inferences codes

.gitattributes

@@ -33,3 +33,104 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+font/gulim.ttc filter=lfs diff=lfs merge=lfs -text
+images_rec/-h6zaExAO5d0SwOLab4CD.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/-LcmZOV4xrQVjv61hX2yF.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/0G5lzNC9wlMjLH4Pgqpiv.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/0Wu-eggp4JBICAoABliTc.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/0xSnz-Yn6dc9yB8eQgzeP.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/13.jpg filter=lfs diff=lfs merge=lfs -text
+images_rec/13121.jpg filter=lfs diff=lfs merge=lfs -text
+images_rec/19.jpg filter=lfs diff=lfs merge=lfs -text
+images_rec/1cnIS16DmJFYH5xGdZDVp.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/1jEP7XVqK_qxMGFELEcBd.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/22.jpg filter=lfs diff=lfs merge=lfs -text
+images_rec/2ZBNwnbnYNpcLL9aBUS4w.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/34gFMj0jKceCkDDHW9H4k.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/390XJDGATbmhPazLWOBc6.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/3Jkl_BANLxoAJkkWodwKH.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/5L7QIIY8EYYhXCyHWUCFr.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/7u5qmwKFsB-walZ34Oe4U.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/8.jpg filter=lfs diff=lfs merge=lfs -text
+images_rec/88rGixQVT8KtfyK_Hq4fH.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/8Byi031_J7Dvvrw60jvTf.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/A020112_111114_0004.JPG filter=lfs diff=lfs merge=lfs -text
+images_rec/A240208_111424_0032_enhanced.JPG.png filter=lfs diff=lfs merge=lfs -text
+images_rec/A240213_111224_0020.JPG filter=lfs diff=lfs merge=lfs -text
+images_rec/A250101_111212_0002.JPG filter=lfs diff=lfs merge=lfs -text
+images_rec/A250101_111424_0032.JPG filter=lfs diff=lfs merge=lfs -text
+images_rec/A260173_111124_0008.JPG filter=lfs diff=lfs merge=lfs -text
+images_rec/a4H8DhGz-O-1rU4SwGYQ9.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/AisrTDiAbj0Y0w5qGpSp-.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/BhzWBe-ZI5ZFgAPzYL23h.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/Bk8E-bGn1i4DJIrzKXGW3.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/cspDWHPbvSOYJZKqF9GCO.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/CVuwRcNm-ny33CMmLdIes.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/d9Q6AafXY_FQeHK-445Z1.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/ddTEFkX2ykSaw8bO-6kXF.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/DpBFo_IdI2MzmGFIi4q8b.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/E7L6NErhRmI5K7NvwtIIv.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/EFcHP3Y89VnQfwzzC12qu.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/Ex-6VCTPgtUYyFDCjI8Rp.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/F1Bv3_6KeZNbRl2NJZUmu.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/F6Pd53sY2kjxRHUUGP0W1.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/f8WRKjYskAIfdAO98AOPB.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/FGwwyukPamfQ4yZG011Qr.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/fjNHe8FUG6vwQ1y1akAt6.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/FtsAHZlrcn07XJB1-751f.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/gcWZJ_7DCZohbRCVFV1Lr.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/gD3Ka3dr1qmZ4iDQer6b-.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/grQmazX8gvt-rcwAQJ_uQ.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/gyXEIgTE9e4t3Kcs8kjth.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/h6AZi1HMOFE-pLsJVlwJX.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/iBsj7uP1blBfP6hNy5TaM.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/ikC0rxHueYH4BKBr2leuH.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/iKKBzqtYzIpATZWqageI2.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/IZQk15ivk8aMyahzd1Kyd.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/JFwtfnlcbUAbcX3KvVU4Y.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/jR1k9PeNpCDNKtMmzGDj_.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/JX1XdrQyXbmK2pgB1ap2r.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/jxrRHHbhTybq6Rmd33dHv.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/K-sDtES-SUaXTQ2hXQvQv.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/kIyrEuiuij5WJvBUHUH0k.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/kTSKWuiMI7U8VYyoovhjT.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/l39yWZjK6Z2dDio3QFHku.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/l5p0VD2H6MKKpC4oOhFvg.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/Lb4Z_vBgjzV5m28h2iGfd.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/LS42MmzTUMa4yAj3R6Kws.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/m5.jpg filter=lfs diff=lfs merge=lfs -text
+images_rec/Mv7tMmiMXwpfpOAHfAKos.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/ndas8VkmE2LkpMNNwk7td.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/NO7tkBlH0ULLFyYwevPS4.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/nRBGhqjYZODA9pfkPu9Pf.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/oQc8PyIJpCZmMid3narQ4.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/OR-yQhdketM5-4-TzbBHW.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/png filter=lfs diff=lfs merge=lfs -text
+images_rec/pZGN4fHO5X5TFfzosrimN.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/qcOpuTiz6dfnkL3vT8YfL.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/qQwNMS0YNb-edEeStfaUF.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/RhYkF88om2i0HZYgWzyhD.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/RmdkMP99cz4at01h4ApMC.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/rNKkY1T8V-QzaKA_Rz-ar.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/rpOll1mVryaUbIJsLRzzF.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/rwbc5u9Ka-jpqUFZcLPon.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/rxqZ12JcF-2-9VcNEBaQm.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/sbGE1-K-fD90tEIxu5_4A.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/ss2jLfJNaQWLMOh2xE_Df.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/SX_KGixDuhAYXJ1eD8vHz.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/synthetic_fig16.png filter=lfs diff=lfs merge=lfs -text
+images_rec/synthetic_fig8.png filter=lfs diff=lfs merge=lfs -text
+images_rec/TYtVYK5D8zxJ1No-2aOfD.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/W5mZdt_jjiAt-vMCNIpZZ.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/wcAM8eTDm07LgUF6VJpY8.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/weSZ0_atWQYcLb5bLGh2E.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/Wfp4cQnnxw2qYnSL8gxjD.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/XBYX8LG-n6pVDiM0x1I_3.jpeg filter=lfs diff=lfs merge=lfs -text
+images_rec/y4gH7gyND28OdhTIuSRsY.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/YQPtBbrGcXrTQFBjy_C5O.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/yxfFbbFNd1dW9m2_P74-l.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/YxZBgC2j8OQyuXBReGbPh.txt filter=lfs diff=lfs merge=lfs -text
+images_rec/zMbYKlma7gRQskg_AFl91.txt filter=lfs diff=lfs merge=lfs -text
+yolo/data/food/images/A250101_111424_0032.JPG filter=lfs diff=lfs merge=lfs -text
+yolo/data/food/weights/food-dark-yolov3-tiny_3l-v3-2_24000.weights filter=lfs diff=lfs merge=lfs -text
+yolo/data/food/weights/food-dark-yolov3-tiny_3l-v3-2_last.weights filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,307 +1,110 @@
-# AIHub 한국 음식 데이터셋 활용 도구
-
-**AIHub "건강관리를 위한 음식이미지" 데이터셋을 효율적으로 활용하기 위한 Python 도구 모음**
-
-[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
-[![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](https://www.python.org/)
-
-## 📋 개요
-
-이 프로젝트는 [AIHub의 "건강관리를 위한 음식이미지"](https://www.aihub.or.kr/aihubdata/data/view.do?dataSetSn=242) 데이터셋을 활용하기 위한 전처리, 분석, 시각화 도구를 제공합니다.
-
-### 🎯 주요 특징
-
-- **500+ 카테고리** 한국 음식 분류 시스템
-- **300만장** 고해상도 음식 이미지 처리
-- **영양정보** 자동 추정 및 분석
-- **이미지 품질** 자동 검증
-- **데이터 증강** 및 전처리 파이프라인
-- **시각화** 및 통계 분석 도구
-
-## 📊 데이터셋 정보
-
-| 항목 | 내용 |
-|------|------|
-| **데이터명** | 건강관리를 위한 음식이미지 |
-| **제공기관** | AIHub |
-| **데이터 크기** | 약 300만장 |
-| **카테고리** | 500여개 |
-| **해상도** | 1,280 x 720 이상 (HD급) |
-| **포맷** | JPEG, PNG + JSON 어노테이션 |
-| **라이선스** | AIHub 이용약관 준수 |
-
-### 🍽️ 주요 카테고리
-
-- **밥류**: 짬뽕밥, 달걀볶음밥, 누룽지 등
-- **국물류**: 된장국, 김치찌개, 순대국 등  
-- **면류**: 라면, 우동, 파스타 등
-- **육류**: 불고기, 치킨, 족발 등
-- **음료**: 커피, 주스, 차류 등
-- **과일/채소**: 사과, 배, 토마토 등
-- **기타**: 빵류, 과자, 견과류 등
-
-## 🚀 빠른 시작
-
-### 1. 환경 설정
-
-**Ubuntu24.04(amd64), tf_2.15, python_3.10, CUDA_12.4, and... plz see the requirements.txt**
-
-```bash
-# 저장소 클론
-git clone https://huggingface.co/spaces/speedpointer/Kfoods
-
-# 가상환경 생성 (권장)
-python -m venv venv
-source venv/bin/activate  # Windows: venv\Scripts\activate
-
-# 패키지 설치
-pip install -r requirements.txt
-```
-
-### 2. 데이터 준비
-
-**⚠️ 중요: 데이터는 AIHub에서 직접 다운로드해야 합니다**
-
-1. [AIHub 웹사이트](https://www.aihub.or.kr/aihubdata/data/view.do?dataSetSn=242) 접속
-2. 회원가입 및 데이터 이용신청
-3. 승인 후 데이터 다운로드 (약 840GB+)
-4. 압축 해제 후 다음과 같은 구조로 배치:
-
-```
-/your/data/path/
-├── category1/
-│   ├── image1.jpg
-│   ├── image2.jpg  
-│   ├── ...
-│   └── annotations.json
-├── category2/
-│   ├── image1.jpg
-│   ├── ...
-└── ...
-```
-
-### 3. 기본 사용법
-
-```python
-from data_loader import KoreanFoodDataLoader
-from preprocessing import FoodImagePreprocessor
-from category_mapping import KoreanFoodCategoryMapper
-
-# 데이터 로더 초기화
-loader = KoreanFoodDataLoader("/path/to/your/data")
-
-# 카테고리 목록 확인
-categories = loader.get_category_list()
-print(f"총 {len(categories)}개 카테고리")
-
-# 특정 카테고리 이미지 로드
-images_data = loader.load_images_from_category("김치찌개", max_images=10)
-
-# 이미지 전처리
-preprocessor = FoodImagePreprocessor(target_size=(224, 224))
-for image, metadata in images_data:
-    processed = preprocessor.preprocess_image(image)
-    print(f"전처리 완료: {processed.shape}")
-```
-
-## 📁 프로젝트 구조
-
-```
-korean-food-dataset-tools/
-├── data_loader.py          # 데이터 로딩 및 관리
-├── category_mapping.py     # 카테고리 분류 및 매핑
-├── preprocessing.py        # 이미지 전처리 및 증강
-├── example_usage.py        # 사용 예제 및 데모
-├── requirements.txt        # 필요 패키지 목록
-├── README.md              # 프로젝트 설명서
-└── examples/              # 추가 예제들
-    ├── basic_usage.ipynb
-    ├── training_example.py
-    └── visualization.py
-```
-
-## 🔧 주요 기능
-
-### 1. 데이터 로딩 (`data_loader.py`)
-
-```python
-# 카테고리별 이미지 로드
-images = loader.load_images_from_category("불고기", max_images=100)
-
-# 영양정보 추출
-nutrition = loader.get_nutrition_info("김치찌개")
-
-# 데이터셋 통계
-stats = loader.get_data_statistics()
-```
-
-### 2. 카테고리 분류 (`category_mapping.py`)
-
-```python
-mapper = KoreanFoodCategoryMapper()
-
-# 음식명으로 카테고리 자동 분류
-result = mapper.classify_food_name("된장찌개")
-print(result)  # {'category_id': 1, 'category_name': '밥, 국, 탕, 찌개, 전골', ...}
-
-# 어노테이션 템플릿 생성
-template = mapper.create_annotation_template("FOOD_001", "김치찌개", 1280, 720)
-```
-
-### 3. 이미지 전처리 (`preprocessing.py`)
-
-```python
-preprocessor = FoodImagePreprocessor(target_size=(224, 224))
-
-# 기본 전처리
-processed = preprocessor.preprocess_image(image, augment=False)
-
-# 데이터 증강 ���용
-augmented = preprocessor.preprocess_image(image, augment=True)
-
-# 이미지 품질 검증
-quality = preprocessor.validate_image_quality(image)
-
-```
-
-### 4. 전체 분석 실행 (`example_usage.py`)
-
-```bash
-# 데이터 경로 수정 후 실행
-python example_usage.py
-```
-
-출력 파일:
-- `aihub_analysis_results.png`: 데이터 분석 시각화
-- `aihub_analysis_report.json`: 상세 분석 보고서
-
-## 📈 분석 결과 예시
-
-### 카테고리 분포
-- 밥류: 45개 (9.0%)
-- 국물류: 78개 (15.6%)  
-- 면류: 32개 (6.4%)
-- 육류: 56개 (11.2%)
-- 기타: 289개 (57.8%)
-
-### 이미지 품질
-- 해상도 기준 통과: 95.2%
-- 선명도 기준 통과: 88.7%
-- 전체 품질 통과: 85.3%
-
-## 🤝 활용 사례
-
-### 1. 음식 분류 모델 학습
-
-```python
-# PyTorch 데이터셋 클래스 예시
-class KoreanFoodDataset(torch.utils.data.Dataset):
-    def __init__(self, data_root, transform=None):
-        self.loader = KoreanFoodDataLoader(data_root)
-        self.categories = self.loader.get_category_list()
-        self.transform = transform
-        
-    def __getitem__(self, idx):
-        # 구현 내용
-        pass
-```
-
-### 2. 영양성분 분석 앱
-
-```python
-# 음식 사진으로부터 영양정보 추정
-def analyze_food_nutrition(image_path):
-    # 이미지 로드 및 전처리
-    # 모델 추론으로 음식 분류
-    # 영양정보 반환
-    pass
-```
-
-### 3. 데이터 품질 관리
-
-```python
-# 대용량 데이터셋의 품질 일괄 검증
-def batch_quality_check(data_root):
-    loader = KoreanFoodDataLoader(data_root)
-    preprocessor = FoodImagePreprocessor()
-    
-    for category in loader.get_category_list():
-        images_data = loader.load_images_from_category(category)
-        # 품질 검증 로직
-```
-
-## ⚠️ 주의사항
-
-### 라이선스 및 사용 제한
-
-1. **원본 데이터**: AIHub 이용약관을 반드시 준수해야 합니다
-2. **재배포 금지**: 원본 이미지 데이터를 재배포할 수 없습니다
-3. **상업적 이용**: AIHub 약관에 따른 별도 승인이 필요할 수 있습니다
-4. **인용 필수**: 연구/개발 시 AIHub 데이터셋 출처를 명시해야 합니다
-
-### 기술적 요구사항
-
-- **저장공간**: 최소 2TB 이상 권장
-- **메모리**: 8GB RAM 이상 권장  
-- **GPU**: CUDA 지원 GPU 권장 (선택사항)
-- **Python**: 3.10 이상
-
-## 🐛 문제 해결
-
-### 자주 발생하는 문제
-
-1. **메모리 부족**
-   ```python
-   # 배치 크기 줄이기
-   images_data = loader.load_images_from_category(category, max_images=100)
-   ```
-
-2. **CUDA 오류**
-   ```bash
-   # CPU 버전 PyTorch 설치
-   pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
-   ```
-
-3. **한글 인코딩 문제**
-   ```python
-   # 파일 읽기 시 인코딩 명시
-   with open(file_path, 'r', encoding='utf-8') as f:
-       data = json.load(f)
-   ```
-
-## 📚 추가 자료
-
-- [AIHub 공식 문서](https://www.aihub.or.kr)
-- [데이터셋 상세 정보](https://www.aihub.or.kr/aihubdata/data/view.do?dataSetSn=242)
-- [PyTorch 튜토리얼](https://pytorch.org/tutorials/)
-- [Albumentations 문서](https://albumentations.ai/)
-
-## 🤝 기여하기
-
-1. Fork the Project
-2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)  
-3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
-4. Push to the Branch (`git push origin feature/AmazingFeature`)
-5. Open a Pull Request
-
-## 📝 라이선스
-
-이 프로젝트는 Apache 2.0 라이선스 하에 배포됩니다. 자세한 내용은 `LICENSE` 파일을 참조하세요.
-
-**⚠️ 주의**: 이 도구는 Apache 2.0 라이선스이지만, **AIHub 원본 데이터는 별도의 이용약관**을 따릅니다.
-
-## 👥 개발자
-
-- **메인테이너**: speedpointer (mynameis@hajunho.com)
-- **기여자**: [Contributors](https://github.com/speedpointer/Kfoods/contributors)
-
-## 📞 문의
-
-- **이슈**: [GitHub Issues](https://github.com/speedpointer/Kfoods/issues)
-- **디스커션**: [GitHub Discussions](https://github.com/speedpointer/Kfoods/discussions)
-
----
-
-**⭐ 이 프로젝트가 도움이 되었다면 별표를 눌러주세요!**
-
-Made with ❤️ for Korean AI Community
+## Food Classifier Test Platform for Trained model with PHOTOMATO 
+- 마지막 업데이트: 2021/02/21
+- 개발환경: Ubuntu 16.04 (Windows 10) 테스트 완료 
+- python 환경설정: conda env (webdev) with python=3.5
+- 필요 팩키지: Flask, opencv, pillow
+- **최종적으로 Naver Server 에 REST API 를 Flask framework 을  이용하여 서비스함.**
+
+- Windows 10 test: working on anaconda environment with pyTh37-pyTorch-Opencv420-office.yml
+- 테스팅 파라미터는 config/config.py 를 참조.
+ 
+   ```angular2html
+   0. 음식데이터 라벨은 카테코리 표준코드 관리의 최종코드를 참조하여 class 로 분리함...
+   1. 현재 3165종으로 구분함.. 
+   2. YOLO 공식 홈페이지에서 제안하기로는 각 클래스당 적어도 2000개이상의 영상이 필요함.
+   3. 현재 PHOTOMATO dataset 기준으로 1000번 수행 기준 2GPU 로 10시간씩 걸림..
+   4. 적어도 100,000번의 수행이 경험상 필요하다고 판단되나 시간관계상 20000번을 통해 모델 취득
+   5. 중간결과 분석    
+      13000번 이상 수행 후 쑥개떡(class_id:384), 멍게(class_id:563)의 경우 
+      "쑥개떡 0.78852534  - confidence: 0.7352499  - thres :  0.1" :
+      "멍게 0.67948073  - confidence: 0.59680355  - thres :  0.1 " :
+      계속 수행함에 따라(약 200 epoch 더 진행 후, 2시간 후).
+      "쑥개떡 0.78726465  - confidence: 0.6972475  - thres :  0.1" :
+      "멍게  0.7939826  - confidence: 0.69312614  - thres :  0.1 " :
+      로 제대로 인식함.. 
+      15000번: 멍게의 경우를 보면 상당히 성능이 좋아지고 있음. 
+          쑥개떡을 보면 아직 불안정한 것으로 판단할 수 있음
+          쑥개떡: 0.55149615  - confidence: 0.4864739  - thres :  0.1 
+          멍게: 0.93912053  - confidence: 0.65867484    
+  
+   ```
+## Preparing data
+```angular2html
+1. data 준비 
+   영상의 위치는 상관이 없으나 기계적인 처리를 위해 json 파일이 같은 폴더안에 존재하는게 좋다.
+   json-> yolo txt 파일 포맷 (class_id, center_x, center_y, width, height)의 tuple 로 
+   저장이 되어 있어야 한다. 단 좌표는 물체를 둘러쌓는 box의 중심, 가로길이, 세로길이는 반드시 전체영상의 가로와 세로로 정규화 되어야 한다.
+2. data가 준비되면 training data:validation data의 비율에 따라 
+   train.txt 와 val.txt에 나누어 목록을 만든다. 현재의 문서는 (8:2)로 비율을 정하였다.
+3. class/label/category를 나타내는 목록은 
+ ./yolo/data/food/food-classes.names 에 넣어져야 한다. 현재 3165개의 클래스가 들어가 있다. 
+```
+##  Training
+```angular2html
+1. 데이터가 준비가 되면 ./yolo/config/food-darknet-v1.data 에 아래와 같이 훈련에 사용할 train list, validation list, 
+    클래스 정의, 그리고 모델을 임시 저장할 폴더 등에 대하 정의를 다음과 같이 한다. 본인의 환경에 맞게 정해 주면 된다. 
+    classes =3165 
+    train  = /workspace/food_classification/yolo/data/food/food_train_20210211.txt 
+    valid  = /workspace/food_classification/yolo/data/food/food_val_20210211.txt
+    names = /workspace/food_classification/yolo/data/food/food-classes.names
+    backup = /workspace/food_classification/yolo/data/food/weights/
+2. docker를 통해 미리 만들어진 container로 들어간다. (docker 유경험자는 알겠지만 image가 없으면 자동으로 archive에서 받게 된다.) 
+    $ sudo docker run --gpus all -it -v ~/workspace:/workspace --ipc=host sangkny/darknet:yolov4 /bin/bash
+3. docker 내에서 training을 시작한다. 
+    $ ./darknet detector train /workspace/food-classifier/yolo/config/food-darknet-v1.data /workspace/food-classifier/yolo/config/food-dark-yolov3-tiny_3l-v3-2.cfg /workspace/food-classifier/yolo/config/darknet53.conv.74 -gpus 0,1 2>&1 |tee /workspace/food-classifier/yolo/data/food/food-train-v3-highGPU.log
+```
+
+### Inference and WebService
+```angular2html
+1. 훈련된 모델에 대한 테스트는 conda 가상환경을 만들고 그안에서 실시했다. 
+    $ conda create --name webdev python=3.5 flask, opencv=3.4.2, pillow
+    $ conda activate webdev
+    위의 명령까지 정상적으로 실행이 되면 가상환경 내에 있어야 하며 다음과 유사한 프롬프트 상에 놓이게 된다. 
+    $ (webdev)
+2. 본격적인 inference test는 food_classifier_yolo.py 로 구현이 되어 있으면
+    $ python food_classifier_yolo.py 를 실행하면 된다.
+    각종 parameter 조정은 ./config/config.py 를 참조하여 조절하면 된다. 
+3. REST API 를 Flask framework 를 통해 구현하였다. 
+    $ python WebAPI.py 
+    를 실행하면 된다. 특히, local ip(0.0.0.0, 127.0.0.1) 을 ubuntu, windows 각각 local host로 넣어주어야 한다. 
+    local host는 서버 본체이며, 포트(내/외부 경로 공통)를 통해 외부에서 들어오는 요청을 받아들이게 된다. 외부에서의 요청은 
+    공식 웹서버 ip를 통해 전달된다.   
+    각 web환경에 맞게 WebAPI.py 제일 마지막 main 함수에 있는 ip 와 port를 조정하여 서비스를 하면 된다. 
+    현재 네이버 서버는 http://xxx.xxx.xx.x:8080/uploader 로 접속을 하면 파일을 전송하라는 webpage가 뜨고
+    적절한 파일을 선택 후 전송을 하여 request를 하면  
+    잠시후 서버에서  json의 포맷으로 탐지된 물체에 대한 정보를 나타나게 된다.
+4. 이렇게 웹서버에 띄워놓은 framework(Flask)가 로그 아웃이 되어도 지속되게 하기 위해 제일 마지막에 &를 붙여
+    $ python WepAPI.py &
+    로 백그라운드에 daemon 같이 ��� 수 있다. 
+    특정 process가 죽어도 계속 써비스 하기 우해서는 no hang up의 약자인 nohup을 앞에 붙여 사용한다. 
+    $ hohup python WepAPI.py &
+    여기에 로그를 저장하고 싶으면
+    $ nohup python WebAPI.py > app.log &
+    로 사용하면 된다. 하지만 화면에 출력까지 보고 싶다면,
+    $ nohup python WebAPI.py 2>&1 |tee app.log & 
+    하면된다.  
+5. 웹서버의 응답은 WebAPI.py 에 정의된 것과 같이.. 
+    (ClassID, ClassName, x, y, width, height) 를 탐지된 물체의 개수 만큼 json format 으로 넘겨주게 된다.
+
+    ----------- 함수 정의 ------- WepAPI.py 의 food_classifier_Json(image) 은 food_classifier_yolo.py 에 있음 --------------- 
+    def food_classifier_Json(image):
+    # do somthing
+    print(args.showText)
+    locations = food_classifier_pipeline(frame=image) #[(2321, 0, 0, 10, 10)] # list of (id, rect) from classification
+    jsons = []
+    for j,location in enumerate(locations):
+        class_id, x, y, width, height =location
+        res_json = {}
+        res_json["ClassID"] = classes_codes[class_id] # code , class_id (training class)
+        res_json["ClassName"] = classes[class_id]
+        res_json["x"] = int(x)
+        res_json["y"] = int(y)
+        res_json["w"] = int(width)
+        res_json["h"] = int(height)
+        jsons.append(res_json)
+    print(json.dumps(jsons,ensure_ascii=False)) # debug purpose 
+
+    return json.dumps(jsons,ensure_ascii=False)
+    
+6. 서버를 재 부팅시 위의 설정에 요구되는 사항은 CTL 등은 서버관리자에게 설정을 하게 하면 된다. 
+```

