重构程序文件目录结构并更新相关路径引用
- 创建新的目录结构：research/、tools/（含子目录）和apps/ - 移动核心理论文件到research/core-theory/ - 移动天山理论文件到research/specialized/ - 重组tools/目录为多个子目录：content-generation/、data-processing/等 - 更新所有文档中的路径引用，包括README.md、项目结构说明.md等 - 更新工作流文件和脚本中的路径引用 - 更新文档索引文件中的路径引用
This commit is contained in:
ben
173
tools/data-processing/image-processing/analyze_large_image.py
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173
tools/data-processing/image-processing/analyze_large_image.py
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#!/usr/bin/env python3
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"""
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大型图像分析工具
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用于分析《三体》项目的复杂图表结构
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"""
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import os
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import sys
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from PIL import Image, ImageDraw, ImageFont
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import numpy as np
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from collections import Counter
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import json
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def analyze_image_basic_info(image_path):
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"""分析图像基本信息"""
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print(f"正在分析图像: {image_path}")
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try:
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# 使用更节省内存的方式打开图像
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Image.MAX_IMAGE_PIXELS = None # 移除像素数量限制
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with Image.open(image_path) as img:
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print(f"图像格式: {img.format}")
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print(f"图像模式: {img.mode}")
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print(f"图像尺寸: {img.size} (宽 x 高)")
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print(f"总像素数: {img.size[0] * img.size[1]:,}")
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# 计算文件大小
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file_size = os.path.getsize(image_path)
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print(f"文件大小: {file_size / (1024*1024):.1f} MB")
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return img
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except Exception as e:
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print(f"打开图像时出错: {e}")
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return None
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def analyze_image_colors(img, sample_size=1000):
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"""分析图像颜色分布"""
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print("\n=== 颜色分析 ===")
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# 将图像转换为RGB模式（如果不是的话）
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if img.mode != 'RGB':
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img = img.convert('RGB')
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# 采样分析（对于大图像，采样会更快）
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pixels = list(img.getdata())
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if len(pixels) > sample_size * sample_size:
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# 均匀采样
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step = len(pixels) // (sample_size * sample_size)
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pixels = pixels[::step]
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# 统计主要颜色
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color_counter = Counter(pixels)
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print(f"采样像素数: {len(pixels):,}")
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print("主要颜色 (RGB值, 出现次数):")
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for color, count in color_counter.most_common(10):
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percentage = (count / len(pixels)) * 100
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print(f" RGB{color}: {count:,} 次 ({percentage:.1f}%)")
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def detect_content_regions(img, threshold=240):
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"""检测图像中的内容区域"""
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print("\n=== 内容区域检测 ===")
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# 转换为灰度图
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gray = img.convert('L')
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pixels = np.array(gray)
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# 找到非白色区域（假设白色背景）
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non_white = pixels < threshold
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# 找到边界
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rows, cols = np.where(non_white)
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if len(rows) > 0:
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min_row, max_row = rows.min(), rows.max()
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min_col, max_col = cols.min(), cols.max()
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print(f"内容区域边界:")
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print(f" 行范围: {min_row} - {max_row} (高度: {max_row - min_row + 1})")
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print(f" 列范围: {min_col} - {max_col} (宽度: {max_col - min_col + 1})")
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return (min_row, max_row, min_col, max_col)
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else:
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print("未检测到明显的内容区域")
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return None
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def extract_text_regions(img, region_bounds=None):
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"""提取可能的文本区域"""
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print("\n=== 文本区域分析 ===")
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# 如果指定了区域边界，只分析该区域
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if region_bounds:
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min_row, max_row, min_col, max_col = region_bounds
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img_cropped = img.crop((min_col, min_row, max_col, max_row))
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else:
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img_cropped = img
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# 转换为灰度图
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gray = img_cropped.convert('L')
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pixels = np.array(gray)
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# 简单的文本检测：寻找高对比度区域
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# 计算局部方差
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from scipy import ndimage
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# 使用sobel算子检测边缘
