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  • A Comparison of Affine Region Detectors.pdf K. MIKOLAJCZYK University of Oxford论文
  • Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is ...
  • Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by pubhshing worldwide in Oxford ...
  • New York: Oxford university press, 1978, 286 pp., [dollar]11.95 Book Reviews 137 difficult to accept. Children have been labeled and placed in learning disabled classes for the past 15 years. ...
  • 代币化:房地产投资的未来 Tokenisation :the futuire of real estate investment -University of Oxford Research .pdf
  • 房地产科技3.0 –突破性的报告展望房地产的未来 -PropTech 3.0 the future of real estate - University of Oxford Research.pdf
  • Books on big data tend to fall into one of two categories: either they offer no explanation as to how things actually work or they are highly mathematical textbooks suitable only for graduate students...
  • New York: Oxford University Press, 234 pp., [dollar]35.00 Book Reviews 87 REFERENCES ELLIS, A. (1977). Anger: How to iive with and without it. Secaucus, NJ: Citadel Press. RULE, B., & NESDALE,...
  • this one is reasonable, since it is under the consideration of the engineering science

    在这里插入图片描述

    this one is reasonable, since it is under the consideration of the engineering science and my supervisor is in computer science department.

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  • New York: Oxford University Press, 474 pp., [dollar]21.50 (paper) Book Reviews 219 SPREEN, O., TUPPER, D., RISSER, A., TUOKKO, H., & EDGELL, D. (1984). Human developmental neuropsychology. New ...
  • Computer simulation of liquids
  • 'The strength of the book is that examples from classroom research are used to demonstrate how meta-language about grammar and meaning can be included, rather than added separately, in a content ...
  • The Oxford Dictionary of English (ODE) is a single-volume English dictionary published by Oxford University Press, first published in 1998 as The New Oxford Dictionary of English (NODE). The word "new...
  • Oxford Modern English Grammar ,Department of English Language and Literature University College London
  • Oxford University Press, 2015 目录: Preface Features of the Book Detailed Contents SECTION I Basic Concepts of Algorithms Chapter 1 Introduction to Algorithms Chapter 2 Growth of Functions Chapter 3 ...
  • Oxford NLP lecture

    2017-11-17 15:23:37
    This repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an advanced course ...
  • 2021年THE计算机科学专业世界排名: Rank University Overall Score 1 University of Oxford 92.9 2 Stanford University 91.7 3 Massachusetts Institute of Technology 91.1 4 ETH Zurich 90.5 5 Carnegie Mellon...

    2021年THE泰晤士高等教育计算机科学Computer Science专业世界大学排名公布,加州大学尔湾分校UCI计算机科学世界排名第88位,加州大学尔湾分校UCI计算机科学专业实力怎么样呢?下面美英港新留学介绍加州大学尔湾分校UCI计算机科学专业培养计划,加州大学尔湾分校UCI计算机科学专业研究生申请及2021年THE计算机科学专业世界排名详细榜单。

    f17bd3847aceea51156acf6294706f69.png

    计算机科学硕士

    加州欧文大学计算机科学包括计算机系统的设计,分析和实现的理论和实践方面,以及计算应用于许多其他领域。核心研究领域包括:人工智能和机器学习,生物信息学,计算机架构,嵌入式系统,图形和计算机视觉,数据库系统和信息管理,多媒体和游戏,网络和分布式系统,编程语言和编译器,安全,隐私和密码学,算法的设计和分析,以及科学计算。硕士和博士计算机科学(CS)学位是广泛而灵活的课程,为学生提供深入研究生学习和前沿研究的机会,涵盖计算机科学的广泛主题。核心研究领域包括人工智能和机器学习、生物信息学、计算机体系结构、嵌入式系统、图形和视觉计算、数据库和信息管理、多媒体、网络和分布式系统、编程语言和编译器、安全和密码学、算法设计和分析,科学计算和无处不在的计算。

    2021年THE计算机科学专业世界排名:

