add 5K wallpaper crawler.py
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# 爬取5K分辨率超清唯美壁纸
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## 简介
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壁纸的选择其实很大程度上能看出电脑主人的内心世界，有的人喜欢风景，有的人喜欢星空，有的人喜欢美女，有的人喜欢动物。然而，终究有一天你已经产生审美疲劳了，但你下定决定要换壁纸的时候，又发现网上的壁纸要么分辨率低，要么带有水印。
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<br />
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<br />
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这里有一款Mac下的小清新壁纸神器[Pap.er][3]，可能是Mac下最好的壁纸软件，**自带5K超清分辨率壁纸**，富有多种类型壁纸，当我们想在Windows或者Linux下使用的时候，就可以考虑将**5K超清分辨率壁纸**爬取下来。
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## 编写思路
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为了方便快速开发，我们使用python中的wxpy模块完成微信的基本操作。
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首先，打开Charles软件，进行抓包。打开[Pap.er][3]，开始抓包。(由于是Mac系统下的APP，所以非Mac系统的朋友可以直接看抓包结果)
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抓包分析结果如下：
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| 类型 | 地址 |
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| ---- | ------------------------------------------------------------ |
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| 最新 | https://service.paper.meiyuan.in/api/v2/columns/flow/5c68ffb9463b7fbfe72b0db0?page=1&per_page=20 |
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| 最热 | https://service.paper.meiyuan.in/api/v2/columns/flow/5c69251c9b1c011c41bb97be?page=1&per_page=20 |
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| 女生 | https://service.paper.meiyuan.in/api/v2/columns/flow/5c81087e6aee28c541eefc26?page=1&per_page=20 |
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| 星空 | https://service.paper.meiyuan.in/api/v2/columns/flow/5c81f64c96fad8fe211f5367?page=1&per_page=20 |
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参数`page`不用改动，`per_page`指的是每页提取的数量，也就是我们想要提取的图片数量。
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抓完包之后，我们开始编写5K壁纸解析程序
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```python
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# 爬取不同类型图片
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def crawler_photo(type_id, photo_count):
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# 最新 1, 最热 2, 女生 3, 星空 4
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if(type_id == 1):
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url = 'https://service.paper.meiyuan.in/api/v2/columns/flow/5c68ffb9463b7fbfe72b0db0?page=1&per_page=' + str(photo_count)
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elif(type_id == 2):
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url = 'https://service.paper.meiyuan.in/api/v2/columns/flow/5c69251c9b1c011c41bb97be?page=1&per_page=' + str(photo_count)
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elif(type_id == 3):
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url = 'https://service.paper.meiyuan.in/api/v2/columns/flow/5c81087e6aee28c541eefc26?page=1&per_page=' + str(photo_count)
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elif(type_id == 4):
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url = 'https://service.paper.meiyuan.in/api/v2/columns/flow/5c81f64c96fad8fe211f5367?page=1&per_page=' + str(photo_count)
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headers = {"User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36"}
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# 获取图片链接列表数据，json格式
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respond = requests.get(url, headers=headers)
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# 对json格式转化为python对象
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photo_data = json.loads(respond.content)
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# 已经下载的图片张数
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now_photo_count = 1
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# 所有图片张数
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all_photo_count = len(photo_data)
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# 开始下载并保存5K分辨率壁纸
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for photo in photo_data:
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# 创建一个文件夹存放我们下载的图片(若存在则不用重新创建)
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if not os.path.exists('./' + str(type_id)):
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os.makedirs('./' + str(type_id))
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# 准备下载的图片链接，5K超清壁纸链接
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file_url = photo['urls']['raw']
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# 准备下载的图片名称,不包含扩展名
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file_name_only = file_url.split('/')
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file_name_only = file_name_only[len(file_name_only) -1]
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# 准备保存到本地的完整路径
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file_full_name = './' + str(type_id) + '/' + file_name_only
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# 开始下载图片
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Down_load(file_url, file_full_name, now_photo_count, all_photo_count)
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# 已经下载的图片数量加1
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now_photo_count = now_photo_count + 1
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```
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根据不同类型的壁纸，创建不同的文件夹编号进行分类。
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上面的`Down_load()`函数是下载文件的意思，调用`requests`库，具体代码如下：
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```python
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# 文件下载器
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def Down_load(file_url, file_full_name, now_photo_count, all_photo_count):
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headers = {"User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36"}
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# 开始下载图片
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with closing(requests.get(file_url, headers=headers, stream=True)) as response:
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chunk_size = 1024 # 单次请求最大值
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content_size = int(response.headers['content-length']) # 文件总大小
