def __init__(self):
# 创建mtcnn对象，建立一个mtcnn模型
# 检测图片中的人脸
self.mtcnn_model = mtcnn()
# 门限函数
self.threshold = [0.5, 0.8, 0.9]
# 载入facenet模型
# 将检测到的人脸转化为128维的向量
self.facenet_model = InceptionResNetV1()
# model.summary()
model_path = './model_data/facenet_keras.h5'
self.facenet_model.load_weights(model_path)
#-----------------------------------------------#
# 对数据库中的人脸进行编码
# known_face_encodings中存储的是编码后的人脸
# known_face_names为人脸的名字
#-----------------------------------------------#
face_list = os.listdir("face_dataset")
self.known_face_encodings = []
self.known_face_names = []
# 所有的人脸进行遍历，得到仓库里所有人脸的128维特征向量和对应名称
for face in face_list:
# obama.jpg进行'.'分割，取前一段字符串
name = face.split(".")[0]
# 人脸读取出来
img = cv2.imread("./face_dataset/" + face)
# 图像由 BGR -> RGB
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 检测人脸
rectangles = self.mtcnn_model.detectFace(img, self.threshold)
# 转化成正方形
rectangles = utils.rect2square(np.array(rectangles))
# facenet要传入一个160x160的图片
# 进行读取
rectangle = rectangles[0]
# 记下他们的landmark，5个标记点，进行人脸对齐
landmark = (np.reshape(rectangle[5:15], (5, 2)) - np.array(
[int(rectangle[0]), int(rectangle[1])])) / (rectangle[3] -
rectangle[1]) * 160
#人脸截取
crop_img = img[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
crop_img = cv2.resize(crop_img, (160, 160))
# 进行对齐操作
new_img, _ = utils.Alignment_1(crop_img, landmark)
# 增加一个维度
new_img = np.expand_dims(new_img, 0)
# 将检测到的人脸传入到facenet的模型中，实现128维特征向量的提取
face_encoding = utils.calc_128_vec(self.facenet_model, new_img)
self.known_face_encodings.append(face_encoding)
self.known_face_names.append(name)
def __init__(self):
# 创建mtcnn对象用于检测图片中的人脸
self.mtcnn_model = mtcnn()
# 门限函数
self.threshold = [0.5, 0.8, 0.9]
# 载入facenet用于将检测到的人脸转化为128维的向量
self.facenet_model = InceptionResNetV1()
# model.summary() #模型summary信息
model_path = './model_data/facenet_keras.h5' # 模型文件
self.facenet_model.load_weights(model_path)
#-----------------------------------------------#
# 对数据库中的人脸进行编码
# known_face_encodings中存储的是编码后的人脸
# known_face_names为人脸的名字
# face_dataset文件中的图片为人脸数据库
#-----------------------------------------------#
face_list = os.listdir("face_dataset") # 列出文件夹中的人脸图片
self.known_face_encodings = [] # 存储编码后的人脸
self.known_face_names = [] # 人脸的名字及图片的名字
# 遍历人脸
for face in face_list:
# 图片文件名
name = face.split(".")[0]
# 读取图片是BGR格式数据
img = cv2.imread("./face_dataset/" + face)
# 颜色空间转换
# cv2.COLOR_BGR2RGB 将BGR格式转换成RGB格式
# cv2.COLOR_BGR2GRAY 将BGR格式转换成灰度图片
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 检测图版中的人脸
rectangles = self.mtcnn_model.detectFace(img, self.threshold)
# 长方形转化成正方形
rectangles = utils.rect2square(np.array(rectangles))
# facenet要传入一个160x160的图片
rectangle = rectangles[0]
# 记下他们的landmark
landmark = (np.reshape(rectangle[5:15], (5, 2)) - np.array(
[int(rectangle[0]), int(rectangle[1])])) / (rectangle[3] -
rectangle[1]) * 160
crop_img = img[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
# Resize to 160x160
crop_img = cv2.resize(crop_img, (160, 160))
new_img, _ = utils.Alignment_1(crop_img, landmark)
new_img = np.expand_dims(new_img, 0)
# 将检测到的人脸传入到facenet的模型中，实现128维特征向量的提取
face_encoding = utils.calc_128_vec(self.facenet_model, new_img)
# print(face_encoding)
# 记录128维护特征向量到数组
self.known_face_encodings.append(face_encoding)
# 记录文件名到数组
self.known_face_names.append(name)
def recognize(self,draw):
#-----------------------------------------------#
# 人脸识别
# 先定位，再进行数据库匹配
#-----------------------------------------------#
height,width,_ = np.shape(draw)
draw_rgb = cv2.cvtColor(draw,cv2.COLOR_BGR2RGB)
