Python object_detection.core.preprocessor.random_crop_to_aspect_ratio() Examples

The following are 30 code examples of object_detection.core.preprocessor.random_crop_to_aspect_ratio(). 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 Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
def testRandomCropToAspectRatio(self):
    images = self.createTestImages()
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_classes: labels,
    }
    tensor_dict = preprocessor.preprocess(tensor_dict, [])
    images = tensor_dict[fields.InputDataFields.image]

    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
        'aspect_ratio': 2.0
    })]
    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
                                                  preprocessing_options)

    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
    cropped_boxes = cropped_tensor_dict[
        fields.InputDataFields.groundtruth_boxes]
    boxes_shape = tf.shape(boxes)
    cropped_boxes_shape = tf.shape(cropped_boxes)
    images_shape = tf.shape(images)
    cropped_images_shape = tf.shape(cropped_images)

    with self.test_session() as sess:
      (boxes_shape_, cropped_boxes_shape_, images_shape_,
       cropped_images_shape_) = sess.run([
           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
       ])
      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
      self.assertEqual(images_shape_[2], cropped_images_shape_[2])
Example #2
Source File: preprocessor_builder_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def test_build_random_crop_to_aspect_ratio(self):
    preprocessor_text_proto = """
    random_crop_to_aspect_ratio {
      aspect_ratio: 0.85
      overlap_thresh: 0.35
      clip_boxes: False
    }
    """
    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_crop_to_aspect_ratio)
    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
                                        'overlap_thresh': 0.35,
                                        'clip_boxes': False})
Example #3
Source File: preprocessor_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def testRandomCropToAspectRatioWithCache(self):
    preprocess_options = [(preprocessor.random_crop_to_aspect_ratio, {})]
    self._testPreprocessorCache(preprocess_options,
                                test_boxes=True,
                                test_masks=False,
                                test_keypoints=False)
Example #4
Source File: preprocessor_test.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def testRandomCropToAspectRatio(self):
    preprocessing_options = [(preprocessor.normalize_image, {
        'original_minval': 0,
        'original_maxval': 255,
        'target_minval': 0,
        'target_maxval': 1
    })]

    images = self.createTestImages()
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_classes: labels
    }
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
    images = tensor_dict[fields.InputDataFields.image]

    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
        'aspect_ratio': 2.0
    })]
    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
                                                  preprocessing_options)

    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
    cropped_boxes = cropped_tensor_dict[
        fields.InputDataFields.groundtruth_boxes]
    boxes_shape = tf.shape(boxes)
    cropped_boxes_shape = tf.shape(cropped_boxes)
    images_shape = tf.shape(images)
    cropped_images_shape = tf.shape(cropped_images)

    with self.test_session() as sess:
      (boxes_shape_, cropped_boxes_shape_, images_shape_,
       cropped_images_shape_) = sess.run([
           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
       ])
      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
      self.assertEqual(images_shape_[2], cropped_images_shape_[2])
Example #5
Source File: preprocessor_builder_test.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def test_build_random_crop_to_aspect_ratio(self):
    preprocessor_text_proto = """
    random_crop_to_aspect_ratio {
      aspect_ratio: 0.85
      overlap_thresh: 0.35
    }
    """
    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_crop_to_aspect_ratio)
    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
                                        'overlap_thresh': 0.35})
Example #6
Source File: preprocessor_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def testRandomCropToAspectRatio(self):
    images = self.createTestImages()
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    weights = self.createTestGroundtruthWeights()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_classes: labels,
        fields.InputDataFields.groundtruth_weights: weights,
    }
    tensor_dict = preprocessor.preprocess(tensor_dict, [])
    images = tensor_dict[fields.InputDataFields.image]

    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
        'aspect_ratio': 2.0
    })]
    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
                                                  preprocessing_options)

