""" Project: enhanced_cs.AI_2508.15769v1_SceneGen_Single_Image_3D_Scene_Generation_in_One_ Type: transformer Description: Enhanced AI project based on cs.AI_2508.15769v1_SceneGen-Single-Image-3D-Scene-Generation-in-One- with content analysis. """

import logging import os import sys import time from typing import Dict, List, Optional

import numpy as np import pandas as pd import torch from torch import nn

Set up logging

logging.basicConfig( level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", handlers=[ logging.FileHandler("scene_gen.log"), logging.StreamHandler(sys.stdout), ], )

Constants and configuration

CONFIG = { "scene_image_path": "path/to/scene/image.jpg", "object_mask_path": "path/to/object/mask.jpg", "output_path": "path/to/output", "batch_size": 32, "num_epochs": 10, }

class SceneGen(nn.Module): """ SceneGen: Single-Image 3D Scene Generation in One Feedforward Pass """

def __init__(self):
    super(SceneGen, self).__init__()
    self.conv1 = nn.Conv2d(3, 64, kernel_size=3)
    self.conv2 = nn.Conv2d(64, 128, kernel_size=3)
    self.conv3 = nn.Conv2d(128, 256, kernel_size=3)
    self.fc1 = nn.Linear(256 * 256, 128)
    self.fc2 = nn.Linear(128, 3)

def forward(self, x):
    x = torch.relu(self.conv1(x))
    x = torch.relu(self.conv2(x))
    x = torch.relu(self.conv3(x))
    x = x.view(-1, 256 * 256)
    x = torch.relu(self.fc1(x))
    x = self.fc2(x)
    return x

class VelocityThreshold: """ Velocity Threshold Algorithm """

def __init__(self, threshold: float):
    self.threshold = threshold

def calculate(self, velocity: float) -> bool:
    return velocity > self.threshold

class FlowTheory: """ Flow Theory Algorithm """

def __init__(self, threshold: float):
    self.threshold = threshold

def calculate(self, flow: float) -> bool:
    return flow > self.threshold

class SceneGenerator: """ Scene Generator """

def __init__(self, scene_image_path: str, object_mask_path: str, output_path: str):
    self.scene_image_path = scene_image_path
    self.object_mask_path = object_mask_path
    self.output_path = output_path
    self.scene_gen = SceneGen()
    self.velocity_threshold = VelocityThreshold(threshold=0.5)
    self.flow_theory = FlowTheory(threshold=0.5)

def generate_scene(self):
    # Load scene image and object mask
    scene_image = np.load(self.scene_image_path)
    object_mask = np.load(self.object_mask_path)

    # Preprocess scene image and object mask
    scene_image = torch.from_numpy(scene_image).float()
    object_mask = torch.from_numpy(object_mask).float()

    # Generate 3D scene
    output = self.scene_gen(scene_image)

    # Postprocess output
    output = output.detach().numpy()

    # Save output to file
    np.save(os.path.join(self.output_path, "output.npy"), output)

    return output

def main(): scene_generator = SceneGenerator( scene_image_path=CONFIG["scene_image_path"], object_mask_path=CONFIG["object_mask_path"], output_path=CONFIG["output_path"], )

start_time = time.time()
output = scene_generator.generate_scene()
end_time = time.time()

logging.info(f"Scene generation completed in {end_time - start_time} seconds")
logging.info(f"Output saved to {CONFIG['output_path']}")

if name == "main": main()