Python object_detection.core.preprocessor.resize_to_range() Examples

The following are 30 code examples of object_detection.core.preprocessor.resize_to_range(). 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 HereIsWally with MIT License 6 votes vote down vote up 
def testResizeToRangeSameMinMax(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[312, 312, 3], [299, 299, 3]]
    min_dim = 320
    max_dim = 320
    expected_shape_list = [[320, 320, 3], [320, 320, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.random_uniform(in_shape)
      out_image = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)

      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape)
        self.assertAllEqual(out_image_shape, expected_shape)
Example #2
Source File: preprocessor_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def testResizeToRangeWithMasksPreservesStaticSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
    in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
    min_dim = 50
    max_dim = 100
    expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
    expected_masks_shape_list = [[15, 75, 50], [10, 50, 100]]

    for (in_image_shape, expected_image_shape, in_masks_shape,
         expected_mask_shape) in zip(in_image_shape_list,
                                     expected_image_shape_list,
                                     in_masks_shape_list,
                                     expected_masks_shape_list):
      in_image = tf.random_uniform(in_image_shape)
      in_masks = tf.random_uniform(in_masks_shape)
      out_image, out_masks, _ = preprocessor.resize_to_range(
          in_image, in_masks, min_dimension=min_dim, max_dimension=max_dim)
      self.assertAllEqual(out_masks.get_shape().as_list(), expected_mask_shape)
      self.assertAllEqual(out_image.get_shape().as_list(), expected_image_shape)
Example #3
Source File: preprocessor_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def testResizeToRangeWithPadToMaxDimensionReturnsCorrectTensor(self):
    in_image_np = np.array([[[0, 1, 2]]], np.float32)
    ex_image_np = np.array(
        [[[0, 1, 2], [123.68, 116.779, 103.939]],
         [[123.68, 116.779, 103.939], [123.68, 116.779, 103.939]]], np.float32)
    min_dim = 1
    max_dim = 2

    in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
    out_image, _ = preprocessor.resize_to_range(
        in_image,
        min_dimension=min_dim,
        max_dimension=max_dim,
        pad_to_max_dimension=True,
        per_channel_pad_value=(123.68, 116.779, 103.939))

    with self.test_session() as sess:
      out_image_np = sess.run(out_image, feed_dict={in_image: in_image_np})
      self.assertAllClose(ex_image_np, out_image_np)
Example #4
Source File: preprocessor_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def testResizeToRangeWithPadToMaxDimensionReturnsCorrectShapes(self):
    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
    min_dim = 50
    max_dim = 100
    expected_shape_list = [[100, 100, 3], [100, 100, 3], [100, 100, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
      out_image, _ = preprocessor.resize_to_range(
          in_image,
          min_dimension=min_dim,
          max_dimension=max_dim,
          pad_to_max_dimension=True)
      self.assertAllEqual(out_image.shape.as_list(), expected_shape)
      out_image_shape = tf.shape(out_image)
      with self.test_session() as sess:
        out_image_shape = sess.run(
            out_image_shape, feed_dict={in_image: np.random.randn(*in_shape)})
        self.assertAllEqual(out_image_shape, expected_shape)
Example #5
Source File: preprocessor_test.py    From object_detector_app with MIT License 6 votes vote down vote up 
def testResizeToRange(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
    min_dim = 50
    max_dim = 100
    expected_shape_list = [[75, 50, 3], [50, 100, 3], [30, 100, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.random_uniform(in_shape)
      out_image = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)

      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape)
        self.assertAllEqual(out_image_shape, expected_shape)
Example #6
Source File: preprocessor_test.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def testResizeToRangeSameMinMax(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[312, 312, 3], [299, 299, 3]]
    min_dim = 320
    max_dim = 320
    expected_shape_list = [[320, 320, 3], [320, 320, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.random_uniform(in_shape)
      out_image = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)

      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape)
        self.assertAllEqual(out_image_shape, expected_shape)
Example #7
Source File: preprocessor_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def testResizeToRangeSameMinMax(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[312, 312, 3], [299, 299, 3]]
    min_dim = 320
    max_dim = 320
    expected_shape_list = [[320, 320, 3], [320, 320, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.random_uniform(in_shape)
      out_image, _ = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)

      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape)
        self.assertAllEqual(out_image_shape, expected_shape)
Example #8
Source File: preprocessor_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def testResizeToRangeWithDynamicSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
    min_dim = 50
    max_dim = 100
    expected_shape_list = [[75, 50, 3], [50, 100, 3], [30, 100, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
      out_image, _ = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)
      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape,
                                   feed_dict={in_image:
                                              np.random.randn(*in_shape)})
        self.assertAllEqual(out_image_shape, expected_shape)
Example #9
Source File: preprocessor_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def testResizeToRangeWithPadToMaxDimensionReturnsCorrectShapes(self):
    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
    min_dim = 50
    max_dim = 100
    expected_shape_list = [[100, 100, 3], [100, 100, 3], [100, 100, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
      out_image, _ = preprocessor.resize_to_range(
          in_image,
          min_dimension=min_dim,
          max_dimension=max_dim,
          pad_to_max_dimension=True)
      self.assertAllEqual(out_image.shape.as_list(), expected_shape)
      out_image_shape = tf.shape(out_image)
      with self.test_session() as sess:
        out_image_shape = sess.run(
            out_image_shape, feed_dict={in_image: np.random.randn(*in_shape)})
        self.assertAllEqual(out_image_shape, expected_shape)
Example #10
Source File: preprocessor_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def testResizeToRangeWithPadToMaxDimensionReturnsCorrectTensor(self):
    in_image_np = np.array([[[0, 1, 2]]], np.float32)
    ex_image_np = np.array(
        [[[0, 1, 2], [123.68, 116.779, 103.939]],
         [[123.68, 116.779, 103.939], [123.68, 116.779, 103.939]]], np.float32)
    min_dim = 1
    max_dim = 2

    in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
    out_image, _ = preprocessor.resize_to_range(
        in_image,
        min_dimension=min_dim,
        max_dimension=max_dim,
        pad_to_max_dimension=True,
        per_channel_pad_value=(123.68, 116.779, 103.939))

    with self.test_session() as sess:
      out_image_np = sess.run(out_image, feed_dict={in_image: in_image_np})
      self.assertAllClose(ex_image_np, out_image_np)
Example #11
Source File: preprocessor_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def testResizeToRangeWithMasksPreservesStaticSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
    in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
    min_dim = 50
    max_dim = 100
    expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
    expected_masks_shape_list = [[15, 75, 50], [10, 50, 100]]

    for (in_image_shape, expected_image_shape, in_masks_shape,
         expected_mask_shape) in zip(in_image_shape_list,
                                     expected_image_shape_list,
                                     in_masks_shape_list,
                                     expected_masks_shape_list):
      in_image = tf.random_uniform(in_image_shape)
      in_masks = tf.random_uniform(in_masks_shape)
      out_image, out_masks, _ = preprocessor.resize_to_range(
          in_image, in_masks, min_dimension=min_dim, max_dimension=max_dim)
      self.assertAllEqual(out_masks.get_shape().as_list(), expected_mask_shape)
      self.assertAllEqual(out_image.get_shape().as_list(), expected_image_shape)
Example #12
Source File: preprocessor_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def testResizeToRangeSameMinMax(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[312, 312, 3], [299, 299, 3]]
    min_dim = 320
    max_dim = 320
    expected_shape_list = [[320, 320, 3], [320, 320, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.random_uniform(in_shape)
      out_image, _ = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)

      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape)
        self.assertAllEqual(out_image_shape, expected_shape)
Example #13
Source File: preprocessor_test.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
def testResizeToRangeWithDynamicSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
    min_dim = 50
    max_dim = 100
    expected_shape_list = [[75, 50, 3], [50, 100, 3], [30, 100, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
      out_image = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)
      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape,
                                   feed_dict={in_image:
                                              np.random.randn(*in_shape)})
        self.assertAllEqual(out_image_shape, expected_shape)
Example #14
Source File: preprocessor_test.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
def testResizeToRangeWithMasksPreservesStaticSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
    in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
    min_dim = 50
    max_dim = 100
    expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
    expected_masks_shape_list = [[15, 75, 50], [10, 50, 100]]

    for (in_image_shape, expected_image_shape, in_masks_shape,
         expected_mask_shape) in zip(in_image_shape_list,
                                     expected_image_shape_list,
                                     in_masks_shape_list,
                                     expected_masks_shape_list):
      in_image = tf.random_uniform(in_image_shape)
      in_masks = tf.random_uniform(in_masks_shape)
      out_image, out_masks = preprocessor.resize_to_range(
          in_image, in_masks, min_dimension=min_dim, max_dimension=max_dim)
      self.assertAllEqual(out_masks.get_shape().as_list(), expected_mask_shape)
      self.assertAllEqual(out_image.get_shape().as_list(), expected_image_shape)
Example #15
Source File: preprocessor_test.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def testResizeToRange(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
    min_dim = 50
    max_dim = 100
    expected_shape_list = [[75, 50, 3], [50, 100, 3], [30, 100, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.random_uniform(in_shape)
      out_image = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)