    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
    cropped_boxes = cropped_tensor_dict[
        fields.InputDataFields.groundtruth_boxes]
    boxes_shape = tf.shape(boxes)
    cropped_boxes_shape = tf.shape(cropped_boxes)
    images_shape = tf.shape(images)
    cropped_images_shape = tf.shape(cropped_images)

    with self.test_session() as sess:
      (boxes_shape_, cropped_boxes_shape_, images_shape_,
       cropped_images_shape_) = sess.run([
           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
       ])
      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
      self.assertEqual(images_shape_[2], cropped_images_shape_[2])
Example #7
Source File: preprocessor_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def testRandomCropToAspectRatio(self):
    preprocessing_options = [(preprocessor.normalize_image, {
        'original_minval': 0,
        'original_maxval': 255,
        'target_minval': 0,
        'target_maxval': 1
    })]

    images = self.createTestImages()
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_classes: labels
    }
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
    images = tensor_dict[fields.InputDataFields.image]

    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
        'aspect_ratio': 2.0
    })]
    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
                                                  preprocessing_options)

    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
    cropped_boxes = cropped_tensor_dict[
        fields.InputDataFields.groundtruth_boxes]
    boxes_shape = tf.shape(boxes)
    cropped_boxes_shape = tf.shape(cropped_boxes)
    images_shape = tf.shape(images)
    cropped_images_shape = tf.shape(cropped_images)

    with self.test_session() as sess:
      (boxes_shape_, cropped_boxes_shape_, images_shape_,
       cropped_images_shape_) = sess.run([
           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
       ])
      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
      self.assertEqual(images_shape_[2], cropped_images_shape_[2])
Example #8
Source File: preprocessor_builder_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def test_build_random_crop_to_aspect_ratio(self):
    preprocessor_text_proto = """
    random_crop_to_aspect_ratio {
      aspect_ratio: 0.85
      overlap_thresh: 0.35
    }
    """
    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_crop_to_aspect_ratio)
    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
                                        'overlap_thresh': 0.35})
Example #9
Source File: preprocessor_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
def testRandomCropToAspectRatio(self):
    preprocessing_options = [(preprocessor.normalize_image, {
        'original_minval': 0,
        'original_maxval': 255,
        'target_minval': 0,
        'target_maxval': 1
    })]

    images = self.createTestImages()
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_classes: labels
    }
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
    images = tensor_dict[fields.InputDataFields.image]

    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
        'aspect_ratio': 2.0
    })]
    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
                                                  preprocessing_options)

    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
    cropped_boxes = cropped_tensor_dict[
        fields.InputDataFields.groundtruth_boxes]
    boxes_shape = tf.shape(boxes)
    cropped_boxes_shape = tf.shape(cropped_boxes)
    images_shape = tf.shape(images)
    cropped_images_shape = tf.shape(cropped_images)

    with self.test_session() as sess:
      (boxes_shape_, cropped_boxes_shape_, images_shape_,
       cropped_images_shape_) = sess.run([
           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
       ])
      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
      self.assertEqual(images_shape_[2], cropped_images_shape_[2])
Example #10
Source File: preprocessor_builder_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
def test_build_random_crop_to_aspect_ratio(self):
    preprocessor_text_proto = """
    random_crop_to_aspect_ratio {
      aspect_ratio: 0.85
      overlap_thresh: 0.35
    }
    """
    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_crop_to_aspect_ratio)
    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
                                        'overlap_thresh': 0.35})
Example #11
Source File: preprocessor_builder_test.py    From moveo_ros with MIT License 5 votes vote down vote up 
def test_build_random_crop_to_aspect_ratio(self):
    preprocessor_text_proto = """
    random_crop_to_aspect_ratio {
      aspect_ratio: 0.85
      overlap_thresh: 0.35
    }
    """
    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_crop_to_aspect_ratio)
    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
                                        'overlap_thresh': 0.35})
Example #12
Source File: preprocessor_test.py    From moveo_ros with MIT License 5 votes vote down vote up 
def testRandomCropToAspectRatio(self):
    preprocessing_options = [(preprocessor.normalize_image, {
        'original_minval': 0,
        'original_maxval': 255,
        'target_minval': 0,
        'target_maxval': 1
    })]

    images = self.createTestImages()
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_classes: labels
    }
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
    images = tensor_dict[fields.InputDataFields.image]

    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
        'aspect_ratio': 2.0
    })]
    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
                                                  preprocessing_options)

