Update tasks/image.py

tasks/image.py

@@ -67,6 +67,16 @@ def compute_max_iou(true_boxes, pred_box):
         max_iou = max(max_iou, iou)
     return max_iou
 
+def load_model(path_to_model, model_type="YOLO"):
+    if model_type == "YOLO":
+        model = YOLO(path_to_model)
+    else:
+        raise NotImplementedError
+    return model
+
+def get_boxes_list(predictions):
+    return [box.tolist() for box in predictions.boxes.xywhn]
+
 @router.post(ROUTE, tags=["Image Task"],
              description=DESCRIPTION)
 async def evaluate_image(request: ImageEvaluationRequest):
@@ -100,6 +110,11 @@ async def evaluate_image(request: ImageEvaluationRequest):
     # Update the code below to replace the random baseline with your model inference
     #--------------------------------------------------------------------------------------------   
     
+    
+    PATH_TO_MODEL = "models/best_YOLOv8n.pt"
+    model = load_model(PATH_TO_MODEL)
+    
+    print(f"Model info: {model.info()}")
     predictions = []
     true_labels = []
     pred_boxes = []
@@ -111,25 +126,20 @@ async def evaluate_image(request: ImageEvaluationRequest):
         has_smoke = len(annotation) > 0
         true_labels.append(int(has_smoke))
         
-        # Make random classification prediction
-        pred_has_smoke = random.random() > 0.5
+        model_preds = model(example['image'])[0]
+        pred_has_smoke = len(model_preds) > 0
         predictions.append(int(pred_has_smoke))
         
         # If there's a true box, parse it and make random box prediction
         if has_smoke:
+            
             # Parse all true boxes from the annotation
             image_true_boxes = parse_boxes(annotation)
             true_boxes_list.append(image_true_boxes)
             
-            # For baseline, make one random box prediction per image
-            # In a real model, you might want to predict multiple boxes
-            random_box = [
-                random.random(),  # x_center
-                random.random(),  # y_center
-                random.random() * 0.5,  # width (max 0.5)
-                random.random() * 0.5   # height (max 0.5)
-            ]
-            pred_boxes.append(random_box)
+            pred_box_list = get_boxes_list(model_preds)[0] # With one bbox to start with (as in the random baseline)
+            pred_boxes.append(pred_box_list)
+
     
     #--------------------------------------------------------------------------------------------
     # YOUR MODEL INFERENCE STOPS HERE