"image_classification" -> "classification_type"

Author: Mark Bray

Properties_README.txt

@@ -258,17 +258,27 @@ classifier_ignore_columns  =  <your_object_x_location_column>, <your_object_y_lo
 
 image_tile_size  =  50
 
+# OPTIONAL
+# Provides the image width and height. Used for per-image classification.
+# If not set, it will be obtained from the Image_Width and Image_Height 
+# measurements in CellProfiler.
+
+image_width  = 
+image_height = 
+
 # OPTIONAL
 # Image Gallery can use a different tile size (in pixels) to create thumbnails for images
 # If not set, it will be the same as image_tile_size
 
 image_size = 
 
-# ======== Image Classification =======
-# CPA 2.2.0 allows image classification instead of object classification
-# If set to yes, then Classifier will fetch whole images instead of objects 
+# ======== Classification type =======
+# OPTIONAL
+# CPA 2.2.0 allows image classification instead of object classification.
+# If left blank or set to "object", then Classifier will fetch objects (default).
+# If set to "image", then Classifier will fetch whole images instead of objects.
 
-image_classification = no 
+classification_type =  
 
 
 # ======== Auto Load Training Set ========

cpa/classifier.py

@@ -84,7 +84,7 @@ def __init__(self, properties=None, parent=None, id=ID_CLASSIFIER, **kwargs):
                            :]  # used to store previous color mappings when toggling colors on/off with ctrl+1,2,3...
         self.brightness = 1.0
         self.required_fields = []
-        if not p.image_classification == 'yes':
+        if not p.classification_type == 'image':
             self.scale = 1.0
             self.required_fields = ['object_table', 'object_id', 'cell_x_loc', 'cell_y_loc']
         else:
@@ -1509,7 +1509,7 @@ def ScoreAll(self, evt=None):
 
         # GET GROUPING METHOD AND FILTER FROM USER
         enrichments = True
-        if p.image_classification == 'yes':
+        if p.classification_type == 'image':
             enrichments = False
 
         dlg = ScoreDialog(self, groupChoices, filterChoices, enrichments)

cpa/dbconnect.py

@@ -331,7 +331,7 @@ def connect(self, empty_sqlite_db=False):
                 self.connectionInfo[connID] = (p.db_host, p.db_user, 
                                                (p.db_passwd or None), p.db_name)
                 logging.debug('[%s] Connected to database: %s as %s@%s'%(connID, p.db_name, p.db_user, p.db_host))
-                if p.image_classification == 'yes':
+                if p.classification_type == 'image':
                     self.CreateObjectImageTable()
             except DBError(), e:
                 raise DBException, 'Failed to connect to database: %s as %s@%s (connID = "%s").\n  %s'%(p.db_name, p.db_user, p.db_host, connID, e)
@@ -445,7 +445,7 @@ def regexp(expr, item):
                         self.CreateSQLiteDB()
                     else:
                         raise DBException, 'Database at %s appears to be empty.'%(p.db_sqlite_file)
-            if p.image_classification == 'yes':
+            if p.classification_type == 'image':
                 self.CreateObjectImageTable()
             logging.debug('[%s] Connected to database: %s'%(connID, p.db_sqlite_file))
 
@@ -1257,7 +1257,7 @@ def CreateSQLiteDB(self):
         self.execute('DROP TABLE IF EXISTS %s'%(p.image_table))
         self.execute(statement)
 
-        if not p.image_classification == 'yes':
+        if not p.classification_type == 'image':
             # CREATE THE OBJECT TABLE
             # For the object table we assume that all values are type FLOAT
             # except for the primary keys
@@ -1292,7 +1292,7 @@ def CreateSQLiteDB(self):
         f.close()
 
         # POPULATE THE OBJECT TABLE
-        if not p.image_classification == 'yes':
+        if not p.classification_type == 'image':
             f = open(p.object_csv_file, 'U')
             r = csv.reader(f)
             row = r.next() # skip the headers
@@ -1391,7 +1391,7 @@ def CreateSQLiteDBFromCSVs(self):
             logging.info("... loaded %d%% of CSV data"%(pct))
 
         line_count = 0
-        if not p.image_classification == 'yes':
+        if not p.classification_type == 'image':
             assert len(obcsvs)>0, ('Failed to parse object csv filenames from %s. '
                               'Make sure db_sql_file in your properties file is'
                               ' set to the .SQL file output by CellProfiler\'s '

cpa/imagegallery.py

@@ -77,7 +77,7 @@ def __init__(self, properties=None, parent=None, id=ID_IMAGE_GALLERY, **kwargs):
 
         # self.required_fields = []
 
-        # if not p.image_classification == 'yes':
+        # if not p.classification_type == 'image':
         #     self.scale = 1.0
         #     self.required_fields = ['object_table', 'object_id', 'cell_x_loc', 'cell_y_loc']
         # else:
@@ -98,7 +98,7 @@ def __init__(self, properties=None, parent=None, id=ID_IMAGE_GALLERY, **kwargs):
         self.toggleChMap = p.image_channel_colors[
                            :]  # used to store previous color mappings when toggling colors on/off with ctrl+1,2,3...
         self.brightness = 1.0
-        if p.image_classification == 'yes':
+        if p.classification_type == 'image':
             self.scale = 100.0 / float(p.image_tile_size) # guarantee it is parsed as float
         else:
             self.scale = 1.0 
@@ -125,7 +125,8 @@ def __init__(self, properties=None, parent=None, id=ID_IMAGE_GALLERY, **kwargs):
 
         # sorting bins
         self.gallery_panel = wx.Panel(self.bins_splitter)
-        self.gallery_box = wx.StaticBox(self.gallery_panel, label=p.object_name[0] + ' image gallery')
+        o_label = p.object_name[0] if p.classification_type == 'image' else '' + ' image gallery'
+        self.gallery_box = wx.StaticBox(self.gallery_panel, label=o_label)
         self.gallery_sizer = wx.StaticBoxSizer(self.gallery_box, wx.VERTICAL)
         self.galleryBin = sortbin.SortBin(parent=self.gallery_panel,
                                                classifier=self,

cpa/properties.py

@@ -57,7 +57,7 @@
                'link_tables_table',
                'link_columns_table',
                'image_rescale',
-               'image_classification',
+               'classification_type',
                'image_size',
                'image_width',
                'image_height'
@@ -117,7 +117,7 @@
                  'plate_shape',
                  'image_tile_size',
                  'image_size',
-                 'image_classification',
+                 'classification_type',
                  'image_width',
                  'image_height'
                  ]
@@ -294,9 +294,9 @@ def load_file(self, filename):
 
         f.close()
 
-        #if image_classification is defined
-        if self.field_defined('image_classification') and self.image_classification.lower() in ['true', 'yes', 'on', 't', 'y', '1']:
-            self.image_classification = 'yes'
+        #if classification_type is defined
+        if self.field_defined('classification_type') and self.classification_type.lower() in ['image']:
+            self.classification_type = 'image'
             #2 cases:
             # object_table/object_id/cell_x_loc/cell_y_loc exist and point to a table/view of object tables
             # object_table/object_id/cell_x_loc/cell_y_loc don't exist
@@ -319,6 +319,8 @@ def load_file(self, filename):
                 self.image_tile_size = min([self.image_width, self.image_height])
 
             self.image_tile_size = int(self.image_tile_size)
+        else:
+            self.classification_type = "object"
 
         # For image gallery
         if self.field_defined('image_size'):