没有合适的资源？快使用搜索试试~ 我知道了~

首页high_performance_mysql_3rd_edition.pdf

high_performance_mysql_3rd_edition.pdf

mysql

5星 · 超过95%的资源需积分: 10 23 下载量 6 浏览量更新于2023-06-07 评论收藏 12.37MB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

试读

826页

高性能MySQL（第3版）》是MySQL 领域的经典之作，拥有广泛的影响力。第3 版更新了大量的内容，不但涵盖了最新MySQL5.5版本的新特性，也讲述了关于固态盘、高可扩展性设计和云计算环境下的数据库相关的新内容，原有的基准测试和性能优化部分也做了大量的扩展和补充。全书共分为16章和6 个附录，内容涵盖MySQL架构和历史，基准测试和性能剖析，数据库软硬件性能优化，复制、备份和恢复，高可用与高可扩展性，以及云端的MySQL和MySQL相关工具等方面的内容。每一章都是相对独立的主题，读者可以有选择性地单独阅读。　　《高性能MySQL（第3版）》不但适合数据库管理员（DBA）阅读，也适合开发人员参考学习。不管是数据库新手还是专家，相信都能从本书有所收获。

资源详情

资源评论

资源推荐

www.it-ebooks.info

THIRD EDITION

High Performance MySQL

Baron Schwartz, Peter Zaitsev, and Vadim Tkachenko

Beijing

•

Cambridge

•

Farnham

•

Köln

•

Sebastopol

•

Tokyo

www.it-ebooks.info

High Performance MySQL, Third Edition

by Baron Schwartz, Peter Zaitsev, and Vadim Tkachenko

Printed in the United States of America.

Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.

O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions

are also available for most titles (http://my.safaribooksonline.com). For more information, contact our

corporate/institutional sales department: (800) 998-9938 or corporate@oreilly.com.

Editor: Andy Oram

Production Editor: Holly Bauer

Proofreader: Rachel Head

Indexer: Jay Marchand

Cover Designer: Karen Montgomery

Interior Designer: David Futato

Illustrator: Rebecca Demarest

March 2004: First Edition.

June 2008: Second Edition.

March 2012: Third Edition.

Revision History for the Third Edition:

2012-03-01 First release

See http://oreilly.com/catalog/errata.csp?isbn=9781449314286 for release details.

Nutshell Handbook, the Nutshell Handbook logo, and the O’Reilly logo are registered trademarks of

O’Reilly Media, Inc. High Performance MySQL, the image of a sparrow hawk, and related trade dress

are trademarks of O’Reilly Media, Inc.

Many of the designations used by manufacturers and sellers to distinguish their products are claimed as

trademarks. Where those designations appear in this book, and O’Reilly Media, Inc., was aware of a

trademark claim, the designations have been printed in caps or initial caps.

While every precaution has been taken in the preparation of this book, the publisher and authors assume

no responsibility for errors or omissions, or for damages resulting from the use of the information con-

tained herein.

ISBN: 978-1-449-31428-6

[LSI]

1330630256

www.it-ebooks.info

Table of Contents

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii

1. MySQL Architecture and History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

MySQL’s Logical Architecture 1

Connection Management and Security 2

Optimization and Execution 3

Concurrency Control 3

Read/Write Locks 4

Lock Granularity 4

Transactions 6

Isolation Levels 7

Deadlocks 9

Transaction Logging 10

Transactions in MySQL 10

Multiversion Concurrency Control 12

MySQL’s Storage Engines 13

The InnoDB Engine 15

The MyISAM Engine 17

Other Built-in MySQL Engines 19

Third-Party Storage Engines 21

Selecting the Right Engine 24

Table Conversions 28

A MySQL Timeline 29

MySQL’s Development Model 33

Summary 34

2. Benchmarking MySQL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Why Benchmark? 35

Benchmarking Strategies 37

iii

www.it-ebooks.info

剩余825页未读，继续阅读

yeziqing

2015-11-08

英文原版，还行

英文原版，数字版，非影印版，无水印，有目录，第三版。 How can you bring out MySQL’s full power? With High Performance MySQL, you’ll learn advanced techniques for everything from designing schemas, indexes, and queries to tuning your MySQL server, operating system, and hardware to their fullest potential. This guide also teaches you safe and practical ways to scale applications through replication, load balancing, high availability, and failover. Updated to reflect recent advances in MySQL and InnoDB performance, features, and tools, this third edition not only offers specific examples of how MySQL works, it also teaches you why this system works as it does, with illustrative stories and case studies that demonstrate MySQL’s principles in action. With this book, you’ll learn how to think in MySQL. Learn the effects of new features in MySQL 5.5, including stored procedures, partitioned databases, triggers, and views Implement improvements in replication, high availability, and clustering Achieve high performance when running MySQL in the cloud Optimize advanced querying features, such as full-text searches Take advantage of modern multi-core CPUs and solid-state disks Explore backup and recovery strategies—including new tools for hot online backups