WebAPI.py

@@ -0,0 +1,56 @@
+# -*- encoding: utf-8 -*-
+# This file supports web-based object classfication
+# by sangkny
+# modified by speedpointer
+# -------------------------------------------------
+from flask import Flask, render_template, request
+from werkzeug.utils import secure_filename
+
+import cv2
+import numpy as np
+
+from food_classifier_yolo import food_classifier_Json
+# classification function
+
+app = Flask(__name__)
+#App name
+
+def recognize(filename):
+    image = cv2.imread(filename) # be careful for hangul name
+    # read file and put it into an image array
+    return food_classifier_Json(image=image)
+# classification for food images
+
+import base64
+
+# for hangul file name
+def recognizeBase64(base64_code):
+    file_bytes = np.asarray(bytearray(base64.b64decode(base64_code)),dtype=np.uint8)
+    image_data_ndarray = cv2.imdecode(file_bytes,1)
+    return food_classifier_Json(image_data_ndarray)
+
+import time
+
+@app.route('/uploader', methods=['GET', 'POST'])# request routing
+def upload_file():
+    if request.method == 'POST':
+        # if POST case
+        f = request.files['file']
+        f.save("./images_rec/"+secure_filename(f.filename))
+        # saving the requested file
+        t0 = time.time()
+        res = recognize("./images_rec/"+secure_filename(f.filename))
+        print("elapsed time:",time.time() - t0)
+        return res
+        # return the result
+
+        # return 'file uploaded successfully'
+    return render_template('upload.html')
+
+if __name__ == '__main__':
+    # input
+    ip_address = "0.0.0.0"#"127.0.0.1"
+    port_number = 3000 #8000
+    app.run(ip_address,port=int(port_number))
+    # run app with ip and port numbers
+