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sobel_x = ndimage.sobel(pixels, axis=1)
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sobel_y = ndimage.sobel(pixels, axis=0)
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edges = np.sqrt(sobel_x**2 + sobel_y**2)
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# 找到高边缘密度的区域
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edge_threshold = np.percentile(edges, 90)
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high_edge_regions = edges > edge_threshold
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# 统计高边缘区域
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high_edge_pixels = np.sum(high_edge_regions)
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total_pixels = pixels.size
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print(f"高边缘密度像素: {high_edge_pixels:,} / {total_pixels:,} ({high_edge_pixels/total_pixels*100:.1f}%)")
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return high_edge_regions
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def create_overview_image(img, output_path="overview.png", max_dimension=2000):
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"""创建图像概览（缩略图）"""
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print(f"\n=== 创建概览图像 ===")
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# 计算缩放比例
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scale = min(max_dimension / img.size[0], max_dimension / img.size[1], 1.0)
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if scale < 1.0:
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new_size = (int(img.size[0] * scale), int(img.size[1] * scale))
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print(f"缩放到: {new_size}")
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overview = img.resize(new_size, Image.Resampling.LANCZOS)
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else:
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overview = img.copy()
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# 保存概览图
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overview.save(output_path)
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print(f"概览图已保存: {output_path}")
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return overview
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def main():
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"""主函数"""
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image_path = "/home/ben/code/huhan3000/3body/三体结构3.drawio.png"
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print("=" * 50)
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print("《三体》项目大型图像分析工具")
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print("=" * 50)
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# 分析基本信息
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img = analyze_image_basic_info(image_path)
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if img is None:
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return
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# 分析颜色分布
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analyze_image_colors(img)
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# 检测内容区域
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regions = detect_content_regions(img)
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# 提取文本区域
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extract_text_regions(img, regions)
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# 创建概览图
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create_overview_image(img, "/home/ben/code/huhan3000/3body/overview.png")
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print("\n" + "=" * 50)
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print("分析完成！")
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print("=" * 50)
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if __name__ == "__main__":
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main()
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274
tools/data-processing/image-processing/deepzoom_generator.py
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274
tools/data-processing/image-processing/deepzoom_generator.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Deep Zoom 瓦片集生成工具
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用于将高分辨率PNG图像转换为Deep Zoom格式，适用于三体项目的大型历史图表展示
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使用方法:
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python deepzoom_generator.py --input <input_image.png> --output <output_dir> --tile_size <tile_size> --overlap <overlap>
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参数说明:
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--input: 输入的PNG图像文件路径
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--output: 输出的Deep Zoom目录路径
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--tile_size: 瓦片大小，默认512
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--overlap: 瓦片重叠像素，默认1
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--format: 输出瓦片格式，支持jpg或png，默认jpg
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--quality: JPEG图像质量(1-100)，默认90
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示例:
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python deepzoom_generator.py --input "三体结构3.drawio.png" --output deepzoom_output
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"""
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import os
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import argparse
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import math
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from PIL import Image
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from xml.dom import minidom
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import logging
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from tqdm import tqdm
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# 配置日志
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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class DeepZoomGenerator:
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"""Deep Zoom 瓦片集生成器类"""
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def __init__(self, input_image_path, output_dir, tile_size=512, overlap=1,
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output_format='jpg', quality=90):
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"""
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初始化DeepZoomGenerator
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参数:
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input_image_path: 输入图像路径
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output_dir: 输出目录路径
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tile_size: 瓦片大小
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overlap: 瓦片重叠像素
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output_format: 输出格式(jpg或png)
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quality: JPEG质量
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"""
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self.input_image_path = input_image_path
