def test_compute(self):
class_config = ClassConfig(names=['one', 'two'])
class_config.update()
class_config.ensure_null_class()
gt_array = np.zeros((4, 4, 1), dtype=np.uint8)
gt_array[2, 2, 0] = 1
gt_array[0, 0, 0] = 2
gt_raster = MockRasterSource([0], 1)
gt_raster.set_raster(gt_array)
gt_label_source = SemanticSegmentationLabelSource(
raster_source=gt_raster)
p_array = np.zeros((4, 4, 1), dtype=np.uint8)
p_array[1, 1, 0] = 1
p_raster = MockRasterSource([0], 1)
p_raster.set_raster(p_array)
p_label_source = SemanticSegmentationLabelSource(
raster_source=p_raster)
eval = SemanticSegmentationEvaluation(class_config)
eval.compute(gt_label_source.get_labels(), p_label_source.get_labels())
tp0 = 16 - 3 # 4*4 - 3 true positives for class 0
fp0 = 1 # 1 false positive (2,2) and one don't care at (0,0)
fn0 = 1 # one false negative (1,1)
precision0 = float(tp0) / (tp0 + fp0)
recall0 = float(tp0) / (tp0 + fn0)
f10 = 2 * float(precision0 * recall0) / (precision0 + recall0)
tp1 = 0 # 0 true positives for class 1
fn1 = 1 # one false negative (2,2)
precision1 = 0 # float(tp1) / (tp1 + fp1) where fp1 == 1
recall1 = float(tp1) / (tp1 + fn1)
f11 = None
self.assertAlmostEqual(precision0,
eval.class_to_eval_item[0].precision)
self.assertAlmostEqual(recall0, eval.class_to_eval_item[0].recall)
self.assertAlmostEqual(f10, eval.class_to_eval_item[0].f1)
self.assertEqual(precision1, eval.class_to_eval_item[1].precision)
self.assertAlmostEqual(recall1, eval.class_to_eval_item[1].recall)
self.assertAlmostEqual(f11, eval.class_to_eval_item[1].f1)
avg_conf_mat = np.array([[0, 0, 0], [13., 1, 0], [1, 0, 0]])
avg_recall = (14 / 15) * recall0 + (1 / 15) * recall1
self.assertTrue(np.array_equal(avg_conf_mat, eval.avg_item.conf_mat))
self.assertEqual(avg_recall, eval.avg_item.recall)
def test_vector_compute(self):
class_config = ClassConfig(names=['one', 'two'])
class_config.update()
class_config.ensure_null_class()
gt_uri = data_file_path('2-gt-polygons.geojson')
pred_uri = data_file_path('2-pred-polygons.geojson')
eval = SemanticSegmentationEvaluation(class_config)
eval.compute_vector(gt_uri, pred_uri, 'polygons', 0)
# NOTE: The two geojson files referenced above contain three
# unique geometries total, each file contains two geometries,
# and there is one geometry shared between the two.
tp = 1.0
fp = 1.0
fn = 1.0
precision = float(tp) / (tp + fp)
recall = float(tp) / (tp + fn)
self.assertAlmostEqual(precision, eval.class_to_eval_item[0].precision)
self.assertAlmostEqual(recall, eval.class_to_eval_item[0].recall)
def test_compute_ignore_class(self):
class_config = ClassConfig(names=['one', 'two'])
class_config.update()
class_config.ensure_null_class()
gt_array = np.zeros((4, 4, 1), dtype=np.uint8)
gt_array[0, 0, 0] = 2
gt_raster = MockRasterSource([0], 1)
gt_raster.set_raster(gt_array)
gt_label_source = SemanticSegmentationLabelSource(
raster_source=gt_raster)
pred_array = np.zeros((4, 4, 1), dtype=np.uint8)
pred_array[0, 0, 0] = 1
pred_raster = MockRasterSource([0], 1)
pred_raster.set_raster(pred_array)
pred_label_source = SemanticSegmentationLabelSource(
raster_source=pred_raster)
eval = SemanticSegmentationEvaluation(class_config)
eval.compute(gt_label_source.get_labels(),
pred_label_source.get_labels())
self.assertAlmostEqual(1.0, eval.class_to_eval_item[0].f1)
self.assertAlmostEqual(1.0, eval.avg_item.f1)
def create_evaluation(self):
return SemanticSegmentationEvaluation(self.class_config)