app.log

@@ -0,0 +1,781 @@
+nohup: ignoring input
+ * Serving Flask app "WebAPI" (lazy loading)
+ * Environment: production
+   WARNING: This is a development server. Do not use it in a production deployment.
+   Use a production WSGI server instead.
+ * Debug mode: off
+ * Running on http://0.0.0.0:3000/ (Press CTRL+C to quit)
+125.243.32.134 - - [25/Feb/2021 11:45:53] "GET /uploader HTTP/1.1" 200 -
+125.243.32.134 - - [25/Feb/2021 11:46:05] "POST /uploader HTTP/1.1" 200 -
+125.243.32.134 - - [25/Feb/2021 11:47:41] "GET /uploader HTTP/1.1" 200 -
+125.243.32.134 - - [25/Feb/2021 11:48:53] "POST /uploader HTTP/1.1" 200 -
+125.243.32.134 - - [25/Feb/2021 12:02:43] "GET /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 12:27:29] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 12:27:58] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 12:33:11] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 12:43:30] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 12:44:22] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 12:58:19] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 12:59:52] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 13:09:29] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 13:11:49] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 13:14:56] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 13:16:51] "POST /uploader HTTP/1.1" 200 -
+1
+out.shape :  (507, 3170)
+obj score:  0.9699368  - confidence: 0.9692912  - thres :  0.1
+obj score:  0.4339592  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 587, "framewidth": 2449, "ClassName": "바게트빵", "w": 1205, "ClassID": "A020112", "h": 536, "frameheight": 1633, "y": 459}]
+elapsed time: 1.260960578918457
+1
+out.shape :  (507, 3170)
+obj score:  0.9699368  - confidence: 0.9692912  - thres :  0.1
+obj score:  0.4339592  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 587, "framewidth": 2449, "ClassName": "바게트빵", "w": 1205, "ClassID": "A020112", "h": 536, "frameheight": 1633, "y": 459}]
+elapsed time: 1.1979155540466309
+1
+out.shape :  (507, 3170)
+obj score:  0.1948593  - confidence: 0.0  - thres :  0.1
+obj score:  0.12583147  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[]
+elapsed time: 1.3625829219818115
+1
+out.shape :  (507, 3170)
+obj score:  0.37945047  - confidence: 0.0  - thres :  0.1
+obj score:  0.56398195  - confidence: 0.26541716  - thres :  0.1
+obj score:  0.27739364  - confidence: 0.0  - thres :  0.1
+obj score:  0.116471924  - confidence: 0.0  - thres :  0.1
+obj score:  0.2095417  - confidence: 0.0  - thres :  0.1
+obj score:  0.4742067  - confidence: 0.0  - thres :  0.1
+obj score:  0.17590275  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": -296, "framewidth": 3000, "ClassName": "물미역", "w": 2670, "ClassID": "A260206", "h": 2452, "frameheight": 4000, "y": 1119}]
+elapsed time: 1.4194393157958984
+1
+out.shape :  (507, 3170)
+obj score:  0.15587588  - confidence: 0.0  - thres :  0.1
+obj score:  0.28064254  - confidence: 0.0  - thres :  0.1
+obj score:  0.7351788  - confidence: 0.0  - thres :  0.1
+obj score:  0.3750209  - confidence: 0.0  - thres :  0.1
+obj score:  0.9473051  - confidence: 0.0  - thres :  0.1
+obj score:  0.45665538  - confidence: 0.37565553  - thres :  0.1
+obj score:  0.14845182  - confidence: 0.0  - thres :  0.1
+obj score:  0.512298  - confidence: 0.0  - thres :  0.1
+obj score:  0.7468566  - confidence: 0.7288364  - thres :  0.1
+obj score:  0.109174296  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 353, "framewidth": 3024, "ClassName": "인삼", "w": 2374, "ClassID": "A260173", "h": 2266, "frameheight": 4032, "y": 1116}]
+elapsed time: 1.4284100532531738
+1
+out.shape :  (507, 3170)
+obj score:  0.27045858  - confidence: 0.0  - thres :  0.1
+obj score:  0.5211713  - confidence: 0.0  - thres :  0.1
+obj score:  0.25349522  - confidence: 0.0  - thres :  0.1
+obj score:  0.48735508  - confidence: 0.0  - thres :  0.1
+obj score:  0.11656608  - confidence: 0.0  - thres :  0.1
+obj score:  0.6559611  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[]
+elapsed time: 1.4360933303833008
+1
+out.shape :  (507, 3170)
+obj score:  0.3123151  - confidence: 0.0  - thres :  0.1
+obj score:  0.6347401  - confidence: 0.0  - thres :  0.1
+obj score:  0.46009225  - confidence: 0.0  - thres :  0.1
+obj score:  0.9065884  - confidence: 0.0  - thres :  0.1
+obj score:  0.9836128  - confidence: 0.0  - thres :  0.1
+obj score:  0.984327  - confidence: 0.9285645  - thres :  0.1
+obj score:  0.93019205  - confidence: 0.0  - thres :  0.1
+obj score:  0.17871094  - confidence: 0.0  - thres :  0.1
+obj score:  0.69968784  - confidence: 0.0  - thres :  0.1
+obj score:  0.56702  - confidence: 0.0  - thres :  0.1
+obj score:  0.28111064  - confidence: 0.0  - thres :  0.1
+obj score:  0.12126979  - confidence: 0.0  - thres :  0.1
+obj score:  0.6704581  - confidence: 0.669163  - thres :  0.1
+obj score:  0.5470945  - confidence: 0.0  - thres :  0.1
+obj score:  0.3136402  - confidence: 0.0  - thres :  0.1
+obj score:  0.1065458  - confidence: 0.0  - thres :  0.1
+obj score:  0.13982871  - confidence: 0.0  - thres :  0.1
+obj score:  0.18792705  - confidence: 0.0  - thres :  0.1
+obj score:  0.19129182  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 6, "framewidth": 3000, "ClassName": "바게트빵", "w": 2987, "ClassID": "A020112", "h": 2335, "frameheight": 4000, "y": 540}]
+elapsed time: 1.373570442199707
+1
+out.shape :  (507, 3170)
+obj score:  0.12036621  - confidence: 0.0  - thres :  0.1
+obj score:  0.17435054  - confidence: 0.0  - thres :  0.1
+obj score:  0.17181547  - confidence: 0.0  - thres :  0.1
+obj score:  0.938802  - confidence: 0.0  - thres :  0.1
+obj score:  0.96647227  - confidence: 0.3490205  - thres :  0.1
+obj score:  0.9648599  - confidence: 0.3455422  - thres :  0.1
+obj score:  0.96931654  - confidence: 0.9245625  - thres :  0.1
+obj score:  0.4979174  - confidence: 0.0  - thres :  0.1
+obj score:  0.6078051  - confidence: 0.0  - thres :  0.1
+obj score:  0.8428788  - confidence: 0.0  - thres :  0.1
+obj score:  0.9461159  - confidence: 0.31178638  - thres :  0.1
+obj score:  0.9447938  - confidence: 0.0  - thres :  0.1
+obj score:  0.5474485  - confidence: 0.0  - thres :  0.1
+obj score:  0.14338121  - confidence: 0.0  - thres :  0.1
+obj score:  0.10363934  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 331, "framewidth": 3024, "ClassName": "마늘빵", "w": 2811, "ClassID": "A020108", "h": 2269, "frameheight": 4032, "y": 955}]
+elapsed time: 1.460747480392456
+1
+out.shape :  (507, 3170)
+obj score:  0.7571344  - confidence: 0.0  - thres :  0.1
+obj score:  0.95487005  - confidence: 0.89353234  - thres :  0.1
+obj score:  0.9589344  - confidence: 0.0  - thres :  0.1
+obj score:  0.9145004  - confidence: 0.0  - thres :  0.1
+obj score:  0.30208638  - confidence: 0.0  - thres :  0.1
+obj score:  0.30014905  - confidence: 0.0  - thres :  0.1
+obj score:  0.7086738  - confidence: 0.0  - thres :  0.1
+obj score:  0.84738624  - confidence: 0.0  - thres :  0.1
+obj score:  0.7692006  - confidence: 0.0  - thres :  0.1
+obj score:  0.110810764  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": -148, "framewidth": 3024, "ClassName": "마늘빵", "w": 2868, "ClassID": "A020108", "h": 2069, "frameheight": 4032, "y": 1058}]
+elapsed time: 1.3806674480438232
+1
+out.shape :  (507, 3170)
+obj score:  0.18695068  - confidence: 0.0  - thres :  0.1
+obj score:  0.37288356  - confidence: 0.36899096  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 878, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1241, "ClassID": "A240213", "h": 1488, "frameheight": 4032, "y": 1191}]
+elapsed time: 1.474802017211914
+1
+out.shape :  (507, 3170)
+obj score:  0.59684366  - confidence: 0.59596765  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 906, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1260, "ClassID": "A240213", "h": 1528, "frameheight": 4032, "y": 1509}]
+elapsed time: 1.4641056060791016
+1
+out.shape :  (507, 3170)
+obj score:  0.20400363  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[]
+elapsed time: 1.4233872890472412
+1
+out.shape :  (507, 3170)
+obj score:  0.47585425  - confidence: 0.46526697  - thres :  0.1
+obj score:  0.13532557  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 845, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1254, "ClassID": "A240213", "h": 1675, "frameheight": 4032, "y": 936}]
+elapsed time: 1.4147543907165527
+1
+out.shape :  (507, 3170)
+obj score:  0.24081504  - confidence: 0.0  - thres :  0.1
+obj score:  0.5732863  - confidence: 0.0  - thres :  0.1
+obj score:  0.7034317  - confidence: 0.0  - thres :  0.1
+obj score:  0.34889942  - confidence: 0.0  - thres :  0.1
+obj score:  0.8115133  - confidence: 0.5817147  - thres :  0.1
+obj score:  0.96638817  - confidence: 0.0  - thres :  0.1
+obj score:  0.97867537  - confidence: 0.97723  - thres :  0.1
+obj score:  0.5284866  - confidence: 0.2205038  - thres :  0.1
+obj score:  0.9352525  - confidence: 0.0  - thres :  0.1
+obj score:  0.97035486  - confidence: 0.0  - thres :  0.1
+obj score:  0.9616611  - confidence: 0.0  - thres :  0.1
+obj score:  0.93727493  - confidence: 0.0  - thres :  0.1
+obj score: 118.67.133.131 - - [25/Feb/2021 13:21:19] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 13:27:43] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 13:40:44] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 13:54:02] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 13:54:43] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 13:57:56] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 13:58:18] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:00:19] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:00:45] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:01:04] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:01:19] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:01:37] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:01:50] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:02:06] "POST /uploader HTTP/1.1" 200 -
+ 0.9439137  - confidence: 0.0  - thres :  0.1
+obj score:  0.2232606  - confidence: 0.0  - thres :  0.1
+obj score:  0.92962205  - confidence: 0.8811631  - thres :  0.1
+obj score:  0.9636179  - confidence: 0.6999781  - thres :  0.1
+obj score:  0.8984838  - confidence: 0.0  - thres :  0.1
+obj score:  0.67779773  - confidence: 0.0  - thres :  0.1
+obj score:  0.49658996  - confidence: 0.0  - thres :  0.1
+obj score:  0.16618112  - confidence: 0.0  - thres :  0.1
+obj score:  0.14594193  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 999, "framewidth": 3024, "ClassName": "바게트빵", "w": 1878, "ClassID": "A020112", "h": 3373, "frameheight": 4032, "y": 339}]
+elapsed time: 1.428248405456543
+1
+out.shape :  (507, 3170)
+obj score:  0.1124442  - confidence: 0.0  - thres :  0.1
+obj score:  0.19231246  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[]
+elapsed time: 1.394395112991333
+1
+out.shape :  (507, 3170)
+obj score:  0.10592354  - confidence: 0.0  - thres :  0.1
+obj score:  0.23797987  - confidence: 0.0  - thres :  0.1
+obj score:  0.48428386  - confidence: 0.0  - thres :  0.1
+obj score:  0.6837413  - confidence: 0.0  - thres :  0.1
+obj score:  0.9344378  - confidence: 0.0  - thres :  0.1
+obj score:  0.96961516  - confidence: 0.5889796  - thres :  0.1
+obj score:  0.98047364  - confidence: 0.9769158  - thres :  0.1
+obj score:  0.22957435  - confidence: 0.0  - thres :  0.1
+obj score:  0.4286951  - confidence: 0.0  - thres :  0.1
+obj score:  0.7285218  - confidence: 0.0  - thres :  0.1
+obj score:  0.56875646  - confidence: 0.0  - thres :  0.1
+obj score:  0.27573687  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 968, "framewidth": 3024, "ClassName": "바게트빵", "w": 1962, "ClassID": "A020112", "h": 2989, "frameheight": 4032, "y": 796}]
+elapsed time: 1.4690446853637695
+1
+out.shape :  (507, 3170)
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[]
+elapsed time: 1.4028847217559814
+1
+out.shape :  (507, 3170)
+obj score:  0.1482152  - confidence: 0.0  - thres :  0.1
+obj score:  0.23114434  - confidence: 0.23055753  - thres :  0.1
+obj score:  0.22987355  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 797, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1332, "ClassID": "A240213", "h": 1735, "frameheight": 4032, "y": 897}]
+elapsed time: 1.3675835132598877
+1
+out.shape :  (507, 3170)
+obj score:  0.106485896  - confidence: 0.0  - thres :  0.1
+obj score:  0.13856125  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[]