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self.output_dir = output_dir
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self.tile_size = tile_size
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self.overlap = overlap
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self.output_format = output_format.lower()
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self.quality = quality
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# 验证参数
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self._validate_params()
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# 创建输出目录
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self._create_output_dirs()
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# 加载图像
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self.image = self._load_image()
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self.width, self.height = self.image.size
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# 计算金字塔层级
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self.levels = self._calculate_levels()
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logger.info(f"输入图像: {input_image_path}")
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logger.info(f"图像尺寸: {self.width}x{self.height}")
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logger.info(f"输出目录: {output_dir}")
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logger.info(f"瓦片大小: {tile_size}, 重叠像素: {overlap}")
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logger.info(f"输出格式: {output_format}")
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logger.info(f"金字塔层级: {self.levels}")
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def _validate_params(self):
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"""验证输入参数"""
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# 检查输入图像是否存在
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if not os.path.exists(self.input_image_path):
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raise FileNotFoundError(f"输入图像文件不存在: {self.input_image_path}")
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# 检查输出格式
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if self.output_format not in ['jpg', 'png']:
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raise ValueError(f"不支持的输出格式: {self.output_format}，仅支持jpg和png")
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# 检查质量参数
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if not (1 <= self.quality <= 100):
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raise ValueError(f"JPEG质量必须在1-100之间: {self.quality}")
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# 检查瓦片大小
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if self.tile_size <= 0:
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raise ValueError(f"瓦片大小必须大于0: {self.tile_size}")
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# 检查重叠像素
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if self.overlap < 0:
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raise ValueError(f"重叠像素不能为负数: {self.overlap}")
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def _create_output_dirs(self):
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"""创建输出目录结构"""
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# 创建主输出目录
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os.makedirs(self.output_dir, exist_ok=True)
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# 提取基本文件名(不含扩展名)
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base_name = os.path.splitext(os.path.basename(self.input_image_path))[0]
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# 设置DZI文件名和瓦片目录
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self.dzi_filename = f"{base_name}.dzi"
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self.tiles_dir = f"{base_name}_files"
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self.tiles_dir_path = os.path.join(self.output_dir, self.tiles_dir)
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# 创建瓦片目录
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os.makedirs(self.tiles_dir_path, exist_ok=True)
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def _load_image(self):
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"""加载输入图像"""
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try:
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image = Image.open(self.input_image_path)
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# 确保图像为RGB模式
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if image.mode != 'RGB':
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image = image.convert('RGB')
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return image
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except Exception as e:
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raise IOError(f"无法加载图像: {e}")
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def _calculate_levels(self):
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"""计算金字塔层级数量"""
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# 计算最大维度
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max_dim = max(self.width, self.height)
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# 计算需要的层级数，确保最小维度至少为1
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levels = math.floor(math.log2(max_dim)) + 1
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return levels
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def _create_dzi_file(self):
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"""创建DZI XML文件"""
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# 创建XML文档
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doc = minidom.getDOMImplementation().createDocument(None, 'Image', None)
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root = doc.documentElement
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root.setAttribute('xmlns', 'http://schemas.microsoft.com/deepzoom/2008')
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root.setAttribute('Format', self.output_format)
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root.setAttribute('Overlap', str(self.overlap))
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root.setAttribute('TileSize', str(self.tile_size))
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# 创建Size元素
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size_element = doc.createElement('Size')
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size_element.setAttribute('Height', str(self.height))
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size_element.setAttribute('Width', str(self.width))
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root.appendChild(size_element)
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# 保存XML文件
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dzi_file_path = os.path.join(self.output_dir, self.dzi_filename)
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with open(dzi_file_path, 'w', encoding='utf-8') as f:
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root.writexml(f, indent=' ', addindent=' ', newl='\n')
