def _classification_to_kwiver_detections(classification, w, h):
"""
Convert kwarray classifications to kwiver deteted object sets
Args:
classification (bioharn.clf_predict.Classification)
w (int): width of image
h (int): height of image
Returns:
kwiver.vital.types.DetectedObjectSet
"""
detected_objects = DetectedObjectSet()
if classification.data.get('prob', None) is not None:
# If we have a probability for each class, uses that
class_names = list(classification.classes)
class_prob = classification.prob
detected_object_type = DetectedObjectType(class_names, class_prob)
else:
# Otherwise we only have the score for the predicted calss
class_name = classification.classes[classification.cidx]
class_score = classification.conf
detected_object_type = DetectedObjectType(class_name, class_score)
bounding_box = BoundingBox(0, 0, w, h)
detected_object = DetectedObject(bounding_box, classification.conf,
detected_object_type)
detected_objects.add(detected_object)
return detected_objects
def _kwimage_to_kwiver_detections(detections):
"""
Convert kwimage detections to kwiver deteted object sets
Args:
detected_objects (kwimage.Detections)
Returns:
kwiver.vital.types.DetectedObjectSet
"""
# convert segmentation masks
if 'segmentations' in detections.data:
print("Warning: segmentations not implemented")
boxes = detections.boxes.to_tlbr()
scores = detections.scores
class_idxs = detections.class_idxs
# convert to kwiver format, apply threshold
detected_objects = DetectedObjectSet()
for tlbr, score, cidx in zip(boxes.data, scores, class_idxs):
class_name = detections.classes[cidx]
bbox_int = np.round(tlbr).astype(np.int32)
bounding_box = BoundingBox(bbox_int[0], bbox_int[1], bbox_int[2],
bbox_int[3])
detected_object_type = DetectedObjectType(class_name, score)
detected_object = DetectedObject(bounding_box, score,
detected_object_type)
detected_objects.add(detected_object)
return detected_objects
def _create_object_track_set(self):
bbox = BoundingBox(10, 10, 20, 20)
dot = DetectedObjectType("test", 0.4)
do = DetectedObject(bbox, 0.4, dot)
track = Track()
for i in range(10):
track.append(ObjectTrackState(i, i, do))
return ObjectTrackSet([track])
def _create_detected_object_set():
from kwiver.vital.types import DetectedObject, DetectedObjectSet, BoundingBox
dos = DetectedObjectSet()
bbox = BoundingBox(0, 10, 100, 50)
dos.add(DetectedObject(bbox, 0.2))
dos.add(DetectedObject(bbox, 0.5))
dos.add(DetectedObject(bbox, 0.4))
return dos
def _create_detected_object(self):
"""
Helper function to generate a detected object for the track state
:return: Detected object with bounding box coordinates of
(10, 10, 20, 20), confidence of 0.4 and "test" label
"""
bbox = BoundingBox(10, 10, 20, 20)
cm = ClassMap("test", 0.4)
do = DetectedObject(bbox, 0.4, cm)
return do
def test_get_set_bbox(self):
do = DetectedObject(self.bbox)
# Check default
nt.ok_(do.bounding_box, self.bbox)
# Setting to different value
new_bbox = BoundingBox(20, 20, 40, 40)
do.bounding_box = new_bbox
nt.ok_(do.bounding_box == new_bbox)
def detect(self, image_data):
dot = DetectedObjectSet([
DetectedObject(
BoundingBox(
self.m_center_x + self.frame_ct * self.m_dx -
self.m_width / 2.0, self.m_center_y +
self.frame_ct * self.m_dy - self.m_height / 2.0,
self.m_center_x + self.frame_ct * self.m_dx +
self.m_width / 2.0, self.m_center_y +
self.frame_ct * self.m_dy + self.m_height / 2.0))
])
self.frame_ct += 1
return dot
def __getitem__(self, f_id):
try:
bb_info = self._frame_track_dict[f_id]
except KeyError:
print('frame id: {} does not exist!'.format(f_id))
exit(0)
bb_list = []
for item in bb_info:
x, y, w, h = map(float, item[1:])
