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How to use Python to connect to Youpaiyun interface to achieve video transcoding and editing
Abstract: Youpaiyun is a powerful cloud storage platform that provides rich multimedia processing functions. This article will introduce how to use Python to connect to Youpaiyun's API interface to implement video transcoding and editing functions. Specific contents include setting API keys, uploading video files, initiating transcoding tasks, querying task status, downloading transcoded video files, etc.
Before using Youpaiyun’s API interface, we need to register an account on Youpaiyun’s official website and create a storage space. Then, log in to the account, enter the console, find "Key Management" in the left navigation bar, and generate an API key. Save the API key locally for subsequent Python code.
To use Python to connect to the cloud interface, we need to install related libraries, including requests and json.
pip install requests pip install json
To use Youpaiyun’s API interface, you must first upload the video file to Youpaiyun’s storage space. Assuming that the video file we want to upload is named "example.mp4", the Python code is as follows:
import requests def upload_video_file(api_key, api_secret, bucket_name, local_file_path): url = f'https://{bucket_name}.video.upyun.com/{local_file_path}' authorization = api_key + ":" + api_secret headers = { 'Authorization': 'Basic ' + base64.b64encode(authorization.encode()).decode() } with open(local_file_path, 'rb') as file: data = file.read() response = requests.put(url, headers=headers, data=data) if response.status_code == 200: print("视频文件上传成功!") else: print("视频文件上传失败!") api_key = 'your_api_key' api_secret = 'your_api_secret' bucket_name = 'your_bucket_name' local_file_path = 'example.mp4' upload_video_file(api_key, api_secret, bucket_name, local_file_path)
In the code, we use the requests library to send a PUT request to upload the video file to Youpaiyun's storage space. We need to set the Authorization field in the request header to the API key. After the upload is successful, status code 200 will be returned.
After uploading the video file, we can initiate a transcoding task to transcode the original video into different formats and resolutions. Youpaiyun provides a wealth of transcoding parameters, which can be set according to specific needs. The following is a sample code to transcode the video to MP4 format with a resolution of 720p:
import requests def transcode_video(api_key, api_secret, bucket_name, local_file_name, target_file_format, target_resolution): url = f'https://{bucket_name}.video.upyun.com/transcoding/' authorization = api_key + ":" + api_secret headers = { 'Authorization': 'Basic ' + base64.b64encode(authorization.encode()).decode(), 'X-Transcode-Target': target_file_format, 'X-Transcode-Resolution': target_resolution } data = { 'source': local_file_name } response = requests.post(url, headers=headers, data=data) if response.status_code == 201: task_id = response.json()['task_id'] print(f"转码任务已创建,任务ID为{task_id}") else: print("转码任务创建失败!") api_key = 'your_api_key' api_secret = 'your_api_secret' bucket_name = 'your_bucket_name' local_file_name = 'example.mp4' target_file_format = 'mp4' target_resolution = '720p' transcode_video(api_key, api_secret, bucket_name, local_file_name, target_file_format, target_resolution)
In the code, we use the requests library to send a POST request to initiate the transcoding task. We need to set the Authorization field in the request header to the API key, and specify the target file format and resolution in the request header. After the upload is successful, status code 201 will be returned, and the task ID will also be returned.
After initiating the transcoding task, we can use Youpaiyun's API interface to query the status of the task. The following is a sample code:
import requests def query_task_status(api_key, api_secret, bucket_name, task_id): url = f'https://{bucket_name}.video.upyun.com/tasks/{task_id}/' authorization = api_key + ":" + api_secret headers = { 'Authorization': 'Basic ' + base64.b64encode(authorization.encode()).decode() } response = requests.get(url, headers=headers) if response.status_code == 200: task_status = response.json()['status'] print(f"任务状态为{task_status}") else: print("查询任务状态失败!") api_key = 'your_api_key' api_secret = 'your_api_secret' bucket_name = 'your_bucket_name' task_id = 'your_task_id' query_task_status(api_key, api_secret, bucket_name, task_id)
In the code, we use the requests library to send a GET request to query the status of the task. We need to set the Authorization field in the request header to the API key. After the query is successful, status code 200 will be returned, and the status of the task will be returned.
After the video transcoding task is completed, we can use Youpaiyun's API interface to download the transcoded video file. The following is a sample code:
import requests def download_transcoded_video(api_key, api_secret, bucket_name, task_id, local_file_name): url = f'https://{bucket_name}.video.upyun.com/tasks/{task_id}/download' authorization = api_key + ":" + api_secret headers = { 'Authorization': 'Basic ' + base64.b64encode(authorization.encode()).decode() } response = requests.get(url, headers=headers) if response.status_code == 200: with open(local_file_name, 'wb') as file: file.write(response.content) print("视频文件下载成功!") else: print("视频文件下载失败!") api_key = 'your_api_key' api_secret = 'your_api_secret' bucket_name = 'your_bucket_name' task_id = 'your_task_id' local_file_name = 'output.mp4' download_transcoded_video(api_key, api_secret, bucket_name, task_id, local_file_name)
In the code, we use the requests library to send a GET request to download the transcoded video file. We need to set the Authorization field in the request header to the API key. After the download is successful, status code 200 will be returned and the file will be saved locally.
This article introduces how to use Python to connect to the Youpai Cloud interface to implement video transcoding and editing functions. By setting API keys, uploading video files, initiating transcoding tasks, querying task status, and downloading transcoded video files, we can perform video processing in Python very conveniently. This is very useful for scenarios that require batch processing of videos, such as video websites, short video platforms, online education and other fields. I hope this article can help readers make better use of Youpaiyun's functions and add more multimedia processing capabilities to their projects.
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