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logger.info(f"创建DZI文件: {dzi_file_path}")
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def _generate_tiles(self):
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"""生成所有层级的瓦片"""
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current_image = self.image.copy()
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current_width, current_height = current_image.size
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# 从最高分辨率到最低分辨率生成瓦片
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for level in range(self.levels):
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# 创建当前层级的目录
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level_dir = os.path.join(self.tiles_dir_path, str(level))
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os.makedirs(level_dir, exist_ok=True)
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# 计算当前层级的瓦片数量
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tiles_x = max(1, math.ceil((current_width + 2 * self.overlap) / self.tile_size))
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tiles_y = max(1, math.ceil((current_height + 2 * self.overlap) / self.tile_size))
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logger.info(f"生成层级 {level} 的瓦片: {tiles_x}x{tiles_y}")
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# 使用tqdm创建进度条
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total_tiles = tiles_x * tiles_y
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with tqdm(total=total_tiles, desc=f"层级 {level}", unit="tile") as pbar:
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# 生成每个瓦片
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for y in range(tiles_y):
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for x in range(tiles_x):
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self._generate_single_tile(current_image, level, x, y, level_dir)
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pbar.update(1)
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# 如果不是最后一层，缩小图像到下一层
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if level < self.levels - 1:
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new_width = max(1, current_width // 2)
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new_height = max(1, current_height // 2)
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current_image = current_image.resize((new_width, new_height), Image.Resampling.LANCZOS)
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current_width, current_height = current_image.size
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def _generate_single_tile(self, image, level, tile_x, tile_y, level_dir):
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"""生成单个瓦片"""
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width, height = image.size
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# 计算瓦片在原图中的位置
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tile_size_no_overlap = self.tile_size - 2 * self.overlap
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start_x = max(0, tile_x * tile_size_no_overlap - self.overlap)
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start_y = max(0, tile_y * tile_size_no_overlap - self.overlap)
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# 计算瓦片的实际大小
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end_x = min(width, start_x + self.tile_size)
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end_y = min(height, start_y + self.tile_size)
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actual_width = end_x - start_x
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actual_height = end_y - start_y
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# 创建一个新的瓦片图像(空白背景)
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tile = Image.new('RGB', (self.tile_size, self.tile_size), color=(255, 255, 255))
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# 从原图中裁剪瓦片区域
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tile_region = image.crop((start_x, start_y, end_x, end_y))
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# 将裁剪的区域粘贴到瓦片上
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tile.paste(tile_region, (0, 0))
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# 保存瓦片
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tile_filename = os.path.join(level_dir, f"{tile_x}_{tile_y}.{self.output_format}")
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if self.output_format == 'jpg':
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tile.save(tile_filename, 'JPEG', quality=self.quality, optimize=True)
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else:
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tile.save(tile_filename, 'PNG', optimize=True)
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def generate(self):
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"""生成完整的Deep Zoom瓦片集"""
|
||||
logger.info("开始生成Deep Zoom瓦片集...")
|
||||
|
||||
# 创建DZI文件
|
||||
self._create_dzi_file()
|
||||
|
||||
# 生成瓦片
|
||||
self._generate_tiles()
|
||||
|
||||
logger.info("Deep Zoom瓦片集生成完成!")
|
||||
logger.info(f"DZI文件: {os.path.join(self.output_dir, self.dzi_filename)}")
|
||||
logger.info(f"瓦片目录: {self.tiles_dir_path}")
|
||||
|
||||
|
||||
def parse_args():
|
||||
"""解析命令行参数"""
|
||||
parser = argparse.ArgumentParser(description='Deep Zoom瓦片集生成工具')
|
||||
parser.add_argument('--input', '-i', required=True, help='输入的PNG图像文件路径')
|
||||
parser.add_argument('--output', '-o', required=True, help='输出的Deep Zoom目录路径')
|
||||
parser.add_argument('--tile_size', '-t', type=int, default=512, help='瓦片大小，默认512')
|
||||
parser.add_argument('--overlap', '-l', type=int, default=1, help='瓦片重叠像素，默认1')
|
||||
parser.add_argument('--format', '-f', default='jpg', choices=['jpg', 'png'], help='输出瓦片格式，默认jpg')
|
||||
parser.add_argument('--quality', '-q', type=int, default=90, help='JPEG图像质量(1-100)，默认90')
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
args = parse_args()
|
||||
|
||||
try:
|
||||
# 创建DeepZoomGenerator实例
|
||||
generator = DeepZoomGenerator(
|
||||
input_image_path=args.input,
|
||||
output_dir=args.output,
|
||||
tile_size=args.tile_size,
|
||||
overlap=args.overlap,
|
||||
output_format=args.format,
|
||||
quality=args.quality
|
||||
)
|
||||
|
||||
# 生成Deep Zoom瓦片集
|
||||
generator.generate()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"生成Deep Zoom瓦片集时出错: {e}")
|
||||
raise
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
250
tools/data-processing/image-processing/image_converter.py
Normal file
250
tools/data-processing/image-processing/image_converter.py
Normal file
@@ -0,0 +1,250 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
胡汉三千年项目 - 图像转换工具
|
||||
支持 PPM -> PNG/JPG/SVG 转换
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import argparse
|
||||
|
||||
def install_requirements():
|
||||
"""安装必要的依赖包"""
|
||||
import subprocess
|
||||
|
||||
packages = [
|
||||
'Pillow>=10.0.0', # PIL的现代版本
|
||||
'opencv-python>=4.8.0', # OpenCV
|
||||
'svgwrite>=1.4.0', # SVG生成
|
||||
'numpy>=1.24.0', # 数值计算
|
||||
]
|
||||
|
||||
print("🔧 安装必要的Python包...")
