dlibで「おでこ」を推定

dlibで顔のパーツを検出すると眉毛のあたりで切れちゃうので、　おでこというか　頭頂部も　ざっくり推定。
こちらのプログラムを参考というか　だいぶ　コピペしました

qiita.com

facelandmark.py

#!/usr/bin/env python

# -*- coding: utf-8 -*-
import sys

import dlib

import cv2

import time

import copy

import numpy as np
resize_rate=1

npoints=85
predictor_path = "./shape_predictor_68_face_landmarks.dat"
detector = dlib.get_frontal_face_detector()

predictor = dlib.shape_predictor(predictor_path)
class NoFaceException(Exception):pass
def landmark(frame,nfaces=1,zero2one=True):

    if isinstance (frame ,str):

        frame=cv2.imread(frame)

    height,width,c=frame.shape
    # 顔検出

    dets = detector(frame, 1)

    detlist=list(dets)

    if len(detlist)==0:

        raise NoFaceException

    if nfaces==0 or nfaces>=3:

        nfaces=2

    faces=[]

    for k, d in enumerate(detlist[:nfaces]):

        print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(

            k, d.left(), d.top(), d.right(), d.bottom()))
        # 顔器官検出

        shape = predictor(frame, d)

        px=np.zeros(npoints,dtype=int)

        py=np.zeros(npoints,dtype=int)

        for shape_point_count in range(shape.num_parts):

            shape_point = shape.part(shape_point_count)

            px[shape_point_count]=shape_point.x

            py[shape_point_count]=shape_point.y

        px[68]=(px[0]+px[16])//2

        py[68]=(py[0]+py[16])//2       

        px[69]=px[68]+(px[68]-px[8])

        py[69]=py[68]+(py[68]-py[8])

        px[70]=(px[21]+px[22])//2

        py[70]=(py[21]+py[22])//2

        #

        mirror=[ (71,15), (72,14),(73,13), (74,12),(75,11),(76,10),(77,9),

          (78,7),(79,6),(80,5),(81,4),(82,3),(83,2),(84,1)]

        ox=px[68]

        oy=py[68]

        for i1,i2 in mirror:

            px[i1]=px[68]+(px[68]-px[i2])

            py[i1]=py[68]+(py[68]-py[i2])

        if zero2one:

            px=px/width

            py=py/height

        faces.append([ px,py])

    return faces
if __name__=="__main__":

    import glob   

    flist=glob.glob("face/*.jpg")

    np.random.shuffle(flist)

    fn=np.random.choice(flist)

    frame=cv2.imread(fn)

    
    faces=landmark(frame,nfaces=1,zero2one=False)

    px,py=faces[0]

    h,w,c=frame.shape
    frame[:,:,:]=255

    for shape_point_count in range(npoints):

        text=str(shape_point_count)

        org=(px[shape_point_count],py[shape_point_count])

        fontFace=cv2.FONT_HERSHEY_SIMPLEX

        color=(0,0,0)

        fontScale=0.45

        cv2.putText(frame, text, org, fontFace, fontScale, color, thickness=1, lineType=cv2.LINE_8)

    cv2.imshow('face landmark detector',frame)

    cv2.imwrite("test.png",frame)