+elapsed time: 1.4121015071868896
+1
+out.shape :  (507, 3170)
+obj score:  0.4626748  - confidence: 0.3870303  - thres :  0.1
+obj score:  0.1725152  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 804, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1288, "ClassID": "A240213", "h": 1613, "frameheight": 4032, "y": 1495}]
+elapsed time: 1.3647465705871582
+1
+out.shape :  (507, 3170)
+obj score:  0.5277948  - confidence: 0.51834565  - thres :  0.1
+obj score:  0.16254549  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 1119, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1153, "ClassID": "A240213", "h": 1733, "frameheight": 4032, "y": 1486}]
+elapsed time: 1.4336187839508057
+1
+out.shape :  (507, 3170)
+obj score:  0.22919439  - confidence: 0.0  - thres :  0.1
+obj score:  0.10506311  - confidence: 0.0  - thres :  0.1
+obj score:  0.4933572  - confidence: 0.49276498  - thres :  0.1
+obj score:  0.11302084  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 1093, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1271, "ClassID": "A240213", "h": 1594, "frameheight": 4032, "y": 1446}]
+elapsed time: 1.4358241558074951
+1
+out.shape :  (507, 3170)
+obj score:  0.28573737  - confidence: 0.0  - thres :  0.1
+obj score:  0.8015044  - confidence: 0.26266626  - thres :  0.1
+out.shape :  (2028, 3170)
+obj score:  0.19794518  - confidence: 0.0  - thres :  0.1
+obj score:  0.20874026  - confidence: 0.0  - thres :  0.1
+out.shape :  (8112, 3170)
+[{"x": 1058, "framewidth": 3024, "ClassName": "크로와상", "w": 1299, "ClassID": "A020140", "h": 1221, "frameheight": 4032, "y": 1614}]
+elapsed time: 1.489638090133667
+1
+out.shape :  (507, 3170)
+obj score:  0.75523084  - confidence: 0.37170273  - thres :  0.1
+obj score:  0.28939563  - confidence: 0.0  - thres :  0.1
+obj score:  0.7408998  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 501, "framewidth": 3024, "ClassName": "크로와상", "w": 1652, "ClassID": "A020140", "h": 1587, "frameheight": 4032, "y": 1334}]
+elapsed time: 1.3848927021026611
+1
+out.shape :  (507, 3170)
+obj score:  0.3230213  - confidence: 0.0  - thres :  0.1
+obj score:  0.5809328  - confidence: 0.0  - thres :  0.1
+obj score:  0.15432024  - confidence: 0.0  - thres :  0.1
+obj score:  0.25433567  - confidence: 0.0  - thres :  0.1
+obj score:  0.78212863  - confidence: 0.0  - thres :  0.1
+obj score:  0.70299566  - confidence: 0.0  - thres :  0.1
+obj score:  0.37560314  - confidence: 0.0  - thres :  0.1
+obj score:  0.845549  - confidence: 0.8444035  - thres :  0.1
+obj score:  0.7051678  - confidence: 0.0  - thres :  0.1
+obj score:  0.28123766  - confidence: 0.0  - thres :  0.1
+obj score:  0.73580647  - confidence: 0.0  - thres :  0.1
+obj score:  0.37738693  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 714, "framewidth": 3024, "ClassName": "인삼", "w": 1596, "ClassID": "A260173", "h": 3359, "frameheight": 4032, "y": 328}]
+elapsed time: 1.412980079650879
+1
+out.shape :  (507, 3170)
+obj score:  0.31659618  - confidence: 0.0  - thres :  0.1
+obj score:  0.57621807  - confidence: 0.56266874  - thres :  0.1
+obj score:  0.24399075  - confidence: 0.0  - thres :  0.1
+obj score:  0.12713125  - confidence: 0.0  - thres :  0.1
+obj score:  0.38459858  - confidence: 0.0  - thres :  0.1
+obj score:  0.4522718  - confidence: 0.0  - thres :  0.1
+obj score:  0.1006953  - confidence: 0.0  - thres :  0.1
+obj score:  0.4302598  - confidence: 0.0  - thres :  0.1
+obj score:  0.7841026  - confidence: 0.77369595  - thres :  0.1
+obj score:  0.3978546  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 427, "framewidth": 3024, "ClassName": "인삼", "w": 2149, "ClassID": "A260173", "h": 2219, "frameheight": 4032, "y": 1516}]
+elapsed time: 1.4444103240966797
+1
+out.shape :  (507, 3170)
+obj score:  0.34088856  - confidence: 0.0  - thres :  0.1
+obj score:  0.14119281  - confidence: 0.0  - thres :  0.1
+obj score:  0.75490195  - confidence: 0.0  - thres :  0.1
+obj score:  0.25953686  - confidence: 0.0  - thres :  0.1
+obj score:  0.30911738  - confidence: 0.0  - thres :  0.1
+obj score:  0.14204596  - confidence: 0.0  - thres :  0.1
+obj score:  0.76160324  - confidence: 0.0  - thres :  0.1
+obj score:  0.8907277  - confidence: 0.7077889  - thres :  0.1
+obj score:  0.50361794  - confidence: 0.43511122  - thres :  0.1
+obj score:  0.23731157  - confidence: 0.0  - thres :  0.1
+obj score:  0.4075222  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 244, "framewidth": 3024, "ClassName": "인삼", "w": 2577, "ClassID": "A260173", "h": 2124, "frameheight": 4032, "y": 1234}]
+elapsed time: 1.4357001781463623
+1
+out.shape :  (507, 3170)
+obj score:  0.19834714  - confidence: 0.0  - thres :  0.1
+obj score:  0.15484245  - confidence: 0.0  - thres :  0.1
+obj score:  0.17233108  - confidence: 0.0  - thres :  0.1
+obj score:  0.3800572  - confidence: 0.0  - thres :  0.1
+obj score:  0.3494113  - confidence: 0.0  - thres :  0.1
+obj score:  0.4475223  - confidence: 0.4456543  - thres :  0.1
+obj score:  0.2762655  - confidence: 0.0  - thres :  0.1
+obj score:  0.28315282  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 334, "framewidth": 3024, "ClassName": "바게트빵", "w": 2301, "ClassID": "A020112", "h": 1203, "frameheight": 4032, "y": 1674}]
+elapsed time:118.67.133.131 - - [25/Feb/2021 14:02:17] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:02:38] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:04:22] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:05:31] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:10:03] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:10:13] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:10:26] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:10:47] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:11:00] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:11:27] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:17:33] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:18:22] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:18:42] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:20:34] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:21:01] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:21:24] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:24:42] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:24:57] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:30:18] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:31:30] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 14:35:02] "POST /uploader HTTP/1.1" 200 -
+ 1.4776935577392578
+1
+out.shape :  (507, 3170)
+obj score:  0.13977608  - confidence: 0.0  - thres :  0.1
+obj score:  0.40138003  - confidence: 0.0  - thres :  0.1
+obj score:  0.24594568  - confidence: 0.23857212  - thres :  0.1
+obj score:  0.57038766  - confidence: 0.0  - thres :  0.1
+obj score:  0.51924306  - confidence: 0.0  - thres :  0.1
+obj score:  0.3797294  - confidence: 0.0  - thres :  0.1
+obj score:  0.4555872  - confidence: 0.0  - thres :  0.1
+obj score:  0.5556125  - confidence: 0.0  - thres :  0.1
+obj score:  0.63370585  - confidence: 0.0  - thres :  0.1
+obj score:  0.10338429  - confidence: 0.0  - thres :  0.1
+obj score:  0.69885117  - confidence: 0.69734144  - thres :  0.1
+obj score:  0.1428422  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 1190, "framewidth": 3024, "ClassName": "인삼", "w": 1470, "ClassID": "A260173", "h": 2747, "frameheight": 4032, "y": 952}]
+elapsed time: 1.4635474681854248
+1
+out.shape :  (507, 3170)
+obj score:  0.3121353  - confidence: 0.2677779  - thres :  0.1
+obj score:  0.27198926  - confidence: 0.0  - thres :  0.1
+obj score:  0.12370876  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 834, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1207, "ClassID": "A240213", "h": 1923, "frameheight": 4032, "y": 1074}]
+elapsed time: 1.3749988079071045
+1
+out.shape :  (507, 3170)
+obj score:  0.23038213  - confidence: 0.0  - thres :  0.1
+obj score:  0.22028722  - confidence: 0.0  - thres :  0.1
+obj score:  0.51938665  - confidence: 0.51795214  - thres :  0.1
+obj score:  0.11246048  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 899, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1206, "ClassID": "A240213", "h": 1713, "frameheight": 4032, "y": 761}]
+elapsed time: 1.366853952407837
+1
+out.shape :  (507, 3170)
+obj score:  0.10524843  - confidence: 0.0  - thres :  0.1
+obj score:  0.105505824  - confidence: 0.0  - thres :  0.1
+obj score:  0.31181413  - confidence: 0.0  - thres :  0.1
+obj score:  0.16310786  - confidence: 0.0  - thres :  0.1
+obj score:  0.10271885  - confidence: 0.0  - thres :  0.1
+obj score:  0.13271685  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[]
+elapsed time: 1.4340720176696777
+1
+out.shape :  (507, 3170)
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[]
+elapsed time: 1.420990228652954
+1
+out.shape :  (507, 3170)
+obj score:  0.15967932  - confidence: 0.0  - thres :  0.1
+obj score:  0.2252205  - confidence: 0.22299531  - thres :  0.1
+obj score:  0.2346376  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 108, "framewidth": 3024, "ClassName": "인삼", "w": 1959, "ClassID": "A260173", "h": 2938, "frameheight": 4032, "y": 245}]
+elapsed time: 1.4275736808776855
+1
+out.shape :  (507, 3170)
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[]
+elapsed time: 1.4287307262420654
+1
+out.shape :  (507, 3170)
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[]
+elapsed time: 1.440711498260498
+1
+out.shape :  (507, 3170)
+obj score:  0.24393104  - confidence: 0.24038877  - thres :  0.1
+obj score:  0.18885124  - confidence: 0.0  - thres :  0.1
+obj score:  0.17533804  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 985, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1160, "ClassID": "A240213", "h": 1670, "frameheight": 4032, "y": 1160}]
+elapsed time: 1.4569928646087646
+1
+out.shape :  (507, 3170)
+obj score:  0.8831976  - confidence: 0.8825878  - thres :  0.1
+out.shape :  (2028, 3170)
+obj score:  0.100112535  - confidence: 0.0  - thres :  0.1
+obj score:  0.18269368  - confidence: 0.0  - thres :  0.1
+out.shape :  (8112, 3170)
+[{"x": 542, "framewidth": 1440, "ClassName": "쑥개떡", "w": 328, "ClassID": "A240213", "h": 264, "frameheight": 960, "y": 358}]
+elapsed time: 1.1435835361480713
+1
+out.shape :  (507, 3170)
+obj score:  0.8831976  - confidence: 0.8825878  - thres :  0.1
+out.shape :  (2028, 3170)
+obj score:  0.100112535  - confidence: 0.0  - thres :  0.1
+obj score:  0.18269368  - confidence: 0.0  - thres :  0.1
+out.shape :  (8112, 3170)
+[{"x": 542, "framewidth": 1440, "ClassName": "쑥개떡", "w": 328, "ClassID": "A240213", "h": 264, "frameheight": 960, "y": 358}]
+elapsed time: 1.1468613147735596
+1
+out.shape :  (507, 3170)
+obj score:  0.8831976  - confidence: 0.8825878  - thres :  0.1
+out.shape :  (2028, 3170)
+obj score:  0.100112535  - confidence: 0.0  - thres :  0.1
+obj score:  0.18269368  - confidence: 0.0  - thres :  0.1
+out.shape :  (8112, 3170)
+[{"x": 542, "framewidth": 1440, "ClassName": "쑥개떡", "w": 328, "ClassID": "A240213", "h": 264, "frameheight": 960, "y": 358}]
+elapsed time: 1.149827241897583
+1
+out.shape :  (507, 3170)
+obj score:  0.8831976  - confidence: 0.8825878  - thres :  0.1
+out.shape :  (2028, 3170)
+obj score:  0.100112535  - confidence: 0.0  - thres :  0.1
+obj score:  0.18269368  - confidence: 0.0  - thres :  0.1
+out.shape :  (8112, 3170)
+[{"x": 542, "framewidth": 1440, "ClassName": "쑥개떡", "w": 328, "ClassID": "A240213", "h": 264, "frameheight": 960, "y": 358}]
+elapsed time: 1.175755500793457
+1
+out.shape :  (507, 3170)
+obj score:  0.62764424  - confidence: 0.6256275  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 1150, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1212, "ClassID": "A240213", "h": 1548, "frameheight": 4032, "y": 1187}]
+elapsed time: 1.4258933067321777
+1
+out.shape :  (507, 3170)
+obj score:  0.52433866  - confidence: 0.5084444  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 969, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1164, "ClassID": "A240213", "h": 1672, "frameheight": 4032, "y": 1119}]
+elapsed time: 1.4331374168395996
+1
+out.shape :  (507, 3170)
+obj score:  0.787515  - confidence: 0.7868684  - thres :  0.1
+obj score:  0.23706035  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 892, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1283, "ClassID": "A240213", "h": 1604, "frameheight": 4032, "y": 1233}]
+elapsed time: 1.3713304996490479
+1
+out.shape :  (507, 3170)
+obj score:  0.22964941  - confidence: 0.0  - thres :  0.1