    Rank

    University

    Overall Score

    1

    University of Oxford

    92.9

    2

    Stanford University

    91.7

    3

    Massachusetts Institute of Technology

    91.1

    4

    ETH Zurich

    90.5

    5

    Carnegie Mellon University

    89.8

    6

    University of Cambridge

    89.6

    7

    Harvard University

    88.1

    8

    National University of Singapore

    88

    9

    University of California, Berkeley

    87.7

    10

    Imperial College London

    87.1

    11

    Princeton University

    86.6

    12

    Tsinghua University

    86.4

    13

    Georgia Institute of Technology

    86.3

    14

    Cornell University

    85.3

    14

    Technical University of Munich

    85.3

    16

    University of California, Los Angeles

    85.2

    16

    École Polytechnique Fédérale de Lausanne

    85.2

    18

    Nanyang Technological University, Singapore

    84.4

    18

    UCL

    84.4

    20

    University of Illinois at Urbana-Champaign

    83.4

    21

    University of Washington

    83

    22

    University of Edinburgh

    82.8

    23

    University of Toronto

    82.7

    24

    Columbia University

    82.6

    25

    California Institute of Technology

    82.1

    26

    University of Michigan-Ann Arbor

    81.6

    27

    Johns Hopkins University

    81.1

    28

    Peking University

    81

    29

    Yale University

    80.3

    30

    University of Texas at Austin

    79.2

    31

    The Hong Kong University of Science and Technology

    79.1

    32

    The University of Chicago

    78.3

    32

    University of Pennsylvania

    78.3

    34

    University of Montreal

    77

    35

    Shanghai Jiao Tong University

    76.7

    36

    New York University

    75.8

    37

    University of California, San Diego

    75.2

    38

    University of Southern California

    74.3

    39

    University of Hong Kong

    73.3

    40

    Zhejiang University

    72.2

    41

    The University of Tokyo

    71.7

    42

    Korea Advanced Institute of Science and Technology (KAIST)

    71.5

    43

    Chinese University of Hong Kong

    71.4

    44

    University of Wisconsin-Madison

    71

    45

    Paris Sciences et Lettres – PSL Research University Paris

    70.3

    45

    University of Waterloo

    70.3

    47

    Seoul National University

    69.7

    48

    University of British Columbia

    69.2

    49

    University of Maryland, College Park

    69

    50

    KU Leuven

    66.9

    展开全文
  • PacBio vs. Oxford Nanopore sequencing

    千次阅读 2019-12-08 23:12:15
    Oxford Nanoporesequencing PacBio与牛津纳米孔测序 发表于2017年6月16日通过Bhagyashree Birla 由太平洋生物科学公司和牛津纳米孔公司开发的长读测序技术克服了研究人员短读所面临的许多限制。读可改善从头...

    PacBio vs. Oxford Nanopore sequencing

    PacBio与牛津纳米孔测序

    由太平洋生物科学公司和牛津纳米孔公司开发的长读测序技术克服了研究人员短读所面临的许多限制。读可改善从头组装,转录组分析(基因同工型鉴定),并在宏基因组学领域发挥重要作用。当组装包括大片段重复区域的基因组时,较长的读段也很有用。

    当前,有两个长读测序平台。为了帮助研究人员选择哪种平台在其应用中具有更大的实用性,我们比较了PacBio和Oxford Nanopore提供的整体仪器规格以及下一代测序领域中已发布的应用。

    一个牛津纳米孔指责为用户提供了一个奴才/ PromethIon仪器,耗材的启动包,某些数据服务和基于社区的支持接入费

    *数据不足

    尽管与短读Illumina或离子测序相比,PacBio和Oxford纳米孔均产生更长的读取,但是PacBio和Oxford纳米孔测序仪的较高错误率仍然是需要解决的问题。PacBio会多次读取一个分子以生成高质量的共有数据,而Oxford Nanopore只能对一个分子进行两次测序。结果,与牛津纳米孔相比,PacBio产生的错误率更低。PacBio在诸如发现转录组复杂性和灵敏鉴定同工型等应用方面的总体性能稍好一些。另一方面,MinION可以提供更高的通量,因为纳米孔可以同时对多个分子进行测序。因此,它最适合需要大量数据的应用程序9

    由于长的阅读可以提供大型的支架,从头组装是PacBio测序5的主要应用之一。尽管PacBio数据的错误率高于短读Illumina或离子测序的错误率,但增加覆盖率混合测序可以大大提高基因组装配的准确性。PacBio测序已成功用于完成Autoethanogenum 梭状芽胞杆菌  DSM 10061(第III类)的100个重叠群的基因组图  ,这是就重复含量和重复类型而言最复杂的基因组分类。它的GC含量为31.1%,并包含重复序列,原噬菌体和9个rRNA基因操纵子。使用单个PacBio库并用两个SMRT细胞对其进行测序,即可从头组装整个基因组  与一个重叠群。当短读Illumina或离子测序单独用于同一基因组时,需要> 22个重叠群,并且每个装配体至少包含四个折叠的重复区域,PacBio装配体没有10个。

