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/constants_inf.pyx

@@ -0,0 +1,95 @@
+import json
+import os
+import sys
+
+from loguru import logger
+
+cdef str CONFIG_FILE = "config.yaml"
+cdef str AI_ONNX_MODEL_FILE = "azaion.onnx"
+
+cdef str CDN_CONFIG = "cdn.yaml"
+cdef str MODELS_FOLDER = "models"
+
+cdef int SMALL_SIZE_KB = 3
+
+cdef str SPLIT_SUFFIX = "!split!"
+cdef double TILE_DUPLICATE_CONFIDENCE_THRESHOLD = <double>0.01
+cdef int METERS_IN_TILE = 25
+
+cdef class AnnotationClass:
+    def __init__(self, id, name, color, max_object_size_meters):
+        self.id = id
+        self.name = name
+        self.color = color
+        self.max_object_size_meters = max_object_size_meters
+
+    def __str__(self):
+        return f'{self.id} {self.name} {self.color} {self.max_object_size_meters}'
+
+cdef int weather_switcher_increase = 20
+
+WEATHER_MODE_NAMES = {
+    Norm: "Norm",
+    Wint: "Wint",
+    Night: "Night"
+}
+
+_classes_path = os.environ.get("CLASSES_JSON_PATH", "classes.json")
+with open(_classes_path, 'r', encoding='utf-8') as f:
+    j = json.loads(f.read())
+    annotations_dict = {}
+
+    for i in range(0, weather_switcher_increase * 3, weather_switcher_increase):
+        for cl in j:
+            id = i + cl['Id']
+            mode_name = WEATHER_MODE_NAMES.get(i, "Unknown")
+            name = cl['Name'] if i == 0 else f'{cl["Name"]}({mode_name})'
+            annotations_dict[id] = AnnotationClass(id, name, cl['Color'], cl['MaxSizeM'])
+
+_log_dir = os.environ.get("LOG_DIR", "Logs")
+os.makedirs(_log_dir, exist_ok=True)
+logger.remove()
+log_format = "[{time:HH:mm:ss} {level}] {message}"
+logger.add(
+    sink=f"{_log_dir}/log_inference_{{time:YYYYMMDD}}.txt",
+    level="INFO",
+    format=log_format,
+    enqueue=True,
+    rotation="1 day",
+    retention="30 days",
+)
+logger.add(
+    sys.stdout,
+    level="DEBUG",
+    format=log_format,
+    filter=lambda record: record["level"].name in ("INFO", "DEBUG", "SUCCESS"),
+    colorize=True
+)
+logger.add(
+    sys.stderr,
+    level="WARNING",
+    format=log_format,
+    colorize=True
+)
+
+def get_annotation_name(int cls_id):
+    if cls_id in annotations_dict:
+        return (<AnnotationClass>annotations_dict[cls_id]).name
+    return ""
+
+cdef log(str log_message):
+    logger.info(log_message)
+
+cdef logerror(str error):
+    logger.error(error)
+
+cdef format_time(long ms):
+    # Calculate hours, minutes, seconds, and hundreds of milliseconds.
+    h = ms // 3600000  # Total full hours.
+    ms_remaining = ms % 3600000
+    m = ms_remaining // 60000  # Full minutes.
+    ms_remaining %= 60000
+    s = ms_remaining // 1000  # Full seconds.
+    f = (ms_remaining % 1000) // 100  # Hundreds of milliseconds.
+    h = h % 10
+    return f"{h}{m:02}{s:02}{f}"