高性能mysql 第四版英文版 PDF下载 [high performance mysql 4th edition PDF]

high performance mysql 4th edition PDF download

mysql教材——9本金典教材合集1

9.High.Performance.MySQL.3rd.Edition.Mar.2012 九本合集，全都是pdf文件。（所有文件只需要2分哦,一共分6个文件上传）分别为： mysql.part1.rar mysql.part2.rar mysql.part3.rar mysql.part4.rar mysql.part5....

collector.performance_schema.eventswaits - collector.performance_schema.file_events - collector.performance_schema.indexiowaits - collector.performance_schema.tableiowaits - collector.performance_schema.tablelocks这几参数结尾都需要等号吗

- performance_schema.eventswaits - performance_schema.file_events - performance_schema.indexiowaits - performance_schema.tableiowaits - performance_schema.tablelocks ``` 在这个配置中，每个列表...

- collector.performance_schema.eventswaits - collector.performance_schema.file_events - collector.performance_schema.indexiowaits - collector.performance_schema.tableiowaits - collector.performance_schema.tablelocks这几参数都需要等号吗

优化这条sql 解决bug select a.*,rownum num from ( SELECT t.PROJ_ID,t.PROJ_CODE,t.PROJ_NAME,t.CLIENT_CODE,t.CLIENT_NAME,t.SPEC_CODE,t.SPEC_NAME,t.BUS_UNIT_CODE,t.BUS_UNIT,t.PROJ_DEP_CODE,t.PROJ_DEP,t.PROJECT_MANAGER_CODE,t.PROJECT_MANAGER,t.PROJECT_DEP_MANAGER_CODE,t.PROJECT_DEP_MANAGER,t.IS_SUB_PROJ,t.SUB_PROJ_TYPE_CODE,t.SUB_PROJ_TYPE,t.PARENT_CODE,t.PROJ_GROSS,t.CLIENT_AREA_CODE,t.CLIENT_AREA,t.CLIENT_TYPE_FULL_PATH_CODE,t.CLIENT_TYPE_PULL_PATH,t.BUSINESS_TYPE_CODE,t.BUSINESS_TYPE,t.BUSINESS_LEVEL_CODE,t.BUSINESS_LEVEL,t.BUSINESS_AREA_CODE,t.BUSINESS_AREA_NAME,t.IS_CLOSE,t.IS_IN_COO,t.TAX_RATE,t.IS_AUTHORIZED,t.AUTHORIZED_AMOUNT,t.IS_VIRTUAL,t.INCOME_BUDGET,t.EXPENDITURE_BUDGET,t.P_VALUE,t.CREATE_TIME,t.P_BUD_VALUE,t.P1_BUD_VALUE,t.P2_BUD_VALUE,t.ORG_CODE,t.ORG_NAME,t.PROD_RES_TYPE,t.IS_TECH_COO,t.COO_UNIT_RATIO,t.PROJ_ACHIEVEMENTS_BUD,t.REIMBURSEMENT_COST_BUD,t.COO_COST_BUD,t.MATERIAL_COST_BUD,t.PERFORMANCE_PERCENT,t.SCHE_START_TIME,t.SCHE_END_TIME,t.PROJECT_ACCOUNT_CODE,t.CUSTOMER_TYPE_CODE,t.CUSTOMER_TYPE,t.IS_PURE_OUT_PROJ,t.PROJECT_CREATE_TIME,t.IS_RELATE,t.IS_QUOTA,t.MAIN_PROJECT_CODE,t.PROJ_STATUS,t.IS_LARGE_PROJECT,t.MARKET_DIS_COUNT_RATE,t.PROJECT_CAT,t.MGR_PER_FORMANCE_RATIO,t.P1_VALUE,t.S_VALUE,t.COOP_VALUE,t.H_VALUE,t.DEVICE_BUDGET_COST,t.SUR_FEE_DIS_COUNT_RATE,t.DES_FEE_DIS_COUNT_RATE, (select listagg(p.coo_unit_code, ',') within group(order by p.coo_unit_code) from ( select distinct coo_unit_code from t_spdi_proj where is_sub_proj = 'Y' and sub_proj_type_code = 'wbhz' and PROJ_STATUS != 'P_5' AND PROJ_STATUS != 'P_4' and parent_code = t.proj_code )p ) coo_unit_code, (select listagg(to_char(p.coo_unit), ',') within group(order by p.coo_unit) from ( select distinct coo_unit from t_spdi_proj where is_sub_proj = 'Y' and sub_proj_type_code = 'wbhz' and PROJ_STATUS != 'P_5' AND PROJ_STATUS != 'P_4' and parent_code = t.proj_code )p ) coo_unit from T_SPDI_PROJ t where t.PROJ_STATUS NOT IN ('E','H','W') order by t.proj_id )a

