Python object_detection.utils.dataset_util.int64_list_feature() Examples

The following are 30 code examples of object_detection.utils.dataset_util.int64_list_feature(). 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.utils.dataset_util , or try the search function .
Example #1
Source File: input_reader_builder_test.py    From MAX-Object-Detector with Apache License 2.0 6 votes vote down vote up 
def create_tf_record(self):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
        'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
        'image/height': dataset_util.int64_feature(4),
        'image/width': dataset_util.int64_feature(5),
        'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
        'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
        'image/object/class/label': dataset_util.int64_list_feature([2]),
        'image/object/mask': dataset_util.float_list_feature(flat_mask),
    }))
    writer.write(example.SerializeToString())
    writer.close()

    return path
Example #2
Source File: input_reader_builder_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 6 votes vote down vote up 
def create_tf_record(self):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
        'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
        'image/height': dataset_util.int64_feature(4),
        'image/width': dataset_util.int64_feature(5),
        'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
        'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
        'image/object/class/label': dataset_util.int64_list_feature([2]),
        'image/object/mask': dataset_util.float_list_feature(flat_mask),
    }))
    writer.write(example.SerializeToString())
    writer.close()

    return path
Example #3
Source File: input_reader_builder_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 6 votes vote down vote up 
def create_tf_record(self):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
        'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
        'image/height': dataset_util.int64_feature(4),
        'image/width': dataset_util.int64_feature(5),
        'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
        'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
        'image/object/class/label': dataset_util.int64_list_feature([2]),
        'image/object/mask': dataset_util.float_list_feature(flat_mask),
    }))
    writer.write(example.SerializeToString())
    writer.close()

    return path
Example #4
Source File: input_reader_builder_test.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def create_tf_record(self):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
        'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
        'image/height': dataset_util.int64_feature(4),
        'image/width': dataset_util.int64_feature(5),
        'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
        'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
        'image/object/class/label': dataset_util.int64_list_feature([2]),
        'image/object/mask': dataset_util.float_list_feature(flat_mask),
    }))
    writer.write(example.SerializeToString())
    writer.close()

    return path
Example #5
Source File: input_reader_builder_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def create_tf_record(self):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
        'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
        'image/height': dataset_util.int64_feature(4),
        'image/width': dataset_util.int64_feature(5),
        'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
        'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
        'image/object/class/label': dataset_util.int64_list_feature([2]),
        'image/object/mask': dataset_util.float_list_feature(flat_mask),
    }))
    writer.write(example.SerializeToString())
    writer.close()

    return path
Example #6
Source File: input_reader_builder_test.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def create_tf_record(self):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
        'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
        'image/height': dataset_util.int64_feature(4),
        'image/width': dataset_util.int64_feature(5),
        'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
        'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
        'image/object/class/label': dataset_util.int64_list_feature([2]),
        'image/object/mask': dataset_util.float_list_feature(flat_mask),
    }))
    writer.write(example.SerializeToString())
    writer.close()

    return path
Example #7
Source File: tf_example_decoder_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def testDecodeObjectLabel(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes = [0, 1]
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/object/class/label':
                    dataset_util.int64_list_feature(bbox_classes),
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
                         .get_shape().as_list()), [2])

    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(bbox_classes,
                        tensor_dict[fields.InputDataFields.groundtruth_classes])
Example #8
Source File: tf_example_decoder_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def testInstancesNotAvailableByDefault(self):
    num_instances = 4
    image_height = 5
    image_width = 3
    # Randomly generate image.
    image_tensor = np.random.randint(
        256, size=(image_height, image_width, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)

    # Randomly generate instance segmentation masks.
    instance_masks = (
        np.random.randint(2, size=(num_instances, image_height,
                                   image_width)).astype(np.float32))
    instance_masks_flattened = np.reshape(instance_masks, [-1])

    # Randomly generate class labels for each instance.
    object_classes = np.random.randint(
        100, size=(num_instances)).astype(np.int64)

