python使用dlib进行人脸检测和关键点的示例
作者:dangxusheng 时间:2021-12-14 20:56:04
#!/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()
来源:https://www.cnblogs.com/dxscode/p/12676293.html
标签:python,dlib,人脸检测
0
投稿
猜你喜欢
Go Gin实现文件上传下载的示例代码
2023-06-21 15:11:13
PHP _construct()函数讲解
2023-06-14 16:56:43
asp如何编写sql语句来查询|搜索数据记录
2008-10-09 12:35:00
document.createElement()用法及注意事项
2008-04-21 15:16:00
仿淘宝首页商品分类列表效果
2009-01-22 13:39:00
Python实现轻松切割MP3文件
2023-09-23 21:40:32
Python 在局部变量域中执行代码
2023-06-12 04:57:15
asp程序定义变量比不定义变量速度快一倍
2012-12-04 20:06:32
关于Python3 lambda函数的深入浅出
2023-01-12 09:12:41
CSS样式表中SPAN和DIV的区别
2007-10-21 08:47:00
ASP Cookies操作的详细介绍与实例代码
2011-03-10 10:53:00
巧制可全屏拖动的图片
2008-05-09 19:34:00
用CSS制作兼容多浏览量器的隐藏菜单
2007-08-30 09:05:00
用Dreamweaver MX制作导航下拉菜单
2009-05-29 18:37:00
aspjpeg 添加水印教程及生成缩略图教程
2011-04-04 11:04:00
Python解析命令行读取参数--argparse模块使用方法
2023-06-28 22:48:45
window.open被浏览器拦截后的自定义提示
2007-11-23 12:31:00
Dreamweaver如何防止及消除垃圾代码的产生
2007-11-13 17:15:00
ASP连接MSSQL2005 数据库
2009-03-08 19:20:00
网页栅格系统研究:960的秘密
2008-10-24 17:03:00