Add Paper A (IEEE TAI) complete draft with Firm A-calibrated dual-method classification
Paper draft includes all sections (Abstract through Conclusion), 36 references, and supporting scripts. Key methodology: Cosine similarity + dHash dual-method verification with thresholds calibrated against known-replication firm (Firm A). Includes: - 8 section markdown files (paper_a_*.md) - Ablation study script (ResNet-50 vs VGG-16 vs EfficientNet-B0) - Recalibrated classification script (84,386 PDFs, 5-tier system) - Figure generation and Word export scripts - Citation renumbering script ([1]-[36]) - Signature analysis pipeline (12 steps) - YOLO extraction scripts Three rounds of AI review completed (GPT-5.4, Claude Opus 4.6, Gemini 3 Pro). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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#!/usr/bin/env python3
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"""
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Step 4: 生成高相似度案例的視覺化報告
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讀取 high_similarity_pairs.json
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為 Top N 高相似度對生成並排對比圖
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生成 HTML 報告
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"""
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import json
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import cv2
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import numpy as np
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from pathlib import Path
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from tqdm import tqdm
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import base64
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from io import BytesIO
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# 路徑配置
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IMAGES_DIR = Path("/Volumes/NV2/PDF-Processing/yolo-signatures/images")
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REPORTS_PATH = Path("/Volumes/NV2/PDF-Processing/signature-analysis/reports")
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HIGH_SIM_JSON = REPORTS_PATH / "high_similarity_pairs.json"
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# 報告配置
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TOP_N = 100 # 顯示前 N 對
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def load_image(filename: str) -> np.ndarray:
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"""載入圖片"""
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img_path = IMAGES_DIR / filename
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img = cv2.imread(str(img_path))
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if img is None:
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# 返回空白圖片
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return np.ones((100, 200, 3), dtype=np.uint8) * 255
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return img
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def create_comparison_image(file1: str, file2: str, similarity: float) -> np.ndarray:
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"""建立並排對比圖"""
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img1 = load_image(file1)
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img2 = load_image(file2)
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# 統一高度
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h1, w1 = img1.shape[:2]
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h2, w2 = img2.shape[:2]
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target_h = max(h1, h2, 100)
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# 縮放
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if h1 != target_h:
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scale = target_h / h1
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img1 = cv2.resize(img1, (int(w1 * scale), target_h))
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if h2 != target_h:
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scale = target_h / h2
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img2 = cv2.resize(img2, (int(w2 * scale), target_h))
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# 加入分隔線
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separator = np.ones((target_h, 20, 3), dtype=np.uint8) * 200
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# 合併
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comparison = np.hstack([img1, separator, img2])
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return comparison
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def image_to_base64(img: np.ndarray) -> str:
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"""將圖片轉換為 base64"""
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_, buffer = cv2.imencode('.png', img)
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return base64.b64encode(buffer).decode('utf-8')
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def generate_html_report(pairs: list, output_path: Path):
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"""生成 HTML 報告"""
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html_content = """
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="UTF-8">
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<title>簽名相似度分析報告 - 高相似度案例</title>
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<style>
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body {
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font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
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max-width: 1400px;
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margin: 0 auto;
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padding: 20px;
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background-color: #f5f5f5;
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}
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h1 {
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color: #333;
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text-align: center;
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border-bottom: 2px solid #666;
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padding-bottom: 10px;
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}
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.summary {
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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color: white;
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padding: 20px;
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border-radius: 10px;
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margin-bottom: 30px;
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}
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.summary h2 {
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margin-top: 0;
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}
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.pair-card {
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background: white;
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border-radius: 10px;
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padding: 20px;
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margin-bottom: 20px;
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box-shadow: 0 2px 10px rgba(0,0,0,0.1);
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}
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.pair-header {
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display: flex;
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justify-content: space-between;
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align-items: center;
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margin-bottom: 15px;
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padding-bottom: 10px;
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border-bottom: 1px solid #eee;
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}
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.pair-number {
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font-size: 1.2em;
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font-weight: bold;
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color: #333;
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}
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.similarity-badge {
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background: #dc3545;
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color: white;
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padding: 5px 15px;
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border-radius: 20px;
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font-weight: bold;
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}
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.similarity-badge.high {
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background: #dc3545;
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}
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.similarity-badge.very-high {
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background: #8b0000;
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}
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.file-info {
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font-family: monospace;
