Create haarcascade_model.py

ai_security/facial_recognition/models/haarcascade_model.py

@@ -0,0 +1,50 @@
+# ai_security/models/haarcascade_model.py
+
+import cv2
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+class HaarCascadeModel:
+    def __init__(self):
+        """Initialize the Haar Cascade model for face detection."""
+        self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+        logging.info('Haar Cascade model initialized.')
+
+    def detect_faces(self, image):
+        """Detect faces in the given image.
+
+        Args:
+            image (numpy.ndarray): The input image.
+
+        Returns:
+            list: A list of bounding boxes for detected faces.
+        """
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        faces = self.face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
+        logging.info(f'Detected {len(faces)} faces.')
+        return faces
+
+    def draw_face_boxes(self, image, faces):
+        """Draw bounding boxes around detected faces in the image.
+
+        Args:
+            image (numpy.ndarray): The input image.
+            faces (list): A list of bounding boxes for detected faces.
+
+        Returns numpy.ndarray: The image with bounding boxes drawn.
+        """
+        for (startX, startY, endX, endY) in faces:
+            cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 2)
+        return image
+
+# Example usage:
+# if __name__ == "__main__":
+#     haar_model = HaarCascadeModel()
+#     input_image = cv2.imread('path/to/input_image.jpg')
+#     detected_faces = haar_model.detect_faces(input_image)
+#     output_image = haar_model.draw_face_boxes(input_image, detected_faces)
+#     cv2.imshow('Haar Cascade Face Detection', output_image)
+#     cv2.waitKey(0)
+#     cv2.destroyAllWindows()