    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/height':
                    dataset_util.int64_feature(image_height),
                'image/width':
                    dataset_util.int64_feature(image_width),
                'image/object/mask':
                    dataset_util.float_list_feature(instance_masks_flattened),
                'image/object/class/label':
                    dataset_util.int64_list_feature(object_classes)
            })).SerializeToString()
    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
    self.assertTrue(
        fields.InputDataFields.groundtruth_instance_masks not in tensor_dict)
Example #9
Source File: tf_example_decoder_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def testDecodeObjectGroupOf(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    object_group_of = [0, 1]
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/object/group_of':
                    dataset_util.int64_list_feature(object_group_of),
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual(
        (tensor_dict[fields.InputDataFields.groundtruth_group_of].get_shape()
         .as_list()), [2])
    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(
        [bool(item) for item in object_group_of],
        tensor_dict[fields.InputDataFields.groundtruth_group_of])
Example #10
Source File: tf_example_decoder_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def testDecodeObjectDifficult(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    object_difficult = [0, 1]
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/object/difficult':
                    dataset_util.int64_list_feature(object_difficult),
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual(
        (tensor_dict[fields.InputDataFields.groundtruth_difficult].get_shape()
         .as_list()), [2])
    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(
        [bool(item) for item in object_difficult],
        tensor_dict[fields.InputDataFields.groundtruth_difficult])
Example #11
Source File: tf_example_decoder_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def testDecodeObjectIsCrowd(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    object_is_crowd = [0, 1]
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/object/is_crowd':
                    dataset_util.int64_list_feature(object_is_crowd),
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual(
        (tensor_dict[fields.InputDataFields.groundtruth_is_crowd].get_shape()
         .as_list()), [2])
    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(
        [bool(item) for item in object_is_crowd],
        tensor_dict[fields.InputDataFields.groundtruth_is_crowd])
Example #12
Source File: dataset_builder_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def create_tf_record(self, has_additional_channels=False, num_examples=1):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    additional_channels_tensor = np.random.randint(
        255, size=(4, 5, 1)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
      encoded_additional_channels_jpeg = tf.image.encode_jpeg(
          tf.constant(additional_channels_tensor)).eval()
      for i in range(num_examples):
        features = {
            'image/source_id': dataset_util.bytes_feature(str(i)),
            'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
            'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
            'image/height': dataset_util.int64_feature(4),
            'image/width': dataset_util.int64_feature(5),
            'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
            'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
            'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
            'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
            'image/object/class/label': dataset_util.int64_list_feature([2]),
            'image/object/mask': dataset_util.float_list_feature(flat_mask),
        }
        if has_additional_channels:
          additional_channels_key = 'image/additional_channels/encoded'
          features[additional_channels_key] = dataset_util.bytes_list_feature(
              [encoded_additional_channels_jpeg] * 2)
        example = tf.train.Example(features=tf.train.Features(feature=features))
        writer.write(example.SerializeToString())
      writer.close()

    return path
Example #13
Source File: tf_example_decoder_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def testDecodeObjectLabel(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes = [0, 1]
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/object/class/label':
                    dataset_util.int64_list_feature(bbox_classes),
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
                         .get_shape().as_list()), [2])

    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(bbox_classes,
                        tensor_dict[fields.InputDataFields.groundtruth_classes])
Example #14
Source File: tf_example_decoder_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def testInstancesNotAvailableByDefault(self):
    num_instances = 4
    image_height = 5
    image_width = 3
    # Randomly generate image.
    image_tensor = np.random.randint(
        256, size=(image_height, image_width, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)

    # Randomly generate instance segmentation masks.
    instance_masks = (
        np.random.randint(2, size=(num_instances, image_height,
                                   image_width)).astype(np.float32))
    instance_masks_flattened = np.reshape(instance_masks, [-1])