    与使用Illumina测序仪12进行组装相比,PacBio测序还用于组装委陵菜11,酿酒酵母拟南芥黑果果蝇的叶绿体基因组。

    与Sanger测序相比,PCR产品的PacBio测序可通过缩小缺口并通过发夹结构和高GC含量区域进行测序来提高当前草图基因组的质量13。

    太平洋生物科学公司已开发出一种用于转录本测序的方案Iso-Seq。这包括库构建,大小选择,排序数据收集和数据处理。Iso-Seq允许直接测序高达10 kb的转录本,而无需使用参考基因​​组。Iso-Seq已用于表征参与血细胞成分14形成的可变剪接事件。这对于解释导致遗传性疾病和血液癌症的突变的影响至关重要,并且可以应用于设计策略以促进移植和再生医学。

    PacBio测序的另一个主要应用是表观遗传学研究。最近的研究表明,通过PacBio测序15直接检测单个分子中的修饰,可以促进对先前无法检测到的基因组DNA修饰(如m 6 A和m 4 C)中细胞间异质性的研究。

    与PacBio相比,Oxford Nanopore MinION体积小(USB拇指驱动器大小),价格适中,利用简单的文库制备且可现场携带16。这在病毒爆发等情况下很有用,在这种情况下可以使用MinIONS设置移动诊断实验室。在诸如巴西和非洲部分偏远地区,这些地方存在与运送样品进行测序相关的后勤问题,MinION可以向科研人员提供即时和实时数据。MinION最著名的临床用途是在西非病毒爆发期间现场对埃博拉病毒样本进行分析17,18。

    MinION测序仪的低成本测序和便携性也使其成为教学的有用工具。它已被用于向学生提供动手实践,最近在哥伦比亚大学和加利福尼亚大学圣克鲁斯分校,每个学生都在其中进行了自己的MinION测序19。

    MinION最雄心勃勃的应用可能是它有潜力检测和识别载人航天飞行中的细菌和病毒。在概念验证实验中,Castro-Wallace等人。展示了λ噬菌体基因组,大肠杆菌基因组和小鼠线粒体基因组的成功测序和从头组装。他们观察到,在国际空间站上生成的序列数据的质量以及在地球22上并行执行的控制实验中,都没有显着差异。

    最近,Oxford Nanopore开发了台式仪器PromethION,该仪器可提供高通量测序,并且在设计上是模块化的。它包含48个流通池,可以单独运行或并行运行。PromethION流通池每个包含3000个通道,并产生多达40 Gb的数据。

    Posted on June 16, 2017 by Bhagyashree Birla

    Long-read sequencing developed by Pacific Biosciences and Oxford Nanopore overcome many of the limitations researchers face with short reads. Long reads improve de novo assembly, transcriptome analysis (gene isoform identification) and play an important role in the field of metagenomics. Longer reads are also useful when assembling genomes that include large stretches of repetitive regions.

    Currently, there are two long read sequencing platforms. To help a researcher choose between which platform has greater utility for their application, we compare overall instrument specifications offered by PacBio and Oxford Nanopore, and published applications in the next-generation sequencing space.

    a Oxford Nanopore charges an access fee that gives users one MinION/PromethIon instrument, a starter pack of consumables, certain data services, and community-based support

    * Insufficient data

    Although both PacBio and Oxford Nanopore generate longer reads compared to short read Illumina or Ion sequencing, the higher error rate of both the PacBio and Oxford Nanopore sequencers remain an issue needs addressing. Whereas PacBio reads a molecule multiple times to generate high-quality consensus data, Oxford Nanopore can only sequence a molecule twice. As a result, PacBio generates data with lower error rates compared to Oxford Nanopore. PacBio has a slightly better overall performance for applications such as the discovery of transcriptome complexity and sensitive identification of isoforms. On the other hand, MinION provides higher throughput as nanopores can sequence multiple molecules simultaneously. Hence, it is best suited for applications that require a larger amount of data9

    As long reads can provide large scaffolds, de novo assembly is one of the main applications of PacBio sequencing5. Though the error rate of PacBio data is higher than that of short read Illumina or Ion sequencing, increased coverage or hybrid sequencing can greatly improve the accuracy of genome assembly. PacBio sequencing has been successfully used to finish the 100-contig draft genome of Clostridium autoethanogenum DSM 10061, a Class III, the most complex genome classification in terms of repeat content and repeat type. It has a 31.1% GC content and contains repeats, prophage, and nine copies of rRNA gene operons. Using a single PacBio library and sequencing it with two SMRT cells, an entire genome can be assembled de novo with a single contig. When short read Illumina or Ion sequencing was used alone with the same genome, >22 contigs were needed, and each of the assemblies contained at least four collapsed repeat regions, PacBio assemblies had none10.

