def save_all_feature(fileName, targetName, *suffix):
# 文件存在
if os.path.exists(fileName):
# 加载vgg模型
vgg_net = torch.load(VGG_FACE_LOCATION)
vgg_net.modules[31] = nn.View(1, 25088)
# 读取一级目录
f = os.listdir(fileName)
for name in f:
# 读取二级目录
images = os.listdir(fileName + os.sep + name)
for image in images:
if endwith(image, suffix): # 后缀名判断
# 文件读取，获取特征
img = cv.imread(fileName + os.sep + name + os.sep + image)
feature = list(get_feature(img, vgg_net))
# print(feature)
# 创建文件
if not os.path.exists(targetName):
os.makedirs(targetName)
# 保存标签文件
with open(targetName + os.sep + 'id.dat', 'a') as f:
f.write(name)
f.write('\n')
# 保存特征文件
with open(targetName + os.sep + 'feature.dat', 'a') as f:
feature = map(str, feature)
f.writelines(s + ' ' for s in feature)
f.write('\n')
print(name, " successful")
def compare_two_img(img1, img2):
vgg_net = torch.load(VGG_FACE_LOCATION)
vgg_net.modules[31] = nn.View(1, 25088)
feature1 = get_feature(img1, vgg_net)
feature2 = get_feature(img2, vgg_net)
result = compare(feature1, feature2)
return result
def test_Parallel(self):
input = torch.randn(3, 4, 5)
m = nn.Parallel(0, 2)
m.add(nn.View(4, 5, 1))
m.add(nn.View(4, 5, 1))
m.add(nn.View(4, 5, 1))
# Check that these don't raise errors
m.__repr__()
str(m)
output = m.forward(input)
output2 = input.transpose(0, 2).transpose(0, 1)
self.assertEqual(output2, output)
gradInput = m.backward(input, output2)
self.assertEqual(gradInput, input)
def findFeatureZeroCount(fileName):
img = pretreat_img_chinese(fileName)
vgg_net = torch.load(VGG_FACE_LOCATION)
vgg_net.modules[31] = nn.View(1, 25088)
feature = get_feature(img, vgg_net)
count = 0
for i in feature:
if i == 0:
count += 1
print(count)
def simbreak_threshold_test(filename):
if os.path.exists(filename):
images = os.listdir(filename)
sum_count = len(images)
test_point = np.arange(0.3, 0.8, 0.1).astype('float32')
accuracy = np.arange(0.3, 0.8, 0.1).astype('float32')
vgg_net = torch.load(VGG_FACE_LOCATION)
print('模型加载成功')
vgg_net.modules[31] = nn.View(1, 25088)
feature = loadtxt(FEATURE_LOCATION)
print('feature加载成功')
for w in range(test_point.shape[0]):
right_count = 0
for image in images:
num = -1
sim = -1
y = os.path.splitext(image)[0]
img = cv.imread(filename + os.path.sep + image)
image_feature = get_feature(img, vgg_net)
# 找到对应的id在第几行
for i in range(feature.shape[0]):
comp = compare(feature[i].reshape(1, -1),
image_feature.reshape(1, -1))
if comp > sim:
num = i
sim = comp
if sim >= test_point[w]:
break
# 判断找到的id和正确id是否相同
if not num == -1:
count = 0
with open(ID_LOCATION) as f:
for line in f.readlines():
if num == count:
if line[:-1] == y:
right_count += 1
print(test_point[w], ' | ', line[:-1],
' | ', y, ' right ', sim)
break
else:
print(test_point[w], ' | ', line[:-1],
' | ', y, ' wrong ', sim)
break
count += 1
else:
print(test_point[w], ' | ', image, ' | ', y, ' not found')
print(right_count, ' ', sum_count)
accuracy[w] = right_count / sum_count
print(test_point[w], ' | ', accuracy[w])
for i in range(accuracy.shape[0]):
print('threshold: ', i + 1, ' | accuracy:', accuracy[i])
def test_ParallelTable(self):
input = torch.randn(3, 4, 5)
p = nn.ParallelTable()
p.add(nn.View(4, 5, 1))
p.add(nn.View(4, 5, 1))
p.add(nn.View(4, 5, 1))
m = nn.Sequential()
m.add(nn.SplitTable(0))
m.add(p)
m.add(nn.JoinTable(2))
# Check that these don't raise errors
p.__repr__()
str(p)
output = m.forward(input)
output2 = input.transpose(0, 2).transpose(0, 1)
self.assertEqual(output2, output)
gradInput = m.backward(input, output2)
self.assertEqual(gradInput, input)
def save_feature(fileName, targetName, *suffix):
if os.path.exists(fileName):
name = os.path.basename(fileName)
vgg_net = torch.load(VGG_FACE_LOCATION)
vgg_net.modules[31] = nn.View(1, 25088)
images = os.listdir(fileName)
for image in images:
if endwith(image, suffix):
img = cv.imread(fileName + os.sep + image)
feature = list(get_feature(img, vgg_net))
