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face_detector.py
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face_detector.py
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import dlib
from PyQt4 import QtCore
import numpy as np
import time
import align.detect_face
import cv2
from mtcnn_net import *
# 检测出人脸并返回人脸的四个坐标,传递给face_recognition
class Face_detector(QtCore.QThread):
def __init__(self, textBrowser):
super(Face_detector, self).__init__()
#self.face_detector = dlib.get_frontal_face_detector()
#self.ldmark_detector = dlib.shape_predictor('./dlib_model/shape_predictor_68_face_landmarks.dat')
self.face_info = {}
self.textBrowser = textBrowser
self.detecting = True # flag of if detect face
self.ldmarking = False # flag of if detect landmark
self.total = 0
self.pnet,self.rnet,self.onet = mtcnn_net()
self.minsize = 20 # minimum size of face
self.threshold = [0.6, 0.7, 0.7] # three steps's threshold
self.factor = 0.709 # scale factor
def face_detector(self, img):
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 左上横坐标，左上纵坐标，宽度，高度
bounding_boxes, points = align.detect_face.detect_face(img_gray, self.minsize, self.pnet, self.rnet, self.onet,
self.threshold, self.factor)
nrof_faces = bounding_boxes.shape[0] # 人脸数目
#print('找到人脸数目为：{}'.format(nrof_faces))
# crop_faces = []
if nrof_faces != 0:
# crop_faces = []
for face_position in bounding_boxes:
face_position = face_position.astype(int)
# print(face_position[0:4])
# 在图上画出人脸框
cv2.rectangle(img, (face_position[0], face_position[1]), (face_position[2], face_position[3]),
(0, 255, 0), 2)
# crop = img[face_position[1]:face_position[3],
# face_position[0]:face_position[2], ]
#
# crop = cv2.resize(crop, (160, 160), interpolation=cv2.INTER_CUBIC)
# crop_faces.append(crop)
# 在图上画出特征点
for num_feature in range(nrof_faces):
point_size = 1
point_color = (255, 0, 0) # BGR
thickness = 8 # 可以为 0 、4、8
# 要画的点的坐标
points_list = points[:, num_feature]
for num_point in range(int(len(points_list) / 2)):
cv2.circle(img, (points_list[num_point], points_list[num_point + 5]), point_size, point_color,
thickness)
return bounding_boxes, img
def detect_face(self, img):
if self.detecting:
self.face_info = {}
# det_start_time = time.time()
# dets = self.face_detector(img, 0)
if img is not None:
dets,img= self.face_detector(img)
# print('Detection took %s seconds.' % (time.time() - det_start_time))
#print('Number of face detected: {}'.format(len(dets)))
if len(dets) > 0:
nrof_faces = dets.shape[0] # 人脸数目
self.textBrowser.append('Number of face detected: {}'.format(nrof_faces))
num = 0
for face_position in dets:
face_position = face_position.astype(int)
# ldmark detection
landmarks = []
if self.ldmarking:
shape = self.ldmark_detector(img, d)
landmarks = [(shape.part(i).x, shape.part(i).y) for i in range(68)]
crop_face = np.copy(img[max(0, face_position[1]):(face_position[3]),
max(0,face_position[0]):(face_position[2]),])
crop_face = cv2.resize(crop_face, (160, 160), interpolation=cv2.INTER_CUBIC)
crop_face = facenet.prewhiten(crop_face)
self.total += 1
self.face_info[num] = ([face_position[0], face_position[1], face_position[2], face_position[3]],
landmarks[18:], crop_face) # 0:18 are face counture
num +=1
# emit signal when detection finished
self.emit(QtCore.SIGNAL('det(PyQt_PyObject)'), [self.face_info, img])
def startstopdet(self, checkbox):
if checkbox.isChecked():
self.detecting = True
else:
self.detecting = False