def test_precision_recall_per_image():
ds_path = BASE_PATH / 'hair_drier_toaster_bear.json'
df = cocoeval.generate_predictions(ds_path, ds_path)
map, mar = cocoeval.precision_recall_per_image(df, df['img_path'].loc[0])
assert map > 0
assert mar > 0
def test_mean_average_precision_and_recall_per_class():
ds_path = BASE_PATH / 'hair_drier_toaster_bear.json'
df = cocoeval.generate_predictions(ds_path, ds_path)
class_idx_metrics = cocoeval.mean_average_precision_and_recall_per_class(df)
class_idxs = df['true_class_id'].unique()
class_idxs = class_idxs[class_idxs > 0]
print(class_idx_metrics)
assert len(class_idx_metrics) == len(class_idxs)
def test_mean_average_precision_and_recall_per_class_with_name_min_score():
ds_path = BASE_PATH / 'hair_drier_toaster_bear.json'
ds = CocoDataset(ds_path)
idx_class_name = {
idx: cat_meta['name'] for idx, cat_meta in ds.cats.items()
}
df = cocoeval.generate_predictions(ds_path, ds_path)
class_idx_metrics = cocoeval.mean_average_precision_and_recall_per_class(
df, idx_class_dict=idx_class_name, min_score=0.5)
class_idxs = df['true_class_id'].unique()
class_idxs = class_idxs[class_idxs > 0]
print(class_idx_metrics)
assert len(class_idx_metrics) == len(class_idxs)
def test_generate_predictions():
ds_path = BASE_PATH / 'hair_drier_toaster_bear.json'
df = cocoeval.generate_predictions(ds_path, ds_path)
assert len(df) > 0
assert df['IOU'].mean() == 1.
def test_mean_average_precision_and_recall():
ds_path = BASE_PATH / 'hair_drier_toaster_bear.json'
df = cocoeval.generate_predictions(ds_path, ds_path)
map, mar = cocoeval.mean_average_precision_and_recall(df)
assert map == 1.
assert mar == 1.
def test_generate_predictions_one_class():
ds_path = BASE_PATH / 'hair_drier_toaster_bear.json'
df = cocoeval.generate_predictions(ds_path, ds_path, category_idxs=[1])
assert len(df) > 0
assert df['IOU'].mean() > 0.