Python object_detection.box_coders.keypoint_box_coder.KeypointBoxCoder() Examples
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code examples of object_detection.box_coders.keypoint_box_coder.KeypointBoxCoder().
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Example #1
| Source File: keypoint_box_coder_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 6 votes |
|
def test_get_correct_relative_codes_after_encoding(self):
boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
expected_rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #2
| Source File: keypoint_box_coder_test.py From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License | 6 votes |
|
def test_get_correct_boxes_after_decoding_with_scaling(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-1., -1.25, -1.62186, -0.911608,
-1.0, -1.5, -1.666667, 0.],
[-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667]
]
scale_factors = [2, 3, 4, 5]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #3
| Source File: keypoint_box_coder_test.py From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License | 6 votes |
|
def test_get_correct_boxes_after_decoding(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #4
| Source File: box_coder_builder_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 6 votes |
|
def test_build_keypoint_box_coder_with_non_default_parameters(self):
box_coder_text_proto = """
keypoint_box_coder {
num_keypoints: 6
y_scale: 6.0
x_scale: 3.0
height_scale: 7.0
width_scale: 8.0
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertIsInstance(box_coder_object, keypoint_box_coder.KeypointBoxCoder)
self.assertEqual(box_coder_object._num_keypoints, 6)
self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0, 8.0])
Example #5
| Source File: keypoint_box_coder_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 6 votes |
|
def test_get_correct_boxes_after_decoding_with_scaling(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-1., -1.25, -1.62186, -0.911608,
-1.0, -1.5, -1.666667, 0.],
[-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667]
]
scale_factors = [2, 3, 4, 5]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #6
| Source File: keypoint_box_coder_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 6 votes |
|
def test_get_correct_boxes_after_decoding(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #7
| Source File: keypoint_box_coder_test.py From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License | 6 votes |
|
def test_get_correct_relative_codes_after_encoding(self):
boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
expected_rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #8
| Source File: keypoint_box_coder_test.py From tensorflow with BSD 2-Clause "Simplified" License | 6 votes |
|
def test_get_correct_relative_codes_after_encoding_with_scaling(self):
boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
scale_factors = [2, 3, 4, 5]
expected_rel_codes = [
[-1., -1.25, -1.62186, -0.911608,
-1.0, -1.5, -1.666667, 0.],
[-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667]
]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #9
| Source File: keypoint_box_coder_test.py From tensorflow with BSD 2-Clause "Simplified" License | 6 votes |
|
def test_get_correct_boxes_after_decoding(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #10
| Source File: keypoint_box_coder_test.py From tensorflow with BSD 2-Clause "Simplified" License | 6 votes |
|
def test_get_correct_boxes_after_decoding_with_scaling(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-1., -1.25, -1.62186, -0.911608,
-1.0, -1.5, -1.666667, 0.],
[-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667]
]
scale_factors = [2, 3, 4, 5]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #11
| Source File: keypoint_box_coder_test.py From Gun-Detector with Apache License 2.0 | 6 votes |
|
def test_get_correct_relative_codes_after_encoding(self):
boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
expected_rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #12
| Source File: keypoint_box_coder_test.py From Gun-Detector with Apache License 2.0 | 6 votes |
|
def test_get_correct_relative_codes_after_encoding_with_scaling(self):
boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
scale_factors = [2, 3, 4, 5]
expected_rel_codes = [
[-1., -1.25, -1.62186, -0.911608,
-1.0, -1.5, -1.666667, 0.],
[-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667]
]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #13
| Source File: keypoint_box_coder_test.py From Gun-Detector with Apache License 2.0 | 6 votes |
|
def test_get_correct_boxes_after_decoding(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #14
| Source File: keypoint_box_coder_test.py From Gun-Detector with Apache License 2.0 | 6 votes |
|
def test_get_correct_boxes_after_decoding_with_scaling(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-1., -1.25, -1.62186, -0.911608,
-1.0, -1.5, -1.666667, 0.],
[-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667]
]
scale_factors = [2, 3, 4, 5]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #15
| Source File: keypoint_box_coder_test.py From tensorflow with BSD 2-Clause "Simplified" License | 6 votes |
|
def test_get_correct_relative_codes_after_encoding(self):
boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
expected_rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #16
| Source File: keypoint_box_coder_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 6 votes |
|
def test_get_correct_relative_codes_after_encoding_with_scaling(self):
boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
scale_factors = [2, 3, 4, 5]
expected_rel_codes = [
[-1., -1.25, -1.62186, -0.911608,
-1.0, -1.5, -1.666667, 0.],
[-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667]