bb_list.append(BoundingBox(x, y, x + w, y + h))
return bb_list
def _create_track(self):
"""
Helper function to create a track
:return: Track with 10 object track state. Every track state has same
detected object however the fram number and time varies from
[0, 10)
"""
bbox = BoundingBox(10, 10, 20, 20)
cm = ClassMap("test", 0.4)
do = DetectedObject(bbox, 0.4, cm)
track = Track()
for i in range(10):
track.append(ObjectTrackState(i, i, do))
return track
def setUp(self):
bbox = BoundingBox(10, 10, 20, 20)
dot = DetectedObjectType("test", 0.4)
do = DetectedObject(bbox, 0.4, dot)
track = Track()
for i in range(10):
track.append(ObjectTrackState(i, i, do))
self.track_ = track
self.time_1 = Timestamp()
self.time_1.set_time_seconds(1234)
self.time_2 = Timestamp()
self.time_2.set_time_seconds(4321)
self.obj_ts = ObjectTrackSet([self.track_])
self.act_type = ActivityType("self_act", 0.87)
self.act = Activity(1, "self_act", 0.87, self.act_type, self.time_1,
self.time_2, self.obj_ts)
def _step(self):
image_container = self.grab_input_using_trait("image")
timestamp = self.grab_input_using_trait("timestamp")
file_name = self.grab_input_using_trait("file_name")
image = image_container.asarray()
h, w, _ = image.shape
bbox_x = w//2
bbox_y = h//2
bbox = BoundingBox( bbox_x - int(self.config_value("bbox_width"))//2,
bbox_y - int(self.config_value("bbox_height"))//2,
bbox_x + int(self.config_value("bbox_width"))//2,
bbox_y + int(self.config_value("bbox_height"))//2 )
dot = DetectedObjectType("Test", 1.0)
do = DetectedObject(bbox, 1.0, dot)
dos = DetectedObjectSet()
dos.add(do)
self.push_to_port_using_trait("detected_object_set", dos)
def setUp(self):
self.loc1 = np.array([-73.759291, 42.849631])
self.loc2 = np.array([-149.484444, -17.619482])
self.bbox = BoundingBox(10, 10, 20, 20)
self.conf = 0.5
self.dot = DetectedObjectType("example_class", 0.4)
self.mask = ImageContainer(Image(1080, 720))
# Values to set outside of constructor
self.geo_point = GeoPoint(self.loc1, geodesy.SRID.lat_lon_WGS84)
self.index = 5
self.detector_name = "example_detector_name"
self.descriptor = descriptor.new_descriptor(5)
self.descriptor[:] = 10
self.note_to_add = "example_note"
self.keypoint_to_add = Point2d()
self.keypoint_to_add.value = self.loc2
self.keypoint_id = "example_keypoint_id"
def test_id(self):
a = self.act
self.assertEqual(a.id, 1)
a.id = 10
self.assertEqual(a.id, 10)
self.assertEqual(a.label, "self_act")
a.label = "second_act"
self.assertEqual(a.label, "second_act")
self.assertEqual(a.activity_type.score("self_act"), 0.87)
a.activity_type = ActivityType()
self.assertEqual(a.confidence, 0.87)
a.confidence = 1
self.assertEqual(a.confidence, 1)
self.assertEqual(a.start_time.get_time_seconds(), 1234)
tmp_time = Timestamp().set_time_seconds(1237)
a.start_time = tmp_time
self.assertEqual(a.start_time.get_time_seconds(), 1237)
self.assertEqual(a.end_time.get_time_seconds(), 4321)
tmp_time = Timestamp()
tmp_time.set_time_seconds(4322)
a.end_time = tmp_time
self.assertEqual(a.end_time.get_time_seconds(), 4322)
self.assertEqual(a.participants.all_frame_ids(), set(range(10)))
bbox = BoundingBox(10, 10, 20, 20)
dot = DetectedObjectType("test", 0.4)
do = DetectedObject(bbox, 0.4, dot)
track = Track()
for i in range(5):
track.append(ObjectTrackState(i, i, do))
new_t = track
new_ots = ObjectTrackSet([new_t])
a.participants = new_ots
self.assertEqual(a.participants.all_frame_ids(), set(range(5)))
self.assertEqual(a.duration[0].get_time_seconds(),
a.start_time.get_time_seconds())
self.assertEqual(a.duration[1].get_time_seconds(),
a.end_time.get_time_seconds())
def create_bounding_box():
return BoundingBox(1.0, 2.0, 3.0, 4.0)