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| author | jules@lens <julescarbon@gmail.com> | 2019-10-10 13:33:31 +0200 |
|---|---|---|
| committer | jules@lens <julescarbon@gmail.com> | 2019-10-10 13:33:31 +0200 |
| commit | 7d72cbb935ec53ce66c6a0c5cdc68f157be1d35f (patch) | |
| tree | a44049683c3c5e44449fe2698bb080329ecf7e61 /megapixels/app/models/dataset.py | |
| parent | 488a65aa5caba91c1384e7bcb2023056e913fc22 (diff) | |
| parent | cdc0c7ad21eb764cfe36d7583e126660d87fe02d (diff) | |
Merge branch 'master' of asdf.us:megapixels_dev
Diffstat (limited to 'megapixels/app/models/dataset.py')
| -rw-r--r-- | megapixels/app/models/dataset.py | 14 |
1 files changed, 13 insertions, 1 deletions
diff --git a/megapixels/app/models/dataset.py b/megapixels/app/models/dataset.py index a7227a70..c908da1b 100644 --- a/megapixels/app/models/dataset.py +++ b/megapixels/app/models/dataset.py @@ -152,6 +152,8 @@ class Dataset: image_records = [] # list of image matches w/identity if available # find most similar feature vectors indexes #match_idxs = self.similar(query_vec, n_results, threshold) + + # TODO: add cosine similarity sim_scores = np.linalg.norm(np.array([query_vec]) - np.array(self._face_vectors), axis=1) match_idxs = np.argpartition(sim_scores, range(n_results))[:n_results] @@ -180,7 +182,17 @@ class Dataset: s3_url = self.data_store_s3.face(ds_record.uuid) bbox_norm = BBox.from_xywh_norm_dim(ds_roi.x, ds_roi.y, ds_roi.w, ds_roi.h, dim) self.log.debug(f'bbox_norm: {bbox_norm}') - score = sim_scores[match_idx] + self.log.debug(f'match_idx: {match_idx}, record_idx: {record_idx}, roi_index: {roi_index}, len sim_scores: {len(sim_scores)}') + try: + score = sim_scores[match_idx] + except Exception as e: + self.log.error(e) + try: + score = sim_scores[record_idx] + except Exception as e: + self.log.error(e) + + if types.Metadata.IDENTITY in self._metadata.keys(): ds_id = df_identity.loc[df_identity['identity_key'] == ds_record.identity_key].iloc[0] |
