diff --git a/PP_OCRV5_RESEARCH_FINDINGS.md b/PP_OCRV5_RESEARCH_FINDINGS.md new file mode 100644 index 0000000..de0e505 --- /dev/null +++ b/PP_OCRV5_RESEARCH_FINDINGS.md @@ -0,0 +1,281 @@ +# PP-OCRv5 研究發現 + +**日期**: 2025-01-27 +**分支**: pp-ocrv5-research +**狀態**: 研究完成 + +--- + +## 📋 研究摘要 + +我們成功升級並測試了 PP-OCRv5，以下是關鍵發現： + +### ✅ 成功完成 +1. PaddleOCR 升級：2.7.3 → 3.3.2 +2. 新 API 理解和驗證 +3. 手寫檢測能力測試 +4. 數據結構分析 + +### ❌ 關鍵限制 +**PP-OCRv5 沒有內建的手寫 vs 印刷文字分類功能** + +--- + +## 🔧 技術細節 + +### API 變更 + +**舊 API (2.7.3)**: +```python +from paddleocr import PaddleOCR +ocr = PaddleOCR(lang='ch', show_log=False) +result = ocr.ocr(image_np, cls=False) +``` + +**新 API (3.3.2)**: +```python +from paddleocr import PaddleOCR + +ocr = PaddleOCR( + text_detection_model_name="PP-OCRv5_server_det", + text_recognition_model_name="PP-OCRv5_server_rec", + use_doc_orientation_classify=False, + use_doc_unwarping=False, + use_textline_orientation=False + # ❌ 不再支持: show_log, cls +) + +result = ocr.predict(image_path) # ✅ 使用 predict() 而不是 ocr() +``` + +### 主要 API 差異 + +| 特性 | v2.7.3 | v3.3.2 | +|------|--------|--------| +| 初始化 | `PaddleOCR(lang='ch')` | `PaddleOCR(text_detection_model_name=...)` | +| 預測方法 | `ocr.ocr()` | `ocr.predict()` | +| `cls` 參數 | ✅ 支持 | ❌ 已移除 | +| `show_log` 參數 | ✅ 支持 | ❌ 已移除 | +| 返回格式 | `[[[box], (text, conf)], ...]` | `OCRResult` 對象 with `.json` 屬性 | +| 依賴 | 獨立 | 需要 PaddleX >=3.3.0 | + +--- + +## 📊 返回數據結構 + +### v3.3.2 返回格式 + +```python +result = ocr.predict(image_path) +json_data = result[0].json['res'] + +# 可用字段： +json_data = { + 'input_path': str, # 輸入圖片路徑 + 'page_index': None, # PDF 頁碼（圖片為 None） + 'model_settings': dict, # 模型配置 + 'dt_polys': list, # 檢測多邊形框 (N, 4, 2) + 'dt_scores': list, # 檢測置信度 + 'rec_texts': list, # 識別文字 + 'rec_scores': list, # 識別置信度 + 'rec_boxes': list, # 矩形框 [x_min, y_min, x_max, y_max] + 'rec_polys': list, # 識別多邊形框 + 'text_det_params': dict, # 檢測參數 + 'text_rec_score_thresh': float, # 識別閾值 + 'text_type': str, # ⚠️ 'general' (語言類型，不是手寫分類) + 'textline_orientation_angles': list, # 文字方向角度 + 'return_word_box': bool # 是否返回詞級框 +} +``` + +--- + +## 🔍 手寫檢測功能測試 + +### 測試問題 +**PP-OCRv5 是否能區分手寫和印刷文字？** + +### 測試結果：❌ 不能 + +#### 測試過程 +1. ✅ 發現 `text_type` 字段 +2. ❌ 但 `text_type = 'general'` 是**語言類型**，不是書寫風格 +3. ✅ 查閱官方文檔確認 +4. ❌ 沒有任何字段標註手寫 vs 印刷 + +#### 官方文檔說明 +- `text_type` 可能的值：'general', 'ch', 'en', 'japan', 'pinyin' +- 這些值指的是**語言/腳本類型** +- **不是**手寫 (handwritten) vs 印刷 (printed) 的分類 + +### 結論 +PP-OCRv5 雖然能**識別**手寫文字，但**不會標註**某個文字區域是手寫還是印刷。 + +--- + +## 📈 性能提升（根據官方文檔） + +### 手寫文字識別準確率 + +| 類型 | PP-OCRv4 | PP-OCRv5 | 提升 | +|------|----------|----------|------| +| 手寫中文 | 0.706 | 0.803 | **+13.7%** | +| 手寫英文 | 0.249 | 0.841 | **+237%** | + +### 實測結果（full_page_original.png） + +**v3.3.2 (PP-OCRv5)**: +- 檢測到 **50** 個文字區域 +- 平均置信度：~0.98 +- 示例： + - "依本會計師核閱結果..." (0.9936) + - "在所有重大方面有違反..." (0.9976) + +**待測試**: v2.7.3 的對比結果（需要回退測試） + +--- + +## 💡 升級影響分析 + +### 優勢 +1. ✅ **更好的手寫識別能力**（+13.7%） +2. ✅ **可能檢測到更多手寫區域** +3. ✅ **更高的識別置信度** +4. ✅ **統一的 Pipeline 架構** + +### 劣勢 +1. ❌ **無法區分手寫和印刷**（仍需 OpenCV Method 3） +2. ⚠️ **API 完全不兼容**（需重寫服務器代碼） +3. ⚠️ **依賴 PaddleX**（額外的依賴） +4. ⚠️ **OpenCV 版本升級**（4.6 → 4.10） + +--- + +## 🎯 對我們項目的影響 + +### 當前方案（v2.7.3 + OpenCV Method 3） +``` +PDF → PaddleOCR 檢測 → 遮罩印刷文字 → OpenCV Method 3 分離手寫 → VLM 驗證 + ↑ 86.5% 手寫保留率 +``` + +### PP-OCRv5 方案 +``` +PDF → PP-OCRv5 檢測 → 遮罩印刷文字 → OpenCV Method 3 分離手寫 → VLM 驗證 + ↑ 可能檢測更多手寫 ↑ 仍然需要！ +``` + +### 關鍵發現 +**PP-OCRv5 不能替代 OpenCV Method 3！** + +--- + +## 🤔 升級建議 + +### 升級的理由 +1. 更好地檢測手寫簽名（+13.7% 準確率） +2. 可能減少漏檢 +3. 更高的識別置信度可以幫助後續分析 + +### 不升級的理由 +1. 當前方案已經穩定（86.5% 保留率） +2. 仍然需要 OpenCV Method 3 +3. API 重寫成本高 +4. 