+obj score:  0.23363326  - confidence: 0.22887903  - thres :  0.1
+obj score:  0.14445809  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 756, "framewidth": 3024, "ClassName": "쑥개떡", "w": 1559, "ClassID": "A240213", "h": 2601, "frameheight": 4032, "y": 1105}]
+elapsed time: 1.427886724472046
+1
+out.shape :  (507, 3170)
+obj score:  0.8831976  - confidence: 0.8825878  - thres :  0.1
+out.shape :  (2028, 3170)
+obj score:  0.100112535  - confidence: 0.0  - thres :  0.1
+obj score:  0.18269368  - confidence: 0.0  - thres :  0.1
+out.shape :  (8112, 3170)
+[{"x": 542, "framewidth": 1440, "ClassName": "쑥개떡", "w": 328, "ClassID": "A240213", "h": 264, "frameheight": 960, "y": 358}]
+elapsed time: 1.1846718788146973
+1
+out.shape :  (507, 3170)
+obj score:  0.8831976  - confidence: 0.8825878  - thres :  0.1
+out.shape :  (2028, 3170)
+obj score:  0.100112535  - confidence: 0.0  - thres :  0.1
+obj score:  0.18269368  - confidence: 0.0  - thres :  0.1
+out.shape :  (8112, 3170)
+[{"x": 542, "framewidth": 1440, "ClassName": "쑥개떡", "w": 328, "ClassID": "A240213", "h": 264, "frameheight": 960, "y": 358}]
+elapsed time: 1.1431877613067627
+1
+out.shape :  (507, 3170)
+obj score:  0.9699368  - confidence: 0.9692912  - thres :  0.1
+obj score:  0.4339592  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 587, "framewidth": 2449, "ClassName": "바게트빵", "w": 1205, "ClassID": "A020112", "h": 536, "frameheight": 1633, "y": 459}]
+elapsed time: 1.2475743293762207
+1
+out.shape :  (507, 3170)
+obj score:  0.12023228  - confidence: 0.0  - thres :  0.1
+obj score:  0.17567517  - confidence: 0.0  - thres :  0.1
+obj score:  0.6763358  - confidence: 0.0  - thres :  0.1
+obj score:  0.6854099  - confidence: 0.0  - thres :  0.1
+obj score:  0.2520209  - confidence: 0.0  - thres :  0.1
+obj score:  0.6032635118.67.133.131 - - [25/Feb/2021 15:56:04] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 16:13:35] "POST /uploader HTTP/1.1" 200 -
+125.243.32.134 - - [25/Feb/2021 16:13:40] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 18:03:17] "POST /uploader HTTP/1.1" 200 -
+185.176.222.73 - - [25/Feb/2021 18:12:41] code 400, message Bad HTTP/0.9 request type ('\x03\x00\x00/*à\x00\x00\x00\x00\x00Cookie:')
+185.176.222.73 - - [25/Feb/2021 18:12:41] "/*àCookie: mstshash=Administr" HTTPStatus.BAD_REQUEST -
+112.222.233.60 - - [25/Feb/2021 18:26:59] "GET /uploader HTTP/1.1" 200 -
+112.222.233.60 - - [25/Feb/2021 18:26:59] "GET /favicon.ico HTTP/1.1" 404 -
+112.222.233.60 - - [25/Feb/2021 18:27:35] "POST /uploader HTTP/1.1" 200 -
+112.222.233.60 - - [25/Feb/2021 18:29:28] "GET /uploader HTTP/1.1" 200 -
+112.222.233.60 - - [25/Feb/2021 18:30:24] "POST /uploader HTTP/1.1" 200 -
+112.222.233.60 - - [25/Feb/2021 18:32:13] "GET /uploader HTTP/1.1" 200 -
+112.222.233.60 - - [25/Feb/2021 18:32:18] "POST /uploader HTTP/1.1" 200 -
+112.222.233.60 - - [25/Feb/2021 18:33:23] "GET /uploader HTTP/1.1" 200 -
+112.222.233.60 - - [25/Feb/2021 18:33:28] "POST /uploader HTTP/1.1" 200 -
+112.222.233.60 - - [25/Feb/2021 18:33:32] "GET /uploader HTTP/1.1" 200 -
+112.222.233.60 - - [25/Feb/2021 18:34:17] "POST /uploader HTTP/1.1" 200 -
+112.222.233.60 - - [25/Feb/2021 18:34:40] "GET /uploader HTTP/1.1" 200 -
+112.222.233.60 - - [25/Feb/2021 18:46:04] "GET /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 21:02:04] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 21:25:44] "POST /uploader HTTP/1.1" 200 -
+  - confidence: 0.0  - thres :  0.1
+obj score:  0.20871645  - confidence: 0.0  - thres :  0.1
+obj score:  0.34965545  - confidence: 0.0  - thres :  0.1
+obj score:  0.61426884  - confidence: 0.5435659  - thres :  0.1
+obj score:  0.58661675  - confidence: 0.20092449  - thres :  0.1
+obj score:  0.43507951  - confidence: 0.0  - thres :  0.1
+obj score:  0.22605768  - confidence: 0.0  - thres :  0.1
+obj score:  0.32287803  - confidence: 0.0  - thres :  0.1
+obj score:  0.15556556  - confidence: 0.0  - thres :  0.1
+obj score:  0.23649815  - confidence: 0.22256704  - thres :  0.1
+obj score:  0.17545334  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 105, "framewidth": 3000, "ClassName": "인삼", "w": 1851, "ClassID": "A260173", "h": 3264, "frameheight": 4000, "y": 377}]
+elapsed time: 1.863832950592041
+1
+out.shape :  (507, 3170)
+obj score:  0.19051495  - confidence: 0.0  - thres :  0.1
+obj score:  0.48144218  - confidence: 0.458524  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 722, "framewidth": 3000, "ClassName": "쑥개떡", "w": 1134, "ClassID": "A240213", "h": 1978, "frameheight": 4000, "y": 931}]
+elapsed time: 1.3272285461425781
+1
+out.shape :  (507, 3170)
+obj score:  0.11947048  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+obj score:  0.32518157  - confidence: 0.0  - thres :  0.1
+obj score:  0.24873595  - confidence: 0.0  - thres :  0.1
+obj score:  0.65438396  - confidence: 0.2968539  - thres :  0.1
+obj score:  0.4461359  - confidence: 0.0  - thres :  0.1
+out.shape :  (8112, 3170)
+[{"x": 972, "framewidth": 2449, "ClassName": "백설기", "w": 448, "ClassID": "A240208", "h": 397, "frameheight": 1633, "y": 568}]
+elapsed time: 1.5738656520843506
+1
+out.shape :  (507, 3170)
+obj score:  0.9699368  - confidence: 0.9692912  - thres :  0.1
+obj score:  0.4339592  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 587, "framewidth": 2449, "ClassName": "바게트빵", "w": 1205, "ClassID": "A020112", "h": 536, "frameheight": 1633, "y": 459}]
+elapsed time: 1.5469915866851807
+1
+out.shape :  (507, 3170)
+obj score:  0.47187752  - confidence: 0.0  - thres :  0.1
+obj score:  0.73515135  - confidence: 0.0  - thres :  0.1
+obj score:  0.89592934  - confidence: 0.0  - thres :  0.1
+obj score:  0.96933305  - confidence: 0.96238387  - thres :  0.1
+obj score:  0.3389606  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 23, "framewidth": 631, "ClassName": "바게트빵", "w": 578, "ClassID": "A020112", "h": 193, "frameheight": 412, "y": 70}]
+elapsed time: 1.321312665939331
+1
+out.shape :  (507, 3170)
+obj score:  0.50584483  - confidence: 0.0  - thres :  0.1
+obj score:  0.96033764  - confidence: 0.0  - thres :  0.1
+obj score:  0.8447239  - confidence: 0.0  - thres :  0.1
+obj score:  0.7502764  - confidence: 0.59348595  - thres :  0.1
+obj score:  0.93652487  - confidence: 0.0  - thres :  0.1
+obj score:  0.9818812  - confidence: 0.0  - thres :  0.1
+obj score:  0.99484694  - confidence: 0.990013  - thres :  0.1
+obj score:  0.94469273  - confidence: 0.0  - thres :  0.1
+obj score:  0.2582266  - confidence: 0.2394657  - thres :  0.1
+obj score:  0.9921096  - confidence: 0.3964678  - thres :  0.1
+obj score:  0.9838696  - confidence: 0.0  - thres :  0.1
+obj score:  0.9781804  - confidence: 0.0  - thres :  0.1
+obj score:  0.9821507  - confidence: 0.0  - thres :  0.1
+obj score:  0.8417759  - confidence: 0.0  - thres :  0.1
+obj score:  0.24276164  - confidence: 0.0  - thres :  0.1
+obj score:  0.9119776  - confidence: 0.0  - thres :  0.1
+obj score:  0.77694094  - confidence: 0.0  - thres :  0.1
+obj score:  0.56311333  - confidence: 0.0  - thres :  0.1
+obj score:  0.35500804  - confidence: 0.0  - thres :  0.1
+obj score:  0.1245117  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 86, "framewidth": 393, "ClassName": "마늘빵", "w": 284, "ClassID": "A020108", "h": 190, "frameheight": 215, "y": -4}]
+elapsed time: 1.4504072666168213
+1
+out.shape :  (507, 3170)
+obj score:  0.69516087  - confidence: 0.0  - thres :  0.1
+obj score:  0.95567626  - confidence: 0.49878195  - thres :  0.1
+obj score:  0.69158787  - confidence: 0.0  - thres :  0.1
+obj score:  0.13157818  - confidence: 0.0  - thres :  0.1
+obj score:  0.4012235  - confidence: 0.0  - thres :  0.1
+obj score:  0.97148275  - confidence: 0.9670613  - thres :  0.1
+obj score:  0.8570264  - confidence: 0.0  - thres :  0.1
+obj score:  0.29068857  - confidence: 0.0  - thres :  0.1
+obj score:  0.11520081  - confidence: 0.0  - thres :  0.1
+obj score:  0.94443506  - confidence: 0.0  - thres :  0.1
+obj score:  0.9502707  - confidence: 0.0  - thres :  0.1
+obj score:  0.44535166  - confidence: 0.0  - thres :  0.1
+obj score:  0.8839524  - confidence: 0.0  - thres :  0.1
+obj score:  0.94441986  - confidence: 0.0  - thres :  0.1
+obj score:  0.6134788  - confidence: 0.0  - thres :  0.1
+obj score:  0.13275604  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 73, "framewidth": 724, "ClassName": "바게트빵", "w": 471, "ClassID": "A020112", "h": 384, "frameheight": 425, "y": -15}]
+elapsed time: 1.4610087871551514
+1
+out.shape :  (507, 3170)
+obj score:  0.671354  - confidence: 0.0  - thres :  0.1
+obj score:  0.86670345  - confidence: 0.3784275  - thres :  0.1
+obj score:  0.8751435  - confidence: 0.73969567  - thres :  0.1
+obj score:  0.98907405  - confidence: 0.0  - thres :  0.1
+obj score:  0.5332565  - confidence: 0.0  - thres :  0.1
+obj score:  0.8822033  - confidence: 0.0  - thres :  0.1
+obj score:  0.9876573  - confidence: 0.0  - thres :  0.1
+obj score:  0.51913065  - confidence: 0.0  - thres :  0.1
+obj score:  0.20261386  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 68, "framewidth": 585, "ClassName": "미나리", "w": 388, "ClassID": "A260119", "h": 362, "frameheight": 460, "y": 54}]
+elapsed time: 1.5096111297607422
+1
+out.shape :  (507, 3170)
+obj score:  0.13031147  - confidence: 0.0  - thres :  0.1
+obj score:  0.25829256  - confidence: 0.0  - thres :  0.1
+obj score:  0.94687736  - confidence: 0.65943855  - thres :  0.1
+obj score:  0.7907321  - confidence: 0.0  - thres :  0.1
+obj score:  0.9064267  - confidence: 0.0  - thres :  0.1
+obj score:  0.80411166  - confidence: 0.0  - thres :  0.1
+obj score:  0.8483185  - confidence: 0.5794998  - thres :  0.1
+obj score:  0.4439966  - confidence: 0.0  - thres :  0.1
+obj score:  0.89004517  - confidence: 0.0  - thres :  0.1
+obj score:  0.42231804  - confidence: 0.0  - thres :  0.1
+obj score:  0.198893  - confidence: 0.0  - thres :  0.1
+obj score:  0.19763796  - confidence: 0.0  - thres :  0.1
+out.shape :  (2028, 3170)
+out.shape :  (8112, 3170)
+[{"x": 33, "framewidth": 206, "ClassName": "참나물", "w": 106, "ClassID": "A260147", "h": 201, "frameheight": 253, "y": -10}]
+elapsed time: 1.4123897552490234
+1
+out.shape :  (507, 3170)
+obj score:  0.8741836  - confidence: 0.8735819  - thres :  0.1
+out.shape :  (2028, 3170)
+obj score:  0.11526003  - confidence: 0.0  - thres :  0.1
+obj score:  0.1949244  - confidence: 0.0  - thres :  0.1
+out.shape :  (8112, 3170)
+[{"x": 924, "framewidth": 2449, "ClassName": "쑥개떡", "w": 556, "ClassID": "A240213", "h": 450, "frameheight": 1633, "y": 605}]
+elapsed time: 1.184636116027832
+1
+out.shape :  (507, 3170)
+obj score:  0.8741836  - confidence: 0.8735819  - thres :  0.1
+out.shape :  (2028, 3170)
+obj score:  0.11526003  - confidence: 0.0  - thres :  0.1
+obj score:  0.1949244  - confidence: 0.0  - thres :  0.1
+out.shape :  (8112, 3170)
+[{"x": 924, "framewidth": 2449, "ClassName": "쑥개떡", "w": 556, "ClassID": "A240213", "h": 450, "frameheight": 1633, "y": 605}]
+elapsed time: 1.4734368324279785
+1
+out.shape :  (507, 3170)
+obj score:  0.8741836  - confidence: 0.8735819  - thres :  0.1
+out.shape :  (2028, 3170)
+obj score:  0.11526003  - confidence: 0.0  - thres :  0.1
+obj score:  0.1949244  - confidence: 0.0  - thres :  0.1
+out.shape :  (8112, 3170)
+[{"x": 924, "framewidth": 2449, "ClassName": "쑥개떡", "w": 556, "ClassID": "A240213", "h": 450, "frameheight": 1633, "y": 605}]118.67.133.131 - - [25/Feb/2021 22:47:01] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 22:53:48] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [25/Feb/2021 23:21:23] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [26/Feb/2021 08:10:21] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [26/Feb/2021 08:46:37] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [26/Feb/2021 10:51:34] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [26/Feb/2021 10:52:28] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [26/Feb/2021 10:54:33] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [26/Feb/2021 10:54:46] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [26/Feb/2021 10:55:04] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [26/Feb/2021 11:21:01] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [26/Feb/2021 11:21:11] "POST /uploader HTTP/1.1" 200 -
+118.67.133.131 - - [26/Feb/2021 11:43:02] "POST /uploader HTTP/1.1" 200 -
+141.98.10.149 - - [27/Feb/2021 01:16:36] code 400, message Bad HTTP/0.9 request type ('\x03\x00\x00/*à\x00\x00\x00\x00\x00Cookie:')
+141.98.10.149 - - [27/Feb/2021 01:16:36] "/*àCookie: mstshash=Administr" HTTPStatus.BAD_REQUEST -

