#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
分析古籍OCR JSON数据 - 理解物理列聚合逻辑
"""

import json
import numpy as np
from collections import defaultdict

def analyze_physical_columns(json_path):
    with open(json_path, 'r', encoding='utf-8') as f:
        data = json.load(f)

    print("=" * 80)
    print(f"文件: {data['FileName']}")
    print(f"尺寸: {data['Width']} × {data['Height']}")
    print(f"版式: 10列 × 25行")
    print("=" * 80)

    chars = data['chars']
    char_marking = data['charMarking']
    coors = data['coors']
    line_ids = data['line_ids']

    # 按line_id分组
    logical_columns = defaultdict(list)
    for i in range(len(chars)):
        logical_columns[line_ids[i]].append({
            'index': i,
            'char': chars[i],
            'x1': coors[i][0],
            'y1': coors[i][1],
            'x2': coors[i][2],
            'y2': coors[i][3],
            'x_center': (coors[i][0] + coors[i][2]) / 2,
            'y_center': (coors[i][1] + coors[i][3]) / 2,
            'is_small': len(char_marking[i]) > 0
        })

    # 分析每个逻辑列
    print("\n逻辑列(line_id)分析:")
    print("-" * 80)

    logical_col_info = []
    for line_id in sorted(logical_columns.keys()):
        col_chars = logical_columns[line_id]
        x_centers = [c['x_center'] for c in col_chars]
        y_centers = [c['y_center'] for c in col_chars]

        x_min, x_max = min(x_centers), max(x_centers)
        x_avg = np.mean(x_centers)
        y_min, y_max = min(y_centers), max(y_centers)

        char_count = len(col_chars)
        small_count = sum(1 for c in col_chars if c['is_small'])
        big_count = char_count - small_count

        text = ''.join([c['char'] for c in col_chars])
        text_preview = text[:15] + '...' if len(text) > 15 else text

        print(f"line_id={line_id}: x范围[{x_min:.0f}-{x_max:.0f}], 平均x={x_avg:.0f}")
        print(f"  字数: {char_count} (大字:{big_count}, 小字:{small_count})")
        print(f"  内容: {text_preview}")

        logical_col_info.append({
            'line_id': line_id,
            'x_avg': x_avg,
            'x_min': x_min,
            'x_max': x_max,
            'chars': col_chars,
            'char_count': char_count,
            'small_count': small_count
        })

    # 尝试聚合到物理列
    print("\n" + "=" * 80)
    print("物理列聚合分析:")
    print("-" * 80)

    # 计算物理列宽度
    canvas_width = data['Width']
    total_physical_cols = 10
    cell_width = canvas_width / total_physical_cols
    print(f"每个物理列宽度: {cell_width:.0f}px")

    # 按x坐标排序逻辑列（从右到左，x大的在前）
    logical_col_info.sort(key=lambda c: c['x_avg'], reverse=True)

    # 计算10个物理列的理想中心位置（从右到左）
    physical_centers = []
    for i in range(total_physical_cols):
        center_x = canvas_width - (i + 0.5) * cell_width
        physical_centers.append(center_x)
    print(f"物理列理想中心: {[f'{x:.0f}' for x in physical_centers]}")

    # 将每个逻辑列映射到最近的物理列
    physical_columns = defaultdict(list)

    for lc in logical_col_info:
        # 找最近的物理列
        min_dist = float('inf')
        best_col = 0
        for i, center in enumerate(physical_centers):
            dist = abs(lc['x_avg'] - center)
            if dist < min_dist:
                min_dist = dist
                best_col = i

        physical_columns[best_col].append(lc)
        print(f"  line_id={lc['line_id']} (x={lc['x_avg']:.0f}) -> 物理列{best_col+1} (距离{min_dist:.0f}px)")

    # 转换为带索引的列表格式
    physical_columns_with_index = [(i, physical_columns[i]) for i in sorted(physical_columns.keys())]

    print(f"\n聚合结果: {len(physical_columns_with_index)} 个有内容的物理列（共10列）")
    print("-" * 80)

    for physical_col_idx, pc in physical_columns_with_index:
        line_ids_in_pc = [lc['line_id'] for lc in pc]
        x_avgs = [lc['x_avg'] for lc in pc]
        total_chars = sum(lc['char_count'] for lc in pc)
        total_small = sum(lc['small_count'] for lc in pc)

        print(f"\n物理列 {physical_col_idx+1}:")
        print(f"  包含line_ids: {line_ids_in_pc}")
        print(f"  x坐标平均值: {[f'{x:.0f}' for x in x_avgs]}")
        print(f"  总字数: {total_chars} (小字: {total_small})")

        # 合并所有字符，按y排序
        all_chars = []
        for lc in pc:
            all_chars.extend(lc['chars'])
        all_chars.sort(key=lambda c: c['y_center'])

        # 显示字符序列（用符号表示大字/小字）
        sequence = ""
        for c in all_chars:
            if c['is_small']:
                sequence += "s"  # small
            else:
                sequence += "B"  # Big
        print(f"  字符序列: {sequence}")

        # 计算每个字符对应的行号
        cell_height = data['Height'] / 25
        print(f"  行分配 (行高={cell_height:.0f}px):")
        for c in all_chars[:5]:  # 只显示前5个
            row = int(c['y_center'] / cell_height)
            char_type = "小" if c['is_small'] else "大"
            print(f"    '{c['char']}' ({char_type}): y={c['y_center']:.0f} -> 第{row+1}行")
        if len(all_chars) > 5:
            print(f"    ... 还有 {len(all_chars)-5} 个字符")

    # 分析双行小字的配对
    print("\n" + "=" * 80)
    print("双行小字配对分析:")
    print("-" * 80)

    for physical_col_idx, pc in physical_columns_with_index:
        # 找这个物理列中的小字
        all_chars = []
        for lc in pc:
            all_chars.extend(lc['chars'])

        small_chars = [c for c in all_chars if c['is_small']]
        if not small_chars:
            continue

        print(f"\n物理列 {physical_col_idx+1} 的小字分析:")

        # 按x坐标分左右
        x_centers = [c['x_center'] for c in small_chars]
        x_threshold = np.mean(x_centers)

        right_chars = sorted([c for c in small_chars if c['x_center'] >= x_threshold],
                             key=lambda c: c['y_center'])
        left_chars = sorted([c for c in small_chars if c['x_center'] < x_threshold],
                            key=lambda c: c['y_center'])

        print(f"  右列({len(right_chars)}字): {''.join([c['char'] for c in right_chars])}")
        print(f"  左列({len(left_chars)}字): {''.join([c['char'] for c in left_chars])}")

        # 配对
        print(f"  配对结果:")
        max_len = max(len(right_chars), len(left_chars))
        for i in range(max_len):
            r_char = right_chars[i]['char'] if i < len(right_chars) else '□'
            l_char = left_chars[i]['char'] if i < len(left_chars) else '□'
            print(f"    格{i+1}: [{r_char}|{l_char}]")

if __name__ == '__main__':
    analyze_physical_columns('/home/yuuko/test/0011B.json')