    PacBio sequencing has also been used to assemble the chloroplast genome of Potentilla micrantha11, Saccharomyces cerevisiaeAradopsis thaliana and Drosophila melanogaster using fewer contigs and CPU time for assembly compared to assemblies using Illumina sequencers12.

    PacBio sequencing of PCR products can be used to improve the quality of current draft genomes by closing gaps and sequencing through hairpin structures and areas of high GC content more efficiently than Sanger sequencing13.

    Pacific Biosciences has developed a protocol, Iso-Seq, for transcript sequencing. This includes library construction, size selection, sequencing data collection, and data processing. Iso-Seq allows direct sequencing of transcripts up to 10 kb without the use of a reference genome. Iso-Seq has been used to characterize alternative splicing events involved in the formation of blood cellular components14. This is essential for interpreting the effects of mutations leading to inherited disorders and blood cancers, and can be applied to design strategies to advance transplantation and regenerative medicine.

    Another major application of PacBio sequencing is in epigenetics research. Recent studies demonstrate that investigation of intercellular heterogeneity in previously undetectable genome DNA modifications (such as m6A and m4C) is facilitated by the direct detection of modifications in single molecules by PacBio sequencing15.

    Compared to PacBio, the Oxford Nanopore MinION is small (size of a USB thumb drive), affordable, utilizes a simple library prep and is field portable16. This is useful in situations such as a virus outbreak where a mobile diagnostic laboratory can be set up using MinIONS. In remote regions such as parts of Brazil and Africa where there are logistical issues associated with shipping samples for sequencing, MinION can provide immediate and real-time data to scientific investigators. The most notable clinical use of MinION has been the analysis of Ebola samples on-site during the viral outbreak in West Africa17,18.

    The low cost of sequencing and portability of the MinION sequencer also make it a useful tool for teaching. It has been used to provide hands-on experience to students, most recently at Columbia University and the University of California Santa Cruz, where every student performed their own MinION sequencing19.

    Perhaps the most ambitious MinION application is its potential to detect and identify bacteria and viruses on manned space flights. In a proof-of-concept experiment, Castro-Wallace et al. demonstrated successful sequencing and de novo assembly of a lambda phage genome, an E. coli genome, and a mouse mitochondrial genome. They observed that there was no significant difference in the quality of sequence data generated on the International Space Station and in control experiments that were performed in parallel on Earth22.

    Recently, Oxford Nanopore developed a bench-top instrument, PromethION, that provides high-throughput sequencing and is modular in design. It contains 48 flow cells that can be run individually or in parallel. The PromethION flow cells contain 3000 channels each, and produce up to 40 Gb of data.

     

    References:

    1. Pacific Biosciences – AllSeq. Available at: http://allseq.com/knowledge-bank/sequencing-platforms/pacific-biosciences/.
    2. Jain, M., Olsen, H. E., Paten, B. & Akeson, M. The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biol. 17, 239 (2016).
    3. Lu, H., Giordano, F. & Ning, Z. Oxford Nanopore MinION Sequencing and Genome Assembly. Genomics. Proteomics Bioinformatics 14, 265–279 (2016).
    4. Jain, M. et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. bioRxiv (2017).
    5. Jain, M. et al. MinION Analysis and Reference Consortium: Phase 2 data release and analysis of R9.0 chemistry [version 1; referees: awaiting peer review]. F1000Research 6, (2017).
    6. Rhoads, A. & Au, K. F. PacBio Sequencing and Its Applications. Genomics, Proteomics Bioinforma. 13, 278–289 (2015).
    7. MinION. Available at: https://nanoporetech.com/products/minion.
    8. PromethION Early Access Programme. Available at: https://nanoporetech.com/community/promethion-early-access-programme.
    9. Oxford Nanopore in 2016. Available at: http://blog.booleanbiotech.com/nanopore_2016.html.
    10. Weirather, J. L. et al. Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis. F1000Research 6, 100 (2017).
    11. Brown, S. D. et al. Comparison of single-molecule sequencing and hybrid approaches for finishing the genome of Clostridium autoethanogenum and analysis of CRISPR systems in industrial relevant Clostridia. Biotechnol. Biofuels 7, 40 (2014).
    12. Ferrarini, M. et al. An evaluation of the PacBio RS platform for sequencing and de novo assembly of a chloroplast genome. BMC Genomics 14, 670 (2013).
    13. Berlin, K. et al. Assembling large genomes with single-molecule sequencing and locality-sensitive hashing. Nat Biotech 33, 623–630 (2015).
    14. Zhang, X. et al. Improving genome assemblies by sequencing PCR products with PacBio. Biotechniques 53, 61–62 (2012).
    15. Chen, L. et al. Transcriptional diversity during lineage commitment of human blood progenitors. Science (80-. ). 345, (2014).
    16. Feng, Z., Li, J., Zhang, J.-R. & Zhang, X. qDNAmod: a statistical model-based tool to reveal intercellular heterogeneity of DNA modification from SMRT sequencing data. Nucleic Acids Res. 42, 13488–13499 (2014).
    17. Jain, M., Olsen, H. E., Paten, B. & Akeson, M. Erratum to: The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biol. 17, 256 (2016).
    18. Quick, J. et al. Real-time, portable genome sequencing for Ebola surveillance. Nature 530, 228–232 (2016).
    19. Hoenen, T. et al. Nanopore sequencing as a rapidly deployable Ebola outbreak tool. Emerg. Infect. Dis. 22, 331–334 (2016).
    20. Citizen Sequencers: Taking Oxford Nanopore’s MinION to the Classroom and Beyond – Bio-IT World. Available at: http://www.bio-itworld.com/2015/12/9/citizen-sequencers-taking-oxford-nanopores-minion-classroom-beyond.html.
    21. Castro-Wallace, S. L. et al. Nanopore DNA Sequencing and Genome Assembly on the International Space Station. bioRxiv (2016).
    展开全文
  • https://oxford-robotics-institute.github.io/radar-robotcar-dataset/documentation 简介 雷达是 Navtech CTS350-X 调频连续波 (FMCW) 扫描雷达,在所使用的配置中提供 4.38 cm 的距离分辨率和 0.9 度的旋转分辨率...

    数据集来源:https://oxford-robotics-institute.github.io/radar-robotcar-dataset/documentation

    简介

    雷达是 Navtech CTS350-X 调频连续波 (FMCW) 扫描雷达,在所使用的配置中提供 4.38 cm 的距离分辨率和 0.9 度的旋转分辨率,范围高达 163 m,同时提供对天气条件的稳健性这可能会给其他传感器模式带来麻烦。在其他配置中,Navtech CTS350-X 可以提供超过 650 m 的范围和更高的旋转速度。然而,对于城市环境,更高的分辨率是更可取的。

    车辆传感器示意图

    车辆传感器所在位置示意图如下
    车顶一个Bumblebee XB3相机,一个毫米波雷达CTS350,雷达左右各一个HDL-32E激光雷达;
    车前后各一个LMS-151激光雷达,车尾三个Grasshopper相机(紫色),一个SPAN-CPT GPS。
    在这里插入图片描述Cameras:
    1 x Point Grey Bumblebee XB3
    3 x Point Grey Grasshopper2
    LIDAR:
    2 x SICK LMS-151 2D LIDAR
    GPS/INS:
    1 x NovAtel SPAN-CPT ALIGN inertial and GPS navigation system
    As well as additionally collecting:
    Radar:
    1 x Navtech CTS350-X - Mounted in the centre of the roof aligned with the vehicles axes.
    LIDAR
    2 x Velodyne HDL-32E - Mounted to the left and right of the Navtech CTS350-X radar.

    雷达数据集介绍

    雷达数据以极坐标格式的无损 PNG 文件形式发布,每行代表每个方位角的传感器读数(在程序中azimuth为一个(400,1)的矩阵),每列代表特定范围内的原始功率返回(在程序中fft_data是(400,3768,1))。在使用的配置中,每次扫描有 400 个方位角(行)和 3768 个距离区间(列)。这些文件的结构为radar/.png,其中 是捕获的起始UNIX 时间戳,以微秒为单位。
    为了向用户提供他们可能需要的所有原始数据,我们还将以下每个方位角元数据嵌入到 PNG 文件的前 11 列中:
    UNIX 时间戳在第 1-8 列中为 int64
    在第 9-10 列中作为 uint16 的扫描计数器 - 转换为具有方位角的角度(弧度)=扫描计数器 / 编码器大小 * 2 * pi,其中我们的配置编码器大小固定为 5600。
    在第 11 列中作为 uint8 的有效标志 - 很少会从 Navtech 雷达中丢弃极少量携带方位角回波的数据包。为了简化用户的使用,我们对相邻回波进行了插值,以便提供的每个雷达扫描具有 400 个方位角。如果这是不可取的,只需删除有效标志设置为零的任何行。