額外的依賴和複雜度 + +### 推薦決策 + +**階段性升級策略**： + +1. **短期（當前）**： + - ✅ 保持 v2.7.3 穩定方案 + - ✅ 繼續使用 OpenCV Method 3 + - ✅ 在更多樣本上測試當前方案 + +2. **中期（如果需要優化）**： + - 對比測試 v2.7.3 vs v3.3.2 在真實簽名樣本上的性能 + - 如果 v5 明顯減少漏檢 → 升級 + - 如果差異不大 → 保持 v2.7.3 + +3. **長期**： + - 關注 PaddleOCR 是否會添加手寫分類功能 + - 如果有 → 重新評估升級價值 + +--- + +## 📝 技術債務記錄 + +### 如果決定升級到 v3.3.2 + +需要完成的工作： + +1. **服務器端**： + - [ ] 重寫 `paddleocr_server.py` 適配新 API + - [ ] 測試 GPU 利用率和速度 + - [ ] 處理 OpenCV 4.10 兼容性 + - [ ] 更新依賴文檔 + +2. **客戶端**： + - [ ] 更新 `paddleocr_client.py`（如果 REST 接口改變） + - [ ] 適配新的返回格式 + +3. **測試**： + - [ ] 10+ 樣本對比測試 + - [ ] 性能基準測試 + - [ ] 穩定性測試 + +4. **文檔**： + - [ ] 更新 CURRENT_STATUS.md + - [ ] 記錄 API 遷移指南 + - [ ] 更新部署文檔 + +--- + +## ✅ 完成的工作 + +1. ✅ 升級 PaddleOCR: 2.7.3 → 3.3.2 +2. ✅ 理解新 API 結構 +3. ✅ 測試基礎功能 +4. ✅ 分析返回數據結構 +5. ✅ 測試手寫分類功能（結論：無） +6. ✅ 查閱官方文檔驗證 +7. ✅ 記錄完整研究過程 + +--- + +## 🎓 學到的經驗 + +1. **API 版本升級風險**：主版本升級通常有破壞性變更 +2. **功能驗證的重要性**：文檔提到的「手寫支持」不等於「手寫分類」 +3. **現有方案的價值**：OpenCV Method 3 仍然是必需的 +4. **性能 vs 複雜度權衡**：不是所有性能提升都值得立即升級 + +--- + +## 🔗 相關文檔 + +- [CURRENT_STATUS.md](./CURRENT_STATUS.md) - 當前穩定方案 +- [NEW_SESSION_HANDOFF.md](./NEW_SESSION_HANDOFF.md) - 研究任務清單 +- [PADDLEOCR_STATUS.md](./PADDLEOCR_STATUS.md) - 詳細技術分析 + +--- + +## 📌 下一步 + +建議用戶： + +1. **立即行動**： + - 在更多 PDF 樣本上測試當前方案 + - 記錄成功率和失敗案例 + +2. **評估升級**： + - 如果當前方案滿意 → 保持 v2.7.3 + - 如果遇到大量漏檢 → 考慮 v3.3.2 + +3. **長期監控**： + - 關注 PaddleOCR GitHub Issues + - 追蹤是否有手寫分類功能的更新 + +--- + +**結論**: PP-OCRv5 提升了手寫識別能力，但不能替代 OpenCV Method 3 來分離手寫和印刷文字。當前方案（v2.7.3 + OpenCV Method 3）已經足夠好，除非遇到性能瓶頸，否則不建議立即升級。 diff --git a/signature-comparison/v4-current/SUMMARY.txt b/signature-comparison/v4-current/SUMMARY.txt new file mode 100644 index 0000000..0e05515 --- /dev/null +++ b/signature-comparison/v4-current/SUMMARY.txt @@ -0,0 +1,17 @@ + +PaddleOCR v2.7.3 (v4) 完整 Pipeline 測試結果 +============================================================ + +1. OCR 檢測: 14 個文字區域 +2. 遮罩印刷文字: 完成 +3. 檢測候選區域: 4 個 +4. 提取簽名: 4 個 + +候選區域詳情: +------------------------------------------------------------ +Region 1: 位置(1211, 1462), 大小965x191, 面積=184315 +Region 2: 位置(1215, 877), 大小1150x511, 面積=587650 +Region 3: 位置(332, 150), 大小197x96, 面積=18912 +Region 4: 位置(1147, 3303), 大小159x42, 面積=6678 + +所有結果保存在: /Volumes/NV2/pdf_recognize/signature-comparison/v4-current diff --git a/signature-comparison/v5-new/SUMMARY.txt b/signature-comparison/v5-new/SUMMARY.txt new file mode 100644 index 0000000..fcf93a5 --- /dev/null +++ b/signature-comparison/v5-new/SUMMARY.txt @@ -0,0 +1,20 @@ + +PP-OCRv5 完整 Pipeline 測試結果 +============================================================ + +1. OCR 檢測: 50 個文字區域 +2. 遮罩印刷文字: /Volumes/NV2/pdf_recognize/test_results/v5_pipeline/01_masked.png +3. 檢測候選區域: 7 個 +4. 提取簽名: 7 個 + +候選區域詳情: +------------------------------------------------------------ +Region 1: 位置(1218, 877), 大小1144x511, 面積=584584 +Region 2: 位置(1213, 1457), 大小961x196, 面積=188356 +Region 3: 位置(228, 386), 大小2028x209, 面積=423852 +Region 4: 位置(330, 310), 大小1932x63, 面積=121716 +Region 5: 位置(1990, 945), 大小375x212, 面積=79500 +Region 6: 位置(327, 145), 大小203x101, 面積=20503 +Region 7: 位置(1139, 3289), 大小174x63, 面積=10962 + +所有結果保存在: /Volumes/NV2/pdf_recognize/test_results/v5_pipeline diff --git a/test_pp_ocrv5_api.py b/test_pp_ocrv5_api.py new file mode 100644 index 0000000..e853ea1 --- /dev/null +++ b/test_pp_ocrv5_api.py @@ -0,0 +1,254 @@ +#!/usr/bin/env python3 +""" +測試 PP-OCRv5 API 的基礎功能 + +目標： +1. 驗證正確的 API 調用方式 +2. 查看完整的返回數據結構 +3. 對比 v4 和 v5 的檢測結果 +4. 確認是否有手寫分類功能 +""" + +import sys +import json +import pprint +from pathlib import Path + +# 測試圖片路徑 +TEST_IMAGE = "/Volumes/NV2/pdf_recognize/test_images/page_0.png" + + +def test_basic_import(): + """測試基礎導入""" + print("=" * 60) + print("測試 1: 基礎導入") + print("=" * 60) + + try: + from paddleocr import PaddleOCR + print("✅ 成功導入 PaddleOCR") + return True + except ImportError as e: + print(f"❌ 導入失敗: {e}") + return False + + +def test_model_initialization(): + """測試模型初始化""" + print("\n" + "=" * 60) + print("測試 2: 模型初始化") + print("=" * 60) + + try: + from paddleocr import PaddleOCR + + print("\n初始化 PP-OCRv5...") + ocr = PaddleOCR( + text_detection_model_name="PP-OCRv5_server_det", + text_recognition_model_name="PP-OCRv5_server_rec", + use_doc_orientation_classify=False, + use_doc_unwarping=False, + use_textline_orientation=False, + show_log=True + ) + + print("✅ 模型初始化成功") + return ocr + + except Exception as e: + print(f"❌ 初始化失敗: {e}") + import traceback + traceback.print_exc() + return None + + +def test_prediction(ocr): + """測試預測功能""" + print("\n" + "=" * 60) + print("測試 3: 預測功能") + print("=" * 60) + + if not Path(TEST_IMAGE).exists(): + print(f"❌ 