|
||||
for package in packages:
|
||||
try:
|
||||
subprocess.check_call([sys.executable, '-m', 'pip', 'install', package])
|
||||
print(f"✅ {package} 安装成功")
|
||||
except subprocess.CalledProcessError as e:
|
||||
print(f"❌ {package} 安装失败: {e}")
|
||||
return False
|
||||
return True
|
||||
|
||||
def convert_ppm_to_png(ppm_path, output_path=None, quality=95):
|
||||
"""将PPM文件转换为PNG格式"""
|
||||
try:
|
||||
from PIL import Image
|
||||
|
||||
if output_path is None:
|
||||
output_path = str(Path(ppm_path).with_suffix('.png'))
|
||||
|
||||
# 打开PPM文件
|
||||
with Image.open(ppm_path) as img:
|
||||
# 转换为RGB模式（PPM通常是RGB）
|
||||
if img.mode != 'RGB':
|
||||
img = img.convert('RGB')
|
||||
|
||||
# 保存为PNG
|
||||
img.save(output_path, 'PNG', optimize=True)
|
||||
print(f"✅ PPM -> PNG: {ppm_path} -> {output_path}")
|
||||
return output_path
|
||||
|
||||
except ImportError:
|
||||
print("❌ 需要安装 Pillow: pip install Pillow")
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"❌ 转换失败: {e}")
|
||||
return None
|
||||
|
||||
def convert_ppm_to_jpg(ppm_path, output_path=None, quality=95):
|
||||
"""将PPM文件转换为JPG格式"""
|
||||
try:
|
||||
from PIL import Image
|
||||
|
||||
if output_path is None:
|
||||
output_path = str(Path(ppm_path).with_suffix('.jpg'))
|
||||
|
||||
with Image.open(ppm_path) as img:
|
||||
if img.mode != 'RGB':
|
||||
img = img.convert('RGB')
|
||||
|
||||
img.save(output_path, 'JPEG', quality=quality, optimize=True)
|
||||
print(f"✅ PPM -> JPG: {ppm_path} -> {output_path}")
|
||||
return output_path
|
||||
|
||||
except ImportError:
|
||||
print("❌ 需要安装 Pillow: pip install Pillow")
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"❌ 转换失败: {e}")
|
||||
return None
|
||||
|
||||
def create_svg_template(image_path, output_path=None):
|
||||
"""为图像创建SVG模板"""
|
||||
try:
|
||||
import svgwrite
|
||||
from PIL import Image
|
||||
|
||||
if output_path is None:
|
||||
output_path = str(Path(image_path).with_suffix('.svg'))
|
||||
|
||||
# 获取图像尺寸
|
||||
with Image.open(image_path) as img:
|
||||
width, height = img.size
|
||||
|
||||
# 创建SVG文档
|
||||
dwg = svgwrite.Drawing(output_path, size=(f"{width}px", f"{height}px"))
|
||||
|
||||
# 添加背景矩形
|
||||
dwg.add(dwg.rect(insert=(0, 0), size=(width, height),
|
||||
fill='white', stroke='black', stroke_width=1))
|
||||
|
||||
# 添加标题
|
||||
dwg.add(dwg.text('胡汉三千年 - 图像模板',
|
||||
insert=(width//2, 30),
|
||||
text_anchor='middle',
|
||||
font_size=16,
|
||||
font_family='Arial'))
|
||||
|
||||
# 添加说明文字
|
||||
dwg.add(dwg.text('此SVG模板需要手动添加具体内容',
|
||||
insert=(width//2, height-30),
|
||||
text_anchor='middle',
|
||||
font_size=12,
|
||||
font_family='Arial'))
|
||||
|
||||
dwg.save()
|
||||
print(f"✅ SVG模板创建: {output_path}")
|
||||
return output_path
|
||||
|
||||
except ImportError:
|
||||
print("❌ 需要安装 svgwrite: pip install svgwrite")
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"❌ SVG创建失败: {e}")
|
||||
return None
|
||||
|
||||
def batch_convert_directory(directory_path, formats=['png', 'jpg']):
|
||||
"""批量转换目录中的所有PPM文件"""
|
||||
directory = Path(directory_path)
|
||||
if not directory.exists():
|
||||
print(f"❌ 目录不存在: {directory_path}")
|
||||
return
|
||||
|
||||
ppm_files = list(directory.rglob('*.ppm'))
|
||||
if not ppm_files:
|
||||
print(f"❌ 在 {directory_path} 中未找到PPM文件")
|
||||
return
|
||||
|
||||
print(f"🔍 找到 {len(ppm_files)} 个PPM文件")
|
||||
|
||||
converted_count = 0
|
||||
for ppm_file in ppm_files:
|
||||
print(f"\n📁 处理: {ppm_file}")
|
||||
|
||||
for format_type in formats:
|
||||
if format_type == 'png':
|
||||
