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import osfrom PIL import Image# 源目录# MyPath = 'E:/play/FaceMask_CelebA-master/facemask_image/'MyPath = 'E:/play/FaceMask_CelebA-master/save/masks/'# 输出目录OutPath = 'E:/play/FaceMask

import osimport sysimport randomfrom PyQt5.QtGui import *from PyQt5.QtCore import *from PyQt5.QtWidgets import *class DesktopPet(QWidget):    def __init__(self, parent=None, **kwar

import osimport jsonfrom abc import abstractmethodimport pygame as pgfrom . import constants as cclass State():    def __init__(self):       &nbs

import sysfrom PyQt6.QtWidgets import QApplication, QWidget, QLabel, QLineEdit, QPushButton, QHBoxLayout, QVBoxLayoutfrom PyQt6.QtGui import QIconfrom PyQt6.QtCore import Qtclass MyWindow(QWidget):&nb

import osimport pygame as pgkeybinding = {    'action':pg.K_s,    'jump':pg.K_a,    'left':pg.K_LEFT,    'right&

# -*- coding: utf-8 -*-# @Time : 2022/3/30 14:44# @Author : Orange# @File : gm_trigonometric.pyfrom decimal import *import mathclass GM_trig():    def __init__(self, X0, X0_hat, L)

import osimport cv2import sysimport timeimport numpy as npimport face_recognitionfrom PyQt5.QtGui import QPixmapfrom PyQt5.QtWidgets import QApplication,QLabel# 全局变量path = './data'  # 人像存

# 创建一个代表玩家的AIai_players = [0, 0]ai_players[0] = self.user_positionai_players[1] = DeepAgent(self.user_position, self.card_play_model_path_dict[self.user_position])# 初始化游戏环境self.env = GameEnv(ai_player

import torch.nn as nnimport torchimport cv2import os# 路径改为反斜杠，在linux和windows中都可使用pkl_dir = "D:/DCGAN/pkl/25g.pkl"# 在同目录下建立一个bitmap_epoch10文件夹test_dir = "bitmaps_epoch10/"if not os.

"""拆分数据集为 训练集、测试集、验证集"""import osimport random# 一. 设置数据集比例# 训练集的比率train_percent = 0.8# 测试集占测试验证集的百分比test_other_percent = 0.5# 二. 获取训练集,验证集和测试集的索引列表# 标注数据地址voc_annotations

mulpicplus = "3"     #1 for normal,2 for 4pic plus,3 for 9pic plus and so on        assert(int(mulpicplus)>=1)  &nbsp

import java.util.Random;import java.util.Scanner;public class User {    public static void main(String[] args) {        {   &

#define _CRT_SECURE_NO_WARNINGS#include "Heap.h"//初始化结构体void HeapInit(HP* php){       assert(php);       php->a = NULL; &n

# -*- coding: utf-8 -*-# @Modified by: Ruihao# @ProjectName:yolov5-pyqt5import sysfrom datetime import datetimefrom PyQt5 import QtWidgetsfrom PyQt5.QtWidgets import *from utils.id_utils import get_id

import tkinter as tkfrom db import dbfrom tkinter import ttk# 录入类class InputFrame(tk.Frame):    def __init__(self, master=None):        supe

# -*- coding: utf-8 -*-import cv2import dlibimport numpy as np detector = dlib.get_frontal_face_detector()  # dlib的正向人脸检测器predictor = dlib.shape_predictor(r'shape_predictor_68_face_landm

import cv2import face_recognition  # 人脸识别库  99.7%    cmake  dlib  face_recognitionimport subprocessdef video2mp3(file_name):    outfile_name = file_na

# 创建一个代表玩家的AIai_players = [0, 0]ai_players[0] = self.user_positionai_players[1] = DeepAgent(self.user_position, self.card_play_model_path_dict[self.user_position])# 初始化游戏环境self.env = GameEnv(ai_player

import jsonimport randomimport time#别挂配置数据gua_data_path = "data.json"#别卦数据gua_data_map = {}fake_delay = 10#读取别卦数据def init_gua_data(json_path):with open(gua_data_path,'r',encoding=&#3

import osimport face_recognitionimport cv2# 读取到数据库中的人名和面部特征face_databases_dir = 'face_databases'user_names = []user_faces_encodings = []# 得到face_databases中所有文件files = os.listdir('face_data