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data_count = 0 # 当前已传输的大小
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with open(file_full_name, "wb") as file:
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for data in response.iter_content(chunk_size=chunk_size):
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file.write(data)
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done_block = int((data_count / content_size) * 50)
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data_count = data_count + len(data)
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now_jd = (data_count / content_size) * 100
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print("\r %s：[%s%s] %d%% %d/%d" % (file_full_name, done_block * '█', ' ' * (50 - 1 - done_block), now_jd, now_photo_count, all_photo_count), end=" ")
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# 下载完图片后获取图片扩展名，并为其增加扩展名
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file_type = filetype.guess(file_full_name)
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os.rename(file_full_name, file_full_name + '.' + file_type.extension)
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```
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`chunk_size`指的是单次请求的最大值，`content_size`指的就是我们下载5K超清壁纸的大小，为了能够直观显示下载情况，所以添加了下载进度条的显示效果。核心代码为`file.write(data)`。
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下载完毕后，为了方便我们查看文件，所以需要给图片添加对应的扩展名，比如`jpg,png,gif`，这里使用到`filetype`库对文件进行解析，判断其类型。
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最后，开始在main中爬取5K高清壁纸：
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```python
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if __name__ == '__main__':
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# 最新 1, 最热 2, 女生 3, 星空 4
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# 爬取类型为3的图片(女生),一共准备爬取100张
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print("程序已经开始运行,请稍等……")
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crawler_photo(1, 100)
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crawler_photo(2, 100)
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crawler_photo(3, 100)
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crawler_photo(4, 100)
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```
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## 使用教程
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1. 确保以下库均已安装：
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```python
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# 如果没有安装，请使用pip install module安装
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import requests
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import filetype
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import os
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import json
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from contextlib import closing
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```
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## 演示图片
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## 完整源代码
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项目源代码在[GitHub仓库][1]
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项目持续更新，欢迎您[star本项目][1]
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# License
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[The MIT License (MIT)][2]
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[1]:https://github.com/shengqiangzhang/examples-of-web-crawlers
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[2]:http://opensource.org/licenses/MIT
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[3]:http://paper.meiyuan.in/
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# -*- coding:utf-8 -*-
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import requests
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import filetype
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import os
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import json
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from contextlib import closing
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# 文件下载器
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def Down_load(file_url, file_full_name, now_photo_count, all_photo_count):
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headers = {"User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36"}
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# 开始下载图片
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with closing(requests.get(file_url, headers=headers, stream=True)) as response:
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chunk_size = 1024 # 单次请求最大值
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content_size = int(response.headers['content-length']) # 文件总大小
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data_count = 0 # 当前已传输的大小
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with open(file_full_name, "wb") as file:
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for data in response.iter_content(chunk_size=chunk_size):
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file.write(data)
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done_block = int((data_count / content_size) * 50)
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data_count = data_count + len(data)
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now_jd = (data_count / content_size) * 100
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print("\r %s：[%s%s] %d%% %d/%d" % (file_full_name, done_block * '█', ' ' * (50 - 1 - done_block), now_jd, now_photo_count, all_photo_count), end=" ")
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# 下载完图片后获取图片扩展名，并为其增加扩展名
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file_type = filetype.guess(file_full_name)
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os.rename(file_full_name, file_full_name + '.' + file_type.extension)
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# 爬取不同类型图片
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def crawler_photo(type_id, photo_count):
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# 最新 1, 最热 2, 女生 3, 星空 4
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if(type_id == 1):