    雷达数据可视化程序

    学习重点:

    1. .png格式的雷达数据怎么读取信息?得到的信息有哪些?
    2. 了解opencv-python的应用
    3. 将极坐标雷达数据转换为笛卡尔坐标雷达数据,来可视化,原理是什么?

    官方给的程序代码

    ################################################################################
    #
    # Copyright (c) 2017 University of Oxford
    # Authors:
    #  Dan Barnes (dbarnes@robots.ox.ac.uk)
    #
    # This work is licensed under the Creative Commons
    # Attribution-NonCommercial-ShareAlike 4.0 International License.
    # To view a copy of this license, visit
    # http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to
    # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
    #
    ################################################################################
    
    import argparse
    import os
    from radar import load_radar, radar_polar_to_cartesian ##定义的radar.py中的函数,见下一段代码
    import numpy as np
    import cv2
    
    ##找需要显示的radar数据文件
    parser = argparse.ArgumentParser(description='Play back radar data from a given directory')
    
    parser.add_argument('dir', type=str, help='Directory containing radar data.')
    
    args = parser.parse_args()
    
    timestamps_path = os.path.join(os.path.join(args.dir, os.pardir, 'radar.timestamps'))
    if not os.path.isfile(timestamps_path):
        raise IOError("Could not find timestamps file")
    
    #设置笛卡尔可视化参数,分辨率以及显示的长宽
    # Cartesian Visualsation Setup
    # Resolution of the cartesian form of the radar scan in metres per pixel
    cart_resolution = .25
    # Cartesian visualisation size (used for both height and width)
    cart_pixel_width = 501  # pixels
    interpolate_crossover = True
    
    title = "Radar Visualisation Example"
    
    radar_timestamps = np.loadtxt(timestamps_path, delimiter=' ', usecols=[0], dtype=np.int64)
    for radar_timestamp in radar_timestamps:
        filename = os.path.join(args.dir, str(radar_timestamp) + '.png')
    
        if not os.path.isfile(filename):
            raise FileNotFoundError("Could not find radar example: {}".format(filename))
    
        timestamps, azimuths, valid, fft_data, radar_resolution = load_radar(filename)#radar.py中的内容
        cart_img = radar_polar_to_cartesian(azimuths, fft_data, radar_resolution, cart_resolution, cart_pixel_width,
                                            interpolate_crossover)#极坐标转换为雷达坐标
    
        # Combine polar and cartesian for visualisation
        # The raw polar data is resized to the height of the cartesian representation
        downsample_rate = 4#下采样率
        fft_data_vis = fft_data[:, ::downsample_rate]
        resize_factor = float(cart_img.shape[0]) / float(fft_data_vis.shape[0])
        fft_data_vis = cv2.resize(fft_data_vis, (0, 0), None, resize_factor, resize_factor)
        vis = cv2.hconcat((fft_data_vis, fft_data_vis[:, :10] * 0 + 1, cart_img))
    
        cv2.imshow(title, vis * 2.)  # The data is doubled to improve visualisation数据翻倍以改善可视化
        cv2.waitKey(1)
    
    ################################################################################
    #
    # Copyright (c) 2017 University of Oxford
    # Authors:
    #  Dan Barnes (dbarnes@robots.ox.ac.uk)
    #
    # This work is licensed under the Creative Commons
    # Attribution-NonCommercial-ShareAlike 4.0 International License.
    # To view a copy of this license, visit
    # http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to
    # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
    #
    ###############################################################################
    
    from typing import AnyStr, Tuple
    import numpy as np
    import cv2
    
    
    def load_radar(example_path: AnyStr) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, float]:
        """Decode a single Oxford Radar RobotCar Dataset radar example
        Args:
            example_path (AnyStr): Oxford Radar RobotCar Dataset Example png
        Returns:
            timestamps (np.ndarray): Timestamp for each azimuth in int64 (UNIX time)
            azimuths (np.ndarray): Rotation for each polar radar azimuth (radians)
            valid (np.ndarray) Mask of whether azimuth data is an original sensor reading or interpolated from adjacent
                azimuths
            fft_data (np.ndarray): Radar power readings along each azimuth
            radar_resolution (float): Resolution of the polar radar data (metres per pixel)
        """
        # Hard coded configuration to simplify parsing code
        radar_resolution = np.array([0.0432], np.float32)
        encoder_size = 5600
    
