标签：openface format clf args 源码 time import 理解 classifier
OpenFace（0）
《OpenFace: A general-purpose face recognition library with mobile applications》
原文地址：http://elijah.cs.cmu.edu/DOCS/CMU-CS-16-118.pdf
源码地址：https://github.com/cmusatyalab/openface
demos/classifier.py
执行命令
./demos/classifier.py infer ./generated-embeddings/classifier.pkl siyitest1.jpg
main函数
#!/usr/bin/env python2
# coding=utf-8
#
# Example to classify faces.
# Brandon Amos
# 2015/10/11
#
# Copyright 2015-2016 Carnegie Mellon University
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import time
start = time.time()
import argparse
import cv2
import os
import pickle
import sys
from operator import itemgetter
import numpy as np
np.set_printoptions(precision=2)
import pandas as pd
import openface
from sklearn.pipeline import Pipeline
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.preprocessing import LabelEncoder
from sklearn.svm import SVC
from sklearn.grid_search import GridSearchCV
from sklearn.mixture import GMM
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
fileDir = os.path.dirname(os.path.realpath(__file__)) # 获取的__file__所在脚本的路径：./demos
modelDir = os.path.join(fileDir, '..', 'models') # ../models
dlibModelDir = os.path.join(modelDir, 'dlib') # ../models/dlib
openfaceModelDir = os.path.join(modelDir, 'openface') # ../models/openface
def getRep(imgPath, multiple=False):
start = time.time()
bgrImg = cv2.imread(imgPath)
if bgrImg is None:
raise Exception("Unable to load image: {}".format(imgPath))
rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)
if args.verbose:
print(" + Original size: {}".format(rgbImg.shape))
if args.verbose:
print("Loading the image took {} seconds.".format(time.time() - start))
start = time.time()
if multiple:
bbs = align.getAllFaceBoundingBoxes(rgbImg)
else:
bb1 = align.getLargestFaceBoundingBox(rgbImg)
bbs = [bb1]
if len(bbs) == 0 or (not multiple and bb1 is None):
raise Exception("Unable to find a face: {}".format(imgPath))
if args.verbose:
print("Face detection took {} seconds.".format(time.time() - start))
reps = []
for bb in bbs:
start = time.time()
alignedFace = align.align(
args.imgDim,
rgbImg,
bb,
landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
if alignedFace is None:
raise Exception("Unable to align image: {}".format(imgPath))
if args.verbose:
print("Alignment took {} seconds.".format(time.time() - start))
print("This bbox is centered at {}, {}".format(bb.center().x, bb.center().y))
start = time.time()
rep = net.forward(alignedFace)
if args.verbose:
print("Neural network forward pass took {} seconds.".format(
time.time() - start))
reps.append((bb.center().x, rep))
sreps = sorted(reps, key=lambda x: x[0])
return sreps
def train(args):
print("Loading embeddings.")
fname = "{}/labels.csv".format(args.workDir)
labels = pd.read_csv(fname, header=None).as_matrix()[:, 1]
labels = map(itemgetter(1),
map(os.path.split,
map(os.path.dirname, labels))) # Get the directory.
fname = "{}/reps.csv".format(args.workDir)
embeddings = pd.read_csv(fname, header=None).as_matrix()
le = LabelEncoder().fit(labels)
labelsNum = le.transform(labels)
nClasses = len(le.classes_)
print("Training for {} classes.".format(nClasses))
if args.classifier == 'LinearSvm':
clf = SVC(C=1, kernel='linear', probability=True)
elif args.classifier == 'GridSearchSvm':
print("""
Warning: In our experiences, using a grid search over SVM hyper-parameters only
gives marginally better performance than a linear SVM with C=1 and
is not worth the extra computations of performing a grid search.
""")
param_grid = [
{'C': [1, 10, 100, 1000],
'kernel': ['linear']},
{'C': [1, 10, 100, 1000],
'gamma': [0.001, 0.0001],
'kernel': ['rbf']}
]
clf = GridSearchCV(SVC(C=1, probability=True), param_grid, cv=5)
elif args.classifier == 'GMM': # Doesn't work best
clf = GMM(n_components=nClasses)
# ref:
# http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#example-classification-plot-classifier-comparison-py
elif args.classifier == 'RadialSvm': # Radial Basis Function kernel
# works better with C = 1 and gamma = 2
clf = SVC(C=1, kernel='rbf', probability=True, gamma=2)
elif args.classifier == 'DecisionTree': # Doesn't work best
clf = DecisionTreeClassifier(max_depth=20)
elif args.classifier == 'GaussianNB':
clf = GaussianNB()
# ref: https://jessesw.com/Deep-Learning/
elif args.classifier == 'DBN':
from nolearn.dbn import DBN
clf = DBN([embeddings.shape[1], 500, labelsNum[-1:][0] + 1], # i/p nodes, hidden nodes, o/p nodes
