Python tensorflow.truediv() Examples

The following are 30 code examples of tensorflow.truediv(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow , or try the search function .
Example #1
Source File: region_similarity_calculator.py    From training_results_v0.5 with Apache License 2.0 6 votes vote down vote up 
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #2
Source File: box_list_ops.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #3
Source File: box_list_ops.py    From tensorflow with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def ioa(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-area between box collections.

  intersection-over-area (IOA) between two boxes box1 and box2 is defined as
  their intersection area over box2's area. Note that ioa is not symmetric,
  that is, ioa(box1, box2) != ioa(box2, box1).

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise ioa scores.
  """
  with tf.name_scope(scope, 'IOA'):
    intersections = intersection(boxlist1, boxlist2)
    areas = tf.expand_dims(area(boxlist2), 0)
    return tf.truediv(intersections, areas)
Example #4
Source File: box_list_ops.py    From tpu_models with Apache License 2.0 6 votes vote down vote up 
def matched_iou(boxlist1, boxlist2, scope=None):
  """Compute intersection-over-union between corresponding boxes in boxlists.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding N boxes
    scope: name scope.

  Returns:
    a tensor with shape [N] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'MatchedIOU'):
    intersections = matched_intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = areas1 + areas2 - intersections
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #5
Source File: box_list_ops.py    From tpu_models with Apache License 2.0 6 votes vote down vote up 
def ioa(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-area between box collections.

  intersection-over-area (IOA) between two boxes box1 and box2 is defined as
  their intersection area over box2's area. Note that ioa is not symmetric,
  that is, ioa(box1, box2) != ioa(box2, box1).

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise ioa scores.
  """
  with tf.name_scope(scope, 'IOA'):
    intersections = intersection(boxlist1, boxlist2)
    areas = tf.expand_dims(area(boxlist2), 0)
    return tf.truediv(intersections, areas)
Example #6
Source File: cwise_ops_test.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def testFloatBasic(self):
    x = np.linspace(-5, 20, 15).reshape(1, 3, 5).astype(np.float32)
    y = np.linspace(20, -5, 15).reshape(1, 3, 5).astype(np.float32)
    self._compareBoth(x, y, np.add, tf.add, also_compare_variables=True)
    self._compareBoth(x, y, np.subtract, tf.sub)
    self._compareBoth(x, y, np.multiply, tf.mul)
    self._compareBoth(x, y + 0.1, np.true_divide, tf.truediv)
    self._compareBoth(x, y + 0.1, np.floor_divide, tf.floordiv)
    self._compareBoth(x, y, np.add, _ADD)
    self._compareBoth(x, y, np.subtract, _SUB)
    self._compareBoth(x, y, np.multiply, _MUL)
    self._compareBoth(x, y + 0.1, np.true_divide, _TRUEDIV)
    self._compareBoth(x, y + 0.1, np.floor_divide, _FLOORDIV)
    try:
      from scipy import special  # pylint: disable=g-import-not-at-top
      a_pos_small = np.linspace(0.1, 2, 15).reshape(1, 3, 5).astype(np.float32)
      x_pos_small = np.linspace(0.1, 10, 15).reshape(1, 3, 5).astype(np.float32)
      self._compareBoth(a_pos_small, x_pos_small, special.gammainc, tf.igamma)
      self._compareBoth(a_pos_small, x_pos_small, special.gammaincc, tf.igammac)
      # Need x > 1
      self._compareBoth(x_pos_small + 1, a_pos_small, special.zeta, tf.zeta)
      n_small = np.arange(0, 15).reshape(1, 3, 5).astype(np.float32)
      self._compareBoth(n_small, x_pos_small, special.polygamma, tf.polygamma)
    except ImportError as e:
      tf.logging.warn("Cannot test special functions: %s" % str(e))
Example #7
Source File: cwise_ops_test.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def testDoubleBasic(self):
    x = np.linspace(-5, 20, 15).reshape(1, 3, 5).astype(np.float64)
    y = np.linspace(20, -5, 15).reshape(1, 3, 5).astype(np.float64)
    self._compareBoth(x, y, np.add, tf.add)
    self._compareBoth(x, y, np.subtract, tf.sub)
    self._compareBoth(x, y, np.multiply, tf.mul)
    self._compareBoth(x, y + 0.1, np.true_divide, tf.truediv)
    self._compareBoth(x, y + 0.1, np.floor_divide, tf.floordiv)
    self._compareBoth(x, y, np.add, _ADD)
    self._compareBoth(x, y, np.subtract, _SUB)
    self._compareBoth(x, y, np.multiply, _MUL)
    self._compareBoth(x, y + 0.1, np.true_divide, _TRUEDIV)
    self._compareBoth(x, y + 0.1, np.floor_divide, _FLOORDIV)
    try:
      from scipy import special  # pylint: disable=g-import-not-at-top
      a_pos_small = np.linspace(0.1, 2, 15).reshape(1, 3, 5).astype(np.float32)
      x_pos_small = np.linspace(0.1, 10, 15).reshape(1, 3, 5).astype(np.float32)
      self._compareBoth(a_pos_small, x_pos_small, special.gammainc, tf.igamma)
      self._compareBoth(a_pos_small, x_pos_small, special.gammaincc, tf.igammac)
    except ImportError as e:
      tf.logging.warn("Cannot test special functions: %s" % str(e))
Example #8
Source File: cwise_ops_test.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def testInt32Basic(self):
    x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.int32)
    y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.int32)
    self._compareBoth(x, y, np.add, tf.add)
    self._compareBoth(x, y, np.subtract, tf.sub)
    self._compareBoth(x, y, np.multiply, tf.mul)
    self._compareBoth(x, y, np.true_divide, tf.truediv)
    self._compareBoth(x, y, np.floor_divide, tf.floordiv)
    self._compareBoth(x, y, np.mod, tf.mod)
    self._compareBoth(x, y, np.add, _ADD)
    self._compareBoth(x, y, np.subtract, _SUB)
    self._compareBoth(x, y, np.multiply, _MUL)
    self._compareBoth(x, y, np.true_divide, _TRUEDIV)
    self._compareBoth(x, y, np.floor_divide, _FLOORDIV)
    self._compareBoth(x, y, np.mod, _MOD)
    # _compareBoth tests on GPU only for floating point types, so test
    # _MOD for int32 on GPU by calling _compareGpu
    self._compareGpu(x, y, np.mod, _MOD)
Example #9
Source File: box_list_ops.py    From ros_tensorflow with Apache License 2.0 6 votes vote down vote up 
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #10
Source File: box_list_ops.py    From ros_tensorflow with Apache License 2.0 6 votes vote down vote up 
def matched_iou(boxlist1, boxlist2, scope=None):
  """Compute intersection-over-union between corresponding boxes in boxlists.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding N boxes
    scope: name scope.

