research/object_detection/box_coders/keypoint_box_coder_test.py
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for object_detection.box_coder.keypoint_box_coder."""
import numpy as np
import tensorflow.compat.v1 as tf
from object_detection.box_coders import keypoint_box_coder
from object_detection.core import box_list
from object_detection.core import standard_fields as fields
from object_detection.utils import test_case
class KeypointBoxCoderTest(test_case.TestCase):
def test_get_correct_relative_codes_after_encoding(self):
boxes = np.array([[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]], np.float32)
keypoints = np.array([[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]], np.float32)
num_keypoints = len(keypoints[0])
anchors = np.array([[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]], np.float32)
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]
]
def graph_fn(boxes, keypoints, anchors):
boxes = box_list.BoxList(boxes)
boxes.add_field(fields.BoxListFields.keypoints, keypoints)
anchors = box_list.BoxList(anchors)
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
rel_codes = coder.encode(boxes, anchors)
return rel_codes
rel_codes_out = self.execute(graph_fn, [boxes, keypoints, anchors])
self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04,
atol=1e-04)
def test_get_correct_relative_codes_after_encoding_with_scaling(self):
boxes = np.array([[10., 10., 20., 15.],
[0.2, 0.1, 0.5, 0.4]], np.float32)
keypoints = np.array([[[15., 12.], [10., 15.]],
[[0.5, 0.3], [0.2, 0.4]]], np.float32)
num_keypoints = len(keypoints[0])
anchors = np.array([[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]], np.float32)
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]
]
def graph_fn(boxes, keypoints, anchors):
scale_factors = [2, 3, 4, 5]
boxes = box_list.BoxList(boxes)
boxes.add_field(fields.BoxListFields.keypoints, keypoints)
anchors = box_list.BoxList(anchors)
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
rel_codes = coder.encode(boxes, anchors)
return rel_codes
rel_codes_out = self.execute(graph_fn, [boxes, keypoints, anchors])
self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04,
atol=1e-04)
def test_get_correct_boxes_after_decoding(self):
anchors = np.array([[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]], np.float32)
rel_codes = np.array([
[-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]
], np.float32)
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])
def graph_fn(rel_codes, anchors):
anchors = box_list.BoxList(anchors)
coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
boxes = coder.decode(rel_codes, anchors)
return boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)
boxes_out, keypoints_out = self.execute(graph_fn, [rel_codes, anchors])
self.assertAllClose(keypoints_out, expected_keypoints, rtol=1e-04,
atol=1e-04)
self.assertAllClose(boxes_out, expected_boxes, rtol=1e-04,
atol=1e-04)
def test_get_correct_boxes_after_decoding_with_scaling(self):
anchors = np.array([[15., 12., 30., 18.],
[0.1, 0.0, 0.7, 0.9]], np.float32)
rel_codes = np.array([
[-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]
], np.float32)
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])
def graph_fn(rel_codes, anchors):
scale_factors = [2, 3, 4, 5]
anchors = box_list.BoxList(anchors)
coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints, scale_factors=scale_factors)
boxes = coder.decode(rel_codes, anchors)
return boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)
boxes_out, keypoints_out = self.execute(graph_fn, [rel_codes, anchors])
self.assertAllClose(keypoints_out, expected_keypoints, rtol=1e-04,
atol=1e-04)
self.assertAllClose(boxes_out, expected_boxes, rtol=1e-04,
atol=1e-04)
def test_very_small_width_nan_after_encoding(self):
boxes = np.array([[10., 10., 10.0000001, 20.]], np.float32)
keypoints = np.array([[[10., 10.], [10.0000001, 20.]]], np.float32)
anchors = np.array([[15., 12., 30., 18.]], np.float32)
expected_rel_codes = [[-0.833333, 0., -21.128731, 0.510826,
-0.833333, -0.833333, -0.833333, 0.833333]]
def graph_fn(boxes, keypoints, anchors):
boxes = box_list.BoxList(boxes)
boxes.add_field(fields.BoxListFields.keypoints, keypoints)
anchors = box_list.BoxList(anchors)
coder = keypoint_box_coder.KeypointBoxCoder(2)
rel_codes = coder.encode(boxes, anchors)
return rel_codes
rel_codes_out = self.execute(graph_fn, [boxes, keypoints, anchors])
self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04,
atol=1e-04)
if __name__ == '__main__':
tf.test.main()