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font-size: 0.9em;
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color: #666;
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margin-bottom: 10px;
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}
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.comparison-image {
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max-width: 100%;
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border: 1px solid #ddd;
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border-radius: 5px;
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}
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.analysis {
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margin-top: 15px;
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padding: 10px;
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background: #f8f9fa;
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border-radius: 5px;
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font-size: 0.9em;
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}
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.tag {
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display: inline-block;
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padding: 2px 8px;
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border-radius: 3px;
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margin-right: 5px;
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font-size: 0.8em;
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}
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.tag-same-serial { background: #ffebee; color: #c62828; }
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.tag-same-month { background: #fff3e0; color: #e65100; }
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.tag-diff { background: #e8f5e9; color: #2e7d32; }
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</style>
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</head>
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<body>
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<h1>簽名相似度分析報告 - 高相似度案例</h1>
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<div class="summary">
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<h2>摘要</h2>
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<p><strong>分析結果：</strong>發現 659,111 對高相似度簽名 (>0.95)</p>
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<p><strong>本報告顯示：</strong>Top """ + str(TOP_N) + """ 最高相似度案例</p>
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<p><strong>結論：</strong>存在大量相似度接近或等於 1.0 的簽名對，強烈暗示「複製貼上」行為</p>
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</div>
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<div class="pairs-container">
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"""
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for i, pair in enumerate(pairs[:TOP_N], 1):
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sim = pair['similarity']
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file1 = pair['file1']
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file2 = pair['file2']
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p1 = pair.get('parsed1', {})
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p2 = pair.get('parsed2', {})
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# 分析關係
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tags = []
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if p1.get('serial') == p2.get('serial'):
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tags.append(('<span class="tag tag-same-serial">同序號</span>', ''))
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if p1.get('year_month') == p2.get('year_month'):
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tags.append(('<span class="tag tag-same-month">同月份</span>', ''))
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if p1.get('year_month') != p2.get('year_month') and p1.get('serial') != p2.get('serial'):
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tags.append(('<span class="tag tag-diff">完全不同文件</span>', ''))
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badge_class = 'very-high' if sim >= 0.99 else 'high'
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# 建立對比圖
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try:
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comparison_img = create_comparison_image(file1, file2, sim)
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img_base64 = image_to_base64(comparison_img)
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img_html = f'<img src="data:image/png;base64,{img_base64}" class="comparison-image">'
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except Exception as e:
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img_html = f'<p style="color:red">無法載入圖片: {e}</p>'
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tag_html = ''.join([t[0] for t in tags])
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html_content += f"""
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<div class="pair-card">
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<div class="pair-header">
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<span class="pair-number">#{i}</span>
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<span class="similarity-badge {badge_class}">相似度: {sim:.4f}</span>
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</div>
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<div class="file-info">
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<strong>簽名 1:</strong> {file1}<br>
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<strong>簽名 2:</strong> {file2}
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</div>
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{img_html}
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<div class="analysis">
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{tag_html}
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<br><small>日期: {p1.get('year_month', 'N/A')} vs {p2.get('year_month', 'N/A')} |
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序號: {p1.get('serial', 'N/A')} vs {p2.get('serial', 'N/A')}</small>
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</div>
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</div>
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"""
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html_content += """
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</div>
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<div style="text-align: center; margin-top: 30px; color: #666;">
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<p>生成時間: 2024 | 簽名真實性研究計劃</p>
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</div>
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</body>
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</html>
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"""
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with open(output_path, 'w', encoding='utf-8') as f:
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f.write(html_content)
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print(f"HTML 報告已儲存: {output_path}")
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def main():
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print("=" * 60)
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print("Step 4: 生成高相似度案例視覺化報告")
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print("=" * 60)
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# 載入高相似度對
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print("載入高相似度對資料...")
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with open(HIGH_SIM_JSON, 'r', encoding='utf-8') as f:
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pairs = json.load(f)
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print(f"共 {len(pairs):,} 對高相似度簽名")
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# 按相似度排序
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pairs_sorted = sorted(pairs, key=lambda x: x['similarity'], reverse=True)
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# 統計
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sim_1 = len([p for p in pairs_sorted if p['similarity'] >= 0.9999])
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sim_99 = len([p for p in pairs_sorted if p['similarity'] >= 0.99])
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sim_97 = len([p for p in pairs_sorted if p['similarity'] >= 0.97])
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print(f"\n相似度統計:")
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print(f" = 1.0 (完全相同): {sim_1:,}")
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print(f" >= 0.99: {sim_99:,}")
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print(f" >= 0.97: {sim_97:,}")
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# 生成報告
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print(f"\n生成 Top {TOP_N} 視覺化報告...")
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generate_html_report(pairs_sorted, REPORTS_PATH / "high_similarity_report.html")
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print("\n完成！")
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if __name__ == "__main__":
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main()
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