Update core/describe_scene.py

core/describe_scene.py

@@ -1,59 +1,61 @@
-import numpy as np
-import logging
-
-logger = logging.getLogger(__name__)
-
-def describe_scene(detection=None, segmentation=None, depth=None):
-    """
-    Generates a structured scene summary with metrics for detection, segmentation, and depth.
-
-    Args:
-        detection (list): List of detected objects with class names and bounding boxes.
-        segmentation (numpy.ndarray): Segmentation mask as a 2D numpy array.
-        depth (numpy.ndarray): Depth map as a 2D numpy array.
-
-    Returns:
-        dict: Structured scene description with metrics.
-    """
-    logger.info("Generating scene summary...")
-    description = {"scene_summary": {}}
-
-    # Detection Summary with Metrics
-    if detection:
-        logger.info("Adding detection results to scene summary.")
-        description["scene_summary"]["objects"] = detection
-        confidences = [obj.get("confidence", 0) for obj in detection]
-        description["scene_summary"]["detection_metrics"] = {
-            "objects_detected": len(detection),
-            "average_confidence": float(np.mean(confidences)) if confidences else 0.0
-        }
-
-    # Segmentation Summary with Coverage Metrics
-    if segmentation is not None:
-        logger.info("Summarizing segmentation coverage.")
-        unique, counts = np.unique(segmentation, return_counts=True)
-        total = segmentation.size
-        coverage = [
-            {"class_id": int(class_id), "coverage": f"{(count / total) * 100:.2f}%"}
-            for class_id, count in zip(unique, counts)
-        ]
-        dominant_class = max(coverage, key=lambda x: float(x["coverage"].strip('%')))
-        description["scene_summary"]["segmentation_summary"] = coverage
-        description["scene_summary"]["dominant_class"] = dominant_class
-
-    # Depth Summary with Metrics
-    if depth is not None:
-        logger.info("Summarizing depth information.")
-        mean_depth = float(np.mean(depth))
-        min_depth = float(np.min(depth))
-        max_depth = float(np.max(depth))
-        std_depth = float(np.std(depth))
-        description["scene_summary"]["depth_summary"] = {
-            "mean_depth": mean_depth,
-            "min_depth": min_depth,
-            "max_depth": max_depth,
-            "std_depth": std_depth
-        }
-
-    logger.info("Scene summary generation complete.")
-    return description
+import numpy as np
+import logging
+
+# Setup logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def describe_scene(detection=None, segmentation=None, depth=None):
+    """
+    Generates a structured scene summary with metrics for detection, segmentation, and depth.
+
+    Args:
+        detection (list): List of detected objects with class names and bounding boxes.
+        segmentation (numpy.ndarray): Segmentation mask as a 2D numpy array.
+        depth (numpy.ndarray): Depth map as a 2D numpy array.
+
+    Returns:
+        dict: Structured scene description with metrics.
+    """
+    logger.info("Generating scene summary...")
+    description = {"scene_summary": {}}
+
+    # Detection Summary with Metrics
+    if detection:
+        logger.info("Adding detection results to scene summary.")
+        description["scene_summary"]["objects"] = detection
+        confidences = [obj.get("confidence", 0) for obj in detection]
+        description["scene_summary"]["detection_metrics"] = {
+            "objects_detected": len(detection),
+            "average_confidence": float(np.mean(confidences)) if confidences else 0.0
+        }
+
+    # Segmentation Summary with Coverage Metrics
+    if segmentation is not None:
+        logger.info("Summarizing segmentation coverage.")
+        unique, counts = np.unique(segmentation, return_counts=True)
+        total = segmentation.size
+        coverage = [
+            {"class_id": int(class_id), "coverage": f"{(count / total) * 100:.2f}%"}
+            for class_id, count in zip(unique, counts)
+        ]
+        dominant_class = max(coverage, key=lambda x: float(x["coverage"].strip('%')))
+        description["scene_summary"]["segmentation_summary"] = coverage
+        description["scene_summary"]["dominant_class"] = dominant_class
+
+    # Depth Summary with Metrics
+    if depth is not None:
+        logger.info("Summarizing depth information.")
+        mean_depth = float(np.mean(depth))
+        min_depth = float(np.min(depth))
+        max_depth = float(np.max(depth))
+        std_depth = float(np.std(depth))
+        description["scene_summary"]["depth_summary"] = {
+            "mean_depth": mean_depth,
+            "min_depth": min_depth,
+            "max_depth": max_depth,
+            "std_depth": std_depth
+        }
+
+    logger.info("Scene summary generation complete.")
+    return description