quyu = {"left":100,"top":100,"width":300,"height":300} with mss() as sct: quyujieping = sct.grab(quyu) 截图之后保存截图

这个Python代码片段是在使用`mss`库进行屏幕截图并裁剪特定区域。首先，`quyu`是一个字典，它定义了一个矩形区域，左上角坐标为(100, 100)，宽度为300像素，高度为300像素。

`with mss() as sct:`这部分创建了一个名为`sct`的上下文管理器，允许你在代码块中方便地操作截图功能，而无需显式关闭`sct`实例。接下来的`sct.grab(quyu)`函数会截取屏幕上的指定矩形区域`quyu`的内容，并返回一个包含截图信息的对象。

截图获取后，如果想要将截图保存，通常需要对返回的对象进一步处理。例如，你可以将其转换为图像文件，如PNG或JPEG，然后写入到硬盘上。这里没有直接保存图片的操作，你需要额外添加一些代码来完成保存，比如：

# 假设save_path是你要保存截图的路径
screen_data = quyujieping.rgb  # 获取截图数据
img = Image.frombytes('RGB', (quyujieping.width, quyujieping.height), screen_data)
img.save(save_path)

相关问题

帮我优化如下sql：SELECT T1.quyu, T2.lastNum, T3.jdNum, T4.xcNum FROM (SELECT STREET AS quyu FROM STREET_YXW WHERE 1=1 GROUP BY STREET ) T1 LEFT JOIN ( SELECT T2.STREET_NAME AS quyu,COUNT(*) AS lastNum FROM V_FIRESAFETYREGISTER_DYC T1 LEFT JOIN V_HSE_COMB_BUILDING_INFO_PA T2 ON LEFT(T1.HOUSEID,19) = T2.BUILDING_CODE WHERE T1.LASTLOGOUTTIME IS NOT NULL and date_format(T1.LASTLOGOUTTIME,'%Y') = ? GROUP BY T2.STREET_NAME ) T2 ON T1.quyu = T2.quyu LEFT JOIN ( SELECT T3.STREET_NAME AS quyu,COUNT(DISTINCT T1.FIREID) AS jdNum FROM V_SX_FIRESAFETYPLAN T1 LEFT JOIN V_FIRESAFETYREGISTER_DYC T2 ON T1.FIREID = T2.ID LEFT JOIN V_HSE_COMB_BUILDING_INFO_PA T3 ON LEFT(T2.HOUSEID,19) = T3.BUILDING_CODE WHERE T1.DATE_NEW IS NOT NULL and date_format(T1.DATE_NEW,'%Y') = ? GROUP BY T3.STREET_NAME ) T3 ON T1.quyu = T3.quyu LEFT JOIN ( SELECT T3.STREET_NAME AS quyu,COUNT(*) as xcNum FROM V_SX_FIRESAFETYTRAINING T1 LEFT JOIN V_FIRESAFETYREGISTER_DYC T2 ON T1.FIREID = T2.ID LEFT JOIN V_HSE_COMB_BUILDING_INFO_PA T3 ON LEFT(T2.HOUSEID,19) = T3.BUILDING_CODE WHERE T1.TRAININGTIME IS NOT NULL and date_format(T1.TRAININGTIME,'%Y') = ? GROUP BY T3.STREET_NAME ) T4 ON T1.quyu = T4.quyu ORDER BY T2.lastNum limit ?,?

可以尝试以下优化：

1. 对于子查询中的表，添加合适的索引以提高查询效率；

2. 可以考虑将子查询改为临时表，避免多次重复查询；

3. 在需要排序的字段上添加索引；

4. 尽量避免在 JOIN 条件中使用函数，可以将日期比较条件提取到 WHERE 子句中。

优化后的 SQL 如下：

CREATE TEMPORARY TABLE tmp_street AS
SELECT STREET AS quyu FROM STREET_YXW WHERE 1=1 GROUP BY STREET;

CREATE TEMPORARY TABLE tmp_lastNum AS
SELECT T2.STREET_NAME AS quyu, COUNT(*) AS lastNum
FROM V_FIRESAFETYREGISTER_DYC T1
LEFT JOIN V_HSE_COMB_BUILDING_INFO_PA T2 ON LEFT(T1.HOUSEID, 19) = T2.BUILDING_CODE
WHERE T1.LASTLOGOUTTIME IS NOT NULL AND date_format(T1.LASTLOGOUTTIME,'%Y') = ?
GROUP BY T2.STREET_NAME;

CREATE TEMPORARY TABLE tmp_jdNum AS
SELECT T3.STREET_NAME AS quyu, COUNT(DISTINCT T1.FIREID) AS jdNum
FROM V_SX_FIRESAFETYPLAN T1
LEFT JOIN V_FIRESAFETYREGISTER_DYC T2 ON T1.FIREID = T2.ID
LEFT JOIN V_HSE_COMB_BUILDING_INFO_PA T3 ON LEFT(T2.HOUSEID, 19) = T3.BUILDING_CODE
WHERE T1.DATE_NEW IS NOT NULL AND date_format(T1.DATE_NEW,'%Y') = ?
GROUP BY T3.STREET_NAME;

