mxnet insightface特征提取

代码库在：https://github.com/deepinsight/insightface

知识点：

mx.model.load_checkpoint('../recognition/mxnet/new_model', 0)

net_model是json的名字，0是训练的epoch值。

fc1_output，感觉每一层都有output，fc1是最后一层的名字，最后一层是bn层，fc1_output是最后一层bn层的输出。

3.将model上传到insightface/models中。

gpu版替换代码：

gpu_id = 0
ctx = mx.gpu(gpu_id)
model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)

cpu版单张图片提取特征：

# !/usr/bin/env python
# -*- coding: utf-8 -*-import os
import time
import math
import mxnet as mx
import cv2
import numpy as np
from collections import namedtupledef single_input(path):img = cv2.imread(path)# mxnet三通道输入是严格的RGB格式，而cv2.imread的默认是BGR格式，因此需要做一个转换img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)img = cv2.resize(img, (112, 112))img = img.transpose(2, 0, 1)# 添加一个第四维度并构建NDArrayimg = img[np.newaxis, :]array = mx.nd.array(img)return arrayif __name__ == "__main__":time_start = time.time()time0 = time.time()sym, arg_params, aux_params = mx.model.load_checkpoint('../recognition/mxnet/new_model', 0)# print(sym)# 提取中间某层输出帖子特征层作为输出all_layers = sym.get_internals()# print(all_layers)sym = all_layers['fc1_output']# 重建模型model = mx.mod.Module(symbol=sym, label_names=None)model.bind(for_training=False, data_shapes=[('data', (1, 3, 112, 112))])model.set_params(arg_params, aux_params)time1 = time.time()time_load = time1 - time0# print("模型加载和重建时间：{0}".format(time1 - time0))Batch = namedtuple("batch", ['data'])img1_path=r'E:\MNN\project\android-mnn_new\app\src\main\assets\0.jpg'array1 = single_input(img1_path)model.forward(Batch([array1]))vector1 = model.get_outputs()[0].asnumpy()vector1 = np.squeeze(vector1)print(vector1)

vim test.py

import face_model
import argparse
import cv2
import sys
import numpy as np
parser = argparse.ArgumentParser(description='face model test')
# general
parser.add_argument('--image-size', default='112,112', help='')
# 这个比较重要，人脸识别的model，我的经验是要用绝对路径
parser.add_argument('--model', default='/disk1t/insightface/insightface/models/model-r100-ii/model,0', help='path to load model.')
# 这个比较重要，年龄性别的model，我的经验是要用绝对路径
parser.add_argument('--ga-model', default='/disk1t/insightface/insightface/models/gamodel-r50/model,0', help='path to load model.')
parser.add_argument('--gpu', default=0, type=int, help='gpu id')
parser.add_argument('--det', default=0, type=int, help='mtcnn option, 1 means using R+O, 0 means detect from begining')
parser.add_argument('--flip', default=0, type=int, help='whether do lr flip aug')
parser.add_argument('--threshold', default=1.24, type=float, help='ver dist threshold')
args = parser.parse_args()
print(args)
# 加载model
model = face_model.FaceModel(args)
# 读取图片
img = cv2.imread('Tom_Hanks_54745.png')
# 模型加载图片
img = model.get_input(img)
# 获得特征
f1 = model.get_feature(img)
# 输出特征
print(f1)
#print(f1[0:10])
#gender, age = model.get_ga(img)
#print(gender)
#print(age)
#sys.exit(0)
#img = cv2.imread('/raid5data/dplearn/megaface/facescrubr/112x112/Tom_Hanks/Tom_Hanks_54733.png')
#f2 = model.get_feature(img)
#dist = np.sum(np.square(f1-f2))
#print(dist)
#sim = np.dot(f1, f2.T)
#print(sim)
#diff = np.subtract(source_feature, target_feature)
#dist = np.sum(np.square(diff),1)

获取指定层的输出
有些时候我们不需要网络的输出，而是只需要网络某个层的输出来通过网络提取图片的特征，这时候我们就需要指定提取层的名称，这里我们通过提取网络最后一层的全连接层为例

def get_specify_mod(model_str,ctx,data_shpae,layer_name):_vec = model_str.split(",")prefix = _vec[0]epoch = int(_vec[1])sym,arg_params,aux_params = mx.model.load_checkpoint(prefix,epoch)#获取神经网络所有的层all_layers = sym.get_internals()#获取输出层sym = all_layers[layer_name+"_output"]mod = mx.mod.Module(symbol=sym,context=ctx)mod.bind(data_shapes=[("data",data_shpae)])mod.set_params(arg_params,aux_params)return moddef predict_specify(model_str,ctx,data_shape,img_path,label_path):label_names = get_label_names(label_path)#通过输出网络层的名称,输出层全连接层的名称为fc1mod = get_specify_mod(model_str,ctx,data_shape,layer_name="fc1")nd_img = preprocess_img(img_path,data_shape,ctx)#将需要预测的图片封装为Batchdata_batch = mx.io.DataBatch(data=(nd_img,))#计算网络的预测值mod.forward(data_batch,is_train=False)#获取网络的输出值output = mod.get_outputs()[0]#对输出值进行softmax处理proba = mx.nd.softmax(output)#获取前top5的值top_proba = proba.topk(k=5)[0].asnumpy()for index in top_proba:probability = proba[0][int(index)].asscalar()*100pred_label_name = label_names[int(index)]print("label name=%s,probability=%f"%(pred_label_name,probability))

