API Documentation Endpoints 1.Upload and Process of Optical Character Recognition POST /img-ocr/
Upload an image and perform OCR (Optical Character Recognition) to extract text.
Request Body: file (form-data): The image file to be processed.
Responses: 200 OK: Returns a JSON object with the recognized text. json Копировать код { "text": "Recognized text from the image." } 500 Internal Server Error: If an error occurs during processing.
2.Get Face Embedding POST /face_embedding/
Upload an image and get the face embedding. Request Body: file (form-data): The image file to be processed. inIsDeep (query parameter, optional): Boolean to enforce deep face embedding. inEnforceFaceDetection (query parameter, optional): Boolean to enforce face detection. Responses: 200 OK: Returns a JSON object with the face embedding. json { "embedding": [0.1, 0.2, ..., 0.5] } 400 Bad Request: If the uploaded file is not an image or processing fails.
3.Detect Faces POST /detect_faces/ Upload an image and detect faces in it. Request Body: file (form-data): The image file to be processed. classifier_type (query parameter, optional): Type of face classifier to use for face detection. Available options include: eye: Detects eyes in the image. eyeglasses: Detects eyeglasses. catface: Detects faces of cats. catface_extended: Detects faces of cats with extended features. frontalface_alt: Detects frontal faces (alternative model). frontalface_alt2: Detects frontal faces (second alternative model). frontalface_alt_tree: Detects frontal faces using a tree-based model. frontalface_default: Detects frontal faces (default model). fullbody: Detects full bodies. lefteye: Detects left eyes. license_plate: Detects license plates. lowerbody: Detects lower bodies. profileface: Detects profile faces. righteye: Detects right eyes. russian_plate: Detects Russian license plates. smile: Detects smiles. upperbody: Detects upper bodies.
Responses: 200 OK: Returns a JSON object with the detected faces' coordinates. json { "faces": [[x1, y1, x2, y2], [x3, y3, x4, y4]] } 400 Bad Request: If the uploaded file is not an image or processing fails.
File Handling Files are saved with a SHA-256 hash to ensure unique filenames. Uploaded files are temporarily stored in the uploaded_images/ directory and deleted after processing.
To install the required dependencies, create a requirements.txt file with the following contents:
fastapi==0.95.1
uvicorn==0.23.1
deepface==0.0.75
pytesseract==0.3.10
opencv-python==4.7.0.72
pillow==9.5.0
python-multipart==0.0.6
numpy==1.25.2
tensorflow==2.15.0
keras==2.15.0
opencv-python-headless==4.7.0.72
fastapi_example/ │ ├── app/ │ ├── main.py # Точка входа в приложение │ ├── services.py # Логика бизнес-слоя │ ├── controllers.py # Контроллеры для обработки запросов │ └── image_processing.py # Логика обработки изображений ├── requirements.txt # Зависимости проекта └── Dockerfile # Файл для сборки Docker-образа
To build the Docker image, run: bash: docker build -t fastapi-example .
Run Docker Container To run the Docker container in detached mode, use: bash: docker run -d --name fastapi-container -p 8000:8000 fastapi-example
Local Development To run the application locally (without Docker), use: bash: python -m uvicorn app.main:app --reload
Dependencies The project depends on the following packages:
fastapi uvicorn pytesseract opencv-python pillow python-multipart These dependencies are listed in requirements.txt.