    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
    cropped_boxes = cropped_tensor_dict[
        fields.InputDataFields.groundtruth_boxes]
    boxes_shape = tf.shape(boxes)
    cropped_boxes_shape = tf.shape(cropped_boxes)
    images_shape = tf.shape(images)
    cropped_images_shape = tf.shape(cropped_images)

    with self.test_session() as sess:
      (boxes_shape_, cropped_boxes_shape_, images_shape_,
       cropped_images_shape_) = sess.run([
           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
       ])
      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
      self.assertEqual(images_shape_[2], cropped_images_shape_[2])
Example #13
Source File: preprocessor_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def testRandomCropToAspectRatio(self):
    images = self.createTestImages()
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_classes: labels,
    }
    tensor_dict = preprocessor.preprocess(tensor_dict, [])
    images = tensor_dict[fields.InputDataFields.image]

    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
        'aspect_ratio': 2.0
    })]
    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
                                                  preprocessing_options)

    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
    cropped_boxes = cropped_tensor_dict[
        fields.InputDataFields.groundtruth_boxes]
    boxes_shape = tf.shape(boxes)
    cropped_boxes_shape = tf.shape(cropped_boxes)
    images_shape = tf.shape(images)
    cropped_images_shape = tf.shape(cropped_images)

    with self.test_session() as sess:
      (boxes_shape_, cropped_boxes_shape_, images_shape_,
       cropped_images_shape_) = sess.run([
           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
       ])
      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
      self.assertEqual(images_shape_[2], cropped_images_shape_[2])
Example #14
Source File: preprocessor_builder_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def test_build_random_crop_to_aspect_ratio(self):
    preprocessor_text_proto = """
    random_crop_to_aspect_ratio {
      aspect_ratio: 0.85
      overlap_thresh: 0.35
    }
    """
    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_crop_to_aspect_ratio)
    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
                                        'overlap_thresh': 0.35})
Example #15
Source File: preprocessor_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
def testRandomCropToAspectRatioWithCache(self):
    preprocess_options = [(preprocessor.random_crop_to_aspect_ratio, {})]
    self._testPreprocessorCache(preprocess_options,
                                test_boxes=True,
                                test_masks=False,
                                test_keypoints=False)
Example #16
Source File: preprocessor_test.py    From MBMD with MIT License 5 votes vote down vote up 
def testRandomCropToAspectRatio(self):
    preprocessing_options = [(preprocessor.normalize_image, {
        'original_minval': 0,
        'original_maxval': 255,
        'target_minval': 0,
        'target_maxval': 1
    })]

    images = self.createTestImages()
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_classes: labels
    }
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
    images = tensor_dict[fields.InputDataFields.image]

    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
        'aspect_ratio': 2.0
    })]
    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
                                                  preprocessing_options)

    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
    cropped_boxes = cropped_tensor_dict[
        fields.InputDataFields.groundtruth_boxes]
    boxes_shape = tf.shape(boxes)
    cropped_boxes_shape = tf.shape(cropped_boxes)
    images_shape = tf.shape(images)
    cropped_images_shape = tf.shape(cropped_images)

    with self.test_session() as sess:
      (boxes_shape_, cropped_boxes_shape_, images_shape_,
       cropped_images_shape_) = sess.run([
           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
       ])
      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
      self.assertEqual(images_shape_[2], cropped_images_shape_[2])
Example #17
Source File: preprocessor_builder_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
def test_build_random_crop_to_aspect_ratio(self):
    preprocessor_text_proto = """
    random_crop_to_aspect_ratio {
      aspect_ratio: 0.85
      overlap_thresh: 0.35
    }
    """
    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_crop_to_aspect_ratio)
    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
                                        'overlap_thresh': 0.35})
Example #18
Source File: preprocessor_builder_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 5 votes vote down vote up 
def test_build_random_crop_to_aspect_ratio(self):
    preprocessor_text_proto = """
    random_crop_to_aspect_ratio {
      aspect_ratio: 0.85
      overlap_thresh: 0.35
    }
    """
    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_crop_to_aspect_ratio)
    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
                                        'overlap_thresh': 0.35})
Example #19
Source File: preprocessor_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 5 votes vote down vote up 
def testRandomCropToAspectRatio(self):
    images = self.createTestImages()
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_classes: labels,
    }
    tensor_dict = preprocessor.preprocess(tensor_dict, [])
    images = tensor_dict[fields.InputDataFields.image]