        raw_example_data = cv2.imread(example_path, cv2.IMREAD_GRAYSCALE)
        timestamps = raw_example_data[:, :8].copy().view(np.int64)
        azimuths = (raw_example_data[:, 8:10].copy().view(np.uint16) / float(encoder_size) * 2 * np.pi).astype(np.float32)
        #在第 9-10 列中作为 uint16 的扫描计数器 - 转换为具有方位角的角度(弧度)=扫描计数器 / 编码器大小 * 2 * pi,其中我们的配置编码器大小固定为 5600。
        valid = raw_example_data[:, 10:11] == 255
        #在第 11 列中作为 uint8 的有效标志 - 很少会从 Navtech 雷达中丢弃极少量携带方位角回波的数据包。为了简化用户的使用,我们对相邻回波进行了插值,以便提供的每个雷达扫描具有 400 个方位角。如果这是不可取的,只需删除有效标志设置为零的任何行。
        fft_data = raw_example_data[:, 11:].astype(np.float32)[:, :, np.newaxis] / 255.
        ##Polar radar power readings
    
        return timestamps, azimuths, valid, fft_data, radar_resolution
    
    
    def radar_polar_to_cartesian(azimuths: np.ndarray, fft_data: np.ndarray, radar_resolution: float,
                                 cart_resolution: float, cart_pixel_width: int, interpolate_crossover=True) -> np.ndarray:
        """Convert a polar radar scan to cartesian.
        Args:
            azimuths (np.ndarray): Rotation for each polar radar azimuth (radians)
            fft_data (np.ndarray): Polar radar power readings
            radar_resolution (float): Resolution of the polar radar data (metres per pixel)
            cart_resolution (float): Cartesian resolution (metres per pixel)
            cart_pixel_size (int): Width and height of the returned square cartesian output (pixels). Please see the Notes
                below for a full explanation of how this is used.
            interpolate_crossover (bool, optional): If true interpolates between the end and start  azimuth of the scan. In
                practice a scan before / after should be used but this prevents nan regions in the return cartesian form.
            interpolate_crossover (bool, optional): 如果为真,则在扫描的结束和开始方位角之间进行插值。在应该使用之前/之后的扫描练习,但这可以防止返回笛卡尔形式的 nan 区域。
    
        Returns:
            np.ndarray: Cartesian radar power readings
        Notes:
            After using the warping grid the output radar cartesian is defined as as follows where
            X and Y are the `real` world locations of the pixels in metres:
            #X 和 Y 是以米为单位的像素的“真实”世界位置:
             If 'cart_pixel_width' is odd:
             如果是奇数
                            +------ Y = -1 * cart_resolution (m)
                            |+----- Y =  0 (m) at centre pixel
                            ||+---- Y =  1 * cart_resolution (m)
                            |||+--- Y =  2 * cart_resolution (m)
                            |||| +- Y =  cart_pixel_width // 2 * cart_resolution (m) (at last pixel)
                            |||| +-----------+
                            vvvv             v
             +---------------+---------------+
             |               |               |
             |               |               |
             |               |               |
             |               |               |
             |               |               |
             |               |               |
             |               |               |
             +---------------+---------------+ <-- X = 0 (m) at centre pixel
             |               |               |
             |               |               |
             |               |               |
             |               |               |
             |               |               |
             |               |               |
             |               |               |
             +---------------+---------------+
             <------------------------------->
                 cart_pixel_width (pixels)
             If 'cart_pixel_width' is even:
             如果是偶数
                            +------ Y = -0.5 * cart_resolution (m)
                            |+----- Y =  0.5 * cart_resolution (m)
                            ||+---- Y =  1.5 * cart_resolution (m)
                            |||+--- Y =  2.5 * cart_resolution (m)
                            |||| +- Y =  (cart_pixel_width / 2 - 0.5) * cart_resolution (m) (at last pixel)
                            |||| +----------+
                            vvvv            v
             +------------------------------+
             |                              |
             |                              |
             |                              |
             |                              |
             |                              |
             |                              |
             |                              |
             |                              |
             |                              |
             |                              |
             |                              |
             |                              |
             |                              |
             |                              |
             |                              |
             +------------------------------+
             <------------------------------>
                 cart_pixel_width (pixels)
        """
        if (cart_pixel_width % 2) == 0:
            cart_min_range = (cart_pixel_width / 2 - 0.5) * cart_resolution
        else:
            cart_min_range = cart_pixel_width // 2 * cart_resolution
        coords = np.linspace(-cart_min_range, cart_min_range, cart_pixel_width, dtype=np.float32)
        Y, X = np.meshgrid(coords, -coords)
        sample_range = np.sqrt(Y * Y + X * X)
        sample_angle = np.arctan2(Y, X)
        sample_angle += (sample_angle < 0).astype(np.float32) * 2. * np.pi
    