这条 SQL 可以进行如下优化： 1. 使用 WITH 语句将子查询提取出来，以降低重复代码并提高可读性。 2. 使用 EXISTS 替代 DISTINCT 子查询，以提高查询效率。 3. 使用 JOIN 替代子查询，以提高查询效率。...

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn import preprocessing from LAMDA_SSL.Algorithm.Regression.CoReg import CoReg from LAMDA_SSL.Evaluation.Regressor.Mean_Squared_Error import Mean_Squared_Error pd.set_option('display.max_columns', None) # 所有列 pd.set_option('display.max_rows', None) # 所有行 data = pd.read_excel('半监督数据.xlsx') X = data.drop(columns=['label']) # 特征矩阵 y = data['label'] # 标签列 # 划分数据集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, stratify=None, shuffle=True, random_state=0) # 划分带标签数据集 labeled_size = 0.3 n_labeled = int(labeled_size * len(X_train)) indices = np.arange(len(X_train)) unlabeled_indices = np.delete(indices, y_train.index[:n_labeled]) X_unlabeled = X_train.iloc[unlabeled_indices] y_unlabeled = y_train.iloc[unlabeled_indices] X_labeled = X_train.iloc[y_train.index[:n_labeled]] y_labeled = y_train.iloc[y_train.index[:n_labeled]] # 数据预处理 pre_transform=preprocessing.StandardScaler() pre_transform.fit(np.vstack([X_train, X_test])) X_train = pre_transform.transform(X_train) X_test = pre_transform.transform(X_test) # 构建和训练模型 model = CoReg() model.fit(X=X_train, y=y_labeled, test_datas=X_unlabeled) pred_y = model.predict(X=X_test) # 计算性能指标 performance = Mean_Squared_Error().scoring(y_test, pred_y)代码运行不了，怎么修改？

在代码的开头，需要加上注释，这样代码才能正常运行。具体来说，需要在第一行前面加上 # 注释符号，来注释导入 pandas 和 numpy 库的语句。...performance = Mean_Squared_Error().scoring(y_test, pred_y) ```

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split pd.set_option('display.max_columns', None) # 所有列 pd.set_option('display.max_rows', None) # 所有行 data = pd.read_excel('半监督数据.xlsx') X = data.drop(columns=['label']) # 特征矩阵 y = data['label'] # 标签列 # 划分数据集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, stratify=None, shuffle=True, random_state=0) # 划分带标签数据集 labeled_size = 0.3 n_labeled = int(labeled_size * len(X_train)) indices = np.arange(len(X_train)) unlabeled_indices = np.delete(indices, y_train.index[:n_labeled]) X_unlabeled = X_train.iloc[unlabeled_indices] y_unlabeled = y_train.iloc[unlabeled_indices] X_labeled = X_train.iloc[y_train.index[:n_labeled]] y_labeled = y_train.iloc[y_train.index[:n_labeled]] from sklearn import preprocessing pre_transform=preprocessing.StandardScaler() pre_transform.fit(np.vstack([train_datas, test_datas])) train_datas=pre_transform.transform(train_datas) test_datas=pre_transform.transform(train_datas) from LAMDA_SSL.Algorithm.Regression.CoReg import CoReg model=CoReg() model.fit(X=train_datas,y=labeled_y,test_datas=unlabeled_X) pred_y=model.predict(X=test_X) from LAMDA_SSL.Evaluation.Regressor.Mean_Squared_Error import Mean_Squared_Error performance = Mean_Squared_Error().scoring(test_y, pred_y)帮我看一下这段代码有什么问题？怎么修改？