# 检测人脸
rectangles = self.mtcnn_model.detectFace(draw_rgb, self.threshold)
if len(rectangles)==0:
return
# 转化成正方形
rectangles = utils.rect2square(np.array(rectangles,dtype=np.int32))
rectangles[:,0] = np.clip(rectangles[:,0],0,width)
rectangles[:,1] = np.clip(rectangles[:,1],0,height)
rectangles[:,2] = np.clip(rectangles[:,2],0,width)
rectangles[:,3] = np.clip(rectangles[:,3],0,height)
#-----------------------------------------------#
# 对检测到的人脸进行编码
#-----------------------------------------------#
face_encodings = []
for rectangle in rectangles:
landmark = (np.reshape(rectangle[5:15],(5,2)) - np.array([int(rectangle[0]),int(rectangle[1])]))/(rectangle[3]-rectangle[1])*160
crop_img = draw_rgb[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
crop_img = cv2.resize(crop_img,(160,160))
new_img,_ = utils.Alignment_1(crop_img,landmark)
new_img = np.expand_dims(new_img,0)
face_encoding = utils.calc_128_vec(self.facenet_model,new_img)
face_encodings.append(face_encoding)
face_names = []
for face_encoding in face_encodings:
# 取出一张脸并与数据库中所有的人脸进行对比，计算得分
matches = utils.compare_faces(self.known_face_encodings, face_encoding, tolerance = 0.6)
name = "Unknown"
# 找出距离最近的人脸
face_distances = utils.face_distance(self.known_face_encodings, face_encoding)
# 取出这个最近人脸的评分
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = self.known_face_names[best_match_index]
face_names.append(name)
rectangles = rectangles[:,0:4]
#-----------------------------------------------#
# 画框~!~
#-----------------------------------------------#
for (left, top, right, bottom), name in zip(rectangles, face_names):
cv2.rectangle(draw, (left, top), (right, bottom), (0, 0, 255), 2)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(draw, name, (left , bottom - 15), font, 0.75, (255, 255, 255), 2)
return draw
def __init__(self):
# 创建mtcnn对象
# 检测图片中的人脸
self.mtcnn_model = mtcnn()
# 门限函数
self.threshold = [0.5, 0.8, 0.9]
# 载入facenet
# 将检测到的人脸转化为128维的向量
self.facenet_model = InceptionResNetV1()
# model.summary()
model_path = './model_data/facenet_keras.h5'
self.facenet_model.load_weights(model_path)
# -----------------------------------------------#
# 对数据库中的人脸进行编码
# known_face_encodings中存储的是编码后的人脸
# known_face_names为人脸的名字
# -----------------------------------------------#
face_list = os.listdir("face_dataset")
# 存放编码后的人脸
self.known_face_encodings = []
# 存放编码后人脸的名字
self.known_face_names = []
for face in face_list:
name = face.split(".")[0]
img = cv2.imread("./face_dataset/" + face)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 检测人脸
rectangles = self.mtcnn_model.detectFace(img, self.threshold)
# 转化成正方形
rectangles = utils.rect2square(np.array(rectangles))
# facenet要传入一个160x160的图片
rectangle = rectangles[0]
# 记下他们的landmark
landmark = (np.reshape(rectangle[5:15], (5, 2)) - np.array(
[int(rectangle[0]), int(rectangle[1])])) / (rectangle[3] -
rectangle[1]) * 160
crop_img = img[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
crop_img = cv2.resize(crop_img, (160, 160))
new_img, _ = utils.Alignment_1(crop_img, landmark)
new_img = np.expand_dims(new_img, 0)
# 将检测到的人脸传入到facenet的模型中，实现128维特征向量的提取
face_encoding = utils.calc_128_vec(self.facenet_model, new_img)
self.known_face_encodings.append(face_encoding)
self.known_face_names.append(name)
def __init__(self):
#-------------------------#
# 创建mtcnn的模型
# 用于检测人脸
#-------------------------#
self.mtcnn_model = mtcnn()
self.threshold = [0.5, 0.6, 0.8]
#-----------------------------------#
# 载入facenet
# 将检测到的人脸转化为128维的向量
#-----------------------------------#
self.facenet_model = InceptionResNetV1()
