set jitter 25

2 files changed, 13 insertions, 7 deletions

diff --git a/megapixels/app/models/data_store.py b/megapixels/app/models/data_store.py
index 244aba60..7b6bef21 100644
--- a/megapixels/app/models/data_store.py
+++ b/megapixels/app/models/data_store.py
@@ -21,7 +21,7 @@ class DataStore:
   def metadata(self, enum_type):
     return join(self.dir_metadata, f'{enum_type.name.lower()}.csv')
 
-  def metadata(self, enum_type):
+  def metadata_dir(self):
     return join(self.dir_metadata)
 
   def media_images_original(self):
diff --git a/megapixels/app/models/dataset.py b/megapixels/app/models/dataset.py
index 35e10465..eb0109a7 100644
--- a/megapixels/app/models/dataset.py
+++ b/megapixels/app/models/dataset.py
@@ -40,7 +40,7 @@ class Dataset:
       self._metadata[metadata_type] = pd.read_csv(fp_csv).set_index('index')
       # convert DataFrame to list of floats
       self._face_vectors = self.df_vecs_to_dict(self._metadata[metadata_type])
-      self._face_vector_idxs = self.df_vec_idxs_to_dict(self._metadata[metadata_type])
+      self._face_vector_roi_idxs = self.df_vec_roi_idxs_to_dict(self._metadata[metadata_type])
       self.log.info(f'build face vector dict: {len(self._face_vectors)}')
       # remove the face vector column, it can be several GB of memory
       self._metadata[metadata_type].drop('vec', axis=1, inplace=True)
@@ -81,8 +81,8 @@ class Dataset:
     # future datasets can have multiple identities per images
     ds_identities = df_identity.iloc[identity_index]
     # get filepath and S3 url
-    fp_im = self.data_store.face_image(ds_record.subdir, ds_record.fn, ds_record.ext)
-    s3_url = self.data_store_s3.face_image(ds_record.uuid) 
+    fp_im = self.data_store.face(ds_record.subdir, ds_record.fn, ds_record.ext)
+    s3_url = self.data_store_s3.face(ds_record.uuid) 
     image_record = ImageRecord(ds_record, fp_im, s3_url, ds_identities=ds_identities)
     return image_record
 
@@ -147,8 +147,14 @@ class Dataset:
     for match_idx in match_idxs:
       # get the corresponding face vector row
       roi_index = self._face_vector_roi_idxs[match_idx]
+      df_record = self._metadata[types.Metadata.FILE_RECORD]
+      ds_record = df_record.iloc[roi_index]
       self.log.debug(f'find match index: {match_idx}, --> roi_index: {roi_index}')
-      image_record = self.roi_idx_to_record(roi_index)
+      fp_im = self.data_store.face(ds_record.subdir, ds_record.fn, ds_record.ext)
+      s3_url = self.data_store_s3.face(ds_record.uuid) 
+      image_record = ImageRecord(ds_record, fp_im, s3_url)
+      #roi_index = self._face_vector_roi_idxs[match_idx]
+      #image_record = self.roi_idx_to_record(roi_index)
       image_records.append(image_record)
     return image_records
 
@@ -159,10 +165,10 @@ class Dataset:
     # convert the DataFrame CSV to float list of vecs
     return [list(map(float,x.vec.split(','))) for x in df.itertuples()]
 
-  def df_vec_idxs_to_dict(self, df):
+  def df_vec_roi_idxs_to_dict(self, df):
     # convert the DataFrame CSV to float list of vecs
     #return [x.roi_index for x in df.itertuples()]
-    return [x.image_index for x in df.itertuples()]
+    return [x.roi_index for x in df.itertuples()]
 
   def similar(self, query_vec, n_results):
     '''Finds most similar N indices of query face vector