FaceSwap/face_detection.py

import cv2
import dlib
import numpy as np

## Face detection
def face_detection(img,upsample_times=1):
    # Ask the detector to find the bounding boxes of each face. The 1 in the
    # second argument indicates that we should upsample the image 1 time. This
    # will make everything bigger and allow us to detect more faces.
    detector = dlib.get_frontal_face_detector()
    faces = detector(img, upsample_times)

    return faces

PREDICTOR_PATH = 'models/shape_predictor_68_face_landmarks.dat'
predictor = dlib.shape_predictor(PREDICTOR_PATH)

## Face and points detection
def face_points_detection(img, bbox:dlib.rectangle):
    # Get the landmarks/parts for the face in box d.
    shape = predictor(img, bbox)

    # loop over the 68 facial landmarks and convert them
    # to a 2-tuple of (x, y)-coordinates
    coords = np.asarray(list([p.x, p.y] for p in shape.parts()), dtype=np.int)

    # return the array of (x, y)-coordinates
    return coords


def select_face(im, r=10, choose=True):
    faces = face_detection(im)

    if len(faces) == 0:
        return None, None, None

    if len(faces) == 1 or not choose:
        idx = int(np.argmax([(face.right() - face.left()) * (face.bottom() - face.top()) for face in faces]))
        bbox = faces[idx]
    else:
        bbox = []

        def click_on_face(event, x, y, flags, params):
            if event != cv2.EVENT_LBUTTONDOWN:
                return

            for face in faces:
                if face.left() < x < face.right() and face.top() < y < face.bottom():
                    bbox.append(face)
                    break

        im_copy = im.copy()
        for face in faces:
            # draw the face bounding box
            cv2.rectangle(im_copy, (face.left(), face.top()), (face.right(), face.bottom()), (0, 0, 255), 1)
        cv2.imshow('Click the Face:', im_copy)
        cv2.setMouseCallback('Click the Face:', click_on_face)
        while len(bbox) == 0:
            cv2.waitKey(1)
        cv2.destroyAllWindows()
        bbox = bbox[0]

    points = np.asarray(face_points_detection(im, bbox))

    im_w, im_h = im.shape[:2]
    left, top = np.min(points, 0)
    right, bottom = np.max(points, 0)

    x, y = max(0, left - r), max(0, top - r)
    w, h = min(right + r, im_h) - x, min(bottom + r, im_w) - y

    return points - np.asarray([[x, y]]), (x, y, w, h), im[y:y + h, x:x + w]


def get_landmarks(im):
    """
    Function that returns the face landmarks.
    """
    rects = face_detection(im, upsample_times=1)
    
    if len(rects) > 1:
        #print("Too Many Faces") #raise TooManyFaces
        return []
    if len(rects) == 0:
        #print("No Faces") #raise NoFaces
        return []

    return np.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])

SCALE_FACTOR = 1 
def read_im_and_landmarks(fname):
    """
    Function that read the images and gets the face landmarks
    in the read images.
    """
    im = cv2.imread(fname, cv2.IMREAD_COLOR)
    im = cv2.resize(im, (im.shape[1] * SCALE_FACTOR,
                         im.shape[0] * SCALE_FACTOR))
    s = get_landmarks(im)

    return im, s


def mask_mouth(source_img):
    img, marks = read_im_and_landmarks(source_img)
    
    tmp_img=img.copy()
    tmp_blank_img=img.copy()
    tmp_blank_img=tmp_blank_img*0
    
    mask_list=marks[49:61]

    draw_img=cv2.drawContours(tmp_blank_img,[mask_list],-1,(255,255,255),-1)
    draw_img_re=cv2.bitwise_not(draw_img)
    tmp_img=tmp_img&draw_img_re
    
    return tmp_img