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

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

In modern Internet applications, we often encounter scenarios where images need to be cropped, such as avatar uploading, product display, etc. Qiniu Cloud, as a leading cloud storage and content distribution platform, provides a rich image processing interface, which can easily perform operations such as cropping, scaling, and rotating images. This article will introduce how to use Python language to connect to the Qiniu Cloud interface to implement the image cropping function.

First, we need to create a storage space on Qiniu Cloud and obtain the Access Key and Secret Key. These two keys are important credentials for accessing Qiniu Cloud storage space, please keep them properly.

Next, we need to install the Python Qiniu Cloud SDK, which can be installed through the pip command:

pip install qiniu

After the installation is completed, we can start writing code. First, we need to import the qiniu module:

import qiniu

Then, we need to build a manager object for Qiniu Cloud storage space:

access_key = 'your_access_key'
secret_key = 'your_secret_key'
bucket_name = 'your_bucket_name'
manager = qiniu.Auth(access_key, secret_key)

Among them, access_key and secret_key need to be replaced with what you have on Qiniu Cloud The key of the created storage space, bucket_name is the name of your storage space.

Next, we can use Qiniu Cloud’s image processing interface to crop the image. For example, we want to crop a picture named "example.jpg" to a width of 200 pixels and a height of 300 pixels. The cropped picture is saved as "example_cropped.jpg":

source_url = 'http://your_bucket_name.qiniudn.com/example.jpg'
target_url = 'http://your_bucket_name.qiniudn.com/example_cropped.jpg'
fops = 'imageView2/2/w/200/h/300'
url = manager.private_download_url(source_url)
ret, info = qiniu.urlretrieve(url, 'example.jpg')
if info.status_code == 200:
    ret, info = qiniu.put_file(manager.upload_token(bucket_name, key='example_cropped.jpg'), 'example_cropped.jpg', 'example.jpg', mime_type='image/jpeg')
    if info.status_code == 200:
        print('图片裁剪成功！')
    else:
        print('图片裁剪失败！')

In the above code, source_url is the URL of the image to be cropped. Images stored on Qiniu Cloud have corresponding URLs, which can be obtained by concatenating the storage space name and image file name. target_url is the URL of the cropped image. fops is the parameter of the cropping operation, indicating that the picture view is to be zoomed. The url is the download URL of the image before cropping, generated through the private_download_url method. ret, info = qiniu.urlretrieve(url, 'example.jpg') is used to download the image before cropping and save it as "example.jpg". Then, upload the cropped image to Qiniu Cloud storage space through the put_file method.

After executing the above code, we can see the cropped picture in the Qiniu cloud storage space.

Of course, Qiniu Cloud's image processing interface not only supports cropping, but also supports zoom, rotate, blur and other operations. We can choose the appropriate operation according to actual needs.

This article introduces how to use Python language to connect to the Qiniu Cloud interface to realize the image cropping function. Through the powerful functions of Qiniu Cloud, we can process images quickly and efficiently to improve user experience. At the same time, Qiniu Cloud also provides a powerful CDN acceleration service to ensure that users can quickly access and download cropped images.

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