OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products.
Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code.
Face recognition based on YOLO, You Only Look Once: Unified, Real-Time Object Detection.
自YOLO算法提出以来，至今已经发展到了v3，性能、集成性等都得到了极大的提升，用YOLO来实现人脸识别算法，其特点是模型训练参数较少，可移植并且实时性很高。目前为止，集成现有技术实现一个基于YOLO算法的人脸识别系统是一项很有挑战性的工作。近几年来，目标检测算法取得了很大的突破。比较流行的算法可以分为两类，一类是基于Region Proposal的R-CNN系算法（R-CNN，Fast R-CNN, Faster R-CNN），它们是two-stage的，需要先使用启发式方法（selective search）或者CNN网络（RPN）产生Region Proposal，然后再在Region Proposal上做分类与回归。另一类是Yolo，SSD这类one-stage算法，其仅仅使用一个CNN网络直接预测不同目标的类别与位置。第一类方法准确度高，但是速度慢，第二类算法速度快，但是准确性较低。本文将介绍Yolo算法，其全称是You Only Look Once: Unified, Real-Time Object Detection，You Only Look Once说的是只需要一次CNN运算，Unified指的是这是一个统一的框架，提供end-to-end的预测，而Real-Time体现是Yolo算法速度快。这里我们谈的是Yolo-v1版本算法，其性能差于后来的SSD算法的，但是Yolo后来也继续进行改进，产生了Yolo9000算法。本文主要讲述Yolo-v1算法的原理。
This paper, Through-wall Human Pose Estimation Using Radio Signals, is extracted from a paper in CVPR2018 published by Dina Katabi, a famous team in the wireless communication field, and demonstrates accurate human pose estimation through walls and occlusions.
In this paper, the system RF-pose designed by wireless signals can accurately predict human activities, and it also has very accurate prediction results when the environment is blocked by walls and other obstacles.
Tensorflow中一些简单但是容易忘记的：
import tensorflow as tf
a = tf.matmul(x,w1) #用来表示矩阵的乘法操作
weight = tf.Variable(tf.random_normal([2,3],stddev = 2))
bias = tf.Variable(tf.zeros([3]))
#偏置项
Taking cifar10 as an example,
DATA_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'
filename = DATA_URL.split('/')[-1]
filepath = os.path.join(path, filename)
#output: path\filename