def generate_negative_2d_bb(obj_label, boxes2d, iou_threshold_min,
iou_threshold_max, samples):
"""Generates negative 2D bounding boxes.
This is computed in a brute force fashion. For any given
bounding box, we randomly shift the centroid and generate
new bounding boxes and if it lies within the desired IoU
threshold bound, we will keep it. Otherwise it is thrown
out and this is repeated until number of valid samples is
satisfied.
Keyword arguments:
obj_label: single label for a detected 2D object
boxes2d: a list of numpy array representing all
bounding boxes in the image
iou_threshold_min: determines the min variation
between the original iou and the negative samples
iou_threshold_max: determines the max variation
between the original iou and the negative samples
samples: number of negative samples per detected object
Returns: a list of generated ObjectLabels
"""
x1 = obj_label.x1
y1 = obj_label.y1
x2 = obj_label.x2
y2 = obj_label.y2
diff_x = (x2 - x1) / 2
diff_y = (y2 - y1) / 2
current_samples = 0
new_objects = []
while current_samples < samples:
# Keep trying to generate samples that
# lie within reasonable bound
new_xp = np.random.uniform(x1, x2, 1)
new_yp = np.random.uniform(y1, y2, 1)
new_x1 = float(new_xp - diff_x)
new_x2 = float(new_xp + diff_x)
new_y1 = float(new_yp - diff_y)
new_y2 = float(new_yp + diff_y)
new_obj = od.ObjectLabel()
new_obj.x1 = new_x1
new_obj.x2 = new_x2
new_obj.y1 = new_y1
new_obj.y2 = new_y2
new_box = np.array([new_x1, new_y1, new_x2, new_y2])
# calculate the IoU
iou = evaluation.two_d_iou(new_box, boxes2d)
if iou_threshold_min < max(iou) < iou_threshold_max:
# keep the new object label
current_samples += 1
new_objects.append(new_obj)
return new_objects
def _construct_new_2d_object(new_xp, half_w, new_yp, half_l):
"""Helper function to construct a
new object label and prepare
arguments to calculate IoU. Used
inside generate_negative_2d_bb
"""
new_x1 = float(new_xp - half_w)
new_x2 = float(new_xp + half_w)
new_y1 = float(new_yp - half_l)
new_y2 = float(new_yp + half_l)
new_obj = od.ObjectLabel()
new_obj.x1 = new_x1
new_obj.x2 = new_x2
new_obj.y1 = new_y1
new_obj.y2 = new_y2
new_box = np.array([new_x1, new_y1, new_x2, new_y2])
return new_obj, new_box
def box_3d_to_object_label(box_3d, obj_type='Car'):
"""Turns a box_3d into an ObjectLabel
Args:
box_3d: 3D box in the format [x, y, z, l, w, h, ry]
obj_type: Optional, the object type
Returns:
ObjectLabel with the location, size, and rotation filled out
"""
obj_label = obj_utils.ObjectLabel()
obj_label.type = obj_type
obj_label.t = box_3d.take((0, 1, 2))
obj_label.l = box_3d[3]
obj_label.w = box_3d[4]
obj_label.h = box_3d[5]
obj_label.ry = box_3d[6]
return obj_label
def test_check_object_label_format(self):
test_obj = obj_utils.ObjectLabel()
test_obj.h = 1
test_obj.w = 1
test_obj.l = 1
test_obj.t = [1, 1, 1]
test_obj.ry = 0
# Case 1, Single instance of object label
test_obj_list = [test_obj]
fc.check_object_label_format(test_obj_list)
test_obj_list = [test_obj, test_obj, test_obj]
fc.check_object_label_format(test_obj_list)
test_obj_list = [test_obj, test_obj, '0']
np.testing.assert_raises(TypeError, fc.check_object_label_format,
test_obj_list)
# Case 2, Range check
test_obj.t = [1, 1]
test_obj_list = [test_obj]
np.testing.assert_raises(TypeError, fc.check_object_label_format,
test_obj_list)
def box_2d_to_object_label(box_2d, obj_type='Car'):
"""Turns a box_2d into an ObjectLabel
Args:
box_2d: 3D box in the format [x1, y1, x2, y2]
obj_type: Optional, the object type
Returns:
ObjectLabel with the location, size, and rotation filled out
"""
# fc.check_box_3d_format(box_3d)
assert box_2d.shape[1] == 4, "the box_2d should be Nx4"
obj_label = obj_utils.ObjectLabel()
obj_label.type = obj_type
obj_label.x1 = box_2d[0]
obj_label.y1 = box_2d[1]
obj_label.x2 = box_2d[2]
obj_label.y2 = box_2d[3]
return obj_label
def test_object_label_eq(self):
# Case 1, positive case
object_1 = obj_utils.ObjectLabel()
object_2 = obj_utils.ObjectLabel()
self.assertTrue(object_1 == object_2)
object_1.t = (1., 2., 3.)
object_2.t = (1., 2., 3.)
self.assertTrue(object_1 == object_2)
# Case 2, negative case (single value)
object_1 = {} # Not a object label type
object_2 = obj_utils.ObjectLabel()
self.assertFalse(object_1 == object_2)
object_1 = obj_utils.ObjectLabel()
object_1.truncation = 1.
self.assertFalse(object_1 == object_2)
object_1 = obj_utils.ObjectLabel()
object_1.occlusion = 1.
self.assertFalse(object_1 == object_2)
object_1 = obj_utils.ObjectLabel()
object_1.alpha = 1.
self.assertFalse(object_1 == object_2)
object_1 = obj_utils.ObjectLabel()
object_1.x1 = 1.
self.assertFalse(object_1 == object_2)
object_1 = obj_utils.ObjectLabel()
object_1.y1 = 1.
self.assertFalse(object_1 == object_2)
object_1 = obj_utils.ObjectLabel()
object_1.x2 = 1.
self.assertFalse(object_1 == object_2)
object_1 = obj_utils.ObjectLabel()
object_1.y2 = 1.
self.assertFalse(object_1 == object_2)
object_1 = obj_utils.ObjectLabel()
object_1.h = 1.
self.assertFalse(object_1 == object_2)
object_1 = obj_utils.ObjectLabel()
object_1.w = 1.
self.assertFalse(object_1 == object_2)
object_1 = obj_utils.ObjectLabel()
object_1.l = 1.
self.assertFalse(object_1 == object_2)
object_1 = obj_utils.ObjectLabel()
object_1.t = (1., 1., 1.)
self.assertFalse(object_1 == object_2)
object_1 = obj_utils.ObjectLabel()
object_1.ry = 1.
self.assertFalse(object_1 == object_2)
object_1 = obj_utils.ObjectLabel()
object_1.score = 1.
self.assertFalse(object_1 == object_2)
# Case 2, negative case (multiple values)
object_1 = obj_utils.ObjectLabel()
object_1.type = "" # Type of object
object_1.truncation = 1.
object_1.occlusion = 1.
object_1.alpha = 1.
object_1.x1 = 1.
object_1.y1 = 1.
object_1.x2 = 1.
object_1.y2 = 1.
object_1.h = 1.
object_1.w = 1.
object_1.l = 1.
object_1.t = [1., 1., 1.]
object_1.ry = 1.
object_1.score = 1.
self.assertFalse(object_1 == object_2)
