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retinaface.py
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import time
import cv2
import numpy as np
import torch
import torch.nn as nn
from PIL import Image, ImageDraw, ImageFont
from tqdm import tqdm
from nets.facenet import Facenet
from nets_retinaface.retinaface import RetinaFace
from utils.anchors import Anchors
from utils.config import cfg_mnet, cfg_re50
from utils.utils import (Alignment_1, compare_faces, letterbox_image,
preprocess_input)
from utils.utils_bbox import (decode, decode_landm, non_max_suppression,
retinaface_correct_boxes)
#--------------------------------------#
# 写中文需要转成PIL来写。
#--------------------------------------#
def cv2ImgAddText(img, label, left, top, textColor=(255, 255, 255)):
img = Image.fromarray(np.uint8(img))
#---------------#
# 设置字体
#---------------#
font = ImageFont.truetype(font='model_data/simhei.ttf', size=20)
draw = ImageDraw.Draw(img)
label = label.encode('utf-8')
draw.text((left, top), str(label,'UTF-8'), fill=textColor, font=font)
return np.asarray(img)
#--------------------------------------#
# 一定注意backbone和model_path的对应。
# 在更换facenet_model后，
# 一定要注意重新编码人脸。
#--------------------------------------#
class Retinaface(object):
_defaults = {
#----------------------------------------------------------------------#
# retinaface训练完的权值路径
#----------------------------------------------------------------------#
"retinaface_model_path" : 'model_data/Retinaface_mobilenet0.25.pth',
#----------------------------------------------------------------------#
# retinaface所使用的主干网络，有mobilenet和resnet50
#----------------------------------------------------------------------#
"retinaface_backbone" : "mobilenet",
#----------------------------------------------------------------------#
# retinaface中只有得分大于置信度的预测框会被保留下来
#----------------------------------------------------------------------#
"confidence" : 0.5,
#----------------------------------------------------------------------#
# retinaface中非极大抑制所用到的nms_iou大小
#----------------------------------------------------------------------#
"nms_iou" : 0.3,
#----------------------------------------------------------------------#
# 是否需要进行图像大小限制。
# 输入图像大小会大幅度地影响FPS，想加快检测速度可以减少input_shape。
# 开启后，会将输入图像的大小限制为input_shape。否则使用原图进行预测。
# 会导致检测结果偏差，主干为resnet50不存在此问题。
# 可根据输入图像的大小自行调整input_shape，注意为32的倍数，如[640, 640, 3]
#----------------------------------------------------------------------#
"retinaface_input_shape": [640, 640, 3],
#----------------------------------------------------------------------#
# 是否需要进行图像大小限制。
#----------------------------------------------------------------------#
"letterbox_image" : True,
#----------------------------------------------------------------------#
# facenet训练完的权值路径
#----------------------------------------------------------------------#
"facenet_model_path" : 'model_data/facenet_mobilenet.pth',
#----------------------------------------------------------------------#
# facenet所使用的主干网络， mobilenet和inception_resnetv1
#----------------------------------------------------------------------#
"facenet_backbone" : "mobilenet",
#----------------------------------------------------------------------#
# facenet所使用到的输入图片大小
#----------------------------------------------------------------------#
"facenet_input_shape" : [160, 160, 3],
#----------------------------------------------------------------------#
# facenet所使用的人脸距离门限
#----------------------------------------------------------------------#
"facenet_threhold" : 0.9,
#--------------------------------#
# 是否使用Cuda
# 没有GPU可以设置成False
#--------------------------------#
"cuda" : True
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化Retinaface
#---------------------------------------------------#
def __init__(self, encoding=0, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
#---------------------------------------------------#
# 不同主干网络的config信息
#---------------------------------------------------#
if self.retinaface_backbone == "mobilenet":
self.cfg = cfg_mnet
else:
self.cfg = cfg_re50
#---------------------------------------------------#
# 先验框的生成
#---------------------------------------------------#
self.anchors = Anchors(self.cfg, image_size=(self.retinaface_input_shape[0], self.retinaface_input_shape[1])).get_anchors()
self.generate()
try:
self.known_face_encodings = np.load("model_data/{backbone}_face_encoding.npy".format(backbone=self.facenet_backbone))
self.known_face_names = np.load("model_data/{backbone}_names.npy".format(backbone=self.facenet_backbone))
except:
if not encoding:
print("载入已有人脸特征失败，请检查model_data下面是否生成了相关的人脸特征文件。")
pass
#---------------------------------------------------#
# 获得所有的分类
#---------------------------------------------------#
def generate(self):
#-------------------------------#
# 载入模型与权值
#-------------------------------#
self.net = RetinaFace(cfg=self.cfg, phase='eval', pre_train=False).eval()
self.facenet = Facenet(backbone=self.facenet_backbone, mode="predict").eval()
device = torch.device('cuda' if self.cuda else 'cpu')
print('Loading weights into state dict...')