# print(feature)
if not os.path.exists(targetName):
os.makedirs(targetName)
with open(targetName + os.sep + 'id.dat', 'a') as f:
f.write(name)
f.write('\n')
with open(targetName + os.sep + 'feature.dat', 'a') as f:
feature = map(str, feature)
f.writelines(s + ' ' for s in feature)
f.write('\n')
print(name, " successfule")
) else 'torch.FloatTensor'
# two-dimensional SparseConvNet
model = nn.Sequential()
sparseModel = scn.Sequential()
denseModel = nn.Sequential()
model.add(sparseModel).add(denseModel)
sparseModel.add(scn.ValidConvolution(2, 3, 16, 3, False))
sparseModel.add(scn.MaxPooling(2, 3, 2))
sparseModel.add(
scn.SparseResNet(
2, 16,
[['b', 16, 2, 1], ['b', 32, 2, 2], ['b', 48, 2, 2], ['b', 96, 2, 2]]))
sparseModel.add(scn.Convolution(2, 96, 128, 4, 1, False))
sparseModel.add(scn.BatchNormReLU(128))
sparseModel.add(scn.SparseToDense(2))
denseModel.add(nn.View(-1, 128))
denseModel.add(nn.Linear(128, 3755))
model.type(dtype)
print(model)
spatial_size = sparseModel.suggestInputSize(torch.LongTensor([1, 1]))
print('input spatial size', spatial_size)
dataset = getIterators(spatial_size, 63, 3)
scn.ClassificationTrainValidate(model, dataset, {
'nEpochs': 100,
'initial_LR': 0.1,
'LR_decay': 0.05,
'weightDecay': 1e-4
})
import socket
from numpy import loadtxt
from face_recognition import compare_feature
import torch
from torch.legacy import nn
from flask import Flask, request
VGG_FACE_LOCATION = 'D://vgg.pkl'
FEATURE_LOCATION = 'D:\\feature\\feature.dat'
vgg_net = torch.load(VGG_FACE_LOCATION)
vgg_net.modules[31] = nn.View(1, 25088)
feature = loadtxt(FEATURE_LOCATION)
print('文件加载成功！')
app = Flask(__name__)
app.debug = True
@app.route('/findsim', methods=['GET', 'POST'])
def findsim():
if request.method == 'POST':
name = request.form['name']
name, sim = compare_feature(name, vgg_net, feature)
result = name + ',' + str(sim[0][0])
print(result)
return result
else:
return 'method is not right'
@app.route('/hello')
def hello():
denseModel = nn.Sequential()
model.add(sparseModel).add(denseModel)
sparseModel.add(scn.ValidConvolution(2, 3, 16, 3, False))\
.add(scn.SparseDenseNet(2, 16, [
{'pool': 'MP', 'compression': 0},
{'nExtraLayers': 2, 'growthRate': 16},
{'pool': 'BN-R-C-AP', 'compression': 0},
{'nExtraLayers': 2, 'growthRate': 16},
{'pool': 'BN-R-C-AP', 'compression': 0},
{'nExtraLayers': 2, 'growthRate': 16},
{'pool': 'BN-R-C-AP', 'compression': 0},
{'nExtraLayers': 2, 'growthRate': 16}]))
n_out = sparseModel.modules[-1].nOutputPlanes
sparseModel.add(scn.Convolution(2, n_out, 256, 4, 1, False))
sparseModel.add(scn.BatchNormReLU(256))
sparseModel.add(scn.SparseToDense(2))
denseModel.add(nn.View(-1, 256))
denseModel.add(nn.Linear(256, 3755))
model.type(dtype)
print(model)
spatial_size = sparseModel.suggestInputSize(torch.LongTensor([1, 1]))
print('input spatial size', spatial_size)
dataset = getIterators(spatial_size, 64, 2)
scn.ClassificationTrainValidate(model, dataset, {
'nEpochs': 100,
'initial_LR': 0.1,
'LR_decay': 0.05,
'weightDecay': 1e-4
})
dtype = 'torch.cuda.FloatTensor' # Uncomment this to run on GPU
# two-dimensional SparseConvNet
model = nn.Sequential()
sparseModel = scn.Sequential()
denseModel = nn.Sequential()
model.add(sparseModel).add(denseModel)
sparseModel.add(
scn.SparseVggNet(2, 3, [[
'C',
8,
], ['C', 8], 'MP', ['C', 16], ['C', 16], 'MP', ['C', 16, 8], ['C', 16, 8],
'MP', ['C', 24, 8], ['C', 24, 8], 'MP']))
sparseModel.add(scn.Convolution(2, 32, 64, 5, 1, False))
sparseModel.add(scn.BatchNormReLU(64))
sparseModel.add(scn.SparseToDense(2))
denseModel.add(nn.View(-1, 64))
denseModel.add(nn.Linear(64, 183))
model.type(dtype)
print(model)
spatial_size = sparseModel.suggestInputSize(torch.LongTensor([1, 1]))
print('input spatial size', spatial_size)
dataset = getIterators(spatial_size, 63, 3)
scn.ClassificationTrainValidate(model, dataset, {
'nEpochs': 100,
'initial_LR': 0.1,
'LR_decay': 0.05,
'weightDecay': 1e-4
})
