def face_recognition_video(model_path,dataset_path, filename,video_path):
# 加载数据库的数据
dataset_emb, names_list = load_dataset(dataset_path, filename)
# 初始化mtcnn人脸检测
face_detect = face_recognition.Facedetection()
# 初始化facenet
face_net = face_recognition.facenetEmbedding(model_path)
cap = cv2.VideoCapture(video_path) # 获取视频数据
face_cnt = 0 # 控制视频读取时候，每秒截取指定数量图片进行一次识别
while cap.isOpened():
ok, frame = cap.read()
face_cnt += 1
if not ok:
break
if(face_cnt % 5 == 0):
frame = image_processing.read_image1(frame)
bboxes, landmarks = face_detect.detect_face(frame)
bboxes, landmarks = face_detect.get_square_bboxes(bboxes, landmarks, fixed="height")
if bboxes == [] or landmarks == []:
print("-----no face")
continue
print("-----image have {} faces".format(len(bboxes)))
face_images = image_processing.get_bboxes_image(frame, bboxes, resize_height, resize_width)
face_images = image_processing.get_prewhiten_images(face_images)
pred_emb = face_net.get_embedding(face_images)
pred_name, pred_score = compare_embadding(pred_emb, dataset_emb, names_list)
# 在图像上绘制人脸边框和识别的结果
show_info = [n + ':' + str(s)[:5] for n, s in zip(pred_name, pred_score)]
print("showinfo:", show_info)
image_processing.show_image_bboxes_text("face_recognition", frame, bboxes, show_info)
cap.release()
cv2.destroyWindow("face_recognition")
def face_recognition_image(model_path, dataset_path, filename, image_path):
# 加载数据库的数据
dataset_emb, names_list = load_dataset(dataset_path, filename)
# 初始化mtcnn人脸检测
face_detect = face_recognition.Facedetection()
# 初始化facenet
face_net = face_recognition.facenetEmbedding(model_path)
image = image_processing.read_image_gbk(image_path)
# 获取 判断标识 bounding_box crop_image
bboxes, landmarks = face_detect.detect_face(image)
bboxes, landmarks = face_detect.get_square_bboxes(bboxes,
landmarks,
fixed="height")
if bboxes == [] or landmarks == []:
print("-----no face")
exit(0)
# print("-----image have {} faces".format(len(bboxes)))
face_images = image_processing.get_bboxes_image(image, bboxes,
resize_height,
resize_width)
face_images = image_processing.get_prewhiten_images(face_images)
pred_emb = face_net.get_embedding(face_images)
pred_name, pred_score = compare_embadding(pred_emb, dataset_emb,
names_list)
# 在图像上绘制人脸边框和识别的结果
show_info = [n + ':' + str(s)[:5] for n, s in zip(pred_name, pred_score)]
print(show_info)
def create_face(images_dir, out_face_dir):
'''
生成人脸数据图库，保存在out_face_dir中，这些数据库将用于生成embedding数据库
:param images_dir:
:param out_face_dir:
:return:
'''
# image_list=file_processing.get_files_list(images_dir, postfix='jpg')
image_list, names_list = file_processing.gen_files_labels(images_dir,
postfix='jpg')
face_detect = face_recognition.Facedetection()
for image_path, name in zip(image_list, names_list):
image = image_processing.read_image(image_path,
resize_height=0,
resize_width=0,
normalization=False)
# 获取 判断标识 bounding_box crop_image
bounding_box, points = face_detect.detect_face(image)
bounding_box = bounding_box[:, 0:4].astype(int)
bounding_box = bounding_box[0, :]
print("face box:{}".format(bounding_box))
face_image = image_processing.crop_image(image, bounding_box)
# image_processing.show_image("face", face_image)
# image_processing.show_image_box("face",image,bounding_box)
out_path = os.path.join(out_face_dir, name)
face_image = image_processing.resize_image(face_image, resize_height,
resize_width)
if not os.path.exists(out_path):
os.mkdir(out_path)
basename = os.path.basename(image_path)
out_path = os.path.join(out_path, basename)
image_processing.save_image(out_path, face_image)
def face_recognition_image(model_path, dataset_path, filename, image_path):
# 加载数据库的数据
dataset_emb, names_list = load_dataset(dataset_path, filename)
# 初始化mtcnn人脸检测
face_detect = face_recognition.Facedetection()
# 初始化facenet
face_net = face_recognition.facenetEmbedding(model_path)
image = image_processing.read_image(image_path)
