使用dlib进行人脸检测和关键点-python版

#!/usr/bin/env python
# -*- coding:utf-8-*-
# file: {NAME}.py
# @author: jory.d
# @contact: [email protected]
# @time: 2020/04/10 19:42
# @desc:  使用dlib进行人脸检测和人脸关键点

import cv2
import numpy as np
import glob
import dlib

FACE_DETECT_PATH = '/home/build/dlib-v19.18/data/mmod_human_face_detector.dat'
FACE_LANDMAKR_5_PATH = '/home/build/dlib-v19.18/data/shape_predictor_5_face_landmarks.dat'
FACE_LANDMAKR_68_PATH = '/home/build/dlib-v19.18/data/shape_predictor_68_face_landmarks.dat'


def face_detect():
    root = '/media/dangxs/E/Project/DataSet/VGG Face Dataset/vgg_face_dataset/vgg_face_dataset/vgg_face_dataset'
    imgs = glob.glob(root + '/**/*.jpg', recursive=True)
    assert len(imgs) > 0

    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor(FACE_LANDMAKR_68_PATH)
    for f in imgs:
        img = cv2.imread(f)
        # 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.
        dets = detector(img, 1)
        print("Number of faces detected: {}".format(len(dets)))
        for i, d in enumerate(dets):
            x1, y1, x2, y2 = d.left(), d.top(), d.right(), d.bottom()
            print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
                i, x1, y1, x2, y2))

            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 1)

            # Get the landmarks/parts for the face in box d.
            shape = predictor(img, d)
            print("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1)))
            # # Draw the face landmarks on the screen.
            '''
            # landmark 顺序： 外轮廓 - 左眉毛 - 右眉毛 - 鼻子 - 左眼 - 右眼 - 嘴巴
            '''
            for i in range(shape.num_parts):
                x, y = shape.part(i).x, shape.part(i).y
                cv2.circle(img, (x, y), 2, (0, 0, 255), 1)
                cv2.putText(img, str(i), (x,y),cv2.FONT_HERSHEY_COMPLEX, 0.3 ,(0,0,255),1)

            # # draw lines
            # for i in range(0, shape.num_parts, 2):
            #     x1, y1 = shape.part(i).x, shape.part(i).y
            #     x2, y2 = shape.part(i+1).x, shape.part(i+1).y
            #     cv2.line(img, (x1,y1), (x2,y2), (0,0,255),1)

        cv2.resize(img, dsize=None, dst=img, fx=2,fy=2)
        cv2.imshow('w', img)
        cv2.waitKey(0)


if __name__ == '__main__':
    face_detect()