虚拟世界的AI魔法：AIGC引领元宇宙创作革命

import openai

# 设置OpenAI API密钥
openai.api_key = 'YOUR_API_KEY'

# 定义生成虚拟环境图像的函数
def generate_virtual_environment(prompt):
    response = openai.Image.create(
        prompt=prompt,
        n=1,
        size="1024x1024"
    )
    image_url = response['data'][0]['url']
    return image_url

# 示例：生成一个未来城市的虚拟环境
prompt = "A futuristic city with skyscrapers, neon lights, flying cars, and advanced technology"
image_url = generate_virtual_environment(prompt)
print(f"Generated Image URL: {image_url}")

import openai

# 设置OpenAI API密钥
openai.api_key = 'YOUR_API_KEY'

# 定义虚拟角色对话的函数
def generate_dialogue(character_name, user_input):
    prompt = f"Character {character_name}: {user_input}\nResponse:"

    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7
    )

    return response.choices[0].text.strip()

# 示例：生成虚拟角色的对话
character_name = "AI Guide"
user_input = "Hello, can you tell me about the virtual world?"

dialogue = generate_dialogue(character_name, user_input)
print(dialogue)

import openai

# 设置OpenAI API密钥
openai.api_key = 'YOUR_API_KEY'

# 定义生成虚拟物品的函数
def generate_virtual_item(prompt):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=100,
        n=1,
        stop=None,
        temperature=0.7
    )
    return response.choices[0].text.strip()

# 示例：生成一个虚拟物品的描述
prompt = "Generate a description for a futuristic helmet that protects users in a virtual world"
virtual_item = generate_virtual_item(prompt)
print(f"Generated Virtual Item: {virtual_item}")

import torch
from torchvision import transforms
from PIL import Image
import matplotlib.pyplot as plt

# 加载训练好的GAN模型（假设已有预训练模型）
generator = torch.load("pretrained_gan_generator.pth")

# 生成随机噪声输入
noise = torch.randn(1, 100)  # 100维的随机噪声

# 使用生成器模型生成虚拟角色图像
generated_image = generator(noise)

# 转换为图片格式
generated_image = generated_image.squeeze().detach().numpy()
generated_image = (generated_image + 1) / 2  # 将像素值归一化到[0, 1]

# 显示生成的虚拟角色图像
plt.imshow(generated_image[0], cmap='gray')
plt.axis('off')
plt.show()

import openai

# 设置OpenAI API密钥
openai.api_key = 'YOUR_API_KEY'

# 定义生成虚拟角色对话的函数
def generate_virtual_character_dialogue(character_name, user_input):
    prompt = f"{character_name} is a helpful AI guide in a virtual world. They respond to user queries and give advice.\nUser: {user_input}\n{character_name}:"

    # 生成对话
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=150,
        temperature=0.9
    )

    return response.choices[0].text.strip()

# 示例：生成虚拟角色的对话
character_name = "AI Guide"
user_input = "What can I do in this virtual world?"

dialogue = generate_virtual_character_dialogue(character_name, user_input)
print(dialogue)

import cv2
import numpy as np

# 加载预训练的动作识别模型（如OpenPose）
pose_model = cv2.dnn.readNetFromTensorflow('pose_model.pb')

# 打开摄像头
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # 输入图像预处理
    blob = cv2.dnn.blobFromImage(frame, 1.0, (368, 368), (127.5, 127.5, 127.5), swapRB=True, crop=False)
    pose_model.setInput(blob)
    
    # 获取关键点检测
    output = pose_model.forward()

    # 可视化检测到的人体姿势
    for i in range(output.shape[2]):
        key_point = output[0, 0, i, 0]
        cv2.circle(frame, (int(key_point[0] * frame.shape[1]), int(key_point[1] * frame.shape[0])), 5, (0, 255, 0), -1)
    
    cv2.imshow('User Pose Detection', frame)
    
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

import openai
import random

# 设置OpenAI API密钥
openai.api_key = 'YOUR_API_KEY'

# 定义生成个性化虚拟角色的函数
def generate_personalized_character(user_data, character_name):
    # 根据用户数据（如爱好、个性等）定制虚拟角色
    interests = user_data.get("interests", "exploring new worlds")
    personality = user_data.get("personality", "adventurous")

    prompt = f"Character {character_name} is a {personality} and curious character, interested in {interests}. They respond to user queries in a friendly and helpful way.\nUser: {user_data['last_query']}\n{character_name}:"

    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=150,
        temperature=0.8
    )

    return response.choices[0].text.strip()

# 示例：根据用户数据生成个性化的虚拟角色
user_data = {"name": "John", "last_query": "Tell me about the latest adventure!", "interests": "space exploration", "personality": "curious"}
character_name = "AI Adventurer"

personalized_dialogue = generate_personalized_character(user_data, character_name)
print(personalized_dialogue)