# 进行人脸检测，获得bounding_box
bounding_box, points = face_detect.detect_face(image)
bounding_box = bounding_box[:, 0:4].astype(int)
# 获得人脸区域
face_images = image_processing.get_crop_images(image,
bounding_box,
resize_height,
resize_width,
whiten=True)
# image_processing.show_image("face", face_images[0,:,:,:])
pred_emb = face_net.get_embedding(face_images)
pred_name = compare_embadding(pred_emb, dataset_emb, names_list)
# 在图像上绘制人脸边框和识别的结果
bgr_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
image_processing.cv_show_image_text("face_recognition", bgr_image,
bounding_box, pred_name)
cv2.waitKey(0)
def create_face(images_dir, out_face_dir):
print(images_dir)
'''
生成人脸数据图库，保存在out_face_dir中，这些数据库将用于生成embedding数据库
:param images_dir:
:param out_face_dir:
:return:
'''
# image_list=file_processing.get_files_list(images_dir, postfix='jpg')
image_list, names_list = file_processing.gen_files_labels(images_dir)
face_detect = face_recognition.Facedetection()
for image_path, name in zip(image_list, names_list):
print("图片的路径", str(image_path))
if image_path == 'dataset/images/.DS_Store':
continue
if image_path == 'dataset/images/face1/.DS_Store':
continue
if image_path == 'dataset/images/face2/.DS_Store':
continue
if image_path == 'dataset/images/face3/.DS_Store':
continue
if image_path == 'dataset/images/face4/.DS_Store':
continue
if image_path == './dataset/emb_face/.DS_Store':
continue
image = image_processing.read_image(image_path,
resize_height=0,
resize_width=0,
normalization=False)
# 获取 判断标识 bounding_box crop_image
bounding_box, points = face_detect.detect_face(image)
bounding_box = bounding_box[:, 0:4].astype(int)
if (len(bounding_box) < 1):
continue
bounding_box = bounding_box[0, :]
print("face box:{}".format(bounding_box))
face_image = image_processing.crop_image(image, bounding_box)
# image_processing.show_image("face", face_image)
# image_processing.show_image_box("face",image,bounding_box)
out_path = os.path.join(out_face_dir, name)
if (len(face_image) > 0):
face_image = image_processing.resize_image(face_image,
resize_height,
resize_width)
else:
continue
if not os.path.exists(out_path):
os.mkdir(out_path)
basename = os.path.basename(image_path)
out_path = os.path.join(out_path, basename)
image_processing.save_image(out_path, face_image)
def get_face_embedding(model_path, files_list, names_list):
'''
获得embedding数据
:param files_list: 图像列表
:param names_list: 与files_list一一的名称列表
:return:
'''
# 转换颜色空间RGB or BGR
colorSpace = "RGB"
# 初始化mtcnn人脸检测
face_detect = face_recognition.Facedetection()
# 初始化facenet
face_net = face_recognition.facenetEmbedding(model_path)
embeddings = [] # 用于保存人脸特征数据库
label_list = [] # 保存人脸label的名称，与embeddings一一对应
for image_path, name in zip(files_list, names_list):
print("processing image :{}".format(image_path))
# image_path='E:/Face/dataset/bzl/subjectphoto_with_name/谢伟林_179_180.jpg'
image = image_processing.read_image_gbk(image_path,
colorSpace=colorSpace)
# 进行人脸检测，获得bounding_box
bboxes, landmarks = face_detect.detect_face(image)
bboxes, landmarks = face_detect.get_square_bboxes(bboxes,
landmarks,
fixed="height")
# image_processing.show_image_boxes("image",image,bboxes)
if bboxes == [] or landmarks == []:
print("-----no face")
continue
if len(bboxes) >= 2 or len(landmarks) >= 2:
print("-----image have {} faces".format(len(bboxes)))
continue
# 获得人脸区域
face_images = image_processing.get_bboxes_image(
image, bboxes, resize_height, resize_width)
# 人脸预处理，归一化
face_images = image_processing.get_prewhiten_images(face_images,
normalization=True)
# 获得人脸特征
pred_emb = face_net.get_embedding(face_images)
embeddings.append(pred_emb)
# 可以选择保存image_list或者names_list作为人脸的标签
# 测试时建议保存image_list，这样方便知道被检测人脸与哪一张图片相似
# label_list.append(image_path)
label_list.append(name)
return embeddings, label_list
def create_face(images_dir, out_face_dir):
'''
生成人脸数据图库，保存在out_face_dir中，这些数据库将用于生成embedding数据库
'''
