Add detailed logging for debugging detection pipeline

model.py

@@ -84,8 +84,10 @@ class TrafficSignDetector:
             if hasattr(self.model, 'model') and hasattr(self.model.model, 'parameters'):
                 total_params = sum(p.numel() for p in self.model.model.parameters())
                 trainable_params = sum(p.numel() for p in self.model.model.parameters() if p.requires_grad)
+                weights_sum = sum(p.sum().item() for p in self.model.model.parameters())
                 print(f"  - Total parameters: {total_params:,}")
                 print(f"  - Trainable parameters: {trainable_params:,}")
+                print(f"  - Weights sum: {weights_sum:.6f}")
         except Exception as e:
             print(f"  - Could not retrieve architecture details: {e}")
         
@@ -154,16 +156,27 @@ class TrafficSignDetector:
         :return: tuple of (image with drawn bounding boxes, preprocessed image for visualization)
         """
         print(f"\n{'='*80}")
-        print(f"INPUT IMAGE shape: {image.shape}")
+        print(f"DETECTION PIPELINE START")
+        print(f"{'='*80}")
+        print(f"[STEP 1] INPUT IMAGE")
+        print(f"  - Shape: {image.shape}")
+        print(f"  - dtype: {image.dtype}")
+        print(f"  - Range: [{image.min()}, {image.max()}]")
+        print(f"  - Mean: {image.mean():.2f}, Std: {image.std():.2f}")
         
         # Store original image for drawing (uint8)
         original_image = image.copy()
         
         # Apply letterbox preprocessing to ensure 640x640 matching training size
         # Returns both processed image and transformation info
+        print(f"\n[STEP 2] LETTERBOX PREPROCESSING")
         image, scale, pad_x, pad_y = self._ensure_square(image, target_size=640)
+        print(f"  - Letterboxed shape: {image.shape}")
+        print(f"  - Scale factor: {scale:.3f}")
+        print(f"  - Padding X: {pad_x}, Y: {pad_y}")
         
         # Normalize pixel values for inference
+        print(f"\n[STEP 3] IMAGE NORMALIZATION")
         image = self._preprocess(image)
         
         # Store preprocessed image for visualization (convert back to 0-255 for display)
@@ -171,22 +184,35 @@ class TrafficSignDetector:
         
         # Use imgsz=640 to match training size
         # Use iou_threshold for NMS (Non-Maximum Suppression) to remove overlapping boxes
+        print(f"\n[STEP 4] MODEL INFERENCE")
+        print(f"  - Input shape to model: {image.shape}")
+        print(f"  - Confidence threshold: {self.conf_threshold}")
+        print(f"  - IOU threshold: 0.45")
         results = self.model(image, conf=self.conf_threshold, imgsz=640, iou=0.45)
-        print(f"Number of results: {len(results)}")
+        print(f"  - Number of results: {len(results)}")
 
         # Get original dimensions for coordinate transformation
         orig_h, orig_w = original_image.shape[:2]
         
+        print(f"\n[STEP 5] DETECTION RESULTS")
         for result in results:
             boxes = result.boxes
-            print(f"Total boxes detected (confidence >= {self.conf_threshold}): {len(boxes)}")
+            print(f"  - Total boxes after NMS (confidence >= {self.conf_threshold}): {len(boxes)}")
+            
+            # Debug: print all raw predictions before NMS
+            if hasattr(result, 'boxes') and len(result.boxes) == 0:
+                print(f"  - Note: Model raw output available but filtered by NMS/confidence")
+                if hasattr(result, 'probs'):
+                    print(f"  - Raw predictions present: {result.probs}")
             
             # Debug: print summary
             if len(boxes) > 0:
                 confidences = [float(box.conf[0]) for box in boxes]
-                print(f"Confidence range: {min(confidences):.4f} - {max(confidences):.4f}")
+                print(f"  - Confidence range: {min(confidences):.4f} - {max(confidences):.4f}")
+                print(f"  - Mean confidence: {np.mean(confidences):.4f}")
             else:
-                print(f"No detections above threshold {self.conf_threshold}")
+                print(f"  - No detections above threshold {self.conf_threshold}")
+                print(f"  - Model may not have detected any objects in this image")
             
             for box in boxes:
                 # Get bounding box coordinates from letterboxed image
@@ -211,5 +237,7 @@ class TrafficSignDetector:
                     label = f"{self.classes[cls]}: {conf:.2f}"
                     cv2.putText(original_image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, self.text_color, 2)
 
-        print("="*80 + "\n")
+        print(f"\n{'='*80}")
+        print(f"DETECTION PIPELINE COMPLETE")
+        print(f"{'='*80}\n")
         return original_image, preprocessed_display