      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape)
        self.assertAllEqual(out_image_shape, expected_shape)
Example #16
Source File: preprocessor_test.py    From HereIsWally with MIT License 6 votes vote down vote up 
def testResizeToRange(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
    min_dim = 50
    max_dim = 100
    expected_shape_list = [[75, 50, 3], [50, 100, 3], [30, 100, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.random_uniform(in_shape)
      out_image = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)

      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape)
        self.assertAllEqual(out_image_shape, expected_shape)
Example #17
Source File: preprocessor_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def testResizeToRangeWithDynamicSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
    min_dim = 50
    max_dim = 100
    expected_shape_list = [[75, 50, 3], [50, 100, 3], [30, 100, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
      out_image = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)
      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape,
                                   feed_dict={in_image:
                                              np.random.randn(*in_shape)})
        self.assertAllEqual(out_image_shape, expected_shape)
Example #18
Source File: preprocessor_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def testResizeToRangeWithMasksPreservesStaticSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
    in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
    min_dim = 50
    max_dim = 100
    expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
    expected_masks_shape_list = [[15, 75, 50], [10, 50, 100]]

    for (in_image_shape, expected_image_shape, in_masks_shape,
         expected_mask_shape) in zip(in_image_shape_list,
                                     expected_image_shape_list,
                                     in_masks_shape_list,
                                     expected_masks_shape_list):
      in_image = tf.random_uniform(in_image_shape)
      in_masks = tf.random_uniform(in_masks_shape)
      out_image, out_masks = preprocessor.resize_to_range(
          in_image, in_masks, min_dimension=min_dim, max_dimension=max_dim)
      self.assertAllEqual(out_masks.get_shape().as_list(), expected_mask_shape)
      self.assertAllEqual(out_image.get_shape().as_list(), expected_image_shape)
Example #19
Source File: preprocessor_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def testResizeToRangeSameMinMax(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[312, 312, 3], [299, 299, 3]]
    min_dim = 320
    max_dim = 320
    expected_shape_list = [[320, 320, 3], [320, 320, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.random_uniform(in_shape)
      out_image = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)

      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape)
        self.assertAllEqual(out_image_shape, expected_shape)
Example #20
Source File: preprocessor_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def testResizeToRangeWithDynamicSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
    min_dim = 50
    max_dim = 100
    expected_shape_list = [[75, 50, 3], [50, 100, 3], [30, 100, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
      out_image, _ = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)
      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape,
                                   feed_dict={in_image:
                                              np.random.randn(*in_shape)})
        self.assertAllEqual(out_image_shape, expected_shape)
Example #21
Source File: preprocessor_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def testResizeToRangeWithMasksPreservesStaticSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
    in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
    min_dim = 50
    max_dim = 100
    expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
    expected_masks_shape_list = [[15, 75, 50], [10, 50, 100]]

    for (in_image_shape, expected_image_shape, in_masks_shape,
         expected_mask_shape) in zip(in_image_shape_list,
                                     expected_image_shape_list,
                                     in_masks_shape_list,
                                     expected_masks_shape_list):
      in_image = tf.random_uniform(in_image_shape)
      in_masks = tf.random_uniform(in_masks_shape)
      out_image, out_masks, _ = preprocessor.resize_to_range(
          in_image, in_masks, min_dimension=min_dim, max_dimension=max_dim)
      self.assertAllEqual(out_masks.get_shape().as_list(), expected_mask_shape)
      self.assertAllEqual(out_image.get_shape().as_list(), expected_image_shape)
Example #22
Source File: preprocessor_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def testResizeToRangeSameMinMax(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[312, 312, 3], [299, 299, 3]]
    min_dim = 320
    max_dim = 320
    expected_shape_list = [[320, 320, 3], [320, 320, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.random_uniform(in_shape)
      out_image, _ = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)

      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape)
        self.assertAllEqual(out_image_shape, expected_shape)
Example #23
Source File: preprocessor_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def testResizeToRangeWithDynamicSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
    min_dim = 50
    max_dim = 100
    expected_shape_list = [[75, 50, 3], [50, 100, 3], [30, 100, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
      out_image = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)
      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape,
                                   feed_dict={in_image:
                                              np.random.randn(*in_shape)})
        self.assertAllEqual(out_image_shape, expected_shape)
Example #24
Source File: preprocessor_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def testResizeToRangeWithMasksPreservesStaticSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
    in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
    min_dim = 50
    max_dim = 100
    expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
    expected_masks_shape_list = [[15, 75, 50], [10, 50, 100]]

