main.py

import os
import shutil

from flask import Flask, jsonify, render_template
import pyecharts
from pyecharts import options as opts
from pyecharts.charts import Bar, Pie, Map
import Crawler
import base64
import os
import webbrowser
import sqlite3
from pyecharts.charts import *
from pyecharts import options as opts
from pyecharts.globals import ThemeType  # 主题
from aip import AipFace
import Crawler
import shutil
import os
import shutil
from icrawler.builtin import BingImageCrawler

# class Database:
#     def work(self, continent="Global"):
#         print(f"为 {continent} 准备数据...")
#         if continent == "Asia":
#             return [100, 80, 110, 130, 95]
#         elif continent == "Europe":
#             return [120, 110, 90, 80, 100]
#         elif continent == "Africa":
#             return [90, 85, 95, 105, 100]
#         elif continent == "South America":
#             return [50, 67, 89, 101, 120]
#         elif continent == "North America":
#             return [70, 56, 78, 120, 100]
#         elif continent == "Oceania":
#             return [78, 88, 95, 100, 110]
#         else:
#             return [200, 150, 180, 220, 190]
#

"""三码"""
APP_ID = '119428112'
API_KEY = 'ayVbPc13C8R90cmCGfs30ui5'
SECRET_KEY = 'ClFlNrSPYxsu3wmsFRMrYMqQtF583N7r'
from icrawler.builtin import BingImageCrawler

keyword = ''
count = []  # 存取每个分类的图片数量


def bing_image_crawler(keyword, max_num):
    dic = 'bing_img'
    if os.path.exists(dic):
        shutil.rmtree(dic)
    bing_crawler = BingImageCrawler(
        feeder_threads=1,
        parser_threads=2,
        downloader_threads=4,
        storage={'root_dir': 'bing_img'})  # 修改存储文件夹名称

    # 设置筛选条件
    # filters = dict(
    #     type='photo',  # 确保是照片
    #     color='blackandwhite',  # 黑白风格
    #     size='large',  # 大尺寸
    #     layout='wide'  # 宽幅图片，更可能是合照
    # )

    # 开始爬取
    # 参数1-keyword：关键字（改为"时代少年团 合照"）
    # 参数2-filters：筛选器
    # 参数3-max_num：最大尝试数量
    bing_crawler.crawl(
        keyword=keyword,  # 修改关键词
        # filters=filters,
        max_num=max_num
    )


# 创建人脸检测对象
def get_file_content(file_path):
    """
    文件转BASE64编码字符串
    :param file_path:文件的路径
    :return: 转船之后的结果
    """
    file = open(file_path, 'rb')  # 使用只读模式打开文件
    data = file.read()  # 读取文件数据流
    content = base64.b64encode(data)  # BASE64编码
    file.close()  # 关闭文件流
    base = content.decode('utf-8')  # 8位的通用编码
    return base


def detect_face(base):
    """
    送给百度服务器进行人脸检测
    :param client:百度人脸检测对象
    :param base: BASE64编码之后的图片，支持PNG，JPG，JPEG，BMP
    :return: 服务器返回的的JSON数据
    """
    client = AipFace(APP_ID, API_KEY, SECRET_KEY)
    options = {'face_field': 'beauty,face_shape,landmark,glasses'}
    json = client.detect(base, 'BASE64', options)
    return json


def parse_json(json, keyword):
    """
    解析服务器返回的JSON数据
    :param json:
    :return: 解析出的颜值，如果监测失败返回-1
    """
    # 判断是否成功
    code = json['error_code']
    if code == 0:
        # 成功检测到人脸，返回研颜值分数(十分制)
        beauty = int(json['result']['face_list'][0]['beauty'] / 10) + 1
        face_shape = json['result']['face_list'][0]['face_shape']['type']
        landmark = json['result']['face_list'][0]['landmark']
        glasses = json['result']['face_list'][0]['glasses']['type']
        eyes_instance = landmark[1]['x'] - landmark[0]['x']
        mouth_instance = landmark[3]['y'] - landmark[2]['y']
        proportion = eyes_instance / mouth_instance
    else:
        beauty = -1
        face_shape = 'null'
        landmark = 'null'
        glasses = 'null'
        eyes_instance = 'null'
        mouth_instance = 'null'
        proportion = 'null'
    result = (keyword, beauty, face_shape, proportion, glasses)
    return result


