GDAL (Geospatial Data Abstraction Library) is an open source raster spatial data conversion library under the X/MIT license. It utilizes an abstract data model to express the various file formats supported. It also has a range of command line tools for data conversion and processing.
Method 1: Download the whl file corresponding to the python version at the URL https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal. In the command line pip install whl file full path installation (windows mode). (Recommended learning: Python video tutorial)
Method 2:
Command line conda/pip search gdal View version , select the appropriate version (mine is 2.2.4), if not, use method one.
Command line conda/pip install gdal=version number, be careful to add the version number, otherwise an old version may be installed (available for windows/linux).
The gdal package is used to process raster data, and ogr is used to process vector data.
The following program is a simple application of gdal for raster processing.
from osgeo import gdal import numpy as np np.set_printoptions(threshold=np.inf)#使print大量数据不用符号...代替而显示所有 dataset = gdal.Open("E:/RS_data/caijian1214/caijian.tif") print(dataset.GetDescription())#数据描述 print(dataset.RasterCount)#波段数 cols=dataset.RasterXSize#图像长度 rows=(dataset.RasterYSize)#图像宽度 xoffset=cols/2 yoffset=rows/2 band = dataset.GetRasterBand(3)#取第三波段 r=band.ReadAsArray(xoffset,yoffset,1000,1000)#从数据的中心位置位置开始,取1000行1000列数据 band = dataset.GetRasterBand(2) g=band.ReadAsArray(xoffset,yoffset,1000,1000) band = dataset.GetRasterBand(1) b=band.ReadAsArray(xoffset,yoffset,1000,1000) import cv2 import matplotlib.pyplot as plt img2=cv2.merge([r,g,b]) plt.imshow(img2) plt.xticks([]),plt.yticks([]) # 不显示坐标轴 plt.show()
For more Python related technical articles, please visit the Python Tutorial column to learn!
The above is the detailed content of How to install gdal in python. For more information, please follow other related articles on the PHP Chinese website!

TomergelistsinPython,youcanusethe operator,extendmethod,listcomprehension,oritertools.chain,eachwithspecificadvantages:1)The operatorissimplebutlessefficientforlargelists;2)extendismemory-efficientbutmodifiestheoriginallist;3)listcomprehensionoffersf

In Python 3, two lists can be connected through a variety of methods: 1) Use operator, which is suitable for small lists, but is inefficient for large lists; 2) Use extend method, which is suitable for large lists, with high memory efficiency, but will modify the original list; 3) Use * operator, which is suitable for merging multiple lists, without modifying the original list; 4) Use itertools.chain, which is suitable for large data sets, with high memory efficiency.

Using the join() method is the most efficient way to connect strings from lists in Python. 1) Use the join() method to be efficient and easy to read. 2) The cycle uses operators inefficiently for large lists. 3) The combination of list comprehension and join() is suitable for scenarios that require conversion. 4) The reduce() method is suitable for other types of reductions, but is inefficient for string concatenation. The complete sentence ends.

PythonexecutionistheprocessoftransformingPythoncodeintoexecutableinstructions.1)Theinterpreterreadsthecode,convertingitintobytecode,whichthePythonVirtualMachine(PVM)executes.2)TheGlobalInterpreterLock(GIL)managesthreadexecution,potentiallylimitingmul

Key features of Python include: 1. The syntax is concise and easy to understand, suitable for beginners; 2. Dynamic type system, improving development speed; 3. Rich standard library, supporting multiple tasks; 4. Strong community and ecosystem, providing extensive support; 5. Interpretation, suitable for scripting and rapid prototyping; 6. Multi-paradigm support, suitable for various programming styles.

Python is an interpreted language, but it also includes the compilation process. 1) Python code is first compiled into bytecode. 2) Bytecode is interpreted and executed by Python virtual machine. 3) This hybrid mechanism makes Python both flexible and efficient, but not as fast as a fully compiled language.

Useaforloopwheniteratingoverasequenceorforaspecificnumberoftimes;useawhileloopwhencontinuinguntilaconditionismet.Forloopsareidealforknownsequences,whilewhileloopssuitsituationswithundeterminediterations.

Pythonloopscanleadtoerrorslikeinfiniteloops,modifyinglistsduringiteration,off-by-oneerrors,zero-indexingissues,andnestedloopinefficiencies.Toavoidthese:1)Use'i


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Dreamweaver Mac version
Visual web development tools

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment

Notepad++7.3.1
Easy-to-use and free code editor

WebStorm Mac version
Useful JavaScript development tools

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.