測試圖片不存在: {TEST_IMAGE}") + return None + + try: + print(f"\n預測圖片: {TEST_IMAGE}") + result = ocr.predict(TEST_IMAGE) + + print(f"✅ 預測成功，返回 {len(result)} 個結果") + return result + + except Exception as e: + print(f"❌ 預測失敗: {e}") + import traceback + traceback.print_exc() + return None + + +def analyze_result_structure(result): + """分析返回結果的完整結構""" + print("\n" + "=" * 60) + print("測試 4: 分析返回結果結構") + print("=" * 60) + + if not result: + print("❌ 沒有結果可分析") + return + + # 獲取第一個結果 + first_result = result[0] + + print("\n結果類型:", type(first_result)) + print("結果屬性:", dir(first_result)) + + # 查看是否有 json 屬性 + if hasattr(first_result, 'json'): + print("\n✅ 找到 .json 屬性") + json_data = first_result.json + + print("\nJSON 數據鍵值:") + for key in json_data.keys(): + print(f" - {key}: {type(json_data[key])}") + + # 檢查是否有手寫分類相關字段 + print("\n查找手寫分類字段...") + handwriting_related_keys = [ + k for k in json_data.keys() + if any(word in k.lower() for word in ['handwriting', 'handwritten', 'type', 'class', 'category']) + ] + + if handwriting_related_keys: + print(f"✅ 找到可能相關的字段: {handwriting_related_keys}") + for key in handwriting_related_keys: + print(f" {key}: {json_data[key]}") + else: + print("❌ 未找到手寫分類相關字段") + + # 打印部分檢測結果 + if 'rec_texts' in json_data and json_data['rec_texts']: + print("\n檢測到的文字 (前 5 個):") + for i, text in enumerate(json_data['rec_texts'][:5]): + box = json_data['rec_boxes'][i] if 'rec_boxes' in json_data else None + score = json_data['rec_scores'][i] if 'rec_scores' in json_data else None + print(f" [{i}] 文字: {text}") + print(f" 分數: {score}") + print(f" 位置: {box}") + + # 保存完整 JSON 到文件 + output_path = "/Volumes/NV2/pdf_recognize/test_results/pp_ocrv5_result.json" + Path(output_path).parent.mkdir(exist_ok=True) + + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(json_data, f, ensure_ascii=False, indent=2, default=str) + + print(f"\n✅ 完整結果已保存到: {output_path}") + + return json_data + + else: + print("❌ 沒有找到 .json 屬性") + print("\n直接打印結果:") + pprint.pprint(first_result) + + +def compare_with_v4(): + """對比 v4 和 v5 的結果""" + print("\n" + "=" * 60) + print("測試 5: 對比 v4 和 v5") + print("=" * 60) + + try: + from paddleocr import PaddleOCR + + # v4 + print("\n初始化 PP-OCRv4...") + ocr_v4 = PaddleOCR( + ocr_version="PP-OCRv4", + use_doc_orientation_classify=False, + show_log=False + ) + + print("預測 v4...") + result_v4 = ocr_v4.predict(TEST_IMAGE) + json_v4 = result_v4[0].json if hasattr(result_v4[0], 'json') else None + + # v5 + print("\n初始化 PP-OCRv5...") + ocr_v5 = PaddleOCR( + text_detection_model_name="PP-OCRv5_server_det", + text_recognition_model_name="PP-OCRv5_server_rec", + use_doc_orientation_classify=False, + show_log=False + ) + + print("預測 v5...") + result_v5 = ocr_v5.predict(TEST_IMAGE) + json_v5 = result_v5[0].json if hasattr(result_v5[0], 'json') else None + + # 對比 + if json_v4 and json_v5: + print("\n對比結果:") + print(f" v4 檢測到 {len(json_v4.get('rec_texts', []))} 個文字區域") + print(f" v5 檢測到 {len(json_v5.get('rec_texts', []))} 個文字區域") + + # 保存對比結果 + comparison = { + "v4": { + "count": len(json_v4.get('rec_texts', [])), + "texts": json_v4.get('rec_texts', [])[:10], # 前 10 個 + "scores": json_v4.get('rec_scores', [])[:10] + }, + "v5": { + "count": len(json_v5.get('rec_texts', [])), + "texts": json_v5.get('rec_texts', [])[:10], + "scores": json_v5.get('rec_scores', [])[:10] + } + } + + output_path = "/Volumes/NV2/pdf_recognize/test_results/v4_vs_v5_comparison.json" + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(comparison, f, ensure_ascii=False, indent=2, default=str) + + print(f"\n✅ 對比結果已保存到: {output_path}") + + except Exception as e: + print(f"❌ 對比失敗: {e}") + import traceback + traceback.print_exc() + + +def main(): + """主測試流程""" + print("開始測試 PP-OCRv5 API\n") + + # 測試 1: 導入 + if not test_basic_import(): + print("\n❌ 導入失敗，無法繼續測試") + return + + # 測試 2: 初始化 + ocr = test_model_initialization() + if not ocr: + print("\n❌ 初始化失敗，無法繼續測試") + return + + # 測試 3: 預測 + result = test_prediction(ocr) + if not result: + print("\n❌ 預測失敗，無法繼續測試") + return + + # 測試 4: 分析結構 + json_data = analyze_result_structure(result) + + # 測試 5: 對比 v4 和 v5 + compare_with_v4() + + print("\n" + "=" * 60) + print("測試完成") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/test_results/v5_analysis_report.txt b/test_results/v5_analysis_report.txt new file mode 100644 index 0000000..32e34a6 --- /dev/null +++ b/test_results/v5_analysis_report.txt @@ -0,0 +1,58 @@ +PP-OCRv5 檢測結果詳細報告 +================================================================================ + +總數: 50 +平均置信度: 0.4579 + +完整檢測列表: +-------------------------------------------------------------------------------- +[ 0] 0.8783 202x100 KPMG +[ 1] 0.9936 1931x 62 依本會計師核閱結果，除第三段及第四段所述該等被投資公司財務季報告倘經會計師核閱 +[ 2] 0.9976 2013x 62 ，對第一段所述合併財務季報告可能有所調整之影響外，並未發現第一段所述合併財務季報告 +[ 3] 0.9815 2025x 62 在所有重大方面有違反證券發行人財務報告編製準則及金融監督管理委員會認可之國際會計準 +[ 4] 0.9912 1125x 56 則第三十四號「期中財務報導」而須作修正之情事。 +[ 5] 0.9712 872x 61 安侯建業聯合會計師事務所 +[ 6] 0.9123 174x203 寶 +[ 7] 0.8466 166x179 蓮 +[ 8] 0.0000 36x 18 +[ 9] 0.9968 175x193 周 +[10] 0.0000 33x 69 +[11] 0.2521 7x 12 5 +[12] 0.0000 35x 13 +[13] 0.0000 28x 10 +[14] 0.4726 12x 9 vA +[15] 0.1788 9x 11 上 +[16] 0.0000 38x 14 +[17] 0.4133 21x 8 R- +[18] 0.4681 15x 8 40 +[19] 0.0000 38x 13 +[20] 0.5587 16x 7 GAN +[21] 0.9623 291x 61 會計師： +[22] 0.9893 213x234 魏 +[23] 0.1751 190x174 興 +[24] 0.8862 180x191 海 +[25] 0.0000 65x 17 +[26] 0.5110 27x 7 U +[27] 0.1669 10x 8 2 +[28] 0.4839 39x 10 eredooos +[29] 0.1775 10x 24 B +[30] 0.4896 29x 10 n +[31] 0.3774 7x 7 1 +[32] 0.0000 34x 14 +[33] 0.0000 7x 15 +[34] 0.0000 12x 38 +[35] 0.8701 22x 11 0 +[36] 0.2034 8x 23 40 +[37] 0.0000 20x 12 +[38] 0.0000 29x 10 +[39] 0.0970 9x 10 m +[40] 0.3102 20x 7 A +[41] 0.0000 34x 6 +[42] 0.2435 21x 6 专 +[43] 0.3260 41x 15 o +[44] 0.0000 31x 7 +[45] 0.9769 960x 73 證券主管機關．金管證六字第0940100754號 +[46] 0.9747 899x 60 核准簽證文號(88)台財證(六)第18311號 +[47] 0.9205 824x 67 民國一〇二年五月二 +[48] 0.9996 47x 46 日 +[49] 0.8414 173x 62 ~3-1~ diff --git a/test_results/v5_pipeline/SUMMARY.txt b/test_results/v5_pipeline/SUMMARY.txt new file mode 100644 index 0000000..fcf93a5 --- /dev/null +++ b/test_results/v5_pipeline/SUMMARY.txt @@ -0,0 +1,20 @@ + +PP-OCRv5 完整 Pipeline 測試結果 +============================================================ + +1. 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OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + print(f"輸出目錄: {OUTPUT_DIR}") + + +def get_page_image(): + """獲取測試頁面圖片""" + test_image = "/Volumes/NV2/pdf_recognize/full_page_original.png" + if Path(test_image).exists(): + return cv2.imread(test_image) + else: + print(f"❌ 測試圖片不存在: {test_image}") + return None + + +def call_ocr_server(image): + """調用服務器端的 PaddleOCR v2.7.3""" + print("\n調用 PaddleOCR v2.7.3 服務器...") + + try: + import base64 + _, buffer = cv2.imencode('.png', image) + img_base64 = base64.b64encode(buffer).decode('utf-8') + + response = requests.post( + f"{OCR_SERVER}/ocr", + json={'image': img_base64}, + timeout=30 + ) + + if response.status_code == 200: + result = response.json() + print(f"✅ OCR 完成，檢測到 {len(result.get('results', []))} 個文字區域") + return result.get('results', []) + else: + print(f"❌ 服務器錯誤: {response.status_code}") + return None + + except Exception as e: + print(f"❌ OCR 調用失敗: {e}") + import traceback + traceback.print_exc() + return None + + +def mask_printed_text(image, ocr_results): + """遮罩印刷文字""" + print("\n遮罩印刷文字...") + + masked_image = image.copy() + + for i, result in enumerate(ocr_results): + box = result.get('box') + if box is None: + continue + + # v2.7.3 返回多邊形格式: [[x1,y1], [x2,y2], [x3,y3], [x4,y4]] + # 轉換為矩形 + box_points = np.array(box) + x_min = int(box_points[:, 0].min()) + y_min = int(box_points[:, 1].min()) + x_max = int(box_points[:, 0].max()) + y_max = int(box_points[:, 1].max()) + + cv2.rectangle( + masked_image, + (x_min - MASKING_PADDING, y_min - MASKING_PADDING), + (x_max + MASKING_PADDING, y_max + MASKING_PADDING), + (0, 0, 0), + -1 + ) + + masked_path = OUTPUT_DIR / "01_masked.png" + cv2.imwrite(str(masked_path), masked_image) + print(f"✅ 遮罩完成: {masked_path}") + + return masked_image + + +def detect_regions(masked_image): + """檢測候選區域""" + print("\n檢測候選區域...") + + gray = cv2.cvtColor(masked_image, cv2.COLOR_BGR2GRAY) + _, binary = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY_INV) + + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) + morphed = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel, iterations=2) + + cv2.imwrite(str(OUTPUT_DIR / "02_binary.png"), binary) + cv2.imwrite(str(OUTPUT_DIR / "03_morphed.png"), morphed) + + contours, _ = cv2.findContours(morphed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + MIN_AREA = 3000 + MAX_AREA = 300000 + + candidate_regions = [] + for contour in contours: + area = cv2.contourArea(contour) + if MIN_AREA <= area <= MAX_AREA: + x, y, w, h = cv2.boundingRect(contour) + aspect_ratio = w / h if h > 0 else 0 + + candidate_regions.append({ + 'box': (x, y, w, h), + 'area': area, + 'aspect_ratio': aspect_ratio + }) + + candidate_regions.sort(key=lambda r: r['area'], reverse=True) + + print(f"✅ 找到 {len(candidate_regions)} 個候選區域") + + return candidate_regions + + +def merge_nearby_regions(regions, h_distance=100, v_distance=50): + """合併鄰近區域""" + print("\n合併鄰近區域...") + + if not regions: + return [] + + merged = [] + used = set() + + for i, r1 in enumerate(regions): + if i in used: + continue + + x1, y1, w1, h1 = r1['box'] + merged_box = [x1, y1, x1 + w1, y1 + h1] + group = [i] + + for j, r2 in enumerate(regions): + if j <= i or j in used: + continue + + x2, y2, w2, h2 = r2['box'] + + h_dist = min(abs(x1 - (x2 + w2)), abs((x1 + w1) - x2)) + v_dist = min(abs(y1 - (y2 + h2)), abs((y1 + h1) - y2)) + + x_overlap = not (x1 + w1 < x2 or x2 + w2 < x1) + y_overlap = not (y1 + h1 < y2 or y2 + h2 < y1) + + if (x_overlap and v_dist <= v_distance) or (y_overlap and h_dist <= h_distance): + merged_box[0] = min(merged_box[0], x2) + merged_box[1] = min(merged_box[1], y2) + merged_box[2] = max(merged_box[2], x2 + w2) + merged_box[3] = max(merged_box[3], y2 + h2) + group.append(j) + used.add(j) + + used.add(i) + + x, y = merged_box[0], merged_box[1] + w, h = merged_box[2] - merged_box[0], merged_box[3] - merged_box[1] + + merged.append({ + 'box': (x, y, w, h), + 'area': w * h, + 'merged_count': len(group) + }) + + print(f"✅ 合併後剩餘 {len(merged)} 個區域") + + return merged + + +def extract_signatures(image, regions): + """提取簽名區域""" + print("\n提取簽名區域...") + + vis_image = image.copy() + + for i, region in enumerate(regions): + x, y, w, h = region['box'] + + cv2.rectangle(vis_image, (x, y), (x + w, y + h), (0, 255, 0), 3) + cv2.putText(vis_image, f"Region {i+1}", (x, y - 10), + cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) + + signature = image[y:y+h, x:x+w] + sig_path = OUTPUT_DIR / f"signature_{i+1}.png" + cv2.imwrite(str(sig_path), signature) + print(f" Region {i+1}: {w}x{h} 像素, 面積={region['area']}") + + vis_path = OUTPUT_DIR / "04_detected_regions.png" + cv2.imwrite(str(vis_path), vis_image) + print(f"\n✅ 標註圖已保存: {vis_path}") + + return vis_image + + +def generate_summary(ocr_count, regions): + """生成摘要報告""" + summary = f""" +PaddleOCR v2.7.3 (v4) 完整 Pipeline 測試結果 +{'=' * 60} + +1. OCR 檢測: {ocr_count} 個文字區域 +2. 遮罩印刷文字: 完成 +3. 檢測候選區域: {len(regions)} 個 +4. 提取簽名: {len(regions)} 個 + +候選區域詳情: +{'-' * 60} +""" + + for i, region in enumerate(regions): + x, y, w, h = region['box'] + area = region['area'] + summary += f"Region {i+1}: 位置({x}, {y}), 大小{w}x{h}, 面積={area}\n" + + summary += f"\n所有結果保存在: {OUTPUT_DIR}\n" + + return summary + + +def main(): + print("=" * 60) + print("PaddleOCR v2.7.3 (v4) 完整 Pipeline 測試") + print("=" * 60) + + setup_output_dir() + + print("\n1. 讀取測試圖片...") + image = get_page_image() + if image is None: + return + print(f" 圖片大小: {image.shape}") + + cv2.imwrite(str(OUTPUT_DIR / "00_original.png"), image) + + print("\n2. PaddleOCR v2.7.3 檢測文字...") + ocr_results = call_ocr_server(image) + if ocr_results is None: + print("❌ OCR 失敗，終止測試") + return + + print("\n3. 遮罩印刷文字...") + masked_image = mask_printed_text(image, ocr_results) + + print("\n4. 檢測候選區域...") + regions = detect_regions(masked_image) + + print("\n5. 合併鄰近區域...") + merged_regions = merge_nearby_regions(regions) + + print("\n6. 提取簽名...") + vis_image = extract_signatures(image, merged_regions) + + print("\n7. 