state_dict = torch.load(self.retinaface_model_path, map_location=device)
self.net.load_state_dict(state_dict)
state_dict = torch.load(self.facenet_model_path, map_location=device)
self.facenet.load_state_dict(state_dict, strict=False)
if self.cuda:
self.net = nn.DataParallel(self.net)
self.net = self.net.cuda()
self.facenet = nn.DataParallel(self.facenet)
self.facenet = self.facenet.cuda()
print('Finished!')
def encode_face_dataset(self, image_paths, names):
face_encodings = []
for index, path in enumerate(tqdm(image_paths)):
#---------------------------------------------------#
# 打开人脸图片
#---------------------------------------------------#
image = np.array(Image.open(path), np.float32)
#---------------------------------------------------#
# 对输入图像进行一个备份
#---------------------------------------------------#
old_image = image.copy()
#---------------------------------------------------#
# 计算输入图片的高和宽
#---------------------------------------------------#
im_height, im_width, _ = np.shape(image)
#---------------------------------------------------#
# 计算scale，用于将获得的预测框转换成原图的高宽
#---------------------------------------------------#
scale = [
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0]
]
scale_for_landmarks = [
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0]
]
if self.letterbox_image:
image = letterbox_image(image, [self.retinaface_input_shape[1], self.retinaface_input_shape[0]])
anchors = self.anchors
else:
anchors = Anchors(self.cfg, image_size=(im_height, im_width)).get_anchors()
#---------------------------------------------------#
# 将处理完的图片传入Retinaface网络当中进行预测
#---------------------------------------------------#
with torch.no_grad():
#-----------------------------------------------------------#
# 图片预处理，归一化。
#-----------------------------------------------------------#
image = torch.from_numpy(preprocess_input(image).transpose(2, 0, 1)).unsqueeze(0).type(torch.FloatTensor)
if self.cuda:
image = image.cuda()
anchors = anchors.cuda()
loc, conf, landms = self.net(image)
#-----------------------------------------------------------#
# 对预测框进行解码
#-----------------------------------------------------------#
boxes = decode(loc.data.squeeze(0), anchors, self.cfg['variance'])
#-----------------------------------------------------------#
# 获得预测结果的置信度
#-----------------------------------------------------------#
conf = conf.data.squeeze(0)[:, 1:2]
#-----------------------------------------------------------#
# 对人脸关键点进行解码
#-----------------------------------------------------------#
landms = decode_landm(landms.data.squeeze(0), anchors, self.cfg['variance'])
#-----------------------------------------------------------#
# 对人脸检测结果进行堆叠
#-----------------------------------------------------------#
boxes_conf_landms = torch.cat([boxes, conf, landms], -1)
boxes_conf_landms = non_max_suppression(boxes_conf_landms, self.confidence)
if len(boxes_conf_landms) <= 0:
print(names[index], "：未检测到人脸")
continue
#---------------------------------------------------------#
# 如果使用了letterbox_image的话，要把灰条的部分去除掉。
#---------------------------------------------------------#
if self.letterbox_image:
boxes_conf_landms = retinaface_correct_boxes(boxes_conf_landms, \
np.array([self.retinaface_input_shape[0], self.retinaface_input_shape[1]]), np.array([im_height, im_width]))
boxes_conf_landms[:, :4] = boxes_conf_landms[:, :4] * scale
boxes_conf_landms[:, 5:] = boxes_conf_landms[:, 5:] * scale_for_landmarks
#---------------------------------------------------#
# 选取最大的人脸框。
#---------------------------------------------------#
best_face_location = None
biggest_area = 0
for result in boxes_conf_landms:
left, top, right, bottom = result[0:4]
w = right - left
h = bottom - top
if w * h > biggest_area:
biggest_area = w * h
best_face_location = result
#---------------------------------------------------#
# 截取图像
#---------------------------------------------------#
crop_img = old_image[int(best_face_location[1]):int(best_face_location[3]), int(best_face_location[0]):int(best_face_location[2])]