config/config.py

@@ -0,0 +1,48 @@
+# no package for independent configuration management
+
+# Initialize the parameters
+CONF_THRES = 0.1 #0.5  # Confidence threshold
+NMS_THRES  = 0.1 #0.4  # Non-maximum suppression threshold
+
+INPWIDTH  = 416 # 32*10  # 608     #Width of network's input image # 320(32*10)
+INPHEIGHT = 416 #32*9 # 608     #Height of network's input image # 288(32*9) best
+
+# start video frame number
+Video_Start_Frame = (21-4)*60*30+(2-39)*30 # compute the first starting location from a video
+
+# model base dir
+#ModelBaseDir = "C:/Users/mmc/workspace/AI_core/food_classification/yolo"
+ModelBaseDir = "./yolo"
+TEST_IMAGE_PATH ="/home/rtdatum/workspace/food_classification/images_rec/13118.jpg"
+#TEST_IMAGE_PATH ="C:/Users/mmc/workspace/AI_core/food_classification/yolo/data/쑥개떡/A240213_111121_0005.jpg"
+#TEST_IMAGE_PATH ="./yolo/data/food/images/A270309_111112_0002.jpg"
+TEST_VIDEO_PATH = \
+    ""#"E:/Topes_data_related/시나리오 영상/시나리오 영상/20200909PM/6085-20200909-170439-1599638679.mp4"
+SHOW_TEXT_FLAG = 1
+PS_FLAG = 1
+
+# Load names of classes, please don't include the first directory separator like "/data/..."
+CLASSES_FILE = "data/food/food-classes.names"
+CLASSES_FILE_CODE = "data/food/food-classes.codes"
+
+# Give the configuration and weight files for the model and load the network using them.
+# Don't include the first directory separator
+# -- yolov3 ------
+# ------- 3 layers
+# itms
+# Model_Configuration = "config/itms-dark-yolov3-tiny_3l-v3-2.cfg"
+# Model_Weights = "data/food/weights/itms-dark-yolov3-tiny_3l-v3-2_200000.weights"
+# food
+Model_Configuration = "config/food-dark-yolov3-tiny_3l-v3-2.cfg"
+Model_Weights = "data/food/weights/food-dark-yolov3-tiny_3l-v3-2_24000.weights"
+# ------- full layers
+#Model_Configuration = "config/food-dark-yolov3-full-2.cfg"
+# Model_Weights = "data/food/weights/food-dark-yolov3-full-2_100000.weights"
+# -- yolov4 -------
+# 3l layers
+# Model_Configuration = "config/food-dark-yolov4-tiny-3l-v1.cfg"
+# Model_Weights       = "data/food/weights/food-dark-yolov4-tiny-3l-v1_best.weights"
+# full layers
+# Model_Configuration = "config/food-dark-yolov4-full.cfg"
+# Model_Weights       = "data/food/weights/food-dark-yolov4-full_10000.weights"
+