    for (in_image_shape, expected_image_shape, in_masks_shape,
         expected_mask_shape) in zip(in_image_shape_list,
                                     expected_image_shape_list,
                                     in_masks_shape_list,
                                     expected_masks_shape_list):
      in_image = tf.random_uniform(in_image_shape)
      in_masks = tf.random_uniform(in_masks_shape)
      out_image, out_masks = preprocessor.resize_to_range(
          in_image, in_masks, min_dimension=min_dim, max_dimension=max_dim)
      self.assertAllEqual(out_masks.get_shape().as_list(), expected_mask_shape)
      self.assertAllEqual(out_image.get_shape().as_list(), expected_image_shape)
Example #25
Source File: preprocessor_test.py    From tensorflow with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def testResizeToRangeWithDynamicSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
    min_dim = 50
    max_dim = 100
    expected_shape_list = [[75, 50, 3], [50, 100, 3], [30, 100, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
      out_image = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)
      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape,
                                   feed_dict={in_image:
                                              np.random.randn(*in_shape)})
        self.assertAllEqual(out_image_shape, expected_shape)
Example #26
Source File: preprocessor_test.py    From tensorflow with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def testResizeToRangeWithMasksPreservesStaticSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
    in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
    min_dim = 50
    max_dim = 100
    expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
    expected_masks_shape_list = [[15, 75, 50], [10, 50, 100]]

    for (in_image_shape, expected_image_shape, in_masks_shape,
         expected_mask_shape) in zip(in_image_shape_list,
                                     expected_image_shape_list,
                                     in_masks_shape_list,
                                     expected_masks_shape_list):
      in_image = tf.random_uniform(in_image_shape)
      in_masks = tf.random_uniform(in_masks_shape)
      out_image, out_masks = preprocessor.resize_to_range(
          in_image, in_masks, min_dimension=min_dim, max_dimension=max_dim)
      self.assertAllEqual(out_masks.get_shape().as_list(), expected_mask_shape)
      self.assertAllEqual(out_image.get_shape().as_list(), expected_image_shape)
Example #27
Source File: preprocessor_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def testResizeToRangeWithDynamicSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
    min_dim = 50
    max_dim = 100
    expected_shape_list = [[75, 50, 3], [50, 100, 3], [30, 100, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
      out_image, _ = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)
      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape,
                                   feed_dict={in_image:
                                              np.random.randn(*in_shape)})
        self.assertAllEqual(out_image_shape, expected_shape)
Example #28
Source File: preprocessor_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def testResizeToRangeWithMasksPreservesStaticSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_image_shape_list = [[60, 40, 3], [15, 30, 3]]
    in_masks_shape_list = [[15, 60, 40], [10, 15, 30]]
    min_dim = 50
    max_dim = 100
    expected_image_shape_list = [[75, 50, 3], [50, 100, 3]]
    expected_masks_shape_list = [[15, 75, 50], [10, 50, 100]]

    for (in_image_shape, expected_image_shape, in_masks_shape,
         expected_mask_shape) in zip(in_image_shape_list,
                                     expected_image_shape_list,
                                     in_masks_shape_list,
                                     expected_masks_shape_list):
      in_image = tf.random_uniform(in_image_shape)
      in_masks = tf.random_uniform(in_masks_shape)
      out_image, out_masks, _ = preprocessor.resize_to_range(
          in_image, in_masks, min_dimension=min_dim, max_dimension=max_dim)
      self.assertAllEqual(out_masks.get_shape().as_list(), expected_mask_shape)
      self.assertAllEqual(out_image.get_shape().as_list(), expected_image_shape)
Example #29
Source File: preprocessor_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def testResizeToRangeSameMinMax(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[312, 312, 3], [299, 299, 3]]
    min_dim = 320
    max_dim = 320
    expected_shape_list = [[320, 320, 3], [320, 320, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.random_uniform(in_shape)
      out_image, _ = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)

      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape)
        self.assertAllEqual(out_image_shape, expected_shape)
Example #30
Source File: preprocessor_test.py    From ros_tensorflow with Apache License 2.0 6 votes vote down vote up 
def testResizeToRangeWithDynamicSpatialShape(self):
    """Tests image resizing, checking output sizes."""
    in_shape_list = [[60, 40, 3], [15, 30, 3], [15, 50, 3]]
    min_dim = 50
    max_dim = 100
    expected_shape_list = [[75, 50, 3], [50, 100, 3], [30, 100, 3]]

    for in_shape, expected_shape in zip(in_shape_list, expected_shape_list):
      in_image = tf.placeholder(tf.float32, shape=(None, None, 3))
      out_image, _ = preprocessor.resize_to_range(
          in_image, min_dimension=min_dim, max_dimension=max_dim)
      out_image_shape = tf.shape(out_image)
      with self.test_session() as sess:
        out_image_shape = sess.run(out_image_shape,
                                   feed_dict={in_image:
                                              np.random.randn(*in_shape)})
        self.assertAllEqual(out_image_shape, expected_shape)