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

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

Abstract:
With the rapid development of the Internet, the demand for image processing and storage is also increasing. The emergence of cloud storage services provides a convenient and efficient solution for image storage, and Qiniu Cloud is one of the most popular cloud storage services. This article will introduce how to use Python to implement Qiniu Cloud interface docking, and use Qiniu Cloud's image processing function to complete image color adjustment.

Article text:

1. Preparation:
First, we need to create a storage space (Bucket) on Qiniu Cloud and obtain the corresponding Access Key and Secret Key. These two keys will be used to connect to Qiniu Cloud interface. In addition, we also need to install the Python requests library.

2. Connect to Qiniu Cloud interface:
We can use Python's requests library to make HTTP requests, and send requests to Qiniu Cloud's API by constructing the corresponding request URL and request parameters.

import requests
import hashlib
import hmac
import base64

access_key = "your_access_key"
secret_key = "your_secret_key"
bucket_name = "your_bucket_name"

# 构建URL
url = "http://rs.qiniu.com/stat/" + bucket_name  # 查询存储空间信息的API接口

# 构建请求参数
params = {}
params['bucket'] = bucket_name

# 生成AccessToken
sign = hmac.new(secret_key.encode('utf-8'), url.encode('utf-8'), hashlib.sha1).digest()
token = access_key + ':' + base64.urlsafe_b64encode(sign).decode('utf-8')

# 发送GET请求
response = requests.get(url, params=params, headers={'Authorization': 'Qiniu ' + token})

# 处理返回结果
if response.status_code == 200:
    result = response.json()  # 将返回结果转为JSON格式
    print(result)
else:
    print("Request Failed:", response.status_code)

Through the above code, we can obtain the basic information of the storage space.

3. Image color adjustment:
Qiniu Cloud provides rich image processing functions. We can achieve color adjustment effects by adjusting the image processing parameters.

def image_color_adjust(image_url, bucket_name):
    access_key = "your_access_key"
    secret_key = "your_secret_key"
    
    # 构建URL
    url = "http://<domain>/style/<style>/<source>"
    
    # 构建请求参数
    params = {}
    params['bucket'] = bucket_name
    params['source'] = base64.urlsafe_b64encode(image_url.encode('utf-8')).decode('utf-8')
    params['style'] = "your_style"  # 调整图片色彩的样式
    
    # 生成AccessToken
    sign = hmac.new(secret_key.encode('utf-8'), url.encode('utf-8'), hashlib.sha1).digest()
    token = access_key + ':' + base64.urlsafe_b64encode(sign).decode('utf-8')

    # 发送GET请求
    response = requests.get(url, params=params, headers={'Authorization': 'Qiniu ' + token})

    # 处理返回结果
    if response.status_code == 200:
        result = response.json()  # 将返回结果转为JSON格式
        print(result)
    else:
        print("Request Failed:", response.status_code)

# 调用函数
image_url = "http://example.com/path/to/image.jpg"  # 替换为需要调整色彩的图片URL
bucket_name = "your_bucket_name"
image_color_adjust(image_url, bucket_name)

9e6c6f86673efa96e9f4645ec38e5f75, c9ccee2e6ea535a969eb3f532ad9fe89 and e02da388656c3265154666b7c71a8ddc in the above code need to be replaced with the real domain name, Style and image URL. Through the above code, we can achieve the effect of color adjustment on the specified picture.

Summary:
This article uses Python as a tool to teach you how to implement Qiniu Cloud interface docking and use Qiniu Cloud's image processing function to complete picture color adjustment. By studying this article, you can further understand and apply the functions of Qiniu Cloud to achieve richer image processing effects. Hope this article is helpful to you!

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