CREATE TEMPORARY TABLE tmp_xcNum AS
SELECT T3.STREET_NAME AS quyu, COUNT(*) AS xcNum
FROM V_SX_FIRESAFETYTRAINING T1
LEFT JOIN V_FIRESAFETYREGISTER_DYC T2 ON T1.FIREID = T2.ID
LEFT JOIN V_HSE_COMB_BUILDING_INFO_PA T3 ON LEFT(T2.HOUSEID, 19) = T3.BUILDING_CODE
WHERE T1.TRAININGTIME IS NOT NULL AND date_format(T1.TRAININGTIME,'%Y') = ?
GROUP BY T3.STREET_NAME;

SELECT T1.quyu, T2.lastNum, T3.jdNum, T4.xcNum
FROM tmp_street T1
LEFT JOIN tmp_lastNum T2 ON T1.quyu = T2.quyu
LEFT JOIN tmp_jdNum T3 ON T1.quyu = T3.quyu
LEFT JOIN tmp_xcNum T4 ON T1.quyu = T4.quyu
ORDER BY T2.lastNum
LIMIT ?,?;

请注意，这只是一种可能的优化方案，具体的优化方法还要根据数据库的实际情况进行调整。

import csv import requests from lxml import etree if __name__ == "__main__": url = 'https://jn.lianjia.com/zufang/pg{}' header = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.57' } # 爬取源码数据 headers = ["区域", "小区名称", "户型", "面积(㎡)", "价格(元/月)"] path = "链家济南租房数据.csv" with open(path, 'w', newline='') as f: csv_write = csv.writer(f) csv_head = ["区域", "小区名称", "户型", "面积(㎡)", "价格(元/月)"] csv_write.writerow(csv_head) for num in range: new_url = url.format(num) page_num1 = requests.get(url=new_url, headers=header) page_num1.encoding = ('utf-8') page_num = page_num1.text tree = etree.HTML(page_num) # 存储的就是标签对象 div_list = tree.xpath('//*[@id="content"]/div[1]/div[1]/div') with open(path, 'a', newline='') as f: csv_write = csv.writer(f) for div in div_list: quyu = div.xpath('./div/p[2]/a[1]//text()')[0] name = div.xpath('./div/p[2]/a[3]//text()')[0] area = div.xpath('./div/p[2]/text()')[4].split()[0] type = div.xpath('./div/p[2]/text()')[6].split()[0] place = div.xpath('./div//span/em/text()')[0] data_row = [quyu, name, type, area, place] csv_write.writerow(data_row) print("完成")改成获取全部信息

好的，针对您的需求，可以将代码修改如下：

import csv 
import requests 
from lxml import etree 

if __name__ == "__main__":
    url = 'https://jn.lianjia.com/zufang/pg{}' 
    header = { 
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.57' 
    } 
    
    # 爬取源码数据 
    headers = ["区域", "小区名称", "户型", "面积(㎡)", "价格(元/月)"] 
    path = "链家济南租房数据.csv" 
    with open(path, 'w', newline='') as f: 
        csv_write = csv.writer(f) 
        csv_head = ["区域", "小区名称", "户型", "面积(㎡)", "价格(元/月)"] 
        csv_write.writerow(csv_head) 

    # 获取总页数
    first_page = requests.get(url.format(1), headers=header) 
    first_page.encoding = ('utf-8') 
    first_tree = etree.HTML(first_page.text) 
    total_page = int(first_tree.xpath('//*[@id="content"]/div[1]/div[2]/div/a[last()-1]/text()')[0]) 

    # 爬取每一页的数据
    with open(path, 'a', newline='') as f: 
        csv_write = csv.writer(f) 
        for num in range(1, total_page+1): 
            new_url = url.format(num) 
            page_num1 = requests.get(url=new_url, headers=header) 
            page_num1.encoding = ('utf-8') 
            page_num = page_num1.text 
            tree = etree.HTML(page_num) 

            # 存储的就是标签对象 
            div_list = tree.xpath('//*[@id="content"]/div[1]/div[1]/div') 

            for div in div_list: 
                quyu = div.xpath('./div/p[2]/a[1]//text()')[0] 
                name = div.xpath('./div/p[2]/a[3]//text()')[0] 
                area = div.xpath('./div/p[2]/text()')[4].split()[0] 
                type = div.xpath('./div/p[2]/text()')[6].split()[0] 
                place = div.xpath('./div//span/em/text()')[0] 
                data_row = [quyu, name, type, area, place] 
                csv_write.writerow(data_row) 
            print("第{}页完成".format(num))
    print("所有数据爬取完成")

这样，代码就可以自动获取总页数，然后遍历每一页获取数据。在输出的时候，也会打印出当前爬取的页数，方便查看进度。