landmark = np.reshape(best_face_location[5:],(5,2)) - np.array([int(best_face_location[0]),int(best_face_location[1])])
crop_img,_ = Alignment_1(crop_img,landmark)
crop_img = np.array(letterbox_image(np.uint8(crop_img),(self.facenet_input_shape[1],self.facenet_input_shape[0])))/255
crop_img = crop_img.transpose(2, 0, 1)
crop_img = np.expand_dims(crop_img,0)
#---------------------------------------------------#
# 利用图像算取长度为128的特征向量
#---------------------------------------------------#
with torch.no_grad():
crop_img = torch.from_numpy(crop_img).type(torch.FloatTensor)
if self.cuda:
crop_img = crop_img.cuda()
face_encoding = self.facenet(crop_img)[0].cpu().numpy()
face_encodings.append(face_encoding)
np.save("model_data/{backbone}_face_encoding.npy".format(backbone=self.facenet_backbone),face_encodings)
np.save("model_data/{backbone}_names.npy".format(backbone=self.facenet_backbone),names)
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image):
#---------------------------------------------------#
# 对输入图像进行一个备份，后面用于绘图
#---------------------------------------------------#
old_image = image.copy()
#---------------------------------------------------#
# 把图像转换成numpy的形式
#---------------------------------------------------#
image = np.array(image, np.float32)
#---------------------------------------------------#
# Retinaface检测部分-开始
#---------------------------------------------------#
#---------------------------------------------------#
# 计算输入图片的高和宽
#---------------------------------------------------#
im_height, im_width, _ = np.shape(image)
#---------------------------------------------------#
# 计算scale，用于将获得的预测框转换成原图的高宽
#---------------------------------------------------#
scale = [
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0]
]
scale_for_landmarks = [
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0]
]
#---------------------------------------------------------#
# letterbox_image可以给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
if self.letterbox_image:
image = letterbox_image(image, [self.retinaface_input_shape[1], self.retinaface_input_shape[0]])
anchors = self.anchors
else:
anchors = Anchors(self.cfg, image_size=(im_height, im_width)).get_anchors()
#---------------------------------------------------#
# 将处理完的图片传入Retinaface网络当中进行预测
#---------------------------------------------------#
with torch.no_grad():
#-----------------------------------------------------------#
# 图片预处理，归一化。
#-----------------------------------------------------------#
image = torch.from_numpy(preprocess_input(image).transpose(2, 0, 1)).unsqueeze(0).type(torch.FloatTensor)
if self.cuda:
anchors = anchors.cuda()
image = image.cuda()
#---------------------------------------------------------#
# 传入网络进行预测
#---------------------------------------------------------#
loc, conf, landms = self.net(image)
#---------------------------------------------------#
# Retinaface网络的解码，最终我们会获得预测框
# 将预测结果进行解码和非极大抑制
#---------------------------------------------------#
boxes = decode(loc.data.squeeze(0), anchors, self.cfg['variance'])
conf = conf.data.squeeze(0)[:, 1:2]
landms = decode_landm(landms.data.squeeze(0), anchors, self.cfg['variance'])
#-----------------------------------------------------------#
# 对人脸检测结果进行堆叠
#-----------------------------------------------------------#
boxes_conf_landms = torch.cat([boxes, conf, landms], -1)
boxes_conf_landms = non_max_suppression(boxes_conf_landms, self.confidence)
#---------------------------------------------------#
# 如果没有预测框则返回原图
#---------------------------------------------------#
if len(boxes_conf_landms) <= 0:
return old_image
#---------------------------------------------------------#
# 如果使用了letterbox_image的话，要把灰条的部分去除掉。
#---------------------------------------------------------#
if self.letterbox_image:
boxes_conf_landms = retinaface_correct_boxes(boxes_conf_landms, \
np.array([self.retinaface_input_shape[0], self.retinaface_input_shape[1]]), np.array([im_height, im_width]))
boxes_conf_landms[:, :4] = boxes_conf_landms[:, :4] * scale
