Python object_detection.core.preprocessor.random_rotation90() Examples

The following are 30 code examples of object_detection.core.preprocessor.random_rotation90(). 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 object_detection.core.preprocessor , or try the search function .
Example #1
Source File: preprocessor_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #2
Source File: preprocessor_test.py    From MAX-Object-Detector with Apache License 2.0 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #3
Source File: preprocessor_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #4
Source File: preprocessor_test.py    From ros_tensorflow with Apache License 2.0 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #5
Source File: preprocessor_test.py    From Accident-Detection-on-Indian-Roads with GNU Affero General Public License v3.0 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #6
Source File: preprocessor_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #7
Source File: preprocessor_test.py    From AniSeg with Apache License 2.0 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #8
Source File: preprocessor_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #9
Source File: preprocessor_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #10
Source File: preprocessor_test.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #11
Source File: preprocessor_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #12
Source File: preprocessor_test.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #13
Source File: preprocessor_test.py    From Elphas with Apache License 2.0 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #14
Source File: preprocessor_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #15
Source File: preprocessor_builder_test.py    From models with Apache License 2.0 6 votes vote down vote up 
def test_build_random_rotation90(self):
    preprocessor_text_proto = """
    random_rotation90 {
      keypoint_rot_permutation: 3
      keypoint_rot_permutation: 0
      keypoint_rot_permutation: 1
      keypoint_rot_permutation: 2
      probability: 0.5
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_rotation90)
    self.assertEqual(args, {'keypoint_rot_permutation': (3, 0, 1, 2),
                            'probability': 0.5})
Example #16
Source File: preprocessor_test.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createEmptyTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected = self.createEmptyTestBoxes()
    images_expected2 = images
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_,
       boxes_expected_) = sess.run([images_diff, images_diff_expected, boxes,
                                    boxes_expected])
      self.assertAllClose(boxes_, boxes_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #17
Source File: preprocessor_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def testRandomRotation90WithEmptyBoxes(self):

    def graph_fn():
      preprocess_options = [(preprocessor.random_rotation90, {})]
      images = self.expectedImagesAfterNormalization()
      boxes = self.createEmptyTestBoxes()
      tensor_dict = {
          fields.InputDataFields.image: images,
          fields.InputDataFields.groundtruth_boxes: boxes
      }
      images_expected1 = self.expectedImagesAfterRot90()
      boxes_expected = self.createEmptyTestBoxes()
      images_expected2 = images
      tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
      images = tensor_dict[fields.InputDataFields.image]
      boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

      images_diff1 = tf.squared_difference(images, images_expected1)
      images_diff2 = tf.squared_difference(images, images_expected2)
      images_diff = tf.multiply(images_diff1, images_diff2)
      images_diff_expected = tf.zeros_like(images_diff)
      return [images_diff, images_diff_expected, boxes, boxes_expected]

    (images_diff_, images_diff_expected_, boxes_,
     boxes_expected_) = self.execute_cpu(graph_fn, [])
    self.assertAllClose(boxes_, boxes_expected_)
    self.assertAllClose(images_diff_, images_diff_expected_)
Example #18
Source File: preprocessor_builder_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def test_build_random_rotation90(self):
    preprocessor_text_proto = """
    random_rotation90 {}
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_rotation90)
    self.assertEqual(args, {})
Example #19
Source File: preprocessor_builder_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def test_build_random_rotation90(self):
    preprocessor_text_proto = """
    random_rotation90 {}
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_rotation90)
    self.assertEqual(args, {})
Example #20
Source File: preprocessor_builder_test.py    From Elphas with Apache License 2.0 5 votes vote down vote up 
def test_build_random_rotation90(self):
    preprocessor_text_proto = """
    random_rotation90 {}
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_rotation90)
    self.assertEqual(args, {})
Example #21
Source File: preprocessor_builder_test.py    From AniSeg with Apache License 2.0 5 votes vote down vote up 
def test_build_random_rotation90(self):
    preprocessor_text_proto = """
    random_rotation90 {}
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_rotation90)
    self.assertEqual(args, {})
Example #22
Source File: preprocessor_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def testRandomRotation90WithCache(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    self._testPreprocessorCache(preprocess_options,
                                test_boxes=True,
                                test_masks=True,
                                test_keypoints=True)
Example #23
Source File: preprocessor_test.py    From AniSeg with Apache License 2.0 5 votes vote down vote up 
def testRunRandomRotation90WithMaskAndKeypoints(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    image_height = 3
    image_width = 3
    images = tf.random_uniform([1, image_height, image_width, 3])
    boxes = self.createTestBoxes()
    masks = self.createTestMasks()
    keypoints = self.createTestKeypoints()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_instance_masks: masks,
        fields.InputDataFields.groundtruth_keypoints: keypoints
    }
    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
        include_instance_masks=True, include_keypoints=True)
    tensor_dict = preprocessor.preprocess(
        tensor_dict, preprocess_options, func_arg_map=preprocessor_arg_map)
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
    masks = tensor_dict[fields.InputDataFields.groundtruth_instance_masks]
    keypoints = tensor_dict[fields.InputDataFields.groundtruth_keypoints]
    with self.test_session() as sess:
      boxes, masks, keypoints = sess.run([boxes, masks, keypoints])
      self.assertTrue(boxes is not None)
      self.assertTrue(masks is not None)
      self.assertTrue(keypoints is not None)
Example #24
Source File: preprocessor_test.py    From AniSeg with Apache License 2.0 5 votes vote down vote up 
def testRandomRotation90WithCache(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    self._testPreprocessorCache(preprocess_options,
                                test_boxes=True,
                                test_masks=True,
                                test_keypoints=True)
Example #25
Source File: preprocessor_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def testRandomRotation90(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected1 = self.expectedBoxesAfterRot90()
    images_expected2 = images
    boxes_expected2 = boxes
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    boxes_diff1 = tf.squared_difference(boxes, boxes_expected1)
    boxes_diff2 = tf.squared_difference(boxes, boxes_expected2)
    boxes_diff = tf.multiply(boxes_diff1, boxes_diff2)
    boxes_diff_expected = tf.zeros_like(boxes_diff)

