def compute_diou(box1, box2):
"""Calculates the distance intersection of union between box1 and box2.
Args:
box1: a `Tensor` whose last dimension is 4 representing the coordinates of boxes in
x_center, y_center, width, height.
box2: a `Tensor` whose last dimension is 4 representing the coordinates of boxes in
x_center, y_center, width, height.
Returns:
iou: a `Tensor` who represents the distance intersection over union.
"""
with tf.name_scope("diou"):
# compute center distance
dist = _center_distance(box1[..., 0:2], box2[..., 0:2])
# get box corners
box1 = _xcycwh_to_xyxy(box1)
box2 = _xcycwh_to_xyxy(box2)
# compute IOU
intersection, union = _intersection_and_union(box1, box2)
iou = tf.math.divide_no_nan(intersection, union)
iou = tf.clip_by_value(iou, clip_value_min=0.0, clip_value_max=1.0)
# compute max diagnal of the smallest enclosing box
c_mins = tf.math.minimum(box1[..., 0:2], box2[..., 0:2])
c_maxes = tf.math.maximum(box1[..., 2:4], box2[..., 2:4])
diag_dist = _center_distance(c_mins, c_maxes)
regularization = tf.math.divide_no_nan(dist, diag_dist)
diou = iou + regularization
return iou, diou
def compute_giou(box1, box2):
"""Calculates the generalized intersection of union between box1 and box2.
Args:
box1: a `Tensor` whose last dimension is 4 representing the coordinates of boxes in
x_center, y_center, width, height.
box2: a `Tensor` whose last dimension is 4 representing the coordinates of boxes in
x_center, y_center, width, height.
Returns:
iou: a `Tensor` who represents the generalized intersection over union.
"""
with tf.name_scope("giou"):
# get box corners
box1 = _xcycwh_to_xyxy(box1)
box2 = _xcycwh_to_xyxy(box2)
# compute IOU
intersection, union = _intersection_and_union(box1, box2)
iou = tf.math.divide_no_nan(intersection, union)
iou = tf.clip_by_value(iou, clip_value_min=0.0, clip_value_max=1.0)
# find the smallest box to encompase both box1 and box2
c_mins = tf.math.minimum(box1[..., 0:2], box2[..., 0:2])
c_maxes = tf.math.maximum(box1[..., 2:4], box2[..., 2:4])
c = _get_area((c_mins, c_maxes), use_tuple=True)
# compute giou
giou = iou - tf.math.divide_no_nan((c - union), c)
return iou, giou
def compute_iou(box1, box2):
# get box corners
with tf.name_scope("iou"):
box1 = _xcycwh_to_xyxy(box1)
box2 = _xcycwh_to_xyxy(box2)
intersection, union = _intersection_and_union(box1, box2)
iou = tf.math.divide_no_nan(intersection, union)
iou = tf.clip_by_value(iou, clip_value_min=0.0, clip_value_max=1.0)
return iou
def compute_iou(box1, box2):
"""Calculates the intersection of union between box1 and box2.
Args:
box1: a `Tensor` whose last dimension is 4 representing the coordinates of boxes in
x_center, y_center, width, height.
box2: a `Tensor` whose last dimension is 4 representing the coordinates of boxes in
x_center, y_center, width, height.
Returns:
iou: a `Tensor` who represents the intersection over union.
"""
# get box corners
with tf.name_scope("iou"):
box1 = _xcycwh_to_xyxy(box1)
box2 = _xcycwh_to_xyxy(box2)
intersection, union = _intersection_and_union(box1, box2)
iou = tf.math.divide_no_nan(intersection, union)
iou = tf.clip_by_value(iou, clip_value_min=0.0, clip_value_max=1.0)
return iou
def compute_giou(box1, box2):
with tf.name_scope("giou"):
# get box corners
box1 = _xcycwh_to_xyxy(box1)
box2 = _xcycwh_to_xyxy(box2)
# compute IOU
intersection, union = _intersection_and_union(box1, box2)
iou = tf.math.divide_no_nan(intersection, union)
iou = tf.clip_by_value(iou, clip_value_min=0.0, clip_value_max=1.0)
# find the smallest box to encompase both box1 and box2
c_mins = K.minimum(box1[..., 0:2], box2[..., 0:2])
c_maxes = K.maximum(box1[..., 2:4], box2[..., 2:4])
c = _get_area((c_mins, c_maxes), use_tuple=True)
# compute giou
giou = iou - tf.math.divide_no_nan((c - union), c)
return iou, giou
def compute_diou(box1, box2):
with tf.name_scope("diou"):
# compute center distance
dist = _center_distance(box1[..., 0:2], box2[..., 0:2])
# get box corners
box1 = _xcycwh_to_xyxy(box1)
box2 = _xcycwh_to_xyxy(box2)
# compute IOU
intersection, union = _intersection_and_union(box1, box2)
iou = tf.math.divide_no_nan(intersection, union)
iou = tf.clip_by_value(iou, clip_value_min=0.0, clip_value_max=1.0)
# compute max diagnal of the smallest enclosing box
c_mins = K.minimum(box1[..., 0:2], box2[..., 0:2])
c_maxes = K.maximum(box1[..., 2:4], box2[..., 2:4])
diag_dist = _center_distance(c_mins, c_maxes)
regularization = tf.math.divide_no_nan(dist, diag_dist)
diou = iou + regularization
return iou, diou