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

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

In recent years, the rapid development of cloud services has provided developers with more technical support and convenience. As one of the cloud service providers, Qiniu Cloud Platform provides a rich series of functions and interfaces, allowing developers to easily store and manage files.

In this article, we will learn how to use Python to connect to Qiniu Cloud interface and implement image cropping and merging functions. First, we need to create an account on the Qiniu Cloud platform, create a storage space, and obtain our Access Key and Secret Key, as well as the name of the created storage space.

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

pip install qiniu

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

import qiniu import requests from PIL import Image

Then, we need to set the parameters of Qiniu Cloud:

access_key = 'Your Access Key' secret_key = 'Your Secret Key' bucket_name = 'Your Bucket Name'

Next, we need to create an authentication object for Qiniu Cloud:

auth = qiniu.Auth(access_key, secret_key)

Now, we can create a function to upload pictures to Qiniu Cloud. This function can accept the path of the local picture as a parameter and return the URL of the uploaded picture:

def upload_image(filepath): token = auth.upload_token(bucket_name) ret, info = qiniu.put_file(token, None, filepath) if ret is not None and ret['key'] is not None: return f"https://{bucket_name}.qiniudn.com/{ret['key']}" else: return None

Next, Let's create a function that crops the image. This function accepts the URL of an image and the crop parameter as input, and returns the URL of the cropped image.

def crop_image(image_url, x, y, width, height): response = requests.get(image_url) image = Image.open(BytesIO(response.content)) cropped_image = image.crop((x, y, x+width, y+height)) cropped_image_url = f"{image_url}-cropped" cropped_image.save("cropped.jpg") cropped_image_url = upload_image("cropped.jpg") return cropped_image_url

Finally, let’s implement the image merging function. This function accepts the URLs of two images as input and returns the URL of the merged image.

def merge_images(image_url1, image_url2): response1 = requests.get(image_url1) response2 = requests.get(image_url2) image1 = Image.open(BytesIO(response1.content)) image2 = Image.open(BytesIO(response2.content)) merged_image = Image.new('RGB', (image1.width + image2.width, max(image1.height, image2.height))) merged_image.paste(image1, (0, 0)) merged_image.paste(image2, (image1.width, 0)) merged_image_url = f"{image_url1}-merged" merged_image.save("merged.jpg") merged_image_url = upload_image("merged.jpg") return merged_image_url

Now, we can write a simple example to demonstrate how to use these functions:

if __name__ == '__main__': image_url1 = upload_image("image1.jpg") image_url2 = upload_image("image2.jpg") cropped_image_url = crop_image(image_url1, 100, 100, 200, 200) merged_image_url = merge_images(cropped_image_url, image_url2) print("Cropped image url:", cropped_image_url) print("Merged image url:", merged_image_url)

Through the above example code, we have learned how to use Python to implement the docking of Qiniu Cloud interface, and The cropping and merging of images are realized through the interface provided by Qiniu Cloud Platform. It can be said that the powerful functions of Qiniu Cloud and the convenience of Python provide developers with more possibilities. I hope readers can better understand this knowledge during the development process and apply it to actual projects. .

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