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_diff_,
       boxes_diff_expected_) = sess.run([images_diff, images_diff_expected,
                                         boxes_diff, boxes_diff_expected])
      self.assertAllClose(boxes_diff_, boxes_diff_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #26
Source File: preprocessor_test.py    From AniSeg with Apache License 2.0 5 votes vote down vote up 
def testRandomRotation90(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected1 = self.expectedBoxesAfterRot90()
    images_expected2 = images
    boxes_expected2 = boxes
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    boxes_diff1 = tf.squared_difference(boxes, boxes_expected1)
    boxes_diff2 = tf.squared_difference(boxes, boxes_expected2)
    boxes_diff = tf.multiply(boxes_diff1, boxes_diff2)
    boxes_diff_expected = tf.zeros_like(boxes_diff)

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_diff_,
       boxes_diff_expected_) = sess.run([images_diff, images_diff_expected,
                                         boxes_diff, boxes_diff_expected])
      self.assertAllClose(boxes_diff_, boxes_diff_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)
Example #27
Source File: preprocessor_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def testRunRandomRotation90WithMaskAndKeypoints(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    image_height = 3
    image_width = 3
    images = tf.random_uniform([1, image_height, image_width, 3])
    boxes = self.createTestBoxes()
    masks = self.createTestMasks()
    keypoints = self.createTestKeypoints()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_instance_masks: masks,
        fields.InputDataFields.groundtruth_keypoints: keypoints
    }
    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
        include_instance_masks=True, include_keypoints=True)
    tensor_dict = preprocessor.preprocess(
        tensor_dict, preprocess_options, func_arg_map=preprocessor_arg_map)
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
    masks = tensor_dict[fields.InputDataFields.groundtruth_instance_masks]
    keypoints = tensor_dict[fields.InputDataFields.groundtruth_keypoints]
    with self.test_session() as sess:
      boxes, masks, keypoints = sess.run([boxes, masks, keypoints])
      self.assertTrue(boxes is not None)
      self.assertTrue(masks is not None)
      self.assertTrue(keypoints is not None)
Example #28
Source File: preprocessor_builder_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def test_build_random_rotation90(self):
    preprocessor_text_proto = """
    random_rotation90 {}
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_rotation90)
    self.assertEqual(args, {})
Example #29
Source File: preprocessor_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def testRunRandomRotation90WithMaskAndKeypoints(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    image_height = 3
    image_width = 3
    images = tf.random_uniform([1, image_height, image_width, 3])
    boxes = self.createTestBoxes()
    masks = self.createTestMasks()
    keypoints = self.createTestKeypoints()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_instance_masks: masks,
        fields.InputDataFields.groundtruth_keypoints: keypoints
    }
    preprocessor_arg_map = preprocessor.get_default_func_arg_map(
        include_instance_masks=True, include_keypoints=True)
    tensor_dict = preprocessor.preprocess(
        tensor_dict, preprocess_options, func_arg_map=preprocessor_arg_map)
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
    masks = tensor_dict[fields.InputDataFields.groundtruth_instance_masks]
    keypoints = tensor_dict[fields.InputDataFields.groundtruth_keypoints]
    with self.test_session() as sess:
      boxes, masks, keypoints = sess.run([boxes, masks, keypoints])
      self.assertTrue(boxes is not None)
      self.assertTrue(masks is not None)
      self.assertTrue(keypoints is not None)
Example #30
Source File: preprocessor_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def testRandomRotation90(self):
    preprocess_options = [(preprocessor.random_rotation90, {})]
    images = self.expectedImagesAfterNormalization()
    boxes = self.createTestBoxes()
    tensor_dict = {fields.InputDataFields.image: images,
                   fields.InputDataFields.groundtruth_boxes: boxes}
    images_expected1 = self.expectedImagesAfterRot90()
    boxes_expected1 = self.expectedBoxesAfterRot90()
    images_expected2 = images
    boxes_expected2 = boxes
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocess_options)
    images = tensor_dict[fields.InputDataFields.image]
    boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]

    boxes_diff1 = tf.squared_difference(boxes, boxes_expected1)
    boxes_diff2 = tf.squared_difference(boxes, boxes_expected2)
    boxes_diff = tf.multiply(boxes_diff1, boxes_diff2)
    boxes_diff_expected = tf.zeros_like(boxes_diff)

    images_diff1 = tf.squared_difference(images, images_expected1)
    images_diff2 = tf.squared_difference(images, images_expected2)
    images_diff = tf.multiply(images_diff1, images_diff2)
    images_diff_expected = tf.zeros_like(images_diff)

    with self.test_session() as sess:
      (images_diff_, images_diff_expected_, boxes_diff_,
       boxes_diff_expected_) = sess.run([images_diff, images_diff_expected,
                                         boxes_diff, boxes_diff_expected])
      self.assertAllClose(boxes_diff_, boxes_diff_expected_)
      self.assertAllClose(images_diff_, images_diff_expected_)