在代码中，预处理部分中使用了未定义的变量 `train_datas` 和 `test_datas`，应该将其改为 `X_train` 和 `X_test`。另外，在调用 `CoReg` 模型时...performance = Mean_Squared_Error().scoring(y_test, pred_y) ```

import cv2 import mediapipe as mp mp_drawing = mp.solutions.drawing_utils mp_drawing_styles = mp.solutions.drawing_styles mp_holistic = mp.solutions.holistic # For webcam input: filepath='F:\\video000\\ce.mp4' cap = cv2.VideoCapture(filepath) with mp_holistic.Holistic( min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic: while cap.isOpened(): success, image = cap.read() if not success: print("Ignoring empty camera frame.") # If loading a video, use 'break' instead of 'continue'. break # To improve performance, optionally mark the image as not writeable to # pass by reference. image.flags.writeable = False image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = holistic.process(image) # Draw landmark annotation on the image. image.flags.writeable = True image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) mp_drawing.draw_landmarks( image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS, landmark_drawing_spec=None, connection_drawing_spec=mp_drawing_styles .get_default_face_mesh_contours_style()) mp_drawing.draw_landmarks( image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, landmark_drawing_spec=mp_drawing_styles .get_default_pose_landmarks_style()) # Flip the image horizontally for a selfie-view display. cv2.imshow('MediaPipe Holistic', cv2.flip(image, 1)) if cv2.waitKey(5) & 0xFF == 27: break cap.release() 在这段代码中引入注意力机制，使人体步态特征更好的提取

# To improve performance, optionally mark the image as not writeable to # pass by reference. image.flags.writeable = False image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = holistic....

解释test_pta_cpu_feature_multistyle_performance.py

test_pta_cpu_feature_multistyle_performance.py 是一个 Python 脚本，它用于测试 PaddlePaddle 中 PTA（Performance Test Analyzer）工具在多种 CPU 特性下的性能表现。具体来说，该脚本会读取一个配置文件，该...

select a.id,b.is_manager_big, b.is_manager_bcompany,a.one_shoukuan,a.two_shoukuan,b.one_income,b.two_income from (select a.id, SUM(CASE WHEN b.shoukuan_month = '2023-05' and b.is_del = 0 then b.netincome ELSE 0 end) as one_shoukuan, SUM(CASE WHEN b.shoukuan_month = '2023-04' AND b.is_del = 0 THEN b.netincome ELSE 0 END) as two_shoukuan from xw_kefu a left join xw_user_applyloan_checking b on b.kefuid = a.id where a.id in (1748,1749,1776,1834,1913,1921,1922,1778,1830,1829,1833,1835,1750,1753,1754,1756,1758,1761,1892,1905,1762,1764,1902,1915,1751,1773,1792,1774,1788,1917,1791,1793,1795,1909,1775,1787,1752,1757,1771,1772,1914,1768,1898,1760,1767,1769,1863,1770,1827) and a.is_del = 0 and a.is_lizhi = 0 group by a.id) as a,(select a.id,a.is_manager_big, a.is_manager_bcompany, SUM(CASE WHEN b.month = '2023-05' then b.create_income end) as one_income, SUM(CASE WHEN b.month = '2023-04' THEN b.create_income END) as two_income from xw_kefu a left join xw_kefu_performance_target b on b.kefuid = a.id where a.id in (1748,1749,1776,1834,1913,1921,1922,1778,1830,1829,1833,1835,1750,1753,1754,1756,1758,1761,1892,1905,1762,1764,1902,1915,1751,1773,1792,1774,1788,1917,1791,1793,1795,1909,1775,1787,1752,1757,1771,1772,1914,1768,1898,1760,1767,1769,1863,1770,1827) and a.is_del = 0 and a.is_lizhi = 0 group by a.id ) as b where b.id = a.id group by a.id优化一下这个查询统计

2. 添加索引：根据查询条件，可以为 xw_kefu 和 xw_user_applyloan_checking 表的 id 列、shoukuan_month 列添加索引，为 xw_kefu_performance_target 表的 kefuid 列、month 列添加索引。 3. 使用 EXISTS 子查询...

mysql的热表有什么办法查么

可以使用MySQL的Performance Schema来查看热表信息。具体步骤如下： 1. 打开Performance Schema：在MySQL中执行`SET GLOBAL performance_schema = ON;` 2. 查看表的I/O操作次数：执行以下语句： ``` SELECT ...