def save_data2db(keyword, y_axis):

    conn = sqlite3.connect('data_analyze00.db')
    cursor = conn.cursor()
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS facial_features (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            keyword TEXT,
            beauty INT,
            face_shape TEXT,
            proportion REAL,
            glasses TEXT
        )
        ''')

    for i in y_axis:
        sql = '''
            INSERT INTO facial_features(keyword,beauty,face_shape,proportion,glasses)
            VALUES(?,?,?,?,?);
            '''
        # 替换占位符并执行3条插入操作
        # 参数1：预处理的SQL语句（包含占位符）
        # 参数2：要替换的参数列表（本例中包含3个元素）
        cursor.execute(sql, i)

    # 多条数据可以合并为一个列表
    # 准备一个预处理的SQL语句

    # 提交事务
    conn.commit()
    cursor.close()
    conn.close()


def classify():
    """
    先爬取图片，百度检测每一张图片，在windows中按照颜值分拣成不同的文件夹
    :return: TODO
    """
    root_dir = 'bing_img'  # 图片根目录
    file_list = os.listdir(root_dir)  # 把文件夹的文件名都存到列表中
    # 遍历图片
    for i in file_list:
        # 拼接处文件路径
        path = root_dir + '/' + i
        print(path)
        # 判断是否是文件
        if os.path.isfile(path):
            base = get_file_content(path)  # BASE64
            json = detect_face(base)  # 百度处理
            global count
            result = parse_json(json, keyword)
            count.append(result)
            # TODO把图片分类到不同目录下
            dic = root_dir + '/' + str(keyword)  # 文件夹路径
            if not os.path.exists(dic):  # 如果文件夹不存在
                os.makedirs(dic)  # 创建文件夹
            # 移动文件到所属的文件位置
            # 参数1：源文件的位置
            # 参数2：目标文件的位置
            os.rename(path, dic + '/' + i)


def select_data():

    conn = sqlite3.connect('data_analyze00.db')

    # 创建一个游标对象，用于执行SQL语句等操作
    cursor = conn.cursor()

    # 查询
    sql = 'SELECT * FROM facial_features;'
    cursor.execute(sql)
    rows = cursor.fetchall()  # 拿到所有查询的结果
    # # 遍历输出每行数据
    # for row in rows:
    #     print(row)  # 返回值元组

    # 关闭游标和连接
    cursor.close()
    conn.close()
    return rows



app = Flask(__name__, template_folder='.')
name = ["亚洲人", "欧洲人", "非洲人", "南美洲人", "北美洲人", "大洋洲人"]

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/api/data/<string:continent>')
def get_data(continent):
    sample_data = select_data()
    continent_map = {
        "Asia": "亚洲人", "Europe": "欧洲人", "Africa": "非洲人",
        "South America": "南美洲人", "North America": "北美洲人",
        "Oceania": "大洋洲人"
    }
    cnt   = [0]*5
    shape = [0]*5
    arr   = [0]*5
    glass = [0]*2

    if continent == "Global":
        target_list = None
    else:
        target_list = { continent_map[continent] }

    for (_id, kw, beauty, fs, prop, gls) in sample_data:
        if beauty < 0:
            continue
        # 如果 target_list 非空，就过滤掉不在其中的
        if target_list is not None and kw not in target_list:
            continue

        # 颜值分段
        if   beauty <= 2: cnt[0] += 1
        elif beauty <= 4: cnt[1] += 1
        elif beauty <= 6: cnt[2] += 1
        elif beauty <= 8: cnt[3] += 1
        else:               cnt[4] += 1

