Learn Python to implement Qiniu Cloud interface docking and realize image filter synthesis and size adjustment

Learn Python to implement Qiniu Cloud interface docking and realize image filter synthesis and size adjustment

In the field of modern social media and graphic design, image processing and filter effect synthesis have become increasingly important. Today we will learn how to use Python language to implement Qiniu Cloud interface docking and realize the functions of image filter synthesis and size adjustment.

Qiniu Cloud is a leading cloud storage platform that provides a series of rich APIs for developers to use. We will use Qiniu Cloud’s Python SDK for interface docking. First, we need to register an account on the Qiniu Cloud platform, create a storage space, and obtain the corresponding access key and secret key.

Next, we need to install Qiniu Cloud’s Python SDK. Open a terminal or command prompt and execute the following command to install the SDK:

pip install qiniu

After the installation is complete, we can start writing code. First, we need to import the necessary libraries:

import qiniu
from PIL import Image, ImageFilter
import requests

Next, we need to configure the access key and secret key of Qiniu Cloud:

access_key = "<your-access-key>"
secret_key = "<your-secret-key>"

Then, we need to create an Auth for Qiniu Cloud Object:

auth = qiniu.Auth(access_key, secret_key)

Next, we need to define a function to upload pictures to Qiniu Cloud Storage Space:

def upload_image(file_path, key):
    token = auth.upload_token("<your-bucket-name>")
    ret, info = qiniu.put_file(token, key, file_path)
    if info.status_code == 200:
        return True
    else:
        return False

In this function, we first generate an upload credential (token), Then use the qiniu.put_file() method to upload the file. When the upload is successful, the function returns True; when the upload fails, the function returns False.

Next, we can implement a function to apply filter effects to the picture:

def apply_filter(img_path, filter_name):
    img = Image.open(img_path)
    filtered_img = img.filter(filter_name)
    filtered_img.save("filtered_image.jpg")

In this function, we first open a picture using the Image.open() method of the PIL library , and then use the filter function in the ImageFilter module to process the image. After the processing is completed, we use the save() method to save the image after applying the filter effect locally.

Finally, we can write a function to resize the image:

def resize_image(img_path, width, height):
    img = Image.open(img_path)
    resized_img = img.resize((width, height))
    resized_img.save("resized_image.jpg")

In this function, we use the Image.open() method of the PIL library to open an image, and then use resize () method to adjust the size of the image. After the adjustment is completed, we use the save() method to save the adjusted image locally.

Now, we can write a main function to call the above function:

def main():
    file_path = "<your-image-file-path>"
    key = "<your-file-key>"
    
    # 上传图片到七牛云
    if upload_image(file_path, key):
        print("Image upload successful!")
        
        # 应用滤镜效果
        apply_filter("filtered_image.jpg", ImageFilter.BLUR)
        
        # 调整图片尺寸
        resize_image("resized_image.jpg", 800, 600)
    else:
        print("Image upload failed!")

In this main function, we need to set an image file path and a file key in Qiniu Cloud. Then, we first call the upload_image() function to upload the image to Qiniu Cloud. After the upload is successful, we then call the apply_filter() function and the resize_image() function in sequence to apply the filter effect and adjust the image size.

Finally, we call the main() function at the end of the code to run the entire program:

if __name__ == "__main__":
    main()

Now, you can save the above code as a Python script file and execute it. The functions of image filter synthesis and size adjustment have been realized.

To summarize, by learning to use Python to implement Qiniu Cloud interface docking, we can easily upload images to Qiniu Cloud storage space, and can apply filter effects and adjust image size. This allows us to process and optimize images in the world of social media and graphic design. I hope this article will be helpful for you to learn the interface between Python and Qiniu Cloud!

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