我在sys.dm_os_performance_counters里没有找到database_id这个对象

确实，在sys.dm_os_performance_counters中没有名为database_id的对象。你可能会看到一些与数据库相关的计数器，例如“SQLServer:Databases Log Bytes Flushed/sec”，但它们并没有直接提供有关数据库ID的信息。 ...

from pypfopt.efficient_frontier import EfficientFrontier from pypfopt import risk_models from pypfopt import expected_returns yuce = pd.read_excel("E:/应统案例大赛/附件1-股票交易数据/yuceclose.xlsx",index_col=0) # 计算预期收益和样本协方差矩阵 mu3 = expected_returns.mean_historical_return(yuce) # 使用历史数据计算预期收益 S3 = risk_models.sample_cov(yuce) # 使用历史数据计算协方差矩阵 # Optimize for maximal Sharpe ratio ef = EfficientFrontier(mu3, S3) raw_weights = ef.max_sharpe() cleaned_weights = ef.clean_weights() ef.save_weights_to_file("yuceweight1.csv") # saves to file print(cleaned_weights) ef.portfolio_performance(verbose=True) # 设置无风险回报率为0 risk_free = 0 # 计算每项资产的夏普比率 RandomPortfolios['Sharpe'] = (RandomPortfolios.Returns - risk_free) / RandomPortfolios.Volatility # 绘制收益-标准差的散点图，并用颜色描绘夏普比率 plt.scatter(RandomPortfolios.Volatility, RandomPortfolios.Returns, c=RandomPortfolios.Sharpe) plt.colorbar(label='Sharpe Ratio') plt.show()修改后面的代码，与前面匹配

ef.portfolio_performance(verbose=True) # 生成随机投资组合 np.random.seed(1) n_samples = 10000 weights = np.random.dirichlet(np.ones(len(yuce.columns)), n_samples) returns = weights.dot(mu3) ...

yuce = pd.read_excel("E:/应统案例大赛/附件1-股票交易数据/yuceclose.xlsx",index_col=0) # 计算预期收益和样本协方差矩阵 mu3 = expected_returns.mean_historical_return(yuce) # 使用历史数据计算预期收益 S3 = risk_models.sample_cov(yuce) # 使用历史数据计算协方差矩阵 # Optimize for maximal Sharpe ratio ef = EfficientFrontier(mu3, S3) raw_weights = ef.max_sharpe() cleaned_weights = ef.clean_weights() ef.save_weights_to_file("yuceweight.csv") # saves to file print(cleaned_weights) ef.portfolio_performance(verbose=True)如何绘图表示

可以使用matplotlib库绘制有效前沿图。代码示例如下： ``` import numpy as np import matplotlib.pyplot as plt # 计算有效前沿 ef = EfficientFrontier(mu3, S3) raw_frontier = ef.efficient_frontier() ...

MySQL 查找经常使用的表

1. 查看表的I/O操作次数：可以使用MySQL的Performance Schema来查看表的I/O操作次数，具体步骤如下： 1）打开Performance Schema：在MySQL中执行`SET GLOBAL performance_schema = ON;` 2）执行以下语句，查看表...

element_content = [0.0001 0.0003 0.0003 0.0002 0.0004; 0.0017 0.0017 0.001 0.0013 0.0018; 0.0028 0.0027 0.0024 0.0025 0.0029; 0.0011 0.0009 0.0009 0.0008 0.0011; 0.0002 0.0001 0.0002 0.0002 0.0003; 0.0005 0.0004 0.0006 0.0006 0.0003; 0.0019 0.0021 0.0024 0.001 0.0025; 0.0012 0.0013 0.0013 0.0012 0.0015; 0.0024 0.0046 0.0016 0.0013 0.0049; 0.0001 0.0002 0.0003 0.0002 0.0002; 0.0001 0.0001 0.0001 0.0003 0; 0 0.0843 0 0 0.0911; 0 0 0.0883 0 0.0886; 0 0 0 0.0597 0.0786]; cycling_performance = [71.10 83.50 79.30 87.60 91.70; 52.50 74.20 70.10 83.50 87.60]; cycling_performance = cycling_performance / 100; % 转换为百分比 % 检查自变量与因变量列数是否匹配 if size(element_content, 2) ~= numel(cycling_performance) error('自变量与因变量列数不匹配'); end % 构建设计矩阵 X = [ones(size(cycling_performance, 1), 1) element_content]; % 将因变量转换为向量 Y = cycling_performance(:); % 进行多元线性回归 [b, bint, r, rint, stats] = regress(Y, X); % 输出回归结果 disp('回归系数：'); disp(b); disp('回归统计信息：'); disp(stats); 这段matlab代码自变量与因变量列数不匹配，请帮我改正

根据您的说明，我注意到您的自变量 `element_content` 的列数与因变量 `cycling_performance` 的列数不匹配。为了解决这个问题，您可以选择以下两种方法之一：方法一：将 `element_content` 矩阵进行转置，使其列...