        # 脸型
        if   fs == "square":   shape[0] += 1
        elif fs == "triangle": shape[1] += 1
        elif fs == "oval":     shape[2] += 1
        elif fs == "heart":    shape[3] += 1
        elif fs == "round":    shape[4] += 1

        # 五官比例
        if   prop <= 1.5:   arr[0] += 1
        elif prop <= 2.0:   arr[1] += 1
        elif prop <= 2.5:   arr[2] += 1
        elif prop <= 3.0:   arr[3] += 1
        else:               arr[4] += 1

        # 眼镜
        if gls == 'none':
            glass[0] += 1
        else:
            glass[1] += 1

    print(*cnt)
    print(*shape)




    beauty_chart = (
        Bar()
        .add_xaxis(['0-2', '2-4', '4-6', '6-8', '8-10'])
        .add_yaxis('人数', cnt)
        .set_global_opts(
            title_opts=opts.TitleOpts(title=""),
            legend_opts=opts.LegendOpts(is_show=False),

        )
    )
    faceshape_chart = (
        Pie()
        .add("", [("正方形", shape[0]), ("三角形", shape[1]), ("椭圆形", shape[2]),("心形",shape[3]),("圆形",shape[4])], radius=["40%", "75%"])
        .set_global_opts(
             legend_opts=opts.LegendOpts(orient="vertical", pos_top="15%", pos_left="2%"),
        )
    )
    features_chart = (
        Bar()
        .add_xaxis(["1.0~1.5","1.5~2.0","2.0~2.5","2.5~3.0","<1.0 || >3.0"])
        .add_yaxis("人数", arr)
        .set_global_opts(
            title_opts=opts.TitleOpts(title=""),
            legend_opts=opts.LegendOpts(is_show=False),
            xaxis_opts=opts.AxisOpts(name="五官比例", axislabel_opts=opts.LabelOpts(rotate=30)),
            yaxis_opts=opts.AxisOpts(name="人数", axislabel_opts=opts.LabelOpts(formatter="{value}%")),
        )
    )

    glass_data = [
        ("未佩戴", glass[0]),
        ("佩戴", glass[1])
    ]

    glasses_chart = (
        Pie()
        .add("", glass_data, radius=["40%", "75%"])
        .set_global_opts(
            legend_opts=opts.LegendOpts(orient="vertical", pos_top="15%", pos_left="2%"),
        )
    )

    world_map_chart = (
        Map()
        .add("", [("China", 100)], "world", is_map_symbol_show=False)
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            visualmap_opts=opts.VisualMapOpts(is_show=False),
        )
    )

    response_data = {
        "viewName": "全球汇总" if continent == "Global" else continent,
        "beautyScoreOptions": beauty_chart.dump_options_with_quotes(),
        "faceshapeOptions": faceshape_chart.dump_options_with_quotes(),
        "featuresOptions": features_chart.dump_options_with_quotes(),
        "glassesOptions": glasses_chart.dump_options_with_quotes(),
        "worldMapOptions": world_map_chart.dump_options_with_quotes(),
    }

    return jsonify(response_data)






if __name__ == '__main__':

    # keyword=input("请输入关键词:")
    # num=int(input("数量:"))
    name=["亚洲人","欧洲人","非洲人","南美洲人","北美洲人","大洋洲人"]
    # imgpath = 'imgs'
    # for keyword in name:
        # print("正在爬取", keyword, "数据...")
        # bing_image_crawler(keyword, 10)
        # classify()
        # save_data2db(keyword, count)
    #     loc = os.path.join(imgpath, 'test',keyword)
    #     if os.path.exists(loc):
    #         shutil.rmtree(loc)
    #     os.makedirs(loc, exist_ok=True)
    #     clr = Crawler.Crawler(keyword,num,loc)
    #     clr.run()
    sample_data=select_data()

    app.run()
    print("Accepted.")