Fix in AP score in Jupyter notebooks

Before the fix, the computed AP score is slightly
lower than it should be. It doesn’t affect
training, but just shows slightly lower results AP
in the notebooks.

mrcnn/utils.py

@@ -662,8 +662,8 @@ def compute_matches(gt_boxes, gt_class_ids, gt_masks,
 
     # Loop through predictions and find matching ground truth boxes
     match_count = 0
-    pred_match = np.zeros([pred_boxes.shape[0]])
-    gt_match = np.zeros([gt_boxes.shape[0]])
+    pred_match = -1 * np.ones([pred_boxes.shape[0]])
+    gt_match = -1 * np.ones([gt_boxes.shape[0]])
     for i in range(len(pred_boxes)):
         # Find best matching ground truth box
         # 1. Sort matches by score
@@ -709,8 +709,8 @@ def compute_ap(gt_boxes, gt_class_ids, gt_masks,
         iou_threshold)
 
     # Compute precision and recall at each prediction box step
-    precisions = np.cumsum(pred_match > 0) / (np.arange(len(pred_match)) + 1)
-    recalls = np.cumsum(pred_match > 0).astype(np.float32) / len(gt_match)
+    precisions = np.cumsum(pred_match > -1) / (np.arange(len(pred_match)) + 1)
+    recalls = np.cumsum(pred_match > -1).astype(np.float32) / len(gt_match)
 
     # Pad with start and end values to simplify the math
     precisions = np.concatenate([[0], precisions, [0]])