{"id":34519520,"url":"https://github.com/dafwa/aplikasi-incer","last_synced_at":"2026-04-29T23:32:57.090Z","repository":{"id":325766627,"uuid":"1102307641","full_name":"dafwa/aplikasi-incer","owner":"dafwa","description":"Real-time Image Processing \u0026 Face Segmentation Web App built with FastAPI, MediaPipe, and OpenCV. 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Aplikasi ini memanfaatkan performa [**FastAPI**](https://fastapi.tiangolo.com/) sebagai backend dan Model Machine Learning dari [**MediaPipe**](https://pypi.org/project/mediapipe/) untuk memisahkan objek manusia dari latar belakang (*background*).\n\nSistem dirancang dengan efisiensi tinggi menggunakan *In-Memory Processing*, sehingga tidak membebani penyimpanan server dengan file sementara.\n\n## Fitur Utama\n\n### 1. Sumber Citra Fleksibel\n* **Upload File**: Mendukung format gambar umum (JPG, PNG).\n* **Webcam Integrasi**: Pengambilan gambar langsung dari kamera perangkat dengan fitur *toggle* (Nyalakan/Matikan) dan preview *mirrored* (seperti bercermin).\n\n### 2. Pengolahan Citra Digital (Classical Processing)\nFitur-fitur ini menggunakan manipulasi matriks dan algoritma pengolahan citra klasik untuk analisis struktur dan warna.\n\n* **Grayscale**: Konversi citra warna (RGB) menjadi derajat keabuan (Luminance).\n* **Gaussian Blur**: Teknik penghalusan citra (*smoothing*) untuk mereduksi *noise*.\n* **Sepia Filter**: Transformasi matriks warna untuk memberikan efek hangat/klasik sekaligus demonstrasi aljabar linear pada citra.\n* **Canny Edge Detection**: Algoritma deteksi tepi multi-tahap untuk mengekstrak struktur dan garis batas objek dalam citra.\n* **Otsu Thresholding**: Teknik binarisasi otomatis yang memisahkan objek (foreground) dan latar (background) menjadi hitam-putih mutlak berdasarkan histogram.\n\n### 3. Computer Vision \u0026 AI (Advanced)\nFitur-fitur ini memanfaatkan model *Machine Learning* (MediaPipe) untuk pemahaman konteks visual yang lebih kompleks.\n\n* **Face Mesh Visualization**: Memetakan dan memvisualisasikan **468 titik koordinat (landmarks)** geometri pada wajah secara *real-time* untuk analisis biometrik.\n* **Hapus Background (Transparent)**: Menggunakan model *Selfie Segmentation* untuk menghapus latar belakang dan menghasilkan format **PNG Transparan** (Alpha Channel), bukan sekadar layar putih.\n* **Ganti Background**: Memungkinkan penggabungan (*masking*) antara objek manusia (foreground) dengan gambar latar pilihan pengguna.\n\n### 4. Monitoring Performa\n* **Latency Counter**: Menampilkan waktu eksekusi (*execution time*) setiap algoritma dalam satuan **milidetik (ms)** untuk mengukur efisiensi sistem secara kuantitatif.\n\n### 5. Efisiensi Sistem\n* **Tanpa Temporary Files**: Hasil pengolahan citra dikirim langsung ke antarmuka pengguna menggunakan format **Base64**, menjaga kebersihan dan privasi server.\n* **Download Instan**: Hasil olahan dapat langsung diunduh oleh pengguna.\n\n---\n\n## Teknologi yang Digunakan\n\n* **Backend Framework**: FastAPI (Python)\n* **Computer Vision**: OpenCV, MediaPipe, NumPy\n* **Server**: Uvicorn\n* **Frontend**: HTML5, Bootstrap 5, Vanilla JavaScript\n\n---\n\n## Struktur Direktori\n\n```text\naplikasi-incer/\n│\n├── app/\n│   ├── api/\n│   │   └── routes.py           # Endpoint API (Logika Routing)\n│   ├── services/\n│   │   ├── image_processor.py  # Modul OpenCV Dasar\n│   │   └── segmenter.py        # Modul MediaPipe (AI)\n│   ├── static/\n│   │   ├── css/                    # Styling Tambahan\n│   │   └── js/                     # Logika Webcam \u0026 AJAX\n│   ├── templates/\n│   │   └── index.html          # Antarmuka Pengguna\n│   └── main.py                 # Konfigurasi Utama Server\n│\n├── requirements.txt            # Daftar Pustaka Python\n└── README.md                   # Dokumentasi Proyek\n```\n\n## INSTALASI\nSilahkan Clone Repository ini\n```bash\ngit clone https://github.com/dafwa/aplikasi-incer.git\n```\n\nPastikan anda berada didalam ```~aplikasi-incer/```\n```bash\ncd aplikasi-incer\n```\n\nBuat Virtual Environment di Python atau Conda.\n```bash\npython -m venv [name_virtual_environment]\n\n# jika memakai conda\nconda create -n [name_virtual_environment] python=3.10\n```\n\nSetelah Virtual Environment terbuat, Aktifkan Environment.\n```bash\n[name_virtual_environment]/Scripts/activate\n\n# jika memakai conda\nconda activate [name_virtual_environment]\n```\n\nDownload library dari ```requirements.txt```\n```bash\npip install -r requirements.txt\n```\natau Download Manual, lakukan ```pip install [library]```\n```text\nfastapi\nuvicorn[standard]\npython-multipart\nnumpy\nopencv-python\nmediapipe\n```\n\n## 🚀 Jalankan\nJalankan Aplikasi dengan command berikut.\n```bash\nuvicorn app.main:app --reload\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdafwa%2Faplikasi-incer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdafwa%2Faplikasi-incer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdafwa%2Faplikasi-incer/lists"}