FaceDetection.py

'''
detectMultiScale(image, scaleFactor, minNeighbors): 
    This is a general function to detect objects, in this case, it'll detect faces since we called in the face cascade. 
    If it finds a face, it returns a list of positions of said face in the form “Rect(x,y,w,h).”, if not, then returns “None”.
Image: 
    The first input is the grayscale image. So make sure the image is in grayscale.
scaleFactor: 
    This function compensates a false perception in size that occurs when one face appears to be bigger than the other simply because it is closer to the camera.
minNeighbors: 
    This is a detection algorithm that uses a moving window to detect objects,
    it does so by defining how many objects are found near the current one before it can declare the face found.
'''

import cv2

haar_face_cascade = cv2.CascadeClassifier('E://PYTHON//Windows-Folder-Unlock-Using-Face-Recognition-master//Face_Recognition_Script//haarcascade_frontalface_alt.xml')
lbp_face_cascade = cv2.CascadeClassifier('E://PYTHON//Windows-Folder-Unlock-Using-Face-Recognition-master//Face_Recognition_Script//lbpcascade_frontalface.xml')


def detect_faces(f_cascade, colored_img, scaleFactor=1.1):
    img_copy = colored_img
    # convert the test image to gray image as opencv face detector expects gray images
    gray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
    # let's detect multiscale (some i
    # mages may be closer to camera than others) images
    faces = f_cascade.detectMultiScale(gray, scaleFactor=scaleFactor, minNeighbors=5);
    # go over list of faces and draw them as rectangles on original colored img
    x=0
    y=0
    z=0
    w = 0
    if len(faces)==0:
        return img_copy,"None","None"
    for (x, y, w, h) in faces:
        cv2.rectangle(img_copy, (x, y), (x + w, y + h), (0, 255, 0), 2)
    return img_copy,img_copy[y:y+w, x:x+h], faces[0]

def staticFaceDetectHaar(img):
    test1 = cv2.imread(img)
    test1 = detect_faces(haar_face_cascade,test1)
    cv2.imshow('finanl',test1)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

def staticFaceDetectLbp(img):
    test1 = cv2.imread(img)
    test1 = detect_faces(lbp_face_cascade,test1)
    cv2.imshow('finanl',test1)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

def liveFaceDetectLbp():
    cap = cv2.VideoCapture(0)
    while(True):
        ret, frame = cap.read()
        frame = detect_faces(lbp_face_cascade,frame,1.1)
        cv2.imshow("frame",frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    cap.release()
    cv2.destroyAllWindows()

def liveFaceDetectHaar():
    cap = cv2.VideoCapture(0)
    while(True):
        ret, frame = cap.read()
        frame = detect_faces(haar_face_cascade,frame,1.1)
        cv2.imshow("frame",frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    cap.release()
    cv2.destroyAllWindows()


#liveFaceDetectHaar()
#liveFaceDetectLbp()