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url = 'https://service.paper.meiyuan.in/api/v2/columns/flow/5c68ffb9463b7fbfe72b0db0?page=1&per_page=' + str(photo_count)
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elif(type_id == 2):
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url = 'https://service.paper.meiyuan.in/api/v2/columns/flow/5c69251c9b1c011c41bb97be?page=1&per_page=' + str(photo_count)
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elif(type_id == 3):
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url = 'https://service.paper.meiyuan.in/api/v2/columns/flow/5c81087e6aee28c541eefc26?page=1&per_page=' + str(photo_count)
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elif(type_id == 4):
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url = 'https://service.paper.meiyuan.in/api/v2/columns/flow/5c81f64c96fad8fe211f5367?page=1&per_page=' + str(photo_count)
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# 获取图片列表数据
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headers = {"User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36"}
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respond = requests.get(url, headers=headers)
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photo_data = json.loads(respond.content)
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# 已经下载的图片张数
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now_photo_count = 1
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# 所有图片张数
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all_photo_count = len(photo_data)
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# 开始下载并保存5K分辨率壁纸
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for photo in photo_data:
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# 创建一个文件夹存放我们下载的图片
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if not os.path.exists('./' + str(type_id)):
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os.makedirs('./' + str(type_id))
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# 准备下载的图片链接
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file_url = photo['urls']['raw']
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# 准备下载的图片名称,不包含扩展名
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file_name_only = file_url.split('/')
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file_name_only = file_name_only[len(file_name_only) -1]
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# 准备保存到本地的完整路径
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file_full_name = './' + str(type_id) + '/' + file_name_only
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# 开始下载图片
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Down_load(file_url, file_full_name, now_photo_count, all_photo_count)
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now_photo_count = now_photo_count + 1
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if __name__ == '__main__':
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# 最新 1, 最热 2, 女生 3, 星空 4
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# 爬取类型为3的图片(女生),一共准备爬取20000张
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print("程序已经开始运行,请稍等……")
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crawler_photo(1, 10)
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crawler_photo(2, 10)
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crawler_photo(3, 10)
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crawler_photo(4, 10)
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45
README.MD
45
README.MD
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@ -172,6 +172,48 @@
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# 5.爬取5K分辨率超清唯美壁纸
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## 简介
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壁纸的选择其实很大程度上能看出电脑主人的内心世界，有的人喜欢风景，有的人喜欢星空，有的人喜欢美女，有的人喜欢动物。然而，终究有一天你已经产生审美疲劳了，但你下定决定要换壁纸的时候，又发现网上的壁纸要么分辨率低，要么带有水印。
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<br />
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<br />
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这里有一款Mac下的小清新壁纸神器[Pap.er][8]，可能是Mac下最好的壁纸软件，**自带5K超清分辨率壁纸**，富有多种类型壁纸，当我们想在Windows或者Linux下使用的时候，就可以考虑将**5K超清分辨率壁纸**爬取下来。
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## 使用教程
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1. 确保以下库均已安装：
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```python
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# 如果没有安装，请使用pip install module安装
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import requests
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import filetype
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import os
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import json
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from contextlib import closing
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```
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## 演示图片
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# 补充
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项目持续更新，欢迎您[star本项目][5]
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@ -192,4 +234,5 @@
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[4]:https://raw.githubusercontent.com/shengqiangzhang/examples-of-web-crawlers/master/1.%E6%B7%98%E5%AE%9D%E6%A8%A1%E6%8B%9F%E7%99%BB%E5%BD%95/example.gif
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[5]:https://github.com/shengqiangzhang/examples-of-web-crawlers
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[6]:http://opensource.org/licenses/MIT
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[7]:https://raw.githubusercontent.com/shengqiangzhang/examples-of-web-crawlers/master/3.%E6%B7%98%E5%AE%9D%E5%B7%B2%E4%B9%B0%E5%88%B0%E7%9A%84%E5%AE%9D%E8%B4%9D%E6%95%B0%E6%8D%AE%E7%88%AC%E8%99%AB(%E5%B7%B2%E6%A8%A1%E6%8B%9F%E7%99%BB%E5%BD%95)/example.gif
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[7]:https://raw.githubusercontent.com/shengqiangzhang/examples-of-web-crawlers/master/3.%E6%B7%98%E5%AE%9D%E5%B7%B2%E4%B9%B0%E5%88%B0%E7%9A%84%E5%AE%9D%E8%B4%9D%E6%95%B0%E6%8D%AE%E7%88%AC%E8%99%AB(%E5%B7%B2%E6%A8%A1%E6%8B%9F%E7%99%BB%E5%BD%95)/example.gif
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[8]:http://paper.meiyuan.in/
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