Fix low recall when limit_val_batches is set

tests/test_main.py

@@ -10,11 +10,13 @@
 import tempfile
 import copy
 import importlib.util
+import math
 
 from deepforest import main, get_data, model
 from deepforest.utilities import read_file, format_geometry
 from deepforest.datasets import prediction
 from deepforest.visualize import plot_results
+from deepforest.metrics import RecallPrecision
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import Callback
@@ -1167,10 +1169,47 @@ def test_predict_file_mixed_sizes(m, tmp_path):
     preds = m.predict_file(csv_file=csv_path, root_dir=str(tmp_path))
 
     assert preds.ymax.max() > 200  # The larger image should have predictions outside the 200px limit
+
+def test_recall_not_lowered_by_unprocessed_images():
+    """This test checks that recall is only computed for images that were
+    passed to the metric and ignores unprocessed images in the ground truth
+    dataframe."""
+
+    label_dict = {'Tree': 0}
+    metric = RecallPrecision(label_dict=label_dict)
+
+    # Simulate limit_val_batches: only 2 images processed
+    # Using different boxes for each image to catch matching bugs
+    preds = [
+        {'boxes': torch.tensor([[10, 10, 50, 50]], dtype=torch.float32),
+        'labels': torch.tensor([0]), 'scores': torch.tensor([0.9])},
+        {'boxes': torch.tensor([[60, 60, 100, 100]], dtype=torch.float32),
+        'labels': torch.tensor([0]), 'scores': torch.tensor([0.85])}
+    ]
+
+    targets = [
+        {'boxes': torch.tensor([[10, 10, 50, 50]], dtype=torch.float32),
+        'labels': torch.tensor([0])},
+        {'boxes': torch.tensor([[60, 60, 100, 100]], dtype=torch.float32),
+        'labels': torch.tensor([0])}
+    ]
+
+    metric.update(preds, targets, ['img1.jpg', 'img2.jpg'])
+    results = metric.compute()
+
+    # Verify only 2 images were processed (not affected by any external ground truth)
+    assert metric.num_images == 2
+
+    # With perfect matches, recall should be 1.0 (2/2 images matched)
+    assert math.isclose(results['box_recall'], 1.0, rel_tol=1e-5), (
+        f"box_recall={results['box_recall']:.2f}, expected 1.0"
+    )
+
 def test_custom_log_root(m, tmpdir):
     """Test that setting a custom log_root creates logs in the expected location"""
     custom_log_dir = tmpdir.join("custom_logs")
     m.config.log_root = str(custom_log_dir)
+
     m.config.train.fast_dev_run = False
 
     m.create_trainer(limit_train_batches=1, limit_val_batches=1, max_epochs=1)