This API allows users to upload images and receive facial recognition results, including bounding boxes around detected faces and corresponding labels.

1. Clone the repository to your local machine.

2. Install the required dependencies using pip:

   pip install -r requirements.txt
   

3. Download the haarcascade_frontalface_default.xml file from the OpenCV GitHub repository and place it in the models/ directory.

4. Run the Flask server:

   python face_recognition.py test.jpg haarcascade_frontalface_default.xml
   

5. Send a POST request to the /detect endpoint with an image file in the request body. You can use tools such as Postman or cURL to send requests.

   • If using Postman:

     1. Open Postman and create a new request.
     2. Select POST as the HTTP method.
     3. Enter http://localhost:5000/detect as the request URL.
     4. Select the Body tab.
     5. Select form-data as the request body type.
     6. Add a new key-value pair to the request body, where the key is file and the value is the path to the image file.
     7. Click Send.

   • If using cURL:

     curl -X POST -F 'file=@path/to/image.jpg' http://localhost:5000/detect
     

6. Receive the facial recognition results in JSON format, including the number of detected faces, the coordinates of the bounding boxes, and the corresponding labels.

Detect faces in an uploaded image.

Request:

• file: The image file to upload.

Response:

{
  "num_faces": 2,
  "faces": [
    {
      "x": 100,
      "y": 150,
      "width": 200,
      "height": 200,
      "label": "John Doe"
    },
    {
      "x": 400,
      "y": 200,
      "width": 150,
      "height": 150,
      "label": "Jane Doe"
    }
  ]
}

The response contains the following fields:

• num_faces: The number of detected faces in the uploaded image.
• faces: An array of face objects, each containing the following fields:
  • x: The x-coordinate of the top-left corner of the bounding box around the face.
  • y: The y-coordinate of the top-left corner of the bounding box around the face.
  • width: The width of the bounding box around the face.
  • height: The height of the bounding box around the face.
  • label: The label or name associated with the detected face.

• Flask
• OpenCV

• haarcascade_frontalface_default.xml: Cascade classifier for face detection.

Version 1.1 (Updated May 2023)

• Implemented error handling mechanisms to gracefully handle exceptions during execution.
• Added input validation to ensure that the image file and cascade classifier file exist.
• Improved code modularity by separating the face detection logic from file handling and display logic.
• Implemented command-line arguments for specifying the image file and cascade classifier file.
• Added performance optimization techniques, such as reducing image size and exploring more efficient algorithms.
• Extended the code to handle multiple faces by detecting and drawing bounding boxes around all faces.
• Tuned parameters, such as scaleFactor and minNeighbors, to improve face detection accuracy.
• Improved user interface by exploring options for a more user-friendly graphical user interface (GUI).
• Modified the code to perform real-time face detection using a webcam or video stream.

This project is licensed under the MIT License - see the LICENSE file for details.