Face Detection Mini Project

import cv2
import numpy as np
import os

class FaceDetector:
    """A complete face and eye detector with configurable sensitivity."""

    def __init__(self, scale_factor=1.1, min_neighbors=5, min_size=(30, 30)):
        self.face_cascade = cv2.CascadeClassifier(
            cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
        )
        self.eye_cascade = cv2.CascadeClassifier(
            cv2.data.haarcascades + "haarcascade_eye.xml"
        )
        self.scale_factor = scale_factor
        self.min_neighbors = min_neighbors
        self.min_size = min_size

    def detect(self, frame):
        """Detect faces in a single frame. Returns list of face dicts."""
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        gray = cv2.equalizeHist(gray)  # Improve contrast

        faces_raw = self.face_cascade.detectMultiScale(
            gray,
            scaleFactor=self.scale_factor,
            minNeighbors=self.min_neighbors,
            minSize=self.min_size
        )

        results = []
        for (x, y, w, h) in faces_raw:
            # Detect eyes within face
            face_gray = gray[y:y+h, x:x+w]
            eyes = self.eye_cascade.detectMultiScale(face_gray)

            results.append({
                "bbox": (x, y, w, h),
                "eyes": len(eyes),
                "centre": (x + w//2, y + h//2),
                "area": w * h,
            })
        return results

    def draw(self, frame, detections):
        """Draw detections on a frame."""
        result = frame.copy()
        for d in detections:
            x, y, w, h = d["bbox"]
            cv2.rectangle(result, (x,y), (x+w,y+h), (0,255,0), 2)
            label = f"eyes:{d['eyes']}"
            cv2.putText(result, label, (x, y-5),
                       cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0,255,0), 1)
        count_text = f"Faces: {len(detections)}"
        cv2.putText(result, count_text, (10,25),
                   cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,0), 2)
        return result

    def process_image(self, image_path, output_path=None):
        """Process a single image."""
        image = cv2.imread(image_path)
        if image is None:
            return 0
        detections = self.detect(image)
        result = self.draw(image, detections)
        out = output_path or image_path.replace(".", "_detected.")
        cv2.imwrite(out, result)
        print(f"Detected {len(detections)} face(s). Saved: {out}")
        return len(detections)

# Use the detector
detector = FaceDetector(scale_factor=1.1, min_neighbors=5)
detector.process_image("photo.jpg")