#此处引用自定义函数 : file_processing.gen_files_labels(images_dir,postfix='jpg')
#返回值: 图像数组: images_dir下所有文件(是的,你没想错!包括子目录下的所有文件)的路径(Path)会被存储到image_list
# 标签数组: names_list就是相应的的"文件名或子目录名"，在本项目中,子目录名 将作为 作为样本的标签(即ID,e.g.姓名 或 学号之类的)
print('#2nd--Mtcnn人脸库')
image_list,names_list=file_processing.gen_files_labels(images_dir,postfix='jpg')
face_detect=face_recognition.Facedetection()
for image_path ,name in zip(image_list,names_list):
# try:
image=image_processing.read_image(image_path, resize_height=0, resize_width=0, normalization=False)
# 获取 判断标识 bounding_box crop_image
bounding_box, points = face_detect.detect_face(image)
bounding_box = bounding_box[:,0:4].astype(int)
# try:
bounding_box=bounding_box[0,:]
# except:
# print("跳过当前图片")
# continue
print("矩形框坐标为:{}".format(bounding_box))
face_image = image_processing.crop_image(image,bounding_box)
# except:
# print("当前图像帧格式非法，将被忽略")
out_path=os.path.join(out_face_dir,name)
face_image=image_processing.resize_image(face_image, resize_height, resize_width)
if not os.path.exists(out_path):
os.mkdir(out_path)
basename=os.path.basename(image_path)
out_path=os.path.join(out_path,basename)
image_processing.save_image(out_path,face_image)
import copy import time from tkinter.messagebox import askquestion, showinfo import threading # 后端申明 resize_width = 160 resize_height = 160 # 存放facenet预训练模型的路径 model_path = 'models/20180408-102900' # 存放人脸特征数据库的路径 npy_dataset_path = 'dataset/emb/faceEmbedding.npy' filename = 'dataset/emb/name.txt' # 加载facenet face_net = face_recognition.facenetEmbedding(model_path) face_detect = face_recognition.Facedetection() # 数据库声明 model_path = 'models/20180408-102900' # 存放facenet预训练模型的路径 dataset_path = 'dataset/images/photo_database' # 用于建立特征库的照片 out_emb_path = 'dataset/emb/faceEmbedding.npy' # 特征库输出地址 out_filename = 'dataset/emb/name.txt' # 标签保存地址 # 前端声明 camera_switch = False count = 1 # 临时变量 tempimagepath = r"dataset\images\photo_database\\" # 摄像机设置
def face_recognition_for_bzl(model_path, test_dataset, filename):
# 加载数据库的数据
dataset_emb, names_list = predict.load_dataset(dataset_path, filename)
print("loadind dataset...\n names_list:{}".format(names_list))
# 初始化mtcnn人脸检测
face_detect = face_recognition.Facedetection()
# 初始化facenet
face_net = face_recognition.facenetEmbedding(model_path)
#获得测试图片的路径和label
filePath_list, label_list = file_processing.gen_files_labels(test_dataset)
label_list = [name.split('_')[0] for name in label_list]
print("filePath_list:{},label_list{}".format(len(filePath_list),
len(label_list)))
right_num = 0
wrong_num = 0
detection_num = 0
test_num = len(filePath_list)
for image_path, label_name in zip(filePath_list, label_list):
print("image_path:{}".format(image_path))
# 读取图片
image = image_processing.read_image_gbk(image_path)
# 人脸检测
bboxes, landmarks = face_detect.detect_face(image)
bboxes, landmarks = face_detect.get_square_bboxes(bboxes,
landmarks,
fixed="height")
if bboxes == [] or landmarks == []:
print("-----no face")
continue
if len(bboxes) >= 2 or len(landmarks) >= 2:
print("-----image have {} faces".format(len(bboxes)))
continue
# 获得人脸框区域
face_images = image_processing.get_bboxes_image(
image, bboxes, resize_height, resize_width)
face_images = image_processing.get_prewhiten_images(face_images,
normalization=True)
# face_images = image_processing.get_prewhiten_images(face_images,normalization=True)
pred_emb = face_net.get_embedding(face_images)
pred_name, pred_score = predict.compare_embadding(pred_emb,
dataset_emb,
names_list,
threshold=1.3)
# 在图像上绘制人脸边框和识别的结果
# show_info = [n + ':' + str(s)[:5] for n, s in zip(pred_name, pred_score)]
# image_processing.show_image_text("face_recognition", image, bboxes, show_info)
index = 0
pred_name = pred_name[index]
pred_score = pred_score[index]
if pred_name == label_name:
right_num += 1
else:
wrong_num += 1
detection_num += 1
print(
"-------------label_name:{},pred_name:{},score:{:3.4f},status:{}".
