[人脸识别]使用VGG Face Model对一张图片进行测试

  如果你已经按照上一篇的步骤做好了训练过程，那么你会得到一个名为VGG_FACE.caffemodel的模型，下面我们来对该模型进行测试。

一、新建配置文件

  在测试之前我们需要新建一个配置文件，该配置文件与VGG_FACE_deploy.prototxt比较相像，但还是有点不一样。上篇中的VGG_FACE_deploy.prototxt是为了训练模型，而我们需要新建的配置文件是为了测试模型。你可以复制一份VGG_FACE_deploy.prototxt并且命名为deploy.prototxt，deploy.prototxt内容如下：（推荐直接复制）

name: "VGG_FACE_16_Net"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224
force_backward: true
layer {
name: "conv1_1"
type: "Convolution"
bottom: "data"
top: "conv1_1"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
}
}
layer {
name: "relu1_1"
type: "ReLU"
bottom: "conv1_1"
top: "conv1_1"
}
layer {
name: "conv1_2"
type: "Convolution"
bottom: "conv1_1"
top: "conv1_2"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
}
}
layer {
name: "relu1_2"
type: "ReLU"
bottom: "conv1_2"
top: "conv1_2"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1_2"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2_1"
type: "Convolution"
bottom: "pool1"
top: "conv2_1"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
}
}
layer {
name: "relu2_1"
type: "ReLU"
bottom: "conv2_1"
top: "conv2_1"
}
layer {
name: "conv2_2"
type: "Convolution"
bottom: "conv2_1"
top: "conv2_2"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
}
}
layer {
name: "relu2_2"
type: "ReLU"
bottom: "conv2_2"
top: "conv2_2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2_2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv3_1"
type: "Convolution"
bottom: "pool2"
top: "conv3_1"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
}
}
layer {
name: "relu3_1"
type: "ReLU"
bottom: "conv3_1"
top: "conv3_1"
}
layer {
name: "conv3_2"
type: "Convolution"
bottom: "conv3_1"
top: "conv3_2"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
}
}
layer {
name: "relu3_2"
type: "ReLU"
bottom: "conv3_2"
top: "conv3_2"
}
layer {
name: "conv3_3"
type: "Convolution"
bottom: "conv3_2"
top: "conv3_3"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
}
}
layer {
name: "relu3_3"
type: "ReLU"
bottom: "conv3_3"
top: "conv3_3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3_3"
top: "pool3"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv4_1"
type: "Convolution"
bottom: "pool3"
top: "conv4_1"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
}
}
layer {
name: "relu4_1"
type: "ReLU"
bottom: "conv4_1"
top: "conv4_1"
}
layer {
name: "conv4_2"
type: "Convolution"
bottom: "conv4_1"
top: "conv4_2"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
}
}
layer {
name: "relu4_2"
type: "ReLU"
bottom: "conv4_2"
top: "conv4_2"
}
layer {
name: "conv4_3"
type: "Convolution"
bottom: "conv4_2"
top: "conv4_3"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
}
}
layer {
name: "relu4_3"
type: "ReLU"
bottom: "conv4_3"
top: "conv4_3"
}
layer {
name: "pool4"
type: "Pooling"
bottom: "conv4_3"
top: "pool4"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv5_1"
type: "Convolution"
bottom: "pool4"
top: "conv5_1"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
}
}
layer {
name: "relu5_1"
type: "ReLU"
bottom: "conv5_1"
top: "conv5_1"
}
layer {
name: "conv5_2"
type: "Convolution"
bottom: "conv5_1"
top: "conv5_2"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
}
}
layer {
name: "relu5_2"
type: "ReLU"
bottom: "conv5_2"
top: "conv5_2"
}
layer {
name: "conv5_3"
type: "Convolution"
bottom: "conv5_2"
top: "conv5_3"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
}
}
layer {
name: "relu5_3"
type: "ReLU"
bottom: "conv5_3"
top: "conv5_3"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5_3"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
# Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8_flickr"
type: "InnerProduct"
bottom: "fc7"
top: "fc8_flickr"
# lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained
propagate_down: false
inner_product_param {
num_output: 1072   #需修改
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "fc8_flickr"
top: "prob"
}

  需要注意的是在342行num_output，你需要修改为训练的种类数。

  而deploy.prototxt与VGG_FACE_deploy.prototxt的区别，推荐参考：

  http://blog.csdn.net/fx409494616/article/details/53008971

二、模型测试

  测试代码如下：

# -*-coding:utf8-*-#
import caffe

#deploy文件
deployFile =    #修改为第一步新建的配置文件地址
#caffemodel
modelFile =     #修改为模型地址
#测试图片地址
imgPath =       #修改为待测试图片地址

def predictImg(net,imgPath):
#图片预处理设置
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))
# 缩放到【0，255】之间
transformer.set_raw_scale('data', 255)
#交换通道，将图片由RGB变为BGR
transformer.set_channel_swap('data', (2,1,0))
# 加载图片
im = caffe.io.load_image(imgPath)
#执行上面设置的图片预处理操作，并将图片载入到blob中
net.blobs['data'].data[...] = transformer.preprocess('data',im)
# #执行测试
output = net.forward()
output_prob = output['prob'][0]
print(str(output_prob.argmax()))

if __name__ == '__main__':
#使用gpu
caffe.set_mode_gpu()
#加载网络
net = caffe.Net(deployFile,modelFile,caffe.TEST)
predictImg(net,imgPath)

  运行上述python代码，会打印出该图片属于的种类。

  本部分参考：http://www.cnblogs.com/Allen-rg/p/5834551.html