boxes_conf_landms[:, 5:] = boxes_conf_landms[:, 5:] * scale_for_landmarks
#---------------------------------------------------#
# Retinaface检测部分-结束
#---------------------------------------------------#
#-----------------------------------------------#
# Facenet编码部分-开始
#-----------------------------------------------#
face_encodings = []
for boxes_conf_landm in boxes_conf_landms:
#----------------------#
# 图像截取，人脸矫正
#----------------------#
boxes_conf_landm = np.maximum(boxes_conf_landm, 0)
crop_img = np.array(old_image)[int(boxes_conf_landm[1]):int(boxes_conf_landm[3]), int(boxes_conf_landm[0]):int(boxes_conf_landm[2])]
landmark = np.reshape(boxes_conf_landm[5:],(5,2)) - np.array([int(boxes_conf_landm[0]),int(boxes_conf_landm[1])])
crop_img, _ = Alignment_1(crop_img, landmark)
#----------------------#
# 人脸编码
#----------------------#
crop_img = np.array(letterbox_image(np.uint8(crop_img),(self.facenet_input_shape[1],self.facenet_input_shape[0])))/255
crop_img = np.expand_dims(crop_img.transpose(2, 0, 1),0)
with torch.no_grad():
crop_img = torch.from_numpy(crop_img).type(torch.FloatTensor)
if self.cuda:
crop_img = crop_img.cuda()
#-----------------------------------------------#
# 利用facenet_model计算长度为128特征向量
#-----------------------------------------------#
face_encoding = self.facenet(crop_img)[0].cpu().numpy()
face_encodings.append(face_encoding)
#-----------------------------------------------#
# Facenet编码部分-结束
#-----------------------------------------------#
#-----------------------------------------------#
# 人脸特征比对-开始
#-----------------------------------------------#
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, face_distances = compare_faces(self.known_face_encodings, face_encoding, tolerance = self.facenet_threhold)
name = "Unknown"
#-----------------------------------------------------#
# 取出这个最近人脸的评分
# 取出当前输入进来的人脸，最接近的已知人脸的序号
#-----------------------------------------------------#
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = self.known_face_names[best_match_index]
face_names.append(name)
#-----------------------------------------------#
# 人脸特征比对-结束
#-----------------------------------------------#
for i, b in enumerate(boxes_conf_landms):
text = "{:.4f}".format(b[4])
b = list(map(int, b))
#---------------------------------------------------#
# b[0]-b[3]为人脸框的坐标，b[4]为得分
#---------------------------------------------------#
cv2.rectangle(old_image, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
cx = b[0]
cy = b[1] + 12
cv2.putText(old_image, text, (cx, cy),
cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
#---------------------------------------------------#
# b[5]-b[14]为人脸关键点的坐标
#---------------------------------------------------#
cv2.circle(old_image, (b[5], b[6]), 1, (0, 0, 255), 4)
cv2.circle(old_image, (b[7], b[8]), 1, (0, 255, 255), 4)
cv2.circle(old_image, (b[9], b[10]), 1, (255, 0, 255), 4)
cv2.circle(old_image, (b[11], b[12]), 1, (0, 255, 0), 4)
cv2.circle(old_image, (b[13], b[14]), 1, (255, 0, 0), 4)
name = face_names[i]
# font = cv2.FONT_HERSHEY_SIMPLEX
# cv2.putText(old_image, name, (b[0] , b[3] - 15), font, 0.75, (255, 255, 255), 2)
#--------------------------------------------------------------#
# cv2不能写中文，加上这段可以，但是检测速度会有一定的下降。
# 如果不是必须，可以换成cv2只显示英文。
#--------------------------------------------------------------#
old_image = cv2ImgAddText(old_image, name, b[0]+5 , b[3] - 25)
return old_image
def get_FPS(self, image, test_interval):
#---------------------------------------------------#
# 对输入图像进行一个备份，后面用于绘图
#---------------------------------------------------#
old_image = image.copy()
#---------------------------------------------------#
# 把图像转换成numpy的形式
#---------------------------------------------------#
image = np.array(image, np.float32)
#---------------------------------------------------#
# Retinaface检测部分-开始
#---------------------------------------------------#
#---------------------------------------------------#
# 计算输入图片的高和宽
#---------------------------------------------------#
