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Accounted for entropy in evaluation code

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
2019-07-09 13:41:11 +02:00
parent 4cf782f7ab
commit c6e7dd3c50
1 changed files with 9 additions and 7 deletions
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16
src/twomartens/masterthesis/evaluate.py
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@ -59,7 +59,7 @@ def get_number_gt_per_class(labels: Sequence[Sequence[Sequence[int]]],
def prepare_predictions(predictions: Sequence[Sequence[Sequence[Union[int, float]]]], def prepare_predictions(predictions: Sequence[Sequence[Sequence[Union[int, float]]]],
nr_classes: int) -> \ nr_classes: int) -> \
List[List[Tuple[int, float, int, int, int, int]]]: List[List[Tuple[int, float, float, int, int, int, int]]]:
""" """
Prepares the predictions for further processing. Prepares the predictions for further processing.
@ -79,18 +79,19 @@ def prepare_predictions(predictions: Sequence[Sequence[Sequence[Union[int, float
class_id = int(box[0]) class_id = int(box[0])
# Round the box coordinates to reduce the required memory. # Round the box coordinates to reduce the required memory.
confidence = box[1] confidence = box[1]
xmin = round(box[2]) entropy = box[2]
ymin = round(box[3]) xmin = round(box[3])
xmax = round(box[4]) ymin = round(box[4])
ymax = round(box[5]) xmax = round(box[5])
prediction = (image_id, confidence, xmin, ymin, xmax, ymax) ymax = round(box[6])
prediction = (image_id, confidence, entropy, xmin, ymin, xmax, ymax)
# Append the predicted box to the results list for its class. # Append the predicted box to the results list for its class.
results[class_id].append(prediction) results[class_id].append(prediction)
return results return results
def match_predictions(predictions: Sequence[Sequence[Tuple[int, float, int, int, int, int]]], def match_predictions(predictions: Sequence[Sequence[Tuple[int, float, float, int, int, int, int]]],
labels: Sequence[Sequence[Sequence[int]]], labels: Sequence[Sequence[Sequence[int]]],
nr_classes: int, nr_classes: int,
iou_threshold: float = 0.5, iou_threshold: float = 0.5,
@ -153,6 +154,7 @@ def match_predictions(predictions: Sequence[Sequence[Tuple[int, float, int, int,
# Create the data type for the structured array. # Create the data type for the structured array.
preds_data_type = np.dtype([('image_id', np.int32), preds_data_type = np.dtype([('image_id', np.int32),
('confidence', 'f4'), ('confidence', 'f4'),
('entropy', 'f4'),
('xmin', 'f4'), ('xmin', 'f4'),
('ymin', 'f4'), ('ymin', 'f4'),
('xmax', 'f4'), ('xmax', 'f4'),