svjack/Aesthetics_X_Phone_4K_Images

Aesthetics X Image Dataset

Overview

This dataset contains high-quality aesthetic images collected from Twitter user @aestheticsguyy. The collection features visually pleasing digital artwork, wallpapers, and photography with a focus on visual appeal and design inspiration.

Dataset Contents

• Image files in JPEG/PNG format
• High-resolution wallpaper collections
• Thematically organized visual content

Collection Methodology

1. Images were gathered from @aestheticsguyy's public Twitter posts
2. Only high-quality, aesthetically curated content was selected
3. Additional premium wallpapers from creator's exclusive collections

Content Creator Profile

Aesthetics X (@aestheticsguyy)
• Digital Aesthetics Platform
• Active since June 2024
• Portfolio Links:
• Exclusive Content: Link in bio (Twitter profile)
• Support Creator: buymeacoffee.com/aestheticsguyy

Intended Use Cases

• Visual design inspiration
• Wallpaper and background resources
• Aesthetic analysis and research
• AI/ML training for visual content generation
• Digital art reference studies

Technical Specifications

• Formats: JPEG, PNG
• Resolutions: Various (including high-res wallpaper formats)

Usage Guidelines

This dataset is provided for personal, educational and research purposes only. Commercial use requires explicit permission from @aestheticsguyy. Premium content may have additional usage restrictions.

Citation

If using this dataset in academic work, please cite as:

Aesthetics X Image Dataset by @aestheticsguyy. Collected [DATE]. 
Twitter: https://twitter.com/aestheticsguyy
Support: buymeacoffee.com/aestheticsguyy

Contact

For dataset inquiries: [Your Email]
For content permissions: Contact @aestheticsguyy on Twitter or via bio links

import os
import cv2
import numpy as np
import shutil
from tqdm import tqdm

def process_large_images(input_path, output_path, min_width=2000, target_width=2304, target_height=4096):
    """
    遍历文件夹中的图片，筛选宽度大于min_width的图片，
    调整分辨率到target_width x target_height（添加黑边保持比例），
    并保存到输出路径
    
    参数:
        input_path: 输入文件夹路径
        output_path: 输出文件夹路径
        min_width: 最小宽度阈值，只处理宽度大于此值的图片
        target_width: 目标宽度
        target_height: 目标高度
    """
    # 支持的图片扩展名
    image_extensions = ('.png', '.jpg', '.jpeg', '.bmp', '.tiff', '.webp')
    
    # 创建输出目录
    os.makedirs(output_path, exist_ok=True)
    
    # 遍历输入目录
    for root, dirs, files in os.walk(input_path):
        for file in tqdm(files, desc="Processing images"):
            # 检查是否为图片文件
            if file.lower().endswith(image_extensions):
                file_path = os.path.join(root, file)
                
                try:
                    # 读取图片
                    img = cv2.imread(file_path)
                    if img is None:
                        print(f"Warning: Could not read image {file_path}")
                        continue
                        
                    # 获取图片尺寸
                    h, w = img.shape[:2]
                    
                    # 检查宽度是否大于阈值
                    if w > min_width:
                        # 计算缩放比例
                        scale = min(target_width / w, target_height / h)
                        new_w = int(w * scale)
                        new_h = int(h * scale)
                        
                        # 调整图片大小
                        resized_img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_AREA)
                        
                        # 创建黑色背景
                        background = np.zeros((target_height, target_width, 3), dtype=np.uint8)
                        
                        # 计算偏移量以居中图片
                        x_offset = (target_width - new_w) // 2
                        y_offset = (target_height - new_h) // 2
                        
                        # 将调整后的图片放入背景中
                        background[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = resized_img
                        
                        # 构建输出路径（保持原始目录结构）
                        relative_path = os.path.relpath(file_path, input_path)
                        output_file_path = os.path.join(output_path, relative_path)
                        os.makedirs(os.path.dirname(output_file_path), exist_ok=True)
                        
                        # 保存图片
                        cv2.imwrite(output_file_path, background)
                        
                        # 尝试复制对应的txt文件（如果有）
                        base_name = os.path.splitext(file)[0]
                        txt_source = os.path.join(root, f"{base_name}.txt")
                        if os.path.exists(txt_source):
                            txt_target = os.path.join(os.path.dirname(output_file_path), f"{base_name}.txt")
                            shutil.copy2(txt_source, txt_target)
                            
                        print(f"Processed: {file_path} (original size: {w}x{h})")
                    else:
                        print(f"Skipped (width <= {min_width}): {file_path}")
                        
                except Exception as e:
                    print(f"Error processing {file_path}: {str(e)}")

process_large_images("Aesthetics_X_Phone_4K_Images", 
                     "Aesthetics_X_Phone_4K_Images_Rec")