]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #17
| Source File: box_coder_builder_test.py From yolo_v2 with Apache License 2.0 | 6 votes |
|
def test_build_keypoint_box_coder_with_non_default_parameters(self):
box_coder_text_proto = """
keypoint_box_coder {
num_keypoints: 6
y_scale: 6.0
x_scale: 3.0
height_scale: 7.0
width_scale: 8.0
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertIsInstance(box_coder_object, keypoint_box_coder.KeypointBoxCoder)
self.assertEqual(box_coder_object._num_keypoints, 6)
self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0, 8.0])
Example #18
| Source File: keypoint_box_coder_test.py From yolo_v2 with Apache License 2.0 | 6 votes |
|
def test_get_correct_boxes_after_decoding_with_scaling(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-1., -1.25, -1.62186, -0.911608,
-1.0, -1.5, -1.666667, 0.],
[-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667]
]
scale_factors = [2, 3, 4, 5]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #19
| Source File: keypoint_box_coder_test.py From yolo_v2 with Apache License 2.0 | 6 votes |
|
def test_get_correct_boxes_after_decoding(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #20
| Source File: keypoint_box_coder_test.py From yolo_v2 with Apache License 2.0 | 6 votes |
|
def test_get_correct_relative_codes_after_encoding_with_scaling(self):
boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
scale_factors = [2, 3, 4, 5]
expected_rel_codes = [
[-1., -1.25, -1.62186, -0.911608,
-1.0, -1.5, -1.666667, 0.],
[-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667]
]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #21
| Source File: keypoint_box_coder_test.py From yolo_v2 with Apache License 2.0 | 6 votes |
|
def test_get_correct_relative_codes_after_encoding(self):
boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
expected_rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #22
| Source File: keypoint_box_coder_test.py From HereIsWally with MIT License | 6 votes |
|
def test_get_correct_boxes_after_decoding_with_scaling(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-1., -1.25, -1.62186, -0.911608,
-1.0, -1.5, -1.666667, 0.],
[-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667]
]
scale_factors = [2, 3, 4, 5]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #23
| Source File: keypoint_box_coder_test.py From HereIsWally with MIT License | 6 votes |
|
def test_get_correct_boxes_after_decoding(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #24
| Source File: keypoint_box_coder_test.py From HereIsWally with MIT License | 6 votes |
|
def test_get_correct_relative_codes_after_encoding_with_scaling(self):
boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
scale_factors = [2, 3, 4, 5]
expected_rel_codes = [
[-1., -1.25, -1.62186, -0.911608,
-1.0, -1.5, -1.666667, 0.],
[-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667]
]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #25
| Source File: keypoint_box_coder_test.py From HereIsWally with MIT License | 6 votes |
|
def test_get_correct_relative_codes_after_encoding(self):
boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
expected_rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #26
| Source File: keypoint_box_coder_test.py From garbage-object-detection-tensorflow with MIT License | 6 votes |
|
def test_get_correct_boxes_after_decoding_with_scaling(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-1., -1.25, -1.62186, -0.911608,
-1.0, -1.5, -1.666667, 0.],
[-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667]
]
scale_factors = [2, 3, 4, 5]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #27
| Source File: keypoint_box_coder_test.py From garbage-object-detection-tensorflow with MIT License | 6 votes |
|
def test_get_correct_boxes_after_decoding(self):
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
expected_boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
expected_keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(expected_keypoints[0])
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
boxes = coder.decode(rel_codes, anchors)
with self.test_session() as sess:
boxes_out, keypoints_out = sess.run(
[boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
self.assertAllClose(boxes_out, expected_boxes)
self.assertAllClose(keypoints_out, expected_keypoints)
Example #28
| Source File: keypoint_box_coder_test.py From garbage-object-detection-tensorflow with MIT License | 6 votes |
|
def test_get_correct_relative_codes_after_encoding_with_scaling(self):
boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
scale_factors = [2, 3, 4, 5]
expected_rel_codes = [
[-1., -1.25, -1.62186, -0.911608,
-1.0, -1.5, -1.666667, 0.],
[-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667]
]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #29
| Source File: keypoint_box_coder_test.py From garbage-object-detection-tensorflow with MIT License | 6 votes |
|
def test_get_correct_relative_codes_after_encoding(self):
boxes = [[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]]
expected_rel_codes = [
[-0.5, -0.416666, -0.405465, -0.182321,
-0.5, -0.5, -0.833333, 0.],
[-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556]
]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
Example #30
| Source File: box_coder_builder_test.py From Person-Detection-and-Tracking with MIT License | 6 votes |
|
def test_build_keypoint_box_coder_with_non_default_parameters(self):
box_coder_text_proto = """
keypoint_box_coder {
num_keypoints: 6
y_scale: 6.0
x_scale: 3.0
height_scale: 7.0
width_scale: 8.0
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertIsInstance(box_coder_object, keypoint_box_coder.KeypointBoxCoder)
self.assertEqual(box_coder_object._num_keypoints, 6)
self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0, 8.0])