im_height, im_width, _ = np.shape(image)
#---------------------------------------------------#
# 计算scale，用于将获得的预测框转换成原图的高宽
#---------------------------------------------------#
scale = [
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0]
]
scale_for_landmarks = [
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0]
]
#---------------------------------------------------------#
# letterbox_image可以给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
if self.letterbox_image:
image = letterbox_image(image, [self.retinaface_input_shape[1], self.retinaface_input_shape[0]])
anchors = self.anchors
else:
anchors = Anchors(self.cfg, image_size=(im_height, im_width)).get_anchors()
#---------------------------------------------------#
# 将处理完的图片传入Retinaface网络当中进行预测
#---------------------------------------------------#
with torch.no_grad():
#-----------------------------------------------------------#
# 图片预处理，归一化。
#-----------------------------------------------------------#
image = torch.from_numpy(preprocess_input(image).transpose(2, 0, 1)).unsqueeze(0).type(torch.FloatTensor)
if self.cuda:
anchors = anchors.cuda()
image = image.cuda()
#---------------------------------------------------------#
# 传入网络进行预测
#---------------------------------------------------------#
loc, conf, landms = self.net(image)
#---------------------------------------------------#
# Retinaface网络的解码，最终我们会获得预测框
# 将预测结果进行解码和非极大抑制
#---------------------------------------------------#
boxes = decode(loc.data.squeeze(0), anchors, self.cfg['variance'])
conf = conf.data.squeeze(0)[:, 1:2]
landms = decode_landm(landms.data.squeeze(0), anchors, self.cfg['variance'])
#-----------------------------------------------------------#
# 对人脸检测结果进行堆叠
#-----------------------------------------------------------#
boxes_conf_landms = torch.cat([boxes, conf, landms], -1)
boxes_conf_landms = non_max_suppression(boxes_conf_landms, self.confidence)
if len(boxes_conf_landms)>0:
#---------------------------------------------------------#
# 如果使用了letterbox_image的话，要把灰条的部分去除掉。
#---------------------------------------------------------#
if self.letterbox_image:
boxes_conf_landms = retinaface_correct_boxes(boxes_conf_landms, \
np.array([self.retinaface_input_shape[0], self.retinaface_input_shape[1]]), np.array([im_height, im_width]))
boxes_conf_landms[:, :4] = boxes_conf_landms[:, :4] * scale
boxes_conf_landms[:, 5:] = boxes_conf_landms[:, 5:] * scale_for_landmarks
#---------------------------------------------------#
# Retinaface检测部分-结束
#---------------------------------------------------#
#-----------------------------------------------#
# Facenet编码部分-开始
#-----------------------------------------------#
face_encodings = []
for boxes_conf_landm in boxes_conf_landms:
#----------------------#
# 图像截取，人脸矫正
#----------------------#
boxes_conf_landm = np.maximum(boxes_conf_landm, 0)
crop_img = np.array(old_image)[int(boxes_conf_landm[1]):int(boxes_conf_landm[3]), int(boxes_conf_landm[0]):int(boxes_conf_landm[2])]
landmark = np.reshape(boxes_conf_landm[5:],(5,2)) - np.array([int(boxes_conf_landm[0]),int(boxes_conf_landm[1])])
crop_img, _ = Alignment_1(crop_img, landmark)
#----------------------#
# 人脸编码
#----------------------#
crop_img = np.array(letterbox_image(np.uint8(crop_img),(self.facenet_input_shape[1],self.facenet_input_shape[0])))/255
crop_img = np.expand_dims(crop_img.transpose(2, 0, 1),0)
with torch.no_grad():
crop_img = torch.from_numpy(crop_img).type(torch.FloatTensor)
if self.cuda:
crop_img = crop_img.cuda()
#-----------------------------------------------#
# 利用facenet_model计算长度为128特征向量
#-----------------------------------------------#
face_encoding = self.facenet(crop_img)[0].cpu().numpy()
face_encodings.append(face_encoding)
#-----------------------------------------------#
# Facenet编码部分-结束
#-----------------------------------------------#
#-----------------------------------------------#
# 人脸特征比对-开始
#-----------------------------------------------#