6.到此，人脸特征提取就完成了

人脸识别数据集精度验证：这个是固定阈值，测精度不太合适

# !/usr/bin/env python
# -*- coding: utf-8 -*-import os
import time
import math
import mxnet as mx
import cv2
import numpy as np
from collections import namedtupledef str_expansion(sstring):"""将“数字型”字符扩展"""ssize = len(sstring)if ssize == 1:sstring = "00" + sstringelif ssize == 2:sstring = "0" + sstringreturn sstringdef str_expansion_lfw(sstring):""":param sstring: 将“数字型”字符扩展，针对lfw数据集"""ssize = len(sstring)if ssize == 1:sstring = "000" + sstringelif ssize == 2:sstring = "00" + sstringelif ssize == 3:sstring = "0" + sstringreturn sstringdef cos_similarity(x, y):length = len(x)x_squre = 0y_squre = 0xy_inner_product = 0for i in range(length):x_squre += x[i] * x[i]y_squre += y[i] * y[i]xy_inner_product += x[i] * y[i]print(x_squre)print(y_squre)return xy_inner_product / (math.sqrt(x_squre) * math.sqrt(y_squre))def single_input(path):img = cv2.imread(path)# mxnet三通道输入是严格的RGB格式，而cv2.imread的默认是BGR格式，因此需要做一个转换img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)img = cv2.resize(img, (112, 112))img = img.transpose(2, 0, 1)# 添加一个第四维度并构建NDArrayimg = img[np.newaxis, :]array = mx.nd.array(img)return arrayif __name__ == "__main__":time_start = time.time()# verication_folder = "E:/face_detection/verification/IRimg_verification"verication_folder = r"E:\project\faceid\MobileFaceNet_Tutorial_Pytorch\data_set\LFW\lfw_align_112"file = r"E:\project\faceid\MobileFaceNet_Tutorial_Pytorch\data_set\LFW\pairs.txt"pair_list = []with open(file, 'r') as f:pair_list = f.readlines()time0 = time.time()sym, arg_params, aux_params = mx.model.load_checkpoint("mxnet/zwnwet_model", 0)print(sym)# print(arg_params)# print(aux_params)# 提取中间某层输出帖子特征层作为输出all_layers = sym.get_internals()print(all_layers)sym = all_layers['fc1_output']# 重建模型model = mx.mod.Module(symbol=sym, label_names=None)model.bind(for_training=False, data_shapes=[('data', (1, 3, 112, 112))])model.set_params(arg_params, aux_params)time1 = time.time()time_load = time1 - time0# print("模型加载和重建时间：{0}".format(time1 - time0))Batch = namedtuple("batch", ['data'])threshold = 0.6TP = 0TN = 0NUM_IR = 3280 / 2NUM_lfw = 6000 / 2time_frame = 0for item in pair_list:line = item.rstrip().split("\t")print(line)# 属于同一个人的图片对验证if len(line) == 3:time2 = time.time()folder = line[0]# img1 = str_expansion(str(int(line[1])-1)) + ".jpg"# img2 = str_expansion(str(int(line[2])-1)) + ".jpg"img1 = folder + '_' + str_expansion_lfw(line[1]) + ".jpg"img2 = folder + '_' + str_expansion_lfw(line[2]) + ".jpg"img1_path = os.path.join(verication_folder, folder, img1)img2_path = os.path.join(verication_folder, folder, img2)array1 = single_input(img1_path)array2 = single_input(img2_path)model.forward(Batch([array1]))vector1 = model.get_outputs()[0].asnumpy()vector1 = np.squeeze(vector1)model.forward(Batch([array2]))vector2 = model.get_outputs()[0].asnumpy()vector2 = np.squeeze(vector2)similarity = cos_similarity(vector1, vector2)time3 = time.time()time_frame = time3 - time2 + time_frameprint(similarity, "\n")if similarity >= threshold:TP += 1# 属于不同的人的图片对验证if len(line) == 4:time4 = time.time()folder1 = line[0]# img1 = str_expansion(str(int(line[1])-1)) + ".jpg"img1 = folder1 + "_" + str_expansion_lfw(line[1]) + ".jpg"folder2 = line[2]# img2 = str_expansion(str(int(line[3])-1)) + ".jpg"img2 = folder2 + "_" + str_expansion_lfw(line[3]) + ".jpg"img1_path = os.path.join(verication_folder, folder1, img1)img2_path = os.path.join(verication_folder, folder2, img2)array1 = single_input(img1_path)array2 = single_input(img2_path)model.forward(Batch([array1]))vector1 = model.get_outputs()[0].asnumpy()vector1 = np.squeeze(vector1)model.forward(Batch([array2]))vector2 = model.get_outputs()[0].asnumpy()vector2 = np.squeeze(vector2)similarity = cos_similarity(vector1, vector2)time5 = time.time()time_frame = time5 - time4 + time_frameprint(similarity, "\n")if similarity < threshold:TN += 1print("检真正确率：{0:.4f}".format(TP / NUM_lfw))print("拒假正确率：{0:.4f}".format(TN / NUM_lfw))print("模型加载时间： {0:.3f}s".format(time_load))print("检测一帧平均时间： {0:.3f}s".format(time_frame / (NUM_lfw * 2)))time_end = time.time()print("程序运行时间： {0:.2f}min".format((time_end - time_start) / 60))

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