  Returns:
    a tensor with shape [N] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'MatchedIOU'):
    intersections = matched_intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = areas1 + areas2 - intersections
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #11
Source File: box_list_ops.py    From ros_tensorflow with Apache License 2.0 6 votes vote down vote up 
def ioa(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-area between box collections.

  intersection-over-area (IOA) between two boxes box1 and box2 is defined as
  their intersection area over box2's area. Note that ioa is not symmetric,
  that is, ioa(box1, box2) != ioa(box2, box1).

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise ioa scores.
  """
  with tf.name_scope(scope, 'IOA'):
    intersections = intersection(boxlist1, boxlist2)
    areas = tf.expand_dims(area(boxlist2), 0)
    return tf.truediv(intersections, areas)
Example #12
Source File: box_list_ops.py    From tpu_models with Apache License 2.0 6 votes vote down vote up 
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #13
Source File: box_list_ops.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def ioa(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-area between box collections.

  intersection-over-area (IOA) between two boxes box1 and box2 is defined as
  their intersection area over box2's area. Note that ioa is not symmetric,
  that is, ioa(box1, box2) != ioa(box2, box1).

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise ioa scores.
  """
  with tf.name_scope(scope, 'IOA'):
    intersections = intersection(boxlist1, boxlist2)
    areas = tf.expand_dims(area(boxlist2), 0)
    return tf.truediv(intersections, areas)
Example #14
Source File: box_list_ops.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def matched_iou(boxlist1, boxlist2, scope=None):
  """Compute intersection-over-union between corresponding boxes in boxlists.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding N boxes
    scope: name scope.

  Returns:
    a tensor with shape [N] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'MatchedIOU'):
    intersections = matched_intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = areas1 + areas2 - intersections
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #15
Source File: box_list_ops.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #16
Source File: box_list_ops.py    From tensorflow with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #17
Source File: region_similarity_calculator.py    From training_results_v0.5 with Apache License 2.0 6 votes vote down vote up 
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #18
Source File: region_similarity_calculator.py    From training_results_v0.5 with Apache License 2.0 6 votes vote down vote up 
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #19
Source File: region_similarity_calculator.py    From training_results_v0.5 with Apache License 2.0 6 votes vote down vote up 
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #20
Source File: box_list_ops.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def ioa(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-area between box collections.

  intersection-over-area (IOA) between two boxes box1 and box2 is defined as
  their intersection area over box2's area. Note that ioa is not symmetric,
  that is, ioa(box1, box2) != ioa(box2, box1).

