Update utils.py

utils.py

@@ -698,7 +698,7 @@ def display_instances(image, boxes, masks, class_ids, class_names,
 def extract_bboxes(mask):
     """Compute bounding boxes from masks.
     mask: [height, width, num_instances]. Mask pixels are either 1 or 0.
-    Returns: bbox array [num_instances, (y1, x1, y2, x2)].
+    Returns: bbox array [num_instances, (x1, y1, x2, y2)].
     """
     mask = mask.transpose((1,2,0))
     boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)
@@ -1034,41 +1034,6 @@ def init_distributed_mode(args):
     torch.distributed.barrier()
     setup_for_distributed(args.rank == 0)
 
-def get_prediction(model, img, threshold):
-    """
-    This function generates a prediction for a given model an image, then 
-    post-process the prediction given a threshold and the name of the classes.
-    """
-
-    CATEGORY_NAMES = [
-    '__background__', 'hen'
-    ]
-
-    device = torch.device('cuda:0')     # Default CUDA device
-
-    # Uncomment if loading image as a path
-    # img = Image.open(img_path)
-    # transform = T.Compose([T.ToTensor()])
-    # img = transform(img).to(device)
-    pred = model([img.to(device)])
-    
-    masks, pred_boxes, pred_class, features, mask_features = None, None, None, None, None
-    pred_score = list(pred[0]['scores'].cpu().detach().numpy())
-    try:
-        pred_t = [pred_score.index(x) for x in pred_score if x>threshold][-1]
-        masks = (pred[0]['masks']>0.5).squeeze().detach().cpu().numpy()
-        if len(masks.shape)<3:
-            masks = np.expand_dims(masks, 0)
-        pred_class = [CATEGORY_NAMES[i] for i in list(pred[0]['labels'].cpu().numpy())]
-        pred_boxes = [[(i[0], i[1]), (i[2], i[3])] for i in list(pred[0]['boxes'].cpu().detach().numpy())]
-        masks = masks[:pred_t+1]
-        pred_boxes = pred_boxes[:pred_t+1]
-        pred_class = pred_class[:pred_t+1]
-        pred_score = pred_score[:pred_t+1]
-    except:
-        print("No objects detected")
-        pass
-    return masks, pred_boxes, pred_class, pred_score
 
 def random_colour_masks(image):
     """