result = convert_ppm_to_png(str(ppm_file))
|
||||
elif format_type == 'jpg':
|
||||
result = convert_ppm_to_jpg(str(ppm_file))
|
||||
elif format_type == 'svg':
|
||||
result = create_svg_template(str(ppm_file))
|
||||
|
||||
if result:
|
||||
converted_count += 1
|
||||
|
||||
print(f"\n🎉 批量转换完成! 成功转换 {converted_count} 个文件")
|
||||
|
||||
def analyze_image_content(image_path):
|
||||
"""分析图像内容并生成描述"""
|
||||
try:
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
with Image.open(image_path) as img:
|
||||
width, height = img.size
|
||||
mode = img.mode
|
||||
|
||||
# 转换为numpy数组进行分析
|
||||
img_array = np.array(img)
|
||||
|
||||
print(f"📊 图像分析: {image_path}")
|
||||
print(f" 尺寸: {width} x {height}")
|
||||
print(f" 模式: {mode}")
|
||||
print(f" 数据类型: {img_array.dtype}")
|
||||
print(f" 形状: {img_array.shape}")
|
||||
|
||||
# 分析颜色分布
|
||||
if len(img_array.shape) == 3: # RGB图像
|
||||
unique_colors = len(np.unique(img_array.reshape(-1, img_array.shape[-1]), axis=0))
|
||||
print(f" 唯一颜色数: {unique_colors}")
|
||||
|
||||
return {
|
||||
'width': width,
|
||||
'height': height,
|
||||
'mode': mode,
|
||||
'shape': img_array.shape
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 图像分析失败: {e}")
|
||||
return None
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description='胡汉三千年项目 - 图像转换工具')
|
||||
parser.add_argument('--install', action='store_true', help='安装必要的依赖包')
|
||||
parser.add_argument('--convert', type=str, help='转换单个PPM文件')
|
||||
parser.add_argument('--batch', type=str, help='批量转换目录中的所有PPM文件')
|
||||
parser.add_argument('--analyze', type=str, help='分析图像内容')
|
||||
parser.add_argument('--formats', nargs='+', default=['png', 'jpg'],
|
||||
help='转换格式 (png, jpg, svg)')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.install:
|
||||
if install_requirements():
|
||||
print("🎉 所有依赖包安装完成!")
|
||||
else:
|
||||
print("❌ 依赖包安装失败")
|
||||
sys.exit(1)
|
||||
|
||||
elif args.convert:
|
||||
ppm_path = args.convert
|
||||
if not os.path.exists(ppm_path):
|
||||
print(f"❌ 文件不存在: {ppm_path}")
|
||||
sys.exit(1)
|
||||
|
||||
print(f"🔄 转换文件: {ppm_path}")
|
||||
for format_type in args.formats:
|
||||
if format_type == 'png':
|
||||
convert_ppm_to_png(ppm_path)
|
||||
elif format_type == 'jpg':
|
||||
convert_ppm_to_jpg(ppm_path)
|
||||
elif format_type == 'svg':
|
||||
create_svg_template(ppm_path)
|
||||
|
||||
elif args.batch:
|
||||
print(f"🔄 批量转换目录: {args.batch}")
|
||||
batch_convert_directory(args.batch, args.formats)
|
||||
|
||||
elif args.analyze:
|
||||
image_path = args.analyze
|
||||
if not os.path.exists(image_path):
|
||||
print(f"❌ 文件不存在: {image_path}")
|
||||
sys.exit(1)
|
||||
|
||||
analyze_image_content(image_path)
|
||||
|
||||
else:
|
||||
print("🎯 胡汉三千年项目 - 图像转换工具")
|
||||
print("\n使用方法:")
|
||||
print(" python image_converter.py --install # 安装依赖")
|
||||
print(" python image_converter.py --convert file.ppm # 转换单个文件")
|
||||
print(" python image_converter.py --batch images/ # 批量转换目录")
|
||||
print(" python image_converter.py --analyze file.png # 分析图像")
|
||||
print(" python image_converter.py --formats png jpg svg # 指定转换格式")
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
||||
|
||||
103
tools/data-processing/image-processing/quick_convert.py
Normal file
103
tools/data-processing/image-processing/quick_convert.py
Normal file
@@ -0,0 +1,103 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
快速图像转换脚本 - 专门处理胡汉三千年项目的PPM文件
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
def quick_install():
|
||||
"""快速安装依赖"""
|
||||
import subprocess
|
||||
|
||||
print("🔧 安装图像处理依赖...")
