


How to use Python to connect to the cloud interface to display video upload progress
How to use Python to connect to Youpai Cloud interface to display video upload progress
Youpai Cloud is a cloud storage platform that provides services such as image, audio and video storage, acceleration, and intelligent identification. During the development process, we often need to interact with Youpai Cloud for data, including uploading large video files. This article will teach you how to use Python to connect to Youpai Cloud interface and display the video upload progress.
- Installing dependent libraries
First, we need to install Python’s dependent libraries requests and tqdm. Use the following command to install:
pip install requests tqdm
- Get Youpaiyun’s API key
Before using Youpaiyun, we need to install Youpaiyun backend Get the API key. First log in to the cloud backend, click "Service Settings" - "API Settings" to generate the corresponding API key.
- Code example to realize video upload progress display
The following is a simple Python code example, showing how to use Python to connect to the cloud interface to realize the video upload progress Display:
import requests import tqdm def upload_video(file_path, bucket_name, api_key, api_secret): # 构造上传文件的URL url = f'https://v0.api.upyun.com/{bucket_name}/' # 读取视频文件 video_file = open(file_path, 'rb') # 计算视频文件总大小 total_size = len(video_file.read()) video_file.seek(0) # 将文件指针返回到文件开头 # 构造请求头 headers = { 'Content-Length': str(total_size), 'Content-Type': 'video/mp4', 'Authorization': f'UPYUN {api_key}:{api_secret}' } # 构造进度条 progress_bar = tqdm.tqdm(total=total_size, unit='B', unit_scale=True) # 发送文件分块进行上传 for chunk in video_file: # 利用requests发送请求,进行文件分块上传 response = requests.post(url, data=chunk, headers=headers) # 更新进度条 progress_bar.update(len(chunk)) # 关闭进度条 progress_bar.close() # 关闭文件 video_file.close() # 示例用法 if __name__ == '__main__': file_path = 'test.mp4' bucket_name = 'your_bucket_name' api_key = 'your_api_key' api_secret = 'your_api_secret' upload_video(file_path, bucket_name, api_key, api_secret)
In the above code, we first use the requests library to send a chunked request, and control the upload of video files by setting the Content-Length field and Content-Type field in the request header. Then, use the tqdm library to construct a progress bar, and continuously update the progress bar to display the progress of the upload. Finally, we call the upload_video function in the example usage, passing in the file path, the cloud storage space name, the API key, and the key corresponding to the API key to upload the video.
Summary:
This article introduces how to use Python to connect to the cloud interface to display the video upload progress. By using the requests and tqdm libraries, we can easily monitor the progress of video uploads. I hope this article will help you understand the data interaction between Python and Youpaiyun!
The above is the detailed content of How to use Python to connect to the cloud interface to display video upload progress. For more information, please follow other related articles on the PHP Chinese website!

ForhandlinglargedatasetsinPython,useNumPyarraysforbetterperformance.1)NumPyarraysarememory-efficientandfasterfornumericaloperations.2)Avoidunnecessarytypeconversions.3)Leveragevectorizationforreducedtimecomplexity.4)Managememoryusagewithefficientdata

InPython,listsusedynamicmemoryallocationwithover-allocation,whileNumPyarraysallocatefixedmemory.1)Listsallocatemorememorythanneededinitially,resizingwhennecessary.2)NumPyarraysallocateexactmemoryforelements,offeringpredictableusagebutlessflexibility.

InPython, YouCansSpectHedatatYPeyFeLeMeReModelerErnSpAnT.1) UsenPyNeRnRump.1) UsenPyNeRp.DLOATP.PLOATM64, Formor PrecisconTrolatatypes.

NumPyisessentialfornumericalcomputinginPythonduetoitsspeed,memoryefficiency,andcomprehensivemathematicalfunctions.1)It'sfastbecauseitperformsoperationsinC.2)NumPyarraysaremorememory-efficientthanPythonlists.3)Itoffersawiderangeofmathematicaloperation

Contiguousmemoryallocationiscrucialforarraysbecauseitallowsforefficientandfastelementaccess.1)Itenablesconstanttimeaccess,O(1),duetodirectaddresscalculation.2)Itimprovescacheefficiencybyallowingmultipleelementfetchespercacheline.3)Itsimplifiesmemorym

SlicingaPythonlistisdoneusingthesyntaxlist[start:stop:step].Here'showitworks:1)Startistheindexofthefirstelementtoinclude.2)Stopistheindexofthefirstelementtoexclude.3)Stepistheincrementbetweenelements.It'susefulforextractingportionsoflistsandcanuseneg

NumPyallowsforvariousoperationsonarrays:1)Basicarithmeticlikeaddition,subtraction,multiplication,anddivision;2)Advancedoperationssuchasmatrixmultiplication;3)Element-wiseoperationswithoutexplicitloops;4)Arrayindexingandslicingfordatamanipulation;5)Ag

ArraysinPython,particularlythroughNumPyandPandas,areessentialfordataanalysis,offeringspeedandefficiency.1)NumPyarraysenableefficienthandlingoflargedatasetsandcomplexoperationslikemovingaverages.2)PandasextendsNumPy'scapabilitieswithDataFramesforstruc


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

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

Atom editor mac version download
The most popular open source editor

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

SublimeText3 Linux new version
SublimeText3 Linux latest version

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment