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, face_distances = compare_faces(self.known_face_encodings, face_encoding, tolerance = self.facenet_threhold)
name = "Unknown"
#-----------------------------------------------------#
# 取出这个最近人脸的评分
# 取出当前输入进来的人脸，最接近的已知人脸的序号
#-----------------------------------------------------#
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = self.known_face_names[best_match_index]
face_names.append(name)
#-----------------------------------------------#
# 人脸特征比对-结束
#-----------------------------------------------#
t1 = time.time()
for _ in range(test_interval):
with torch.no_grad():
#---------------------------------------------------------#
# 传入网络进行预测
#---------------------------------------------------------#
loc, conf, landms = self.net(image)
#---------------------------------------------------#
# Retinaface网络的解码，最终我们会获得预测框
# 将预测结果进行解码和非极大抑制
#---------------------------------------------------#
boxes = decode(loc.data.squeeze(0), anchors, self.cfg['variance'])
conf = conf.data.squeeze(0)[:, 1:2]
landms = decode_landm(landms.data.squeeze(0), anchors, self.cfg['variance'])
#-----------------------------------------------------------#
# 对人脸检测结果进行堆叠
#-----------------------------------------------------------#
boxes_conf_landms = torch.cat([boxes, conf, landms], -1)
boxes_conf_landms = non_max_suppression(boxes_conf_landms, self.confidence)
if len(boxes_conf_landms) > 0:
#---------------------------------------------------------#
# 如果使用了letterbox_image的话，要把灰条的部分去除掉。
#---------------------------------------------------------#
if self.letterbox_image:
boxes_conf_landms = retinaface_correct_boxes(boxes_conf_landms, \
np.array([self.retinaface_input_shape[0], self.retinaface_input_shape[1]]), np.array([im_height, im_width]))
boxes_conf_landms[:, :4] = boxes_conf_landms[:, :4] * scale
boxes_conf_landms[:, 5:] = boxes_conf_landms[:, 5:] * scale_for_landmarks
#---------------------------------------------------#
# Retinaface检测部分-结束
#---------------------------------------------------#
#-----------------------------------------------#
# Facenet编码部分-开始
#-----------------------------------------------#
face_encodings = []
for boxes_conf_landm in boxes_conf_landms:
#----------------------#
# 图像截取，人脸矫正
#----------------------#
boxes_conf_landm = np.maximum(boxes_conf_landm, 0)
crop_img = np.array(old_image)[int(boxes_conf_landm[1]):int(boxes_conf_landm[3]), int(boxes_conf_landm[0]):int(boxes_conf_landm[2])]
landmark = np.reshape(boxes_conf_landm[5:],(5,2)) - np.array([int(boxes_conf_landm[0]),int(boxes_conf_landm[1])])
crop_img, _ = Alignment_1(crop_img, landmark)
#----------------------#
# 人脸编码
#----------------------#
crop_img = np.array(letterbox_image(np.uint8(crop_img),(self.facenet_input_shape[1],self.facenet_input_shape[0])))/255
crop_img = np.expand_dims(crop_img.transpose(2, 0, 1),0)
with torch.no_grad():
crop_img = torch.from_numpy(crop_img).type(torch.FloatTensor)
if self.cuda:
crop_img = crop_img.cuda()
#-----------------------------------------------#
# 利用facenet_model计算长度为128特征向量
#-----------------------------------------------#
face_encoding = self.facenet(crop_img)[0].cpu().numpy()
face_encodings.append(face_encoding)
#-----------------------------------------------#
# Facenet编码部分-结束
#-----------------------------------------------#
#-----------------------------------------------#
# 人脸特征比对-开始
#-----------------------------------------------#
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, face_distances = compare_faces(self.known_face_encodings, face_encoding, tolerance = self.facenet_threhold)
name = "Unknown"
#-----------------------------------------------------#
# 取出这个最近人脸的评分
# 取出当前输入进来的人脸，最接近的已知人脸的序号
#-----------------------------------------------------#
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = self.known_face_names[best_match_index]
face_names.append(name)
#-----------------------------------------------#
# 人脸特征比对-结束
#-----------------------------------------------#
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time