详细解释一下这段代码，每一句都要进行注解：tgt = f'/kaggle/working/{dataset}-{scene}' # Generate a simple reconstruction with SIFT (https://en.wikipedia.org/wiki/Scale-invariant_feature_transform). if not os.path.isdir(tgt): os.makedirs(f'{tgt}/bundle') os.system(f'cp -r {src}/images {tgt}/images') database_path = f'{tgt}/database.db' sift_opt = pycolmap.SiftExtractionOptions() sift_opt.max_image_size = 1500 # Extract features at low resolution could significantly reduce the overall accuracy sift_opt.max_num_features = 8192 # Generally more features is better, even if behond a certain number it doesn't help incresing accuracy sift_opt.upright = True # rotation invariance device = 'cpu' t = time() pycolmap.extract_features(database_path, f'{tgt}/images', sift_options=sift_opt, verbose=True) print(len(os.listdir(f'{tgt}/images'))) print('TIMINGS --- Feature extraction', time() - t) t = time() matching_opt = pycolmap.SiftMatchingOptions() matching_opt.max_ratio = 0.85 # Ratio threshold significantly influence the performance of the feature extraction method. It varies depending on the local feature but also on the image type # matching_opt.max_distance = 0.7 matching_opt.cross_check = True matching_opt.max_error = 1.0 # The ransac error threshold could help to exclude less accurate tie points pycolmap.match_exhaustive(database_path, sift_options=matching_opt, device=device, verbose=True) print('TIMINGS --- Feature matching', time() - t) t = time() mapper_options = pycolmap.IncrementalMapperOptions() mapper_options.extract_colors = False mapper_options.min_model_size = 3 # Sometimes you want to impose the first image pair for initialize the incremental reconstruction mapper_options.init_image_id1 = -1 mapper_options.init_image_id2 = -1 # Choose which interior will be refined during BA mapper_options.ba_refine_focal_length = True mapper_options.ba_refine_principal_point = True mapper_options.ba_refine_extra_params = True maps = pycolmap.incremental_mapping(database_path=database_path, image_path=f'{tgt}/images', output_path=f'{tgt}/bundle', options=mapper_options) print('TIMINGS --- Mapping', time() - t)

这段代码主要是使用 PyCOLMAP 库实现对图像的特征提取、特征匹配和增量式三维重建。具体解释如下： ```tgt = f'/kaggle/working/{dataset}-{scene}'``` 定义了一个字符串变量 tgt，表示输出路径。...

stayeveryoung

粉丝: 6
资源: 32

上传资源快速赚钱

我的内容管理收起

我的资源快来上传第一个资源

我的收益

登录查看自己的收益

我的积分登录查看自己的积分

我的C币登录后查看C币余额

我的收藏

我的下载

下载帮助

会员权益专享

high_performance_mysql_3rd_edition.pdf

评论5

会员权益专享

最新资源

high_performance_mysql_3rd_edition.pdf

评论5

High Performance MySQL 3rd Edition [mobi]

High Performance MySQL, 3rd Edition

High Performance MySQL_3rd_edition

高性能mysql 第四版 英文版 PDF下载 [high performance mysql 4th edition PDF]

mysql教材——9本金典教材合集1

collector.performance_schema.eventswaits - collector.performance_schema.file_events - collector.performance_schema.indexiowaits - collector.performance_schema.tableiowaits - collector.performance_schema.tablelocks这几参数结尾都需要等号吗

- collector.performance_schema.eventswaits - collector.performance_schema.file_events - collector.performance_schema.indexiowaits - collector.performance_schema.tableiowaits - collector.performance_schema.tablelocks这几参数都需要等号吗

解释test_pta_cpu_feature_multistyle_performance.py

mysql的热表有什么办法查么

我在sys.dm_os_performance_counters里没有找到database_id这个对象

MySQL 查找经常使用的表

会员权益专享

最新资源

高性能mysql 第四版英文版 PDF下载 [high performance mysql 4th edition PDF]