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise ioa scores.
  """
  with tf.name_scope(scope, 'IOA'):
    intersections = intersection(boxlist1, boxlist2)
    areas = tf.expand_dims(area(boxlist2), 0)
    return tf.truediv(intersections, areas)
Example #21
Source File: box_list_ops.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def matched_iou(boxlist1, boxlist2, scope=None):
  """Compute intersection-over-union between corresponding boxes in boxlists.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding N boxes
    scope: name scope.

  Returns:
    a tensor with shape [N] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'MatchedIOU'):
    intersections = matched_intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = areas1 + areas2 - intersections
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #22
Source File: box_list_ops.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #23
Source File: model.py    From attention-ocr with MIT License 6 votes vote down vote up 
def _prepare_image(self, image):
        """Resize the image to a maximum height of `self.height` and maximum
        width of `self.width` while maintaining the aspect ratio. Pad the
        resized image to a fixed size of ``[self.height, self.width]``."""
        img = tf.image.decode_png(image, channels=self.channels)
        dims = tf.shape(img)
        width = self.max_width

        max_width = tf.to_int32(tf.ceil(tf.truediv(dims[1], dims[0]) * self.height_float))
        max_height = tf.to_int32(tf.ceil(tf.truediv(width, max_width) * self.height_float))

        resized = tf.cond(
            tf.greater_equal(width, max_width),
            lambda: tf.cond(
                tf.less_equal(dims[0], self.height),
                lambda: tf.to_float(img),
                lambda: tf.image.resize_images(img, [self.height, max_width],
                                               method=tf.image.ResizeMethod.BICUBIC),
            ),
            lambda: tf.image.resize_images(img, [max_height, width],
                                           method=tf.image.ResizeMethod.BICUBIC)
        )

        padded = tf.image.pad_to_bounding_box(resized, 0, 0, self.height, width)
        return padded
Example #24
Source File: box_list_ops.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def ioa(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-area between box collections.

  intersection-over-area (IOA) between two boxes box1 and box2 is defined as
  their intersection area over box2's area. Note that ioa is not symmetric,
  that is, ioa(box1, box2) != ioa(box2, box1).

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise ioa scores.
  """
  with tf.name_scope(scope, 'IOA'):
    intersections = intersection(boxlist1, boxlist2)
    areas = tf.expand_dims(area(boxlist2), 0)
    return tf.truediv(intersections, areas)
Example #25
Source File: box_list_ops.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def matched_iou(boxlist1, boxlist2, scope=None):
  """Compute intersection-over-union between corresponding boxes in boxlists.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding N boxes
    scope: name scope.

  Returns:
    a tensor with shape [N] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'MatchedIOU'):
    intersections = matched_intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = areas1 + areas2 - intersections
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #26
Source File: box_list_ops.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #27
Source File: box_list_ops.py    From HereIsWally with MIT License 6 votes vote down vote up 
def ioa(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-area between box collections.

  intersection-over-area (IOA) between two boxes box1 and box2 is defined as
  their intersection area over box2's area. Note that ioa is not symmetric,
  that is, ioa(box1, box2) != ioa(box2, box1).

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise ioa scores.
  """
  with tf.name_scope(scope, 'IOA'):
    intersections = intersection(boxlist1, boxlist2)
    areas = tf.expand_dims(area(boxlist2), 0)
    return tf.truediv(intersections, areas)
Example #28
Source File: box_list_ops.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #29
Source File: box_list_ops.py    From HereIsWally with MIT License 6 votes vote down vote up 
def iou(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise iou scores.
  """
  with tf.name_scope(scope, 'IOU'):
    intersections = intersection(boxlist1, boxlist2)
    areas1 = area(boxlist1)
    areas2 = area(boxlist2)
    unions = (
        tf.expand_dims(areas1, 1) + tf.expand_dims(areas2, 0) - intersections)
    return tf.where(
        tf.equal(intersections, 0.0),
        tf.zeros_like(intersections), tf.truediv(intersections, unions))
Example #30
Source File: box_list_ops.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
def ioa(boxlist1, boxlist2, scope=None):
  """Computes pairwise intersection-over-area between box collections.

  intersection-over-area (IOA) between two boxes box1 and box2 is defined as
  their intersection area over box2's area. Note that ioa is not symmetric,
  that is, ioa(box1, box2) != ioa(box2, box1).

  Args:
    boxlist1: BoxList holding N boxes
    boxlist2: BoxList holding M boxes
    scope: name scope.

  Returns:
    a tensor with shape [N, M] representing pairwise ioa scores.
  """
  with tf.name_scope(scope, 'IOA'):
    intersections = intersection(boxlist1, boxlist2)
    areas = tf.expand_dims(area(boxlist2), 0)
    return tf.truediv(intersections, areas)