font/gulim.ttc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b9ac63e8920ed1bae29c068025ed30493464c18ee18617887daa18e59189226
+size 13531200

food_classifier_UI_v099.py

@@ -0,0 +1,250 @@
+import cv2
+import numpy as np
+import glob
+import random
+import pathlib
+import subprocess, os
+from PIL import Image
+# import matplotlib.pyplot as plt
+
+CONF_THRES = 0.1 #0.5  # Confidence threshold
+NMS_THRES  = 0.1 #0.4  # Non-maximum suppression threshold
+
+INPWIDTH  = 32*10  # 608     #Width of network's input image # 320(32*10)
+INPHEIGHT = 32*9 # 608     #Height of network's input image # 288(32*9) best
+
+#ui
+import cv2
+from tkinter import *
+from tkinter import messagebox
+from tkinter import filedialog
+from tkinter.ttk import Button, Style, Progressbar
+import time
+import os
+
+folder_selected=""
+class_name=""
+data_class=""
+v_class=""
+
+def add_class(): 
+    data_class = add_class_field.get()
+    class_name = data_class
+    if data_class == "":
+        messagebox.showinfo("Warning!!", "Class cant be empty")
+    else:
+        print("===============Class Name================")
+        print(class_name)
+        v_class=class_name
+        main()
+
+
+# Load Yolo
+def main():
+    folder_selected = filedialog.askdirectory()
+    v_class =add_class_field.get()
+    print("=========================class name===================")
+    print(v_class)
+    print(folder_selected)
+    print( "========================================")
+    # label_file_explorer.configure(text="File Path: "+folder_selected)
+    # sangkny
+    # original net = cv2.dnn.readNet("yolov3_training_2000.weights", "yolov3_testing.cfg")
+    #modelBaseDir = "C:/Users/mmc/workspace/yolo"
+    modelBaseDir = "./yolo"
+    modelConfiguration = modelBaseDir + "/config/food-dark-yolov3-tiny_3l-v3-2.cfg"
+    modelWeights = modelBaseDir + "/data/food/weights/food-dark-yolov3-tiny_3l-v3-2_200000.weights"
+    net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
+    #
+
+    # Name custom object
+    # classes = [" "]
+    # Load names of classes by sangkny
+    classesFile = modelBaseDir + "/data/food/food-classes.names"
+    classes = None
+    with open(classesFile, 'rt') as f:
+        classes = f.read().rstrip('\n').split('\n')
+
+
+    #path for train
+    train_path = glob.glob(
+        r"" +folder_selected)
+    # Images path
+    images_path = glob.glob(
+        r"" +folder_selected+"\*.jpg")
+    layer_names = net.getLayerNames()
+
+    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+    colors = np.random.uniform(0, 255, size=(len(classes), 3))
+    for img_path in images_path:
+        # Loading image
+        print(img_path)
+        img = cv2.imread(img_path)
+        file_name = img_path.rsplit('\\', 1)[1]
+        file_name = file_name.rsplit('.', 1)[0]
+        print (file_name)
+        img = cv2.resize(img, None, fx=0.7, fy=0.7)
+        height, width, channels = img.shape
+
+        # Detecting objects
+        #  blob = cv2.dnn.blobFromImage(frame, 1 / 255, (inpWidth, inpHeight), [0, 0, 0], 1, crop=False)
+        blob = cv2.dnn.blobFromImage(img, 0.00392, (INPWIDTH, INPHEIGHT), (0, 0, 0), True, crop=False)
+
+        net.setInput(blob)
+        outs = net.forward(output_layers)
+
+        # Showing informations on the screen
+        class_ids = []
+        confidences = []
+        boxes = []
+        boxes2 = []
+        coordinate_list = []  # for .txt
+
+        for out in outs:
+            for detection in out:
+                scores = detection[5:]
+                class_id = np.argmax(scores)
+                confidence = scores[class_id]
+                if confidence > CONF_THRES:
+                    # Object detected
+                    # print("NonArr", class_id)
+                    center_x = int(detection[0] * width)
+                    center_y = int(detection[1] * height)
+                    w = int(detection[2] * width)
+                    h = int(detection[3] * height)
+                    a = class_id
+                    b = detection[0]
+                    c = detection[1]
+                    d = detection[2]
+                    e = detection[3]
+                    print("=====================")
+                    print(a,b,c,d,e,confidence)
+
+                    # Rectangle coordinates
+                    x = int(center_x - w / 2)
+                    y = int(center_y - h / 2)
+
+                    boxes.append([x, y, w, h])
+                    boxes2.append([a, b, c, d,e])
+                    confidences.append(float(confidence))
+                    class_ids.append(class_id)
+
+                    coordinate = class_id, detection[0], detection[1], detection[2], detection[3], "Confidence:", confidence
+
+                    coordinate_for_txt = class_id, detection[0], detection[1], detection[3], detection[4]
+                    coordinate_list.append(coordinate_for_txt)
+   
+                    font = cv2.FONT_HERSHEY_PLAIN
+        indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
+        filename = os.path.basename(img_path).replace('.jpg', '')
+        print("========================All Coordinate=====================")
+        # print(coordinate_to_str)
+        #coordinate.txt
+        with open("result/obj_train_data/%s.txt" %filename, "w") as file: #for .txt
+            for i in range(len(boxes)):
+                if i in indexes:
+                    x, y, w, h = boxes[i]
+                    label = str(classes[class_ids[i]])
+                    color = colors[class_ids[i]]
+                    cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
+                    cv2.putText(img, label, (x, y + 30), font, 3, color, 2)
+                    a, b, c, d, e =boxes2[i]
+                    ab =  a, b, c, d, e, confidence
+                    print("=========================Choosen Coordinate=======================")
+                    print(ab)
+                    printout = str(a)+" "+ str(b)+" "+ str(c)+" "+ str(d)+" "+ str(e)+ "\n"
+                    file.write(printout)
+
+        cv2.imshow('result image', img)
+        cv2.waitKey(0)
+    #train data
+    path = folder_selected
+    listdir = os.listdir(path)
+    
+    with open("result/train.txt", "w") as train:
+        for file in listdir:
+            train.writelines("data/obj_train_data/"+file+"\n")
+            
+    print("Arr", indexes)
+    print("========================End============================")
+
+    #.names
+    with open("result/obj.names", "w") as names:
+        names.write(v_class)
+    
+    #.data
+    with open("result/obj.data", "w") as data:
+        data_content = "classes = "+ "1" + "\n" + "train = data/train.txt" + "\n" + "names = data/obj.names" + "\n" + "backup = backup/" + "\n"
+        data.write(data_content)
+    messagebox.showinfo("Notif", "Process Completed")
+
+root = Tk()
+root.geometry("800x300")
+root.title('SVG Auto Annonate v0.99')
+
+def bar():
+    # progress.start(20)
+    print("============Browse File=============")
+    # print(printed)
+
+def execute():
+    main()
+
+s = Style()
+# path_frame = Frame(root, bg='blue')
+# path_frame.pack(side=TOP)
+
+field_frame = Frame(root)
+field_frame.pack(pady=20)
+
+# add_class_btn_frame = Frame(root)
+# add_class_btn_frame.pack(pady=10)
+
+btn_group_frame = LabelFrame(root, padx=10, pady=10)
+btn_group_frame.pack(pady=20)
+
+execute_info_frame = LabelFrame(root, text="Execution Progress", padx=10, pady=10)
+execute_info_frame.pack(pady=20)
+
+exit_btn_frame = Frame(root)
+execute_info_frame.pack(side=BOTTOM)
+
+# label_file_explorer = Label(path_frame,
+#                             text = "Path . . . .",
+#                             width = 70, height = 2,
+#                             fg = "blue", bg="snow")
+
+field_label = Label(field_frame, text="Class")
+add_class_field = Entry(field_frame)
+
+# add_class_btn = Button(add_class_btn_frame, text="Add Class", command=add_class)
+
+btn_execute = Button(btn_group_frame, text="Browse and Execute", width=25, style='execute_btn.TButton', command=add_class)
+# btn_cancel = Button(btn_group_frame, text="Cancel", width=25, style='cancel_btn.TButton')
+
+# progress = Progressbar(execute_info_frame, length=400 ,mode='indeterminate', orient=HORIZONTAL)
+
+# exit_btn = Button(exit_btn_frame, text="Quit", width=30)
+
+# specifying rows and columns
+# label_file_explorer.grid(column=0, row=0)
+
+btn_execute.grid(column=0, row=1)
+
+field_label.grid(column=0, row=2, padx=15)
+add_class_field.grid(column=1, row=2)
+
+# btn_cancel.grid(column=1, row=0, padx=15)
+
+# progress.grid(column=0, row=0)
+
+#style
+s.configure('execute_btn.TButton', background='blue')
+# s.configure('cancel_btn.TButton', background='red')
+
+
+# show_img = cv2.imshow("Deteksi Gambar Balon", img)
+# key = cv2.waitKey(0)
+
+root.mainloop()
+# cv2.destroyAllWindows()