        # Interpolate Radar Data Coordinates内插雷达数据坐标
        azimuth_step = azimuths[1] - azimuths[0]
        sample_u = (sample_range - radar_resolution / 2) / radar_resolution
        sample_v = (sample_angle - azimuths[0]) / azimuth_step
    
        # We clip the sample points to the minimum sensor reading range so that we
        # do not have undefined results in the centre of the image. In practice
        # this region is simply undefined.
        #我们将样本点剪辑到最小传感器读数范围,以便我们在图像的中心没有未定义的结果。
        sample_u[sample_u < 0] = 0
    
        if interpolate_crossover:
            fft_data = np.concatenate((fft_data[-1:], fft_data, fft_data[:1]), 0)
            sample_v = sample_v + 1
    
        polar_to_cart_warp = np.stack((sample_u, sample_v), -1)
        cart_img = np.expand_dims(cv2.remap(fft_data, polar_to_cart_warp, None, cv2.INTER_LINEAR), -1)
        return cart_img
    

    关于argparse

    详细见:
    https://zhuanlan.zhihu.com/p/56922793

    argsparse是python的命令行解析的标准模块,内置于python,不需要安装。这个库可以让我们直接在命令行中就可以向程序中传入参数并让程序运行。
    
    import argparse
    
    parser = argparse.ArgumentParser(description='命令行中传入一个数字')
    #type是要传入的参数的数据类型  help是该参数的提示信息
    parser.add_argument('integers', type=str, help='传入的数字')
    
    args = parser.parse_args()
    
    #获得传入的参数
    print(args)
    

    opencv-python基础知识

    基础知识学习:
    https://www.cnblogs.com/silence-cho/p/10926248.html

    一些主要函数:

    1. imread(img_path,flag) 读取图片,返回图片对象
      img_path: 图片的路径,即使路径错误也不会报错,但打印返回的图片对象为None
      flag:cv2.IMREAD_COLOR,读取彩色图片,图片透明性会被忽略,为默认参数,也可以传入1
      cv2.IMREAD_GRAYSCALE,按灰度模式读取图像,也可以传入0
      cv2.IMREAD_UNCHANGED,读取图像,包括其alpha通道,也可以传入-1
      这里用到的是黑白图像

    2. imshow(window_name,img):显示图片,窗口自适应图片大小
      window_name: 指定窗口的名字
      img:显示的图片对象
      可以指定多个窗口名称,显示多个图片

    waitKey(millseconds) 键盘绑定事件,阻塞监听键盘按键,返回一个数字(不同按键对应的数字不同)
    millseconds: 传入时间毫秒数,在该时间内等待键盘事件;传入0时,会一直等待键盘事件

    destroyAllWindows(window_name)
    window_name: 需要关闭的窗口名字,不传入时关闭所有窗口

    mmwave雷达毫米波基础知识

    系列培训视频,见B站:
    https://www.bilibili.com/video/BV1k4411C7aW?p=7
    mmwave
    FMCW表示调频连续波
    该雷达主要测量前方物体的距离、速度和到达角。核心是用傅里叶变换去计算距离,速度和到达角信息。
    在这里插入图片描述在这里插入图片描述

    笛卡尔坐标

    笛卡尔坐标系(Cartesian coordinates,法语:les coordonnées cartésiennes)就是直角坐标系和斜坐标系的统称。
    相交于原点的两条数轴,构成了平面仿射坐标系 。如两条数轴上的度量单位相等,则称此仿射坐标系为笛卡尔坐标系。两条数轴互相垂直的笛卡尔坐标系,称为笛卡尔直角坐标系,否则称为笛卡尔斜角坐标系。

    展开全文
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