learn_rates=0.3,
# Smaller steps mean a possibly more accurate result, but the
# training will take longer
learn_rate_decays=0.9,
# a factor the initial learning rate will be multiplied by
# after each iteration of the training
epochs=300, # no of iternation
# dropouts = 0.25, # Express the percentage of nodes that
# will be randomly dropped as a decimal.
verbose=1)
if args.ldaDim > 0:
clf_final = clf
clf = Pipeline([('lda', LDA(n_components=args.ldaDim)),
('clf', clf_final)])
clf.fit(embeddings, labelsNum)
fName = "{}/classifier.pkl".format(args.workDir)
print("Saving classifier to '{}'".format(fName))
with open(fName, 'w') as f:
pickle.dump((le, clf), f)
def infer(args, multiple=False):
with open(args.classifierModel, 'rb') as f:
if sys.version_info[0] < 3:
(le, clf) = pickle.load(f) # 从f中读取一个字符串,并将它重构为原来的python对象。
else:
(le, clf) = pickle.load(f, encoding='latin1')
for img in args.imgs:
print("\n=== {} ===".format(img))
reps = getRep(img, multiple)
if len(reps) > 1:
print("List of faces in image from left to right")
for r in reps:
rep = r[1].reshape(1, -1)
bbx = r[0]
start = time.time()
predictions = clf.predict_proba(rep).ravel()
maxI = np.argmax(predictions)
person = le.inverse_transform(maxI)
confidence = predictions[maxI]
if args.verbose:
print("Prediction took {} seconds.".format(time.time() - start))
if multiple:
print("Predict {} @ x={} with {:.2f} confidence.".format(person.decode('utf-8'), bbx,
confidence))
else:
print("Predict {} with {:.2f} confidence.".format(person.decode('utf-8'), confidence))
if isinstance(clf, GMM):
dist = np.linalg.norm(rep - clf.means_[maxI])
print(" + Distance from the mean: {}".format(dist))
if __name__ == '__main__':
# 执行命令./demos/classifier.py infer ./generated-embeddings/classifier.pkl test1.jpg
# 第一步是创建一个 ArgumentParser 对象。ArgumentParser 对象包含将命令行解析成 Python 数据类型所需的全部信息。
parser = argparse.ArgumentParser()
# 调用add_argument添加参数
# 人脸预测器的路径
parser.add_argument(
'--dlibFacePredictor', # 命名
type=str,
help="Path to dlib's face predictor.",
# default 当参数未在命令行中出现时使用的值
default=os.path.join(
dlibModelDir,
"shape_predictor_68_face_landmarks.dat"))
# 网络模型路径
parser.add_argument(
'--networkModel',
type=str,
help="Path to Torch network model.",
default=os.path.join(
openfaceModelDir,
'nn4.small2.v1.t7'))
# 图片维度
parser.add_argument('--imgDim', type=int,
help="Default image dimension.", default=96)
# action - 当参数在命令行中出现时使用的动作基本类型。
parser.add_argument('--cuda', action='store_true')
# 添加--verbose标签，标签别名可以为-v，这里action的意思是当读取的参数中出现--verbose/-v的时候
# 参数字典的verbose建对应的值为True，而help参数用于描述--verbose参数的用途或意义。
# 将变量以标签-值的字典形式存入args字典
parser.add_argument('--verbose', action='store_true')
# dest - 被添加到 parse_args() 所返回对象上的属性名。
subparsers = parser.add_subparsers(dest='mode', help="Mode")
trainParser = subparsers.add_parser('train',
help="Train a new classifier.")
trainParser.add_argument('--ldaDim', type=int, default=-1)
# 分类器
trainParser.add_argument(
'--classifier',
type=str,
choices=[
'LinearSvm',
'GridSearchSvm',
'GMM',
'RadialSvm',
'DecisionTree',
'GaussianNB',
'DBN'],
help='The type of classifier to use.', # 要使用的分类器的类型
default='LinearSvm')
trainParser.add_argument(
'workDir',
type=str,
help="The input work directory containing 'reps.csv' and 'labels.csv'. Obtained from aligning a directory with 'align-dlib' and getting the representations with 'batch-represent'.")
inferParser = subparsers.add_parser(
'infer', help='Predict who an image contains from a trained classifier.')
inferParser.add_argument(
'classifierModel',
type=str,
help='The Python pickle representing the classifier. This is NOT the Torch network model, which can be set with --networkModel.')
inferParser.add_argument('imgs', type=str, nargs='+',
help="Input image.")
inferParser.add_argument('--multi', help="Infer multiple faces in image",
action="store_true")
# 第三步使用 parse_args() 解析添加的参数
args = parser.parse_args()
# 运行脚本后面跟了–verbose/-v的时候会输出下面的内容
if args.verbose:
print("Argument parsing and import libraries took {} seconds.".format(
time.time() - start))
# 运行脚本后面跟了infer且分类器模型以.t7结尾，报异常
if args.mode == 'infer' and args.classifierModel.endswith(".t7"):
raise Exception("""
Torch network model passed as the classification model,
which should be a Python pickle (.pkl)
See the documentation for the distinction between the Torch
network and classification models:
http://cmusatyalab.github.io/openface/demo-3-classifier/
http://cmusatyalab.github.io/openface/training-new-models/
Use `--networkModel` to set a non-standard Torch network model.""")
start = time.time()
align = openface.AlignDlib(args.dlibFacePredictor)
net = openface.TorchNeuralNet(args.networkModel, imgDim=args.imgDim,
cuda=args.cuda)
if args.verbose:
print("Loading the dlib and OpenFace models took {} seconds.".format(
time.time() - start))
start = time.time()
if args.mode == 'train': # 训练
train(args)
elif args.mode == 'infer': # 推断
infer(args, args.multi)
标签：openface,format,clf,args,源码,time,import,理解,classifier 来源： https://blog.csdn.net/qq_34708977/article/details/109522895
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