|
||||
packages = ['Pillow', 'svgwrite']
|
||||
|
||||
for package in packages:
|
||||
try:
|
||||
subprocess.check_call([sys.executable, '-m', 'pip', 'install', package])
|
||||
print(f"✅ {package} 安装成功")
|
||||
except:
|
||||
print(f"❌ {package} 安装失败")
|
||||
|
||||
def convert_ppm_to_png_simple(ppm_path):
|
||||
"""简单的PPM到PNG转换"""
|
||||
try:
|
||||
from PIL import Image
|
||||
|
||||
# 打开PPM文件
|
||||
img = Image.open(ppm_path)
|
||||
|
||||
# 转换为RGB（如果需要）
|
||||
if img.mode != 'RGB':
|
||||
img = img.convert('RGB')
|
||||
|
||||
# 生成输出文件名
|
||||
output_path = str(Path(ppm_path).with_suffix('.png'))
|
||||
|
||||
# 保存为PNG
|
||||
img.save(output_path, 'PNG', optimize=True)
|
||||
|
||||
# 显示文件大小对比
|
||||
original_size = os.path.getsize(ppm_path) / (1024*1024) # MB
|
||||
new_size = os.path.getsize(output_path) / (1024*1024) # MB
|
||||
|
||||
print(f"✅ {Path(ppm_path).name} -> {Path(output_path).name}")
|
||||
print(f" 原始: {original_size:.1f}MB -> 转换后: {new_size:.1f}MB")
|
||||
print(f" 压缩率: {(1-new_size/original_size)*100:.1f}%")
|
||||
|
||||
return output_path
|
||||
|
||||
except ImportError:
|
||||
print("❌ 需要安装 Pillow: pip install Pillow")
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"❌ 转换失败: {e}")
|
||||
return None
|
||||
|
||||
def batch_convert_images():
|
||||
"""批量转换images目录下的所有PPM文件"""
|
||||
images_dir = Path("images")
|
||||
|
||||
if not images_dir.exists():
|
||||
print("❌ images目录不存在")
|
||||
return
|
||||
|
||||
# 查找所有PPM文件
|
||||
ppm_files = list(images_dir.rglob('*.ppm'))
|
||||
|
||||
if not ppm_files:
|
||||
print("❌ 未找到PPM文件")
|
||||
return
|
||||
|
||||
print(f"🔍 找到 {len(ppm_files)} 个PPM文件")
|
||||
|
||||
total_original_size = 0
|
||||
total_new_size = 0
|
||||
converted_count = 0
|
||||
|
||||
for ppm_file in ppm_files:
|
||||
print(f"\n📁 处理: {ppm_file.relative_to(images_dir)}")
|
||||
|
||||
result = convert_ppm_to_png_simple(str(ppm_file))
|
||||
if result:
|
||||
converted_count += 1
|
||||
total_original_size += os.path.getsize(str(ppm_file))
|
||||
total_new_size += os.path.getsize(result)
|
||||
|
||||
print(f"\n🎉 批量转换完成!")
|
||||
print(f" 转换文件数: {converted_count}/{len(ppm_files)}")
|
||||
print(f" 总大小: {total_original_size/(1024*1024):.1f}MB -> {total_new_size/(1024*1024):.1f}MB")
|
||||
print(f" 总体压缩率: {(1-total_new_size/total_original_size)*100:.1f}%")
|
||||
|
||||
def main():
|
||||
if len(sys.argv) > 1 and sys.argv[1] == '--install':
|
||||
quick_install()
|
||||
else:
|
||||
batch_convert_images()
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user