Python tensorflow.truediv() Examples
The following are 30
code examples of tensorflow.truediv().
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Example #1
| Source File: region_similarity_calculator.py From training_results_v0.5 with Apache License 2.0 | 6 votes |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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)