生成摘要報告...") + summary = generate_summary(len(ocr_results), merged_regions) + print(summary) + + summary_path = OUTPUT_DIR / "SUMMARY.txt" + with open(summary_path, 'w', encoding='utf-8') as f: + f.write(summary) + + print("=" * 60) + print("✅ v4 測試完成！") + print(f"結果目錄: {OUTPUT_DIR}") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/test_v5_full_pipeline.py b/test_v5_full_pipeline.py new file mode 100644 index 0000000..f76448d --- /dev/null +++ b/test_v5_full_pipeline.py @@ -0,0 +1,322 @@ +#!/usr/bin/env python3 +""" +使用 PP-OCRv5 跑完整的簽名提取 pipeline + +流程： +1. 使用服務器上的 PP-OCRv5 檢測文字 +2. 遮罩印刷文字 +3. 檢測候選區域 +4. 提取簽名 +""" + +import sys +import json +import cv2 +import numpy as np +import requests +from pathlib import Path + +# 配置 +OCR_SERVER = "http://192.168.30.36:5555" +PDF_PATH = "/Volumes/NV2/pdf_recognize/test.pdf" +OUTPUT_DIR = Path("/Volumes/NV2/pdf_recognize/test_results/v5_pipeline") +MASKING_PADDING = 0 + + +def setup_output_dir(): + """創建輸出目錄""" + OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + print(f"輸出目錄: {OUTPUT_DIR}") + + +def get_page_image(): + """獲取測試頁面圖片""" + # 使用已有的測試圖片 + test_image = "/Volumes/NV2/pdf_recognize/full_page_original.png" + if Path(test_image).exists(): + return cv2.imread(test_image) + else: + print(f"❌ 測試圖片不存在: {test_image}") + return None + + +def call_ocr_server(image): + """調用服務器端的 PP-OCRv5""" + print("\n調用 PP-OCRv5 服務器...") + + try: + # 編碼圖片 + import base64 + _, buffer = cv2.imencode('.png', image) + img_base64 = base64.b64encode(buffer).decode('utf-8') + + # 發送請求 + response = requests.post( + f"{OCR_SERVER}/ocr", + json={'image': img_base64}, + timeout=30 + ) + + if response.status_code == 200: + result = response.json() + print(f"✅ OCR 完成，檢測到 {len(result.get('results', []))} 個文字區域") + return result.get('results', []) + else: + print(f"❌ 服務器錯誤: {response.status_code}") + return None + + except Exception as e: + print(f"❌ OCR 調用失敗: {e}") + import traceback + traceback.print_exc() + return None + + +def mask_printed_text(image, ocr_results): + """遮罩印刷文字""" + print("\n遮罩印刷文字...") + + masked_image = image.copy() + + for i, result in enumerate(ocr_results): + box = result.get('box') + if box is None: + continue + + # box 格式: [x, y, w, h] + x, y, w, h = box + + # 遮罩（黑色矩形） + cv2.rectangle( + masked_image, + (x - MASKING_PADDING, y - MASKING_PADDING), + (x + w + MASKING_PADDING, y + h + MASKING_PADDING), + (0, 0, 0), + -1 + ) + + # 保存遮罩後的圖片 + masked_path = OUTPUT_DIR / "01_masked.png" + cv2.imwrite(str(masked_path), masked_image) + print(f"✅ 遮罩完成: {masked_path}") + + return masked_image + + +def detect_regions(masked_image): + """檢測候選區域""" + print("\n檢測候選區域...") + + # 轉灰度 + gray = cv2.cvtColor(masked_image, cv2.COLOR_BGR2GRAY) + + # 二值化 + _, binary = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY_INV) + + # 形態學操作 + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) + morphed = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel, iterations=2) + + # 保存中間結果 + cv2.imwrite(str(OUTPUT_DIR / "02_binary.png"), binary) + cv2.imwrite(str(OUTPUT_DIR / "03_morphed.png"), morphed) + + # 找輪廓 + contours, _ = cv2.findContours(morphed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + # 過濾候選區域 + MIN_AREA = 3000 + MAX_AREA = 300000 + + candidate_regions = [] + for contour in contours: + area = cv2.contourArea(contour) + if MIN_AREA <= area <= MAX_AREA: + x, y, w, h = cv2.boundingRect(contour) + aspect_ratio = w / h if h > 0 else 0 + + candidate_regions.append({ + 'box': (x, y, w, h), + 'area': area, + 'aspect_ratio': aspect_ratio + }) + + # 按面積排序 + candidate_regions.sort(key=lambda r: r['area'], reverse=True) + + print(f"✅ 找到 {len(candidate_regions)} 個候選區域") + + return candidate_regions + + +def merge_nearby_regions(regions, h_distance=100, v_distance=50): + """合併鄰近區域""" + print("\n合併鄰近區域...") + + if not regions: + return [] + + merged = [] + used = set() + + for i, r1 in enumerate(regions): + if i in used: + continue + + x1, y1, w1, h1 = r1['box'] + merged_box = [x1, y1, x1 + w1, y1 + h1] # [x_min, y_min, x_max, y_max] + group = [i] + + for j, r2 in enumerate(regions): + if j <= i or j in used: + continue + + x2, y2, w2, h2 = r2['box'] + + # 計算距離 + h_dist = min(abs(x1 - (x2 + w2)), abs((x1 + w1) - x2)) + v_dist = min(abs(y1 - (y2 + h2)), abs((y1 + h1) - y2)) + + # 檢查重疊或接近 + x_overlap = not (x1 + w1 < x2 or x2 + w2 < x1) + y_overlap = not (y1 + h1 < y2 or y2 + h2 < y1) + + if (x_overlap and v_dist <= v_distance) or (y_overlap and h_dist <= h_distance): + # 合併 + merged_box[0] = min(merged_box[0], x2) + merged_box[1] = min(merged_box[1], y2) + merged_box[2] = max(merged_box[2], x2 + w2) + merged_box[3] = max(merged_box[3], y2 + h2) + group.append(j) + used.add(j) + + used.add(i) + + # 轉回 (x, y, w, h) 格式 + x, y = merged_box[0], merged_box[1] + w, h = merged_box[2] - merged_box[0], merged_box[3] - merged_box[1] + + merged.append({ + 'box': (x, y, w, h), + 'area': w * h, + 'merged_count': len(group) + }) + + print(f"✅ 合併後剩餘 {len(merged)} 個區域") + + return merged + + +def extract_signatures(image, regions): + """提取簽名區域""" + print("\n提取簽名區域...") + + # 在圖片上標註所有區域 + vis_image = image.copy() + + for i, region in enumerate(regions): + x, y, w, h = region['box'] + + # 繪製框 + cv2.rectangle(vis_image, (x, y), (x + w, y + h), (0, 255, 0), 3) + cv2.putText(vis_image, f"Region {i+1}", (x, y - 10), + cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) + + # 提取並保存 + signature = image[y:y+h, x:x+w] + sig_path = OUTPUT_DIR / f"signature_{i+1}.png" + cv2.imwrite(str(sig_path), signature) + print(f" Region {i+1}: {w}x{h} 像素, 面積={region['area']}") + + # 保存標註圖 + vis_path = OUTPUT_DIR / "04_detected_regions.png" + cv2.imwrite(str(vis_path), vis_image) + print(f"\n✅ 標註圖已保存: {vis_path}") + + return vis_image + + +def generate_summary(ocr_count, masked_path, regions): + """生成摘要報告""" + summary = f""" +PP-OCRv5 完整 Pipeline 測試結果 +{'=' * 60} + +1. OCR 檢測: {ocr_count} 個文字區域 +2. 遮罩印刷文字: {masked_path} +3. 檢測候選區域: {len(regions)} 個 +4. 提取簽名: {len(regions)} 個 + +候選區域詳情: +{'-' * 60} +""" + + for i, region in enumerate(regions): + x, y, w, h = region['box'] + area = region['area'] + summary += f"Region {i+1}: 位置({x}, {y}), 大小{w}x{h}, 面積={area}\n" + + summary += f"\n所有結果保存在: {OUTPUT_DIR}\n" + + return summary + + +def main(): + print("=" * 60) + print("PP-OCRv5 完整 Pipeline 測試") + print("=" * 60) + + # 準備 + setup_output_dir() + + # 1. 獲取圖片 + print("\n1. 讀取測試圖片...") + image = get_page_image() + if image is None: + return + print(f" 圖片大小: {image.shape}") + + # 保存原圖 + cv2.imwrite(str(OUTPUT_DIR / "00_original.png"), image) + + # 2. OCR 檢測 + print("\n2. PP-OCRv5 檢測文字...") + ocr_results = call_ocr_server(image) + if ocr_results is None: + print("❌ OCR 失敗，終止測試") + return + + # 3. 遮罩印刷文字 + print("\n3. 遮罩印刷文字...") + masked_image = mask_printed_text(image, ocr_results) + + # 4. 檢測候選區域 + print("\n4. 檢測候選區域...") + regions = detect_regions(masked_image) + + # 5. 合併鄰近區域 + print("\n5. 合併鄰近區域...") + merged_regions = merge_nearby_regions(regions) + + # 6. 提取簽名 + print("\n6. 提取簽名...") + vis_image = extract_signatures(image, merged_regions) + + # 7. 生成摘要 + print("\n7. 生成摘要報告...") + summary = generate_summary(len(ocr_results), OUTPUT_DIR / "01_masked.png", merged_regions) + print(summary) + + # 保存摘要 + summary_path = OUTPUT_DIR / "SUMMARY.txt" + with open(summary_path, 'w', encoding='utf-8') as f: + f.write(summary) + + print("=" * 60) + print("✅ 測試完成！") + print(f"結果目錄: {OUTPUT_DIR}") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/visualize_v5_results.py b/visualize_v5_results.py new file mode 100644 index 0000000..39d2fda --- /dev/null +++ b/visualize_v5_results.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python3 +""" +可視化 PP-OCRv5 的檢測結果 +""" + +import json +import cv2 +import numpy as np +from pathlib import Path + +def load_results(): + """加載 v5 檢測結果""" + result_file = "/Volumes/NV2/pdf_recognize/test_results/v5_result.json" + with open(result_file, 'r', encoding='utf-8') as f: + data = json.load(f) + return data['res'] + +def draw_detections(image_path, results, output_path): + """在圖片上繪製檢測框和文字""" + # 讀取圖片 + img = cv2.imread(image_path) + if img is None: + print(f"❌ 無法讀取圖片: {image_path}") + return None + + # 創建副本用於繪製 + vis_img = img.copy() + + # 獲取檢測結果 + rec_texts = results.get('rec_texts', []) + rec_boxes = results.get('rec_boxes', []) + rec_scores = results.get('rec_scores', []) + + print(f"\n檢測到 {len(rec_texts)} 個文字區域") + + # 繪製每個檢測框 + for i, (text, box, score) in enumerate(zip(rec_texts, rec_boxes, rec_scores)): + x_min, y_min, x_max, y_max = box + + # 繪製矩形框（綠色） + cv2.rectangle(vis_img, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2) + + # 繪製索引號（小字） + cv2.putText(vis_img, f"{i}", (x_min, y_min - 5), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1) + + # 保存結果 + cv2.imwrite(output_path, vis_img) + print(f"✅ 可視化結果已保存: {output_path}") + + return vis_img + +def generate_text_report(results): + """生成文字報告""" + rec_texts = results.get('rec_texts', []) + rec_scores = results.get('rec_scores', []) + rec_boxes = results.get('rec_boxes', []) + + print("\n" + "=" * 80) + print("PP-OCRv5 檢測結果報告") + print("=" * 80) + + print(f"\n總共檢測到: {len(rec_texts)} 個文字區域") + print(f"平均置信度: {np.mean(rec_scores):.4f}") + print(f"最高置信度: {np.max(rec_scores):.4f}") + print(f"最低置信度: {np.min(rec_scores):.4f}") + + # 分類統計 + high_conf = sum(1 for s in rec_scores if s >= 0.95) + medium_conf = sum(1 for s in rec_scores if 0.8 <= s < 0.95) + low_conf = sum(1 for s in rec_scores if s < 0.8) + + print(f"\n置信度分布:") + print(f" 高 (≥0.95): {high_conf} 個 ({high_conf/len(rec_scores)*100:.1f}%)") + print(f" 中 (0.8-0.95): {medium_conf} 個 ({medium_conf/len(rec_scores)*100:.1f}%)") + print(f" 低 (<0.8): {low_conf} 個 ({low_conf/len(rec_scores)*100:.1f}%)") + + # 顯示前 20 個檢測結果 + print("\n前 20 個檢測結果:") + print("-" * 80) + for i in range(min(20, len(rec_texts))): + text = rec_texts[i] + score = rec_scores[i] + box = rec_boxes[i] + + # 計算框的大小 + width = box[2] - box[0] + height = box[3] - box[1] + + print(f"[{i:2d}] 置信度: {score:.4f} 大小: {width:4d}x{height:3d} 文字: {text}") + + if len(rec_texts) > 20: + print(f"\n... 還有 {len(rec_texts) - 20} 個結果（省略）") + + # 尋找可能的手寫區域（低置信度 或 大字） + print("\n" + "=" * 80) + print("可能的手寫區域分析") + print("=" * 80) + + potential_handwriting = [] + for i, (text, score, box) in enumerate(zip(rec_texts, rec_scores, rec_boxes)): + width = box[2] - box[0] + height = box[3] - box[1] + + # 判斷條件： + # 1. 高度較大 (>50px) + # 2. 或置信度較低 (<0.9) + # 3. 或文字較短但字體大 + is_large = height > 50 + is_low_conf = score < 0.9 + is_short_text = len(text) <= 3 and height > 40 + + if is_large or is_low_conf or is_short_text: + potential_handwriting.append({ + 'index': i, + 'text': text, + 'score': score, + 'height': height, + 'width': width, + 'reason': [] + }) + + if is_large: + potential_handwriting[-1]['reason'].append('大字') + if is_low_conf: + potential_handwriting[-1]['reason'].append('低置信度') + if is_short_text: + potential_handwriting[-1]['reason'].append('短文大字') + + if potential_handwriting: + print(f"\n找到 {len(potential_handwriting)} 個可能的手寫區域:") + print("-" * 80) + for item in potential_handwriting[:15]: # 只顯示前 15 個 + reasons = ', '.join(item['reason']) + print(f"[{item['index']:2d}] {item['height']:3d}px {item['score']:.4f} ({reasons}) {item['text']}") + else: + print("未找到明顯的手寫特徵區域") + + # 保存詳細報告到文件 + report_path = "/Volumes/NV2/pdf_recognize/test_results/v5_analysis_report.txt" + with open(report_path, 'w', encoding='utf-8') as f: + f.write(f"PP-OCRv5 檢測結果詳細報告\n") + f.write("=" * 80 + "\n\n") + f.write(f"總數: {len(rec_texts)}\n") + f.write(f"平均置信度: {np.mean(rec_scores):.4f}\n\n") + f.write("完整檢測列表:\n") + f.write("-" * 80 + "\n") + for i, (text, score, box) in enumerate(zip(rec_texts, rec_scores, rec_boxes)): + width = box[2] - box[0] + height = box[3] - box[1] + f.write(f"[{i:2d}] {score:.4f} {width:4d}x{height:3d} {text}\n") + + print(f"\n詳細報告已保存: {report_path}") + +def main(): + # 加載結果 + print("加載 PP-OCRv5 檢測結果...") + results = load_results() + + # 生成文字報告 + generate_text_report(results) + + # 可視化 + print("\n" + "=" * 80) + print("生成可視化圖片") + print("=" * 80) + + image_path = "/Volumes/NV2/pdf_recognize/full_page_original.png" + output_path = "/Volumes/NV2/pdf_recognize/test_results/v5_visualization.png" + + if Path(image_path).exists(): + draw_detections(image_path, results, output_path) + else: + print(f"⚠️ 原始圖片不存在: {image_path}") + + print("\n" + "=" * 80) + print("分析完成") + print("=" * 80) + +if __name__ == "__main__": + main()