Learn Python to implement Qiniu Cloud interface docking and image conversion function

Learn Python to implement Qiniu Cloud interface docking and realize image conversion function

Introduction:
With the development of the Internet, pictures play a very important role in our daily lives. In website development, image conversion is a common requirement, such as image scaling, cropping, or format conversion. Qiniu Cloud is a well-known cloud storage service provider in China, which provides powerful and stable image processing functions. This article will introduce how to use Python language to connect to the interface of Qiniu Cloud to realize the image conversion function.

1. Preparation:

1. Register a Qiniu Cloud account and create a storage space.
2. Install Python's requests library for sending HTTP requests.
3. Get the AccessKey and SecretKey of Qiniu Cloud Storage Space.

2. Import dependent libraries:
To use the requests library to send HTTP requests in a Python project, we need to import the requests library first in the code.

import requests

3. Obtain Qiniu Cloud's upload certificate:
Before uploading images, we need to obtain an upload certificate first. Qiniu Cloud's upload certificate is a token used to upload files and is used to verify the legality of the upload behavior. The following code demonstrates how to obtain upload credentials through Qiniu Cloud's API.

access_key = 'your_access_key'  # 七牛云的AccessKey
secret_key = 'your_secret_key'  # 七牛云的SecretKey
bucket_name = 'your_bucket_name'  # 存储空间名称

def get_upload_token(access_key, secret_key, bucket_name):
    url = 'http://api.qiniu.com/put-policy/{}/put-policy'.format(bucket_name)
    auth = requests.auth.HTTPBasicAuth(access_key, secret_key)
    response = requests.get(url, auth=auth)
    result = response.json()
    if 'token' in result:
        return result['token']
    else:
        raise ValueError('Failed to get upload token.')

upload_token = get_upload_token(access_key, secret_key, bucket_name)

4. Upload image files:
After obtaining the upload credentials, we can start uploading image files. In Qiniu Cloud, we can use a custom key to identify uploaded file resources. The following code demonstrates how to use Python language to upload image files to Qiniu Cloud.

def upload_image(file_path, upload_token):
    url = 'http://upload.qiniu.com/'
    headers = {
        'Content-Type': 'multipart/form-data',
    }
    files = {'file': open(file_path, 'rb')}
    data = {'token': upload_token}
    response = requests.post(url, headers=headers, files=files, data=data)
    result = response.json()
    if 'key' in result:
        return result['key']
    else:
        raise ValueError('Failed to upload image.')

image_path = 'your_image_path'  # 待上传的图片文件路径
image_key = upload_image(image_path, upload_token)

5. Perform image conversion operations:
After uploading the image file successfully, we can perform various conversion operations on the image through Qiniu Cloud's API. Qiniu Cloud provides many powerful image processing functions, such as image scaling, cropping, format conversion, etc. The following code demonstrates how to use Python language to call Qiniu Cloud's interface to achieve image scaling and format conversion.

def image_tranformation(image_key, new_image_key, width, height, format):
    url = 'http://api.qiniu.com/image/v2/{}'.format(image_key)
    headers = {
        'Content-Type': 'application/x-www-form-urlencoded',
    }
    params = {
        'imageView2': '/{}.w_{}/h_{}/format/{}'.format(new_image_key, width, height, format),
    }
    response = requests.get(url, headers=headers, params=params)
    with open(new_image_key, 'wb') as f:
        f.write(response.content)

new_image_key = 'your_new_image_key'  # 新生成的图片文件key
width = 500  # 新图片的宽度
height = 500  # 新图片的高度
format = 'jpg'  # 新图片的格式
image_tranformation(image_key, new_image_key, width, height, format)

6. Summary:
This article introduces how to use Python language to connect to the Qiniu Cloud interface to realize the image conversion function. By studying this article, you can master how to use Python language and Qiniu Cloud's API to upload and convert images. I hope this article can be helpful to you when using Qiniu Cloud for image processing.

The above is the detailed content of Learn Python to implement Qiniu Cloud interface docking and image conversion function. For more information, please follow other related articles on the PHP Chinese website!