Add AIAvailabilityStatus and AIRecognitionConfig classes for AI model management

- Introduced `AIAvailabilityStatus` class to manage the availability status of AI models, including methods for setting status and logging messages.
- Added `AIRecognitionConfig` class to encapsulate configuration parameters for AI recognition, with a static method for creating instances from dictionaries.
- Implemented enums for AI availability states to enhance clarity and maintainability.
- Updated related Cython files to support the new classes and ensure proper type handling.

These changes aim to improve the structure and functionality of the AI model management system, facilitating better status tracking and configuration handling.

src/annotation.pyx

@@ -0,0 +1,50 @@
+cimport constants_inf
+
+cdef class Detection:
+    def __init__(self, double x, double y, double w, double h, int cls, double confidence):
+        self.x = x
+        self.y = y
+        self.w = w
+        self.h = h
+        self.cls = cls
+        self.confidence = confidence
+
+    def __str__(self):
+        return f'{self.cls}: {self.x:.2f} {self.y:.2f} {self.w:.2f} {self.h:.2f}, prob: {(self.confidence*100):.1f}%'
+
+    def __eq__(self, other):
+        if not isinstance(other, Detection):
+            return False
+
+        if max(abs(self.x - other.x),
+               abs(self.y - other.y),
+               abs(self.w - other.w),
+               abs(self.h - other.h)) > constants_inf.TILE_DUPLICATE_CONFIDENCE_THRESHOLD:
+            return False
+        return True
+
+    cdef bint overlaps(self, Detection det2, float confidence_threshold):
+        cdef double overlap_x = 0.5 * (self.w + det2.w) - abs(self.x - det2.x)
+        cdef double overlap_y = 0.5 * (self.h + det2.h) - abs(self.y - det2.y)
+        cdef double overlap_area = <double>(max(0.0, overlap_x) * max(0.0, overlap_y))
+        cdef double min_area = min(self.w * self.h, det2.w * det2.h)
+
+        return <bint>(overlap_area / min_area > confidence_threshold)
+
+cdef class Annotation:
+    def __init__(self, str name, str original_media_name, long ms, list[Detection] detections):
+        self.name = name
+        self.original_media_name = original_media_name
+        self.time = ms
+        self.detections = detections if detections is not None else []
+        self.image = b''
+
+    def __str__(self):
+        if not self.detections:
+            return f"{self.name}: No detections"
+
+        detections_str = ", ".join(
+            f"class: {d.cls} {d.confidence * 100:.1f}% ({d.x:.2f}, {d.y:.2f}) ({d.w:.2f}, {d.h:.2f})"
+            for d in self.detections
+        )
+        return f"{self.name}: {detections_str}"