model_path = './model_data/facenet_keras.h5'
self.facenet_model.load_weights(model_path)
#-----------------------------------------------#
# 对数据库中的人脸进行编码
# known_face_encodings中存储的是编码后的人脸
# known_face_names为人脸的名字
#-----------------------------------------------#
face_list = os.listdir("face_dataset")
self.known_face_encodings = []
self.known_face_names = []
for face in face_list:
name = face.split(".")[0]
img = cv2.imread("./face_dataset/" + face)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#---------------------#
# 检测人脸
#---------------------#
rectangles = self.mtcnn_model.detectFace(img, self.threshold)
#---------------------#
# 转化成正方形
#---------------------#
rectangles = utils.rect2square(np.array(rectangles))
#-----------------------------------------------#
# facenet要传入一个160x160的图片
# 利用landmark对人脸进行矫正
#-----------------------------------------------#
rectangle = rectangles[0]
landmark = np.reshape(rectangle[5:15], (5, 2)) - np.array(
[int(rectangle[0]), int(rectangle[1])])
crop_img = img[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
crop_img, _ = utils.Alignment_1(crop_img, landmark)
crop_img = np.expand_dims(cv2.resize(crop_img, (160, 160)), 0)
#--------------------------------------------------------------------#
# 将检测到的人脸传入到facenet的模型中，实现128维特征向量的提取
#--------------------------------------------------------------------#
face_encoding = utils.calc_128_vec(self.facenet_model, crop_img)
self.known_face_encodings.append(face_encoding)
self.known_face_names.append(name)
model_path = './model_data/facenet_keras.h5'
facenet_model.load_weights(model_path)
for rectangle in rectangles:
if rectangle is not None:
landmark = (np.reshape(rectangle[5:15], (5, 2)) - np.array(
[int(rectangle[0]), int(rectangle[1])])) / (rectangle[3] -
rectangle[1]) * 160
# y方向的截取和x方向的截取
crop_img = img[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
# resize后可以输入facenet中
crop_img = cv2.resize(crop_img, (160, 160))
cv2.imshow("before", crop_img)
new_img, _ = utils.Alignment_1(crop_img, landmark)
cv2.imshow("two eyes", new_img)
# std_landmark = np.array([[54.80897114,59.00365493],
# [112.01078961,55.16622207],
# [86.90572522,91.41657571],
# [55.78746897,114.90062758],
# [113.15320624,111.08135986]])
# crop_img = img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
# crop_img = cv2.resize(crop_img,(160,160))
# new_img,_ = utils.Alignment_2(crop_img,std_landmark,landmark)
# cv2.imshow("affine",new_img)
new_img = np.expand_dims(new_img, 0)
feature1 = utils.calc_128_vec(facenet_model, new_img)
cv2.waitKey(0)
#------------------------------#
rectangles = utils.rect2square(np.array(rectangles))
#------------------------------#
# 载入facenet
#------------------------------#
model_path = './model_data/facenet_keras.h5'
facenet_model = InceptionResNetV1()
facenet_model.load_weights(model_path)
for rectangle in rectangles:
#---------------#
# 截取图像
#---------------#
landmark = np.reshape(rectangle[5:15], (5, 2)) - np.array(
[int(rectangle[0]), int(rectangle[1])])
crop_img = img[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
#-----------------------------------------------#
# 利用人脸关键点进行人脸对齐
#-----------------------------------------------#
cv2.imshow("before", cv2.cvtColor(crop_img, cv2.COLOR_RGB2BGR))
crop_img, _ = utils.Alignment_1(crop_img, landmark)
cv2.imshow("two eyes", cv2.cvtColor(crop_img, cv2.COLOR_RGB2BGR))
crop_img = np.expand_dims(cv2.resize(crop_img, (160, 160)), 0)
feature1 = utils.calc_128_vec(facenet_model, crop_img)
print(feature1)
cv2.waitKey(0)
