after scaling back by `size`
        (x, y, w, h) = [v * size for v in face_i]
        face = gray[y:y + h, x:x + w]
        face_resize = cv2.resize(face, (im_width, im_height))

        # Try to recognize the face
        prediction = model.predict(face_resize)
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)

        # [1]
        # Write the name of recognized face
        # cv2.putText(frame,'%s - %.0f' % (names[prediction[0]],prediction[1]),(x-10, y-10), cv2.FONT_HERSHEY_PLAIN,1,(0, 255, 0))
        if prediction[1] < 150:
            cv2.putText(frame, '%s - %.0f' % (names[prediction[0]], prediction[1]), (x - 10, y - 10),
                        cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0))
            # print('%s - %.0f' % (names[prediction[0]],prediction[1]))
            print('%.0f' % (prediction[1]))
            pred = prediction[1]

        else:
            cv2.putText(frame, 'not recognized', (x - 10, y - 10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255))

    # Show the image and check for ESC being pressed
    cv2.imshow('OpenCV', frame)
    # key = cv2.waitKey(3)
    # if key == 27:
    #     break
    # time.sleep(5)
    # Change the range according to the values you obtain in the local test run
    if int(pred) in range(70, 90):
        # print('%.0f' % pred)
        break

webcam.release()
cv2.destroyAllWindows