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python使用dlib进行人脸检测和关键点的示例

来源:互联网 作者:佚名 时间:2021-09-10 03:32
#!/usr/bin/env python# -*- coding:utf-8-*-# file: {NAME}.py# @author: jory.d# @contact: dangxusheng163@163.com# @time: 2020/04/10 19:42# @desc: 使用dlib进行人脸检测和人脸关键点import cv2import numpy as npimport globimport dlibFACE_DETECT_P
#!/usr/bin/env python
# -*- coding:utf-8-*-
# file: {NAME}.py
# @author: jory.d
# @contact: dangxusheng163@163.com
# @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)

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


def face_detect_mask():
  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
      '''
      points = []
      for i in range(shape.num_parts):
        x, y = shape.part(i).x, shape.part(i).y
        if i < 26:
          points.append([x, 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)

      # 只把脸切出来
      points[17:] = points[17:][::-1]
      points = np.asarray(points, np.int32).reshape(-1, 1, 2)
      img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
      black_img = np.zeros_like(img)
      cv2.polylines(black_img, [points], 1, 255)
      cv2.fillPoly(black_img, [points], (1, 1, 1))
      mask = black_img
      masked_bgr = img * mask

      # 位运算时需要转化成灰度图像
      mask_gray = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
      masked_gray = cv2.bitwise_and(img_gray, img_gray, mask=mask_gray)

    cv2.resize(img, dsize=None, dst=img, fx=2, fy=2)
    cv2.imshow('w', img)
    cv2.imshow('mask', mask)
    cv2.imshow('mask2', masked_gray)
    cv2.imshow('mask3', masked_bgr)
    cv2.waitKey(0)


if __name__ == '__main__':
  face_detect()

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