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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", mode="outer_product",
border_pixels="include") border_pixels="include")
image_observations = [] image_observations = []
used_boxes = set() used_boxes = None
for j in range(overlaps.shape[0]): for j in range(overlaps.shape[0]):
# check if box is already in existing observation # check if box is already in existing observation
if j in used_boxes: if j in used_boxes:
@ -420,7 +420,9 @@ def _get_observations(detections: Sequence[np.ndarray]) -> List[List[np.ndarray]
box_overlaps = overlaps[j] box_overlaps = overlaps[j]
overlap_detections = np.nonzero(box_overlaps >= 0.95) 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: for k in overlap_detections:
# check if box was already removed from observation, then skip # check if box was already removed from observation, then skip
if k not in observation_set: 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 # check if other found detections are also overlapping with this
# detection # detection
second_overlaps = overlaps[k] second_overlaps = overlaps[k]
second_detections = set(np.nonzero(second_overlaps >= 0.95)) second_detections = np.unique(np.nonzero(second_overlaps >= 0.95), axis=0)
difference = observation_set - second_detections difference = _set_difference(observation_set, second_detections)
observation_set = observation_set - difference 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) image_observations.append(observation_set)
for observation in image_observations: for observation in image_observations:
observation_detections = detections_image[np.asarray(list(observation))] observation_detections = detections_image[observation]
# average over class probabilities # average over class probabilities
observation_mean = np.mean(observation_detections, axis=0) observation_mean = np.mean(observation_detections, axis=0)
observations[i].append(observation_mean) observations[i].append(observation_mean)
return observations 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))]