    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
        'aspect_ratio': 2.0
    })]
    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
                                                  preprocessing_options)

    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
    cropped_boxes = cropped_tensor_dict[
        fields.InputDataFields.groundtruth_boxes]
    boxes_shape = tf.shape(boxes)
    cropped_boxes_shape = tf.shape(cropped_boxes)
    images_shape = tf.shape(images)
    cropped_images_shape = tf.shape(cropped_images)

    with self.test_session() as sess:
      (boxes_shape_, cropped_boxes_shape_, images_shape_,
       cropped_images_shape_) = sess.run([
           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
       ])
      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
      self.assertEqual(images_shape_[2], cropped_images_shape_[2])
Example #20
Source File: preprocessor_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 5 votes vote down vote up 
def testRandomCropToAspectRatio(self):
    preprocessing_options = [(preprocessor.normalize_image, {
        'original_minval': 0,
        'original_maxval': 255,
        'target_minval': 0,
        'target_maxval': 1
    })]

    images = self.createTestImages()
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_classes: labels
    }
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
    images = tensor_dict[fields.InputDataFields.image]

    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
        'aspect_ratio': 2.0
    })]
    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
                                                  preprocessing_options)

    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
    cropped_boxes = cropped_tensor_dict[
        fields.InputDataFields.groundtruth_boxes]
    boxes_shape = tf.shape(boxes)
    cropped_boxes_shape = tf.shape(cropped_boxes)
    images_shape = tf.shape(images)
    cropped_images_shape = tf.shape(cropped_images)

    with self.test_session() as sess:
      (boxes_shape_, cropped_boxes_shape_, images_shape_,
       cropped_images_shape_) = sess.run([
           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
       ])
      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
      self.assertEqual(images_shape_[2], cropped_images_shape_[2])
Example #21
Source File: preprocessor_builder_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 5 votes vote down vote up 
def test_build_random_crop_to_aspect_ratio(self):
    preprocessor_text_proto = """
    random_crop_to_aspect_ratio {
      aspect_ratio: 0.85
      overlap_thresh: 0.35
    }
    """
    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_crop_to_aspect_ratio)
    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
                                        'overlap_thresh': 0.35})
Example #22
Source File: preprocessor_test.py    From tensorflow with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def testRandomCropToAspectRatio(self):
    preprocessing_options = [(preprocessor.normalize_image, {
        'original_minval': 0,
        'original_maxval': 255,
        'target_minval': 0,
        'target_maxval': 1
    })]

    images = self.createTestImages()
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_classes: labels
    }
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
    images = tensor_dict[fields.InputDataFields.image]

    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
        'aspect_ratio': 2.0
    })]
    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
                                                  preprocessing_options)

    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
    cropped_boxes = cropped_tensor_dict[
        fields.InputDataFields.groundtruth_boxes]
    boxes_shape = tf.shape(boxes)
    cropped_boxes_shape = tf.shape(cropped_boxes)
    images_shape = tf.shape(images)
    cropped_images_shape = tf.shape(cropped_images)

