Calculate mean average precision (mAP) based on predictions.
(
eval_results_obj,
h5_file,
metadata_file,
oks_sigma=0.1,
margin=0,
symmetric_kpts=None,
drop_kpts=None,
)
| 137 | |
| 138 | |
| 139 | def calc_map_from_obj( |
| 140 | eval_results_obj, |
| 141 | h5_file, |
| 142 | metadata_file, |
| 143 | oks_sigma=0.1, |
| 144 | margin=0, |
| 145 | symmetric_kpts=None, |
| 146 | drop_kpts=None, |
| 147 | ): |
| 148 | """Calculate mean average precision (mAP) based on predictions.""" |
| 149 | df = pd.read_hdf(h5_file) |
| 150 | try: |
| 151 | df.drop("single", level="individuals", axis=1, inplace=True) |
| 152 | except KeyError: |
| 153 | pass |
| 154 | n_animals = len(df.columns.get_level_values("individuals").unique()) |
| 155 | kpts = list(df.columns.get_level_values("bodyparts").unique()) |
| 156 | |
| 157 | test_indices = _load_test_indices(metadata_file) |
| 158 | df_test = df.iloc[test_indices] |
| 159 | test_images = load_test_images(h5_file, metadata_file) |
| 160 | missing_images = set(test_images) - set(eval_results_obj.keys()) |
| 161 | if len(missing_images) > 0: |
| 162 | raise ValueError( |
| 163 | f"Failed to compute the test mAP: there are test images missing from theprediction object: {missing_images}" |
| 164 | ) |
| 165 | |
| 166 | ground_truth = df_test.to_numpy().reshape((len(test_images), n_animals, -1, 2)) |
| 167 | temp = np.ones((*ground_truth.shape[:3], 3)) |
| 168 | temp[..., :2] = ground_truth |
| 169 | assemblies_gt_test = { |
| 170 | test_images[i]: assembly for i, assembly in inferenceutils._parse_ground_truth_data(temp).items() |
| 171 | } |
| 172 | |
| 173 | # TODO(stes): remove/rewrite |
| 174 | if drop_kpts is not None: |
| 175 | temp = {} |
| 176 | for k, v in assemblies_gt_test.items(): |
| 177 | lst = [] |
| 178 | for a in v: |
| 179 | arr = np.delete(a.data[:, :3], drop_kpts, axis=0) |
| 180 | a = inferenceutils.Assembly.from_array(arr) |
| 181 | lst.append(a) |
| 182 | temp[k] = lst |
| 183 | assemblies_gt_test = temp |
| 184 | for ind in sorted(drop_kpts, reverse=True): |
| 185 | kpts.pop(ind) |
| 186 | |
| 187 | assemblies_pred = conv_obj_to_assemblies(eval_results_obj, kpts) |
| 188 | with deeplabcut.benchmark.utils.DisableOutput(): |
| 189 | oks = inferenceutils.evaluate_assembly( |
| 190 | assemblies_pred, |
| 191 | assemblies_gt_test, |
| 192 | oks_sigma, |
| 193 | margin=margin, |
| 194 | symmetric_kpts=symmetric_kpts, |
| 195 | greedy_matching=True, |
| 196 | ) |
nothing calls this directly
no test coverage detected