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Fixed incompatibilities between numpy arrays and set

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-08-12 18:11:52 +02:00
parent 82a370c10f
commit 9752f285d1
1 changed files with 30 additions and 7 deletions
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37
src/twomartens/masterthesis/ssd.py
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@ -412,7 +412,7 @@ def _get_observations(detections: Sequence[np.ndarray]) -> List[List[np.ndarray]
mode="outer_product",
border_pixels="include")
image_observations = []
used_boxes = set()
used_boxes = None
for j in range(overlaps.shape[0]):
# check if box is already in existing observation
if j in used_boxes:
@ -420,7 +420,9 @@ def _get_observations(detections: Sequence[np.ndarray]) -> List[List[np.ndarray]
box_overlaps = overlaps[j]
overlap_detections = np.nonzero(box_overlaps >= 0.95)
observation_set = set(overlap_detections)
if not len(overlap_detections[0]):
continue
observation_set = np.unique(overlap_detections, axis=0)
for k in overlap_detections:
# check if box was already removed from observation, then skip
if k not in observation_set:
@ -429,17 +431,38 @@ def _get_observations(detections: Sequence[np.ndarray]) -> List[List[np.ndarray]
# check if other found detections are also overlapping with this
# detection
second_overlaps = overlaps[k]
second_detections = set(np.nonzero(second_overlaps >= 0.95))
difference = observation_set - second_detections
observation_set = observation_set - difference
second_detections = np.unique(np.nonzero(second_overlaps >= 0.95), axis=0)
difference = _set_difference(observation_set, second_detections)
observation_set = _set_difference(observation_set, difference)
used_boxes.update(observation_set)
if used_boxes is None:
used_boxes = observation_set
else:
used_boxes = np.unique(np.concatenate([used_boxes, observation_set],
axis=0), axis=0)
image_observations.append(observation_set)
for observation in image_observations:
observation_detections = detections_image[np.asarray(list(observation))]
observation_detections = detections_image[observation]
# average over class probabilities
observation_mean = np.mean(observation_detections, axis=0)
observations[i].append(observation_mean)
return observations
def _set_difference(first_array: np.ndarray, second_array: np.ndarray) -> np.ndarray:
"""
Removes all elements from first_array that are present in second_array.
Args:
first_array: the first array
second_array: the second array
Returns:
set difference between first_array and second_array
"""
dims = np.maximum(second_array.max(axis=0),
first_array.max(axis=0)) + 1
return second_array[~np.in1d(np.ravel_multi_index(second_array.T, dims),
np.ravel_multi_index(first_array.T, dims))]