    with self.test_session() as sess:
      (boxes_shape_, cropped_boxes_shape_, images_shape_,
       cropped_images_shape_) = sess.run([
           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
       ])
      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
      self.assertEqual(images_shape_[2], cropped_images_shape_[2])
Example #23
Source File: preprocessor_builder_test.py    From tensorflow with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def test_build_random_crop_to_aspect_ratio(self):
    preprocessor_text_proto = """
    random_crop_to_aspect_ratio {
      aspect_ratio: 0.85
      overlap_thresh: 0.35
    }
    """
    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_crop_to_aspect_ratio)
    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
                                        'overlap_thresh': 0.35})
Example #24
Source File: preprocessor_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def testRandomCropToAspectRatioWithCache(self):
    preprocess_options = [(preprocessor.random_crop_to_aspect_ratio, {})]
    self._testPreprocessorCache(preprocess_options,
                                test_boxes=True,
                                test_masks=False,
                                test_keypoints=False)
Example #25
Source File: preprocessor_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 5 votes vote down vote up 
def testRandomCropToAspectRatioWithCache(self):
    preprocess_options = [(preprocessor.random_crop_to_aspect_ratio, {})]
    self._testPreprocessorCache(preprocess_options,
                                test_boxes=True,
                                test_masks=False,
                                test_keypoints=False)
Example #26
Source File: preprocessor_builder_test.py    From ros_tensorflow with Apache License 2.0 5 votes vote down vote up 
def test_build_random_crop_to_aspect_ratio(self):
    preprocessor_text_proto = """
    random_crop_to_aspect_ratio {
      aspect_ratio: 0.85
      overlap_thresh: 0.35
    }
    """
    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_crop_to_aspect_ratio)
    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
                                        'overlap_thresh': 0.35})
Example #27
Source File: preprocessor_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def testRandomCropToAspectRatio(self):
    images = self.createTestImages()
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_classes: labels,
    }
    tensor_dict = preprocessor.preprocess(tensor_dict, [])
    images = tensor_dict[fields.InputDataFields.image]

    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
        'aspect_ratio': 2.0
    })]
    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
                                                  preprocessing_options)

    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
    cropped_boxes = cropped_tensor_dict[
        fields.InputDataFields.groundtruth_boxes]
    boxes_shape = tf.shape(boxes)
    cropped_boxes_shape = tf.shape(cropped_boxes)
    images_shape = tf.shape(images)
    cropped_images_shape = tf.shape(cropped_images)

    with self.test_session() as sess:
      (boxes_shape_, cropped_boxes_shape_, images_shape_,
       cropped_images_shape_) = sess.run([
           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
       ])
      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
      self.assertEqual(images_shape_[2], cropped_images_shape_[2])
Example #28
Source File: preprocessor_builder_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def test_build_random_crop_to_aspect_ratio(self):
    preprocessor_text_proto = """
    random_crop_to_aspect_ratio {
      aspect_ratio: 0.85
      overlap_thresh: 0.35
    }
    """
    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_crop_to_aspect_ratio)
    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
                                        'overlap_thresh': 0.35})
Example #29
Source File: preprocessor_test.py    From ros_tensorflow with Apache License 2.0 5 votes vote down vote up 
def testRandomCropToAspectRatioWithCache(self):
    preprocess_options = [(preprocessor.random_crop_to_aspect_ratio, {})]
    self._testPreprocessorCache(preprocess_options,
                                test_boxes=True,
                                test_masks=False,
                                test_keypoints=False)
Example #30
Source File: preprocessor_test.py    From ros_tensorflow with Apache License 2.0 5 votes vote down vote up 
def testRandomCropToAspectRatio(self):
    images = self.createTestImages()
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    tensor_dict = {
        fields.InputDataFields.image: images,
        fields.InputDataFields.groundtruth_boxes: boxes,
        fields.InputDataFields.groundtruth_classes: labels,
    }
    tensor_dict = preprocessor.preprocess(tensor_dict, [])
    images = tensor_dict[fields.InputDataFields.image]

    preprocessing_options = [(preprocessor.random_crop_to_aspect_ratio, {
        'aspect_ratio': 2.0
    })]
    cropped_tensor_dict = preprocessor.preprocess(tensor_dict,
                                                  preprocessing_options)

    cropped_images = cropped_tensor_dict[fields.InputDataFields.image]
    cropped_boxes = cropped_tensor_dict[
        fields.InputDataFields.groundtruth_boxes]
    boxes_shape = tf.shape(boxes)
    cropped_boxes_shape = tf.shape(cropped_boxes)
    images_shape = tf.shape(images)
    cropped_images_shape = tf.shape(cropped_images)

    with self.test_session() as sess:
      (boxes_shape_, cropped_boxes_shape_, images_shape_,
       cropped_images_shape_) = sess.run([
           boxes_shape, cropped_boxes_shape, images_shape, cropped_images_shape
       ])
      self.assertAllEqual(boxes_shape_, cropped_boxes_shape_)
      self.assertEqual(images_shape_[1], cropped_images_shape_[1] * 2)
      self.assertEqual(images_shape_[2], cropped_images_shape_[2])