food_classifier_yolo.py

@@ -0,0 +1,279 @@
+# -*- encoding: utf-8 -*-
+# ---------------------------- Food Classifier ---------------------------
+# This code is written to test the food classifier using customized YOLO model
+# It supports YOLO v3 and v4 as of 20Dec. 11, 2020
+# For options in detail, please refer to /config/confi.py
+
+# Usage example:  python3 food_classifier_yolo.py --video=run.mp4
+#                 python3 food_classifier_yolo.py --image=bird.jpg
+# -------------------------------------------------------------------------
+# modified by speedpointer, 05 Aug, 2025
+
+import os.path
+import cv2 as cv
+import argparse
+import sys
+import numpy as np
+import json
+from PIL import ImageFont, ImageDraw, Image
+
+from config import config
+
+parser = argparse.ArgumentParser(description='Food Classification and Localization ver. 0.9')
+parser.add_argument('--image', help='Full path to image file.')
+parser.add_argument('--video', help='Full path to video file.')
+parser.add_argument('--showText', type=int, default=1, help='show text in the output.')
+parser.add_argument('--ps', type=int, default=1, help='stop each image in the screen.')
+args = parser.parse_args()
+
+# Initialize the parameters
+args.image      = config.TEST_IMAGE_PATH  # image path
+args.video      =  config.TEST_VIDEO_PATH # video path
+args.showText = config.SHOW_TEXT_FLAG #1
+args.ps = config.PS_FLAG # 1
+
+# refine the inferences
+confThreshold   = config.CONF_THRES # 0.1 #0.5  # Confidence threshold
+nmsThreshold    = config.NMS_THRES #0.1 #0.4  # Non-maximum suppression threshold
+
+# modes inference size regardless of input image size
+inpWidth        = config.INPWIDTH # 32*10  # 608     #Width of network's input image # 320(32*10)
+inpHeight       = config.INPHEIGHT # 32*9 # 608     #Height of network's input image # 288(32*9) best
+
+# model base directory
+modelBaseDir    = config.ModelBaseDir # "C:/Users/mmc/workspace/yolo"
+
+# Load names of classes from a file
+classesFile = os.path.sep.join([modelBaseDir, config.CLASSES_FILE])
+classes = None
+with open(classesFile, 'rt', encoding='utf-8') as f:
+    classes = f.read().rstrip('\n').split('\n')
+
+# Load codes of classes from a file
+classes_File_Codes = os.path.sep.join([modelBaseDir, config.CLASSES_FILE_CODE])
+classes_codes = None
+with open(classes_File_Codes, 'rt', encoding='utf-8') as f:
+    classes_codes = f.read().rstrip('\n').split('\n')
+
+assert (len(classes) == len(classes_codes))
+
+# model configuration and weights paths
+modelConfiguration = os.path.sep.join([modelBaseDir, config.Model_Configuration])
+modelWeights = os.path.sep.join([modelBaseDir, config.Model_Weights])
+
+# load a given model
+net = cv.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
+net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
+net.setPreferableTarget(cv.dnn.DNN_TARGET_OPENCL_FP16)
+
+# Get the names of the output layers
+def getOutputsNames(net):
+    # Get the names of all the layers in the network
+    layersNames = net.getLayerNames()
+    # Get the names of the output layers, i.e. the layers with unconnected outputs
+    # Fix for OpenCV 4.x compatibility
+    unconnected = net.getUnconnectedOutLayers()
+    if len(unconnected.shape) == 1:
+        return [layersNames[i - 1] for i in unconnected]
+    else:
+        return [layersNames[i[0] - 1] for i in unconnected]
+
+# Draw the predicted bounding box
+def drawPred(frame, classId, conf, left, top, right, bottom):
+    # Draw a bounding box.
+    #    cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)
+    cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 3)
+
+    label = '%.2f' % conf
+
+    # Get the label for the class name and its confidence
+    if classes:
+        assert (classId < len(classes))
+        #label = '%s:%s' % (classes[classId], label)
+        label = u'%s' % (classes[classId])
+        #label = u'%s' % (classId)
+        print('label:{}, class_id:{}'.format(label, classId))
+
+
+    # Display the label at the top of the bounding box
+    labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+    top = max(top, labelSize[1])
+    if args.showText:
+        #cv.rectangle(frame, (left, top - round(1.5 * labelSize[1])), (left + round(1.5 * labelSize[0]), top + baseLine),
+        #        (0, 255, 255), cv.FILLED)
+        cv.rectangle(frame, (left, top - round(1.5*labelSize[1])), (left + round(1.5*labelSize[0]), top + baseLine),    (0, 255, 255), cv.FILLED)
+        cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 0), 2)
+
+        #fontpath = "./font/gulim.ttc"
+        #font_ = ImageFont.truetype(fontpath, 14)
+        #img_pil = Image.fromarray(frame)
+        #draw = ImageDraw.Draw(img_pil)
+        #draw.text((left, top), label, font=font_, fill=(0, 0, 0, 0))
+        #frame = np.array(img_pil)
+        #cv.imshow('pil', frame)
+
+
+def postprocess(frame, outs, showimg=False):
+    frameHeight = frame.shape[0]
+    frameWidth = frame.shape[1]
+
+    # Scan through all the bounding boxes output from the network and keep only the
+    # ones with high confidence scores. Assign the box's class label as the class with the highest score.
+    classIds = []
+    confidences = []
+    boxes = []
+    for out in outs:
+        if(args.showText):
+            print("out.shape : ", out.shape)
+        for detection in out:
+            # if detection[4]>0.001:
+            scores = detection[5:]
+            classId = np.argmax(scores)
+            # if scores[classId]>confThreshold:
+            confidence = scores[classId]
+            if detection[4] >= confThreshold:
+                if(args.showText):
+                    print('obj score: ', detection[4], " - confidence:", scores[classId], " - thres : ", confThreshold)
+                    #print(detection)
+            if confidence >= confThreshold:
+                center_x = int(detection[0] * frameWidth)
+                center_y = int(detection[1] * frameHeight)
+                width = int(detection[2] * frameWidth)
+                height = int(detection[3] * frameHeight)
+                left = int(center_x - width / 2)
+                top = int(center_y - height / 2)
+                classIds.append(classId)
+                confidences.append(float(confidence))
+                boxes.append([left, top, width, height])
+                # cv.rectangle(frame, (left, top), (left+width, top+height), (255, 0, 255),2)
+                # cv.imshow('test', frame)
+                # cv.waitKey(1)
+
+    # Perform non maximum suppression to eliminate redundant overlapping boxes with
+    # lower confidences.
+    indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
+    rests =[]
+    for i in indices:
+        # Fix for OpenCV 4.x compatibility
+        idx = i[0] if isinstance(i, (list, tuple, np.ndarray)) and len(i) > 0 else i
+        box = boxes[idx]
+        left = box[0]
+        top = box[1]
+        width = box[2]
+        height = box[3]
+        rests.append([classIds[idx], left, top, width, height, frameWidth, frameHeight])
+        if(showimg):
+            drawPred(frame, classIds[idx], confidences[idx], left, top, left + width, top + height)
+
+    return rests
+
+def food_classifier_Json(image):
+    # do somthing
+    print(args.showText)
+    locations = food_classifier_pipeline(frame=image) #[(2321, 0, 0, 10, 10)] # list of (id, rect) from classfication
+    jsons = []
+    for j,location in enumerate(locations):
+        class_id, x, y, width, height, framewidth, frameheight =location
+        res_json = {}
+        res_json["ClassID"] = classes_codes[class_id] # code , class_id (training class)
+        res_json["ClassName"] = classes[class_id]
+        res_json["x"] = int(x)
+        res_json["y"] = int(y)
+        res_json["w"] = int(width)
+        res_json["h"] = int(height)
+        res_json["framewidth"] = int(framewidth)
+        res_json["frameheight"]= int(frameheight)
+        jsons.append(res_json)
+    print(json.dumps(jsons,ensure_ascii=False))
+
+    return json.dumps(jsons,ensure_ascii=False)
+
+def food_classifier_pipeline(frame):
+
+    # Create a 4D blob from a frame.
+    blob = cv.dnn.blobFromImage(frame, 1 / 255, (inpWidth, inpHeight), [0, 0, 0], 1, crop=False)
+    # Sets the input to the network
+    net.setInput(blob)
+    # Runs the forward pass to get output of the output layers
+    outs = net.forward(getOutputsNames(net))
+    final_infos = postprocess(frame, outs)
+
+    return final_infos
+
+# Process inputs
+def main(main_args):
+    winName = 'Food Classification Results'
+    #cv.namedWindow(winName, cv.WINDOW_AUTOSIZE)
+    m_startFrame = np.maximum(0, config.Video_Start_Frame)
+
+    outputFile = "yolo_out_py.avi"
+    if (main_args.image):
+        # Open the image file
+        if not os.path.isfile(main_args.image):
+            print("Input image file ", main_args.image, " doesn't exist")
+            sys.exit(1)
+        cap = cv.VideoCapture(main_args.image)
+        outputFile = args.image[:-4] + '_yolo_out_py.jpg'
+    elif (main_args.video):
+        # Open the video file
+        if not os.path.isfile(main_args.video):
+            print("Input video file ", main_args.video, " doesn't exist")
+            sys.exit(1)
+        cap = cv.VideoCapture(main_args.video)
+        cap.set(cv.CAP_PROP_POS_FRAMES, m_startFrame)
+        outputFile = main_args.video[:-4] + '_yolo_out_py.avi'
+    else:
+        # Webcam input
+        cap = cv.VideoCapture(0)
+
+    # Get the video writer initialized to save the output video
+    if (not main_args.image):
+        vid_writer = cv.VideoWriter(outputFile, cv.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30,
+                                    (round(cap.get(cv.CAP_PROP_FRAME_WIDTH)), round(cap.get(cv.CAP_PROP_FRAME_HEIGHT))))
+    pcontinue = True
+    while pcontinue:
+
+        # get frame from the video
+        hasFrame, frame = cap.read()
+
+        # Stop the program if reached end of video
+        if not hasFrame:
+            print("Done processing !!!")
+            print("Output file is stored as ", outputFile)
+            # if(main_args.ps):
+            #    cv.waitKey(0)
+            #else:
+            #    cv.waitKey(1)
+
+            #break
+
+        # Create a 4D blob from a frame.
+        blob = cv.dnn.blobFromImage(frame, 1 / 255, (inpWidth, inpHeight), [0, 0, 0], 1, crop=False)
+        # Sets the input to the network
+        net.setInput(blob)
+        # Runs the forward pass to get output of the output layers
+        outs = net.forward(getOutputsNames(net))
+        if main_args.showText:
+            print(getOutputsNames(net))
+
+        postprocess(frame, outs, showimg=True)
+
+        # Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)
+        if main_args.showText:
+            t, _ = net.getPerfProfile()
+            label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
+            print(label)
+            cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
+
+        # Write the frame with the detection boxes
+        if (main_args.image):
+            cv.imwrite(outputFile, frame.astype(np.uint8));
+        else:
+            vid_writer.write(frame.astype(np.uint8))
+
+        #cv.imshow(winName, frame)
+        #cv.waitKey(1)
+        pcontinue=False
+
+if __name__ == "__main__":
+    main(main_args=args)

images_rec/-h6zaExAO5d0SwOLab4CD.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61b83852c206e82aeda78fb4d64d72a357d9a272fc7a509f08de959b3ec78daa
+size 5478482

images_rec/0G5lzNC9wlMjLH4Pgqpiv.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44b1a55c7f3281b73dbbb5ac248881835727206721abdaeaf812d6f235f70167
+size 191375

images_rec/1cnIS16DmJFYH5xGdZDVp.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77602490434ec7ce247958834d5c3e796a5c944a170c66d51ca78c8bac7c98a7
+size 1459594

images_rec/1jEP7XVqK_qxMGFELEcBd.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44b1a55c7f3281b73dbbb5ac248881835727206721abdaeaf812d6f235f70167
+size 191375

images_rec/2ZBNwnbnYNpcLL9aBUS4w.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f86344dd3134033330bc9ab92b06c45df6c5714462301443665a6a7edd701373
+size 2936393

images_rec/34gFMj0jKceCkDDHW9H4k.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a76b1364719285bdb60ba26e1a0e83f5c278105301fd2a7668dcdf8c841ce5ba
+size 5129949

images_rec/390XJDGATbmhPazLWOBc6.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77602490434ec7ce247958834d5c3e796a5c944a170c66d51ca78c8bac7c98a7
+size 1459594

images_rec/3Jkl_BANLxoAJkkWodwKH.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:805bf4d64b839d964c8fba5cc3be99d9ac4e76929578acbd48ca8a3c073dfc19
+size 4615403

images_rec/5L7QIIY8EYYhXCyHWUCFr.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:437d6a1f645f3a31f99c44a956d11d9276f35b2f99e2d0eb7ac67eafce5b2e2f
+size 5157898

images_rec/7u5qmwKFsB-walZ34Oe4U.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f7a1ac7b47d023e7d7125f88e73961e9c977f58e70929f1317630991cbe43372
+size 4578697

images_rec/88rGixQVT8KtfyK_Hq4fH.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d663020fc8a05cfdc3c9348872192692beb2627359bd860c9c7f9235d3e81b4
+size 4046352

images_rec/8Byi031_J7Dvvrw60jvTf.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7839fca13e0191b4c21e01dd52b39a30ed1b07cea3ae5ac1cbbd107ce22cbe7b
+size 4521136

images_rec/AisrTDiAbj0Y0w5qGpSp-.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d25f6843909654a3d9e16d5e62d24edf5658efc73f220a12c962631b23f327ba
+size 3718151

images_rec/BhzWBe-ZI5ZFgAPzYL23h.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77602490434ec7ce247958834d5c3e796a5c944a170c66d51ca78c8bac7c98a7
+size 1459594

images_rec/Bk8E-bGn1i4DJIrzKXGW3.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44b1a55c7f3281b73dbbb5ac248881835727206721abdaeaf812d6f235f70167
+size 191375

images_rec/CVuwRcNm-ny33CMmLdIes.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db85fa9122e8052970efd36eb25246bb3dc4919d553c8fde8f66f6b1e5cc5b1d
+size 5163362

images_rec/DpBFo_IdI2MzmGFIi4q8b.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a6ba2613ee5f5a0aa51b81d272667beb87810b05311c87db52b7592415d8249
+size 3623474

images_rec/E7L6NErhRmI5K7NvwtIIv.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ec0cd4d79338c6ba600b436618c21a1c427bd359bed1ff3c781c67420dfce40
+size 1408858

images_rec/EFcHP3Y89VnQfwzzC12qu.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44b1a55c7f3281b73dbbb5ac248881835727206721abdaeaf812d6f235f70167
+size 191375

images_rec/Ex-6VCTPgtUYyFDCjI8Rp.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c1ad7a42829a623abcf8c31f0c18b94b4f4437f2b46691444d0de401eeb477cc
+size 4913741