update site, white

2 files changed, 25 insertions, 3 deletions

diff --git a/megapixels/app/models/bbox.py b/megapixels/app/models/bbox.py
index 8ecc8971..c840ea1b 100644
--- a/megapixels/app/models/bbox.py
+++ b/megapixels/app/models/bbox.py
@@ -207,11 +207,21 @@ class BBox:
   # -----------------------------------------------------------------
   # Convert to
 
-  def to_square(self, bounds):
+  def to_square(self):
     '''Forces bbox to square dimensions
-    :param bounds: (int, int) w, h of the image
     :returns (BBox) in square ratio
     '''
+    if self._width > self._height:
+      delta = (self._width - self._height) / 2
+      self._y1 -= delta
+      self._y2 += delta
+    elif self._height > self._width:
+      delta = (self._height - self._width) / 2
+      self._x1 -= delta
+      self._x2 += delta
+    return BBox(self._x1, self._y1, self._x2, self._y2)
+
+
 
   def to_dim(self, dim):
     """scale is (w, h) is tuple of dimensions"""
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]