Building a Face Detection API with OpenCV and Flask
Algorithm Explanation
1. Import the necessary libraries: Flask for creating the web application and OpenCV for face detection.
2. Load the pre-trained face cascade classifier using ‘CascadeClassifier’ from OpenCV. This classifier is used to detect faces in an image.
3. Initialize the Flask app.
4. Define a route for the home page (“/”) which renders an HTML template.
5. Define a route for face detection (“/detect”) which accepts both GET and POST requests.
6. If a POST request is received, retrieve the uploaded image file from the request, read it using OpenCV’s ‘imdecode’ function, and convert it to grayscale using ‘cvtColor’.
7. Perform face detection on the grayscale image using ‘detectMultiScale’ from the face cascade classifier. This function returns a list of rectangles representing the detected faces.
8. Iterate over the detected faces and draw rectangles around them using ‘rectangle’.
9. Save the output image with the detected faces to a static directory.
10. Redirect the user to the result page (“/result”).
11. Define a route for displaying the result (“/result”) which renders an HTML template showing the output image.
12. Run the Flask app.
This code demonstrates how to create a Flask web application for face detection using OpenCV. The web application provides a form to upload an image and performs face detection on the uploaded image. The detected faces are marked with rectangles, and the resulting image is displayed on a separate result page.
