194 lines
6.8 KiB
Python
194 lines
6.8 KiB
Python
|
#!/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')
|