Made evaluation compatible with Caffe-like decoding

Signed-off-by: Jim Martens <github@2martens.de>

src/twomartens/masterthesis/evaluate.py

@@ -61,7 +61,7 @@ def get_number_gt_per_class(labels: Sequence[Sequence[Sequence[int]]],
 
 def prepare_predictions(predictions: Sequence[Sequence[Sequence[Union[int, float]]]],
                         nr_classes: int) -> \
-        List[List[Tuple[int, float, float, int, int, int, int]]]:
+        List[List[Tuple[int, float, int, int, int, int]]]:
     """
     Prepares the predictions for further processing.
     
@@ -73,27 +73,38 @@ def prepare_predictions(predictions: Sequence[Sequence[Sequence[Union[int, float
         list of predictions per class
     """
     results = [list() for _ in range(nr_classes + 1)]
+    # index positions for bounding box coordinates
+    xmin = 2
+    ymin = 3
+    xmax = 4
+    ymax = 5
     
     for i, batch_item in enumerate(predictions):
         image_id = i
         
         for box in batch_item:
+            if len(box) == 7:
+                # entropy is in box list
+                xmin += 1
+                ymin += 1
+                xmax += 1
+                ymax += 1
+                
             class_id = int(box[0])
             # Round the box coordinates to reduce the required memory.
             confidence = box[1]
-            entropy = box[2]
-            xmin = round(box[3])
-            ymin = round(box[4])
-            xmax = round(box[5])
-            ymax = round(box[6])
-            prediction = (image_id, confidence, entropy, xmin, ymin, xmax, ymax)
+            xmin = round(box[xmin])
+            ymin = round(box[ymin])
+            xmax = round(box[xmax])
+            ymax = round(box[ymax])
+            prediction = (image_id, confidence, xmin, ymin, xmax, ymax)
             # Append the predicted box to the results list for its class.
             results[class_id].append(prediction)
     
     return results
 
 
-def match_predictions(predictions: Sequence[Sequence[Tuple[int, float, float, int, int, int, int]]],
+def match_predictions(predictions: Sequence[Sequence[Tuple[int, float, int, int, int, int]]],
                       labels: Sequence[Sequence[Sequence[int]]],
                       iou_func: callable,
                       nr_classes: int,
@@ -169,7 +180,6 @@ def match_predictions(predictions: Sequence[Sequence[Tuple[int, float, float, in
         # Create the data type for the structured array.
         preds_data_type = np.dtype([('image_id', np.int32),
                                     ('confidence', 'f4'),
-                                    ('entropy', 'f4'),
                                     ('xmin', 'f4'),
                                     ('ymin', 'f4'),
                                     ('xmax', 'f4'),