173 lines
5.2 KiB
Python
173 lines
5.2 KiB
Python
#!/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() |