format(label_name, pred_name, pred_score,
(label_name == pred_name)))
# 准确率
accuracy = right_num / detection_num
# 漏检率
misdetection = (test_num - detection_num) / test_num
print("-------------right_num/detection_num:{}/{},accuracy rate:{}".format(
right_num, detection_num, accuracy))
print(
"-------------misdetection/all_num:{}/{},misdetection rate:{}".format(
(test_num - detection_num), test_num, misdetection))
def face_reco(model_path, dataset_path, filename):
##############################################################
# my_knn_model = KNeighborsClassifier()
# my_knn_model = joblib.load('./models/knn.model')#######################################################
#TIPS
print("人脸识别系统正在启动>>>\n")
# 先传入 "Embedding特征向量数据集" ,再 传入标签数组
dataset_emb, names_list = load_dataset(dataset_path, filename)
# 构造 MTCNN人脸检测器
face_detect = face_recognition.Facedetection()
print("加载Google-Facenet模型>>>")
# 传入系统库模型 从而 构造 "Facenet特征提取器"
face_net = face_recognition.facenetEmbedding(model_path)
#打开摄像头，传入待检测的 VideoFrame
camera = cv2.VideoCapture(0)
#见L87
# counter = 1
#############################################################################################################
#############################################################################################################
#############################################################################################################
print("KNN>>>\n")
print("\n加载预训练模型，即将开始人脸检测>>>\n")
while True:
read, image = camera.read() #每一张视频帧都是一个三维矩阵
#传入待检测的 StillImage
# image=cv2.imread("/home/oliver/test_images/05.png")#first image_path
# image = cv2.imread('/home/oliver/test_images/001.png')
# ########
bgr_image = image
resize_height = 0
resize_width = 0
# normalization=False
# 若是灰度图（即二维矩阵）则转为三通道（即：三维矩阵）
if len(bgr_image.shape) == 2:
print("非法的灰度图！正在转换为BGR图>>>", image)
bgr_image = cv2.cvtColor(bgr_image, cv2.COLOR_GRAY2BGR)
rgb_image = cv2.cvtColor(bgr_image,
cv2.COLOR_BGR2RGB) # 将BGR转为RGB 格式转化
if resize_height > 0 and resize_width > 0:
rgb_image = cv2.resize(rgb_image, (resize_width, resize_height))
rgb_image = np.asarray(rgb_image) #几何图像代数化，便于运算、分析
# #########
#刷新参数：宽度 和 高度
resize_width = 160
resize_height = 160
# 对StillImage 进行 MTCNN人脸检测，获得bounding_box
bounding_box, points = face_detect.detect_face(rgb_image)
bounding_box = bounding_box[:, 0:4].astype(int)
if rgb_image is None:
continue
# 在StillImage上 ,基于bounding_box 进行Crop裁剪操作
face_images = image_processing.get_crop_images(rgb_image,
bounding_box,
resize_height,
resize_width,
whiten=True)
if face_images is True:
continue
# 基于Facenet特征提取器 , 得到 摄像头实时返回的视频帧 的特征向量
pred_emb = face_net.get_embedding(face_images) #FaceNET 特征向量
# 把 StillImage特征向量 和 "Embedding特征向量数据集" 进行比较 ,最后返回一个 "数据库中的已有标签" 或者 "Unknown标签"
pred_name = compare_embadding(pred_emb, dataset_emb,
names_list) #FaceNet 标签数组
if pred_name is True:
continue
# 因为L37 把图片转化为RGB的格式 - 所以此处 还原 测试图片 为BGR图像
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
# bgr_image = cv2.resize(bgr_image,(450,550))
# while True:
# 调用函数,在StillImage上绘制人脸边框和 "相对应的标签"
image_processing.cv_show_image_text("【@ZTJ-FaceReco人脸识别系统】", bgr_image,
bounding_box, pred_name)
#
if cv2.waitKey(1) & 0xff == ord("q"):
print("由于您按下了\"Q\"键，系统即将退出！")
break
#############################################################################################################
#############################################################################################################
#############################################################################################################
camera.release() #释放占用的摄像头
cv2.destroyAllWindows() #杀死窗口进程
#-*- coding: UTF-8 -*-
import cv2 as cv
import face_recognition
import numpy as np
import time
import pickle
name = ['1', '2', '3', '4'] #预测的标签样本
model_path = 'models/20180408-102900' #face_net的路径
mtcnn = face_recognition.Facedetection() #导入mtcnn
face_net = face_recognition.facenetEmbedding(model_path)
with open('face_svm.pkl', 'rb') as infile:
(classifymodel1, class_names1) = pickle.load(infile)
with open('face_xg.pkl', 'rb') as infile:
(classifymodel2, class_names2) = pickle.load(infile)
'''
以下三个函数为人脸的裁剪和图像归一化处理
'''
def prewhiten(x):
mean = np.mean(x)
std = np.std(x)
std_adj = np.maximum(std, 1.0 / np.sqrt(x.size))
y = np.multiply(np.subtract(x, mean), 1 / std_adj)
return y
def crop_image(image, box):
crop_img = image[box[1]:box[3], box[0]:box[2]]
return crop_img