前言
面部表情跟踪的原理就是检测人脸特征点，根据特定的特征点可以对应到特定的器官，比如眼睛、鼻子、嘴巴、耳朵等等，以此来跟踪各个面部器官的动作。
程序实现
原理很简单，下面直接上程序了：
# *_*coding:utf-8 *_*
# author: 许鸿斌
import sys
import cv2
import dlib
import os
import logging
import datetime
import numpy as np
def cal_face_boundary(img, shape):
for index_, pt in enumerate(shape.parts()):
if index_ == 0:
x_min = pt.x
x_max = pt.x
y_min = pt.y
y_max = pt.y
else:
if pt.x < x_min:
x_min = pt.x
if pt.x > x_max:
x_max = pt.x
if pt.y < y_min:
y_min = pt.y
if pt.y > y_max:
y_max = pt.y
# print('x_min:{}'.format(x_min))
# print('x_max:{}'.format(x_max))
# print('y_min:{}'.format(y_min))
# print('y_max:{}'.format(y_max))
# 如果出现负值，即人脸位于图像框之外的情况，应当忽视图像外的部分，将负值置为0
if x_min < 0:
x_min = 0
if y_min < 0:
y_min = 0
if x_min == x_max or y_min == y_max:
return None
else:
return img[y_min:y_max, x_min:x_max]
def draw_left_eyebrow(img, shape):
# 17 - 21
pt_pos = []
for index, pt in enumerate(shape.parts()[17:21 + 1]):
pt_pos.append((pt.x, pt.y))
for num in range(len(pt_pos)-1):
cv2.line(img, pt_pos[num], pt_pos[num+1], 255, 2)
def draw_right_eyebrow(img, shape):
# 22 - 26
pt_pos = []
for index, pt in enumerate(shape.parts()[22:26 + 1]):
pt_pos.append((pt.x, pt.y))
for num in range(len(pt_pos) - 1):
cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)
def draw_left_eye(img, shape):
# 36 - 41
pt_pos = []
for index, pt in enumerate(shape.parts()[36:41 + 1]):
pt_pos.append((pt.x, pt.y))
for num in range(len(pt_pos) - 1):
cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)
cv2.line(img, pt_pos[0], pt_pos[-1], 255, 2)
def draw_right_eye(img, shape):
# 42 - 47
pt_pos = []
for index, pt in enumerate(shape.parts()[42:47 + 1]):
pt_pos.append((pt.x, pt.y))
for num in range(len(pt_pos) - 1):
cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)
cv2.line(img, pt_pos[0], pt_pos[-1], 255, 2)
def draw_nose(img, shape):
# 27 - 35
pt_pos = []
for index, pt in enumerate(shape.parts()[27:35 + 1]):
pt_pos.append((pt.x, pt.y))
for num in range(len(pt_pos) - 1):
cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)
cv2.line(img, pt_pos[0], pt_pos[4], 255, 2)
cv2.line(img, pt_pos[0], pt_pos[-1], 255, 2)
cv2.line(img, pt_pos[3], pt_pos[-1], 255, 2)
def draw_mouth(img, shape):
# 48 - 59
pt_pos = []
for index, pt in enumerate(shape.parts()[48:59 + 1]):
pt_pos.append((pt.x, pt.y))
for num in range(len(pt_pos) - 1):
cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)
cv2.line(img, pt_pos[0], pt_pos[-1], 255, 2)
# 60 - 67
pt_pos = []
for index, pt in enumerate(shape.parts()[60:]):
pt_pos.append((pt.x, pt.y))
for num in range(len(pt_pos) - 1):
cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)
cv2.line(img, pt_pos[0], pt_pos[-1], 255, 2)
def draw_jaw(img, shape):
# 0 - 16
pt_pos = []
for index, pt in enumerate(shape.parts()[0:16 + 1]):
pt_pos.append((pt.x, pt.y))
for num in range(len(pt_pos) - 1):
cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)
# 获取logger实例，如果参数为空则返回root logger
logger = logging.getLogger("PedestranDetect")
# 指定logger输出格式
formatter = logging.Formatter('%(asctime)s %(levelname)-8s: %(message)s')
# 文件日志
# file_handler = logging.FileHandler("test.log")
# file_handler.setFormatter(formatter) # 可以通过setFormatter指定输出格式
# 控制台日志
console_handler = logging.StreamHandler(sys.stdout)
console_handler.formatter = formatter # 也可以直接给formatter赋值
# 为logger添加的日志处理器
# logger.addHandler(file_handler)
logger.addHandler(console_handler)
# 指定日志的最低输出级别，默认为WARN级别
logger.setLevel(logging.INFO)
pwd = os.getcwd()
predictor_path = os.path.join(pwd, 'shape_predictor_68_face_landmarks.dat')
logger.info(u'导入人脸检测器')
detector = dlib.get_frontal_face_detector()
logger.info(u'导入人脸特征点检测器')
predictor = dlib.shape_predictor(predictor_path)
cap = cv2.VideoCapture(0)
cnt = 0
total_time = 0
start_time = 0
while(1):
ret, frame = cap.read()
# cv2.imshow("window", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
dets = detector(img, 1)
if dets:
logger.info('Face detected')
else:
logger.info('No face detected')
for index, face in enumerate(dets):
# print('face {}; left {}; top {}; right {}; bottom {}'.format(index, face.left(), face.top(), face.right(),
# face.bottom()))
shape = predictor(img, face)
# for index_, pt in enumerate(shape.parts()):
# pt_pos = (pt.x, pt.y)
# cv2.circle(frame, pt_pos, 2, (255, 0, 0), 1)
features = np.zeros(img.shape[0:-1], dtype=np.uint8)
for index_, pt in enumerate(shape.parts()):
pt_pos = (pt.x, pt.y)
cv2.circle(features, pt_pos, 2, 255, 1)
draw_left_eyebrow(features, shape)
draw_right_eyebrow(features, shape)
draw_left_eye(features, shape)
draw_right_eye(features, shape)
draw_nose(features, shape)
draw_mouth(features, shape)
draw_jaw(features, shape)
logger.info('face shape: {} {}'.format(face.right()-face.left(), face.bottom()-face.top()))
faceROI = cal_face_boundary(features, shape)
logger.info('ROI shape: {}'.format(faceROI.shape))
# faceROI = features[face.top():face.bottom(), face.left():face.right()]
faceROI = cv2.resize(faceROI, (500, 500), interpolation=cv2.INTER_LINEAR)
# logger.info('face {}'.format(index))
cv2.imshow('face {}'.format(index), faceROI)
if cnt == 0:
start_time = datetime.datetime.now()
cnt += 1
elif cnt == 100:
end_time = datetime.datetime.now()
frame_rate = float(100) / (end_time-start_time).seconds
# logger.info(start_time)
# logger.info(end_time)
logger.info(u'帧率：{:.2f}fps'.format(frame_rate))
cnt = 0
else:
cnt += 1
# logger.info(cnt)
程序不算复杂，所以就只解释要点。
- 首先开启摄像头，可以自行修改程序选择打开默认摄像头还是外置摄像头，默认选择电脑自带的默认摄像头；
- 检测人脸，并检测人脸68个特征点；
- 根据特征点对应关系，在新建的图像中，绘制面部各个器官的轮廓，图像大小统一缩放为 ；
运行结果
按q键退出；如果摄像头中检测到人脸会自动提取信息，并在如下窗口中重新绘制面部器官轮廓。
