# -*- coding: utf-8 -*-
import os
import sys
from config import config
root=config.Config.root
caffe_root=config.Config.caffe_root
sys.path.insert(0, caffe_root + 'python')
import random
import skimage
import numpy as np
import caffe
import sklearn.metrics.pairwise as pw
import time
import cv2
from django.http import HttpResponse
# GPU加速
caffe.set_device(0)
caffe.set_mode_gpu()
# 加载caffe模型
global net
try:
net = caffe.Net(root + 'model/vgg/deploy.prototxt'
, root + 'model/vgg/VGG_FACE.caffemodel'
, caffe.TEST)
except:
print("请先下载VGG FACE神经网络模型，详情请看model/vgg/README.md")
exit();
# 提取特征数组
def get_feature(path1):
global net
# 加载图片
X = read_image(path1)
test_num = np.shape(X)[0]
# X 作为 模型的输入
out = net.forward(data=X)
# print out.keys()
feature1 = np.float64(out["fc7"])
feature1 = np.reshape(feature1, (test_num, 4096))
# 加载注册图片
return feature1
# 保存上传文件
def handle_uploaded_file(file, filename):
if not os.path.exists('upload/'):
os.mkdir('upload/')
with open('upload/' + filename, 'wb+') as destination:
for chunk in file.chunks():
destination.write(chunk)
#图片预处理
def read_image(filename):
averageImg = [129.1863, 104.7624, 93.5940]
X = np.empty((1, 3, 224, 224))
im1 = skimage.io.imread(filename, as_grey=False)
# 归一化
image = skimage.transform.resize(im1, (224, 224)) * 255
X[0, 0, :, :] = image[:, :, 0] - averageImg[0]
X[0, 1, :, :] = image[:, :, 1] - averageImg[1]
X[0, 2, :, :] = image[:, :, 2] - averageImg[2]
return X
# api返回函数
def compared(request):
if request.method == 'POST':
if len(request.FILES) != 2:
return HttpResponse('{"status":false,"data":"","msg":"图片参数错误！"}')
starttime = time.time()
name1 = str(random.randint(10000, 99999)) + str(time.time()) # 随机名字
name2 = str(random.randint(10000, 99999)) + str(time.time())
handle_uploaded_file(request.FILES['face1'], str(name1))
handle_uploaded_file(request.FILES['face2'], str(name2))
tz1 = get_feature(root + "RestServer/upload/" + str(name1))
tz2 = get_feature(root + "RestServer/upload/" + str(name2))
comparedValue = pw.cosine_similarity(tz1, tz2)[0][0]
os.remove(root + "RestServer/upload/" + str(name1))
os.remove(root + "RestServer/upload/" + str(name2))
endtime = time.time()
Runtime=endtime-starttime
return HttpResponse('{"status":true,"data":"' + str(comparedValue) + '","msg":"成功","runtime": ' + str(Runtime) + ' }')
else:
return HttpResponse('{"status":false,"data":"","msg":"请求不合法"}')
return HttpResponse('{"status":false,"data":"","msg":"未知错误"}')
def comparedByFaceID(request):
if request.method=='POST':
if request.POST['faceID1'] and request.POST['faceID2'] :
tz1=request.POST['faceID1'][2:-2]
tz2=request.POST['faceID2'][2:-2]
tz1=[tz1.split(",")]
tz2=[tz2.split(",")]
faceID1=np.float64(tz1)
faceID2=np.float64(tz2)
com=pw.cosine_similarity(faceID1, faceID2)[0][0]
return HttpResponse('{"status":true,"data":'+str(com)+',"msg":"成功！"}')
return HttpResponse('{"status":false,"data":"","msg":"失败！"}')
def faceid(request):
if request.method == 'POST':
if len(request.FILES) != 1:
return HttpResponse('{"status":false,"data":"","msg":"图片参数错误！"}')
starttime = time.time()
name = str(random.randint(10000, 99999)) + str(time.time()) # 随机名字
handle_uploaded_file(request.FILES['pic'], str(name))
tz = get_feature(root + "RestServer/upload/" + str(name))
endtime = time.time()
Runtime=endtime-starttime
return HttpResponse('{"status":true,"data":' + str(tz.tolist()) + ',"msg":"成功","runtime": '+ str(Runtime) +' }')
else:
return HttpResponse('{"status":false,"data":"","msg":"请求不合法"}')
return HttpResponse('{"status":false,"data":"","msg":"未知错误"}')
#获取图片中人脸的位置
def detectFaces(image_name):
starttime=time.time()
img = cv2.imread(image_name)
face_cascade = cv2.CascadeClassifier(root+"model/haarcascade_frontalface_default.xml")
if img.ndim == 3:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
gray = img #if语句：如果img维度为3，说明不是灰度图，先转化为灰度图gray，如果不为3，也就是2，原图就是灰度图
faces = face_cascade.detectMultiScale(gray, 1.2, 5)#1.3和5是特征的最小、最大检测窗口，它改变检测结果也会改变
result = "["
for (x,y,width,height) in faces:
result+= '{ "x":'+ str(x) +' ,"y":'+ str(y) +' ,"height":'+ str(height)+',"width":'+ str(width)+' } ' +','
endtime=time.time()
result=result[:-1]+']'
return '{"status":true, "data":'+ result +' ,"msg":"成功","runtime":'+ str(endtime-starttime)+'}'
#api返回函数
def locate(request):
if request.method == 'POST':
if len(request.FILES) != 1:
return HttpResponse('{"status":false,"data":"","msg":"图片参数错误！"}')
name = str(random.randint(10000, 99999)) + str(time.time()) # 随机保存图片的名字
handle_uploaded_file(request.FILES['pic'], str(name))
returnData= HttpResponse(detectFaces(root+"RestServer/upload/"+name))
os.remove(root + "RestServer/upload/" + str(name))
return returnData