    # Randomly generate class labels for each instance.
    object_classes = np.random.randint(
        100, size=(num_instances)).astype(np.int64)

    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/height':
                    dataset_util.int64_feature(image_height),
                'image/width':
                    dataset_util.int64_feature(image_width),
                'image/object/mask':
                    dataset_util.float_list_feature(instance_masks_flattened),
                'image/object/class/label':
                    dataset_util.int64_list_feature(object_classes)
            })).SerializeToString()
    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
    self.assertTrue(
        fields.InputDataFields.groundtruth_instance_masks not in tensor_dict)
Example #15
Source File: generate_tfrecord.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def create_tf_example(group, path):
    with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
        encoded_jpg = fid.read()
    encoded_jpg_io = io.BytesIO(encoded_jpg)
    image = Image.open(encoded_jpg_io)
    width, height = image.size

    filename = group.filename.encode('utf8')
    image_format = b'jpg'
    xmins = []
    xmaxs = []
    ymins = []
    ymaxs = []
    classes_text = []
    classes = []

    for index, row in group.object.iterrows():
        xmins.append(row['xmin'] / width)
        xmaxs.append(row['xmax'] / width)
        ymins.append(row['ymin'] / height)
        ymaxs.append(row['ymax'] / height)
        classes_text.append(row['class'].encode('utf8'))
        classes.append(class_text_to_int(row['class']))

    tf_example = tf.train.Example(features=tf.train.Features(feature={
        'image/height': dataset_util.int64_feature(height),
        'image/width': dataset_util.int64_feature(width),
        'image/filename': dataset_util.bytes_feature(filename),
        'image/source_id': dataset_util.bytes_feature(filename),
        'image/encoded': dataset_util.bytes_feature(encoded_jpg),
        'image/format': dataset_util.bytes_feature(image_format),
        'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
        'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
        'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
        'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
        'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
        'image/object/class/label': dataset_util.int64_list_feature(classes),
    }))
    return tf_example
Example #16
Source File: generate_tf_records.py    From Emojinator with MIT License 5 votes vote down vote up 
def create_tf_example(group, path):
    with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
        encoded_jpg = fid.read()
    encoded_jpg_io = io.BytesIO(encoded_jpg)
    image = Image.open(encoded_jpg_io)
    width, height = image.size

    filename = group.filename.encode('utf8')
    image_format = b'jpg'
    xmins = []
    xmaxs = []
    ymins = []
    ymaxs = []
    classes_text = []
    classes = []

    for index, row in group.object.iterrows():
        xmins.append(row['xmin'] / width)
        xmaxs.append(row['xmax'] / width)
        ymins.append(row['ymin'] / height)
        ymaxs.append(row['ymax'] / height)
        classes_text.append(row['class'].encode('utf8'))
        classes.append(class_text_to_int(row['class']))

    tf_example = tf.train.Example(features=tf.train.Features(feature={
        'image/height': dataset_util.int64_feature(height),
        'image/width': dataset_util.int64_feature(width),
        'image/filename': dataset_util.bytes_feature(filename),
        'image/source_id': dataset_util.bytes_feature(filename),
        'image/encoded': dataset_util.bytes_feature(encoded_jpg),
        'image/format': dataset_util.bytes_feature(image_format),
        'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
        'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
        'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
        'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
        'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
        'image/object/class/label': dataset_util.int64_list_feature(classes),
    }))
    return tf_example
Example #17
Source File: dataset_builder_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def create_tf_record(self, has_additional_channels=False, num_examples=1):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    additional_channels_tensor = np.random.randint(
        255, size=(4, 5, 1)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
      encoded_additional_channels_jpeg = tf.image.encode_jpeg(
          tf.constant(additional_channels_tensor)).eval()
      for i in range(num_examples):
        features = {
            'image/source_id': dataset_util.bytes_feature(str(i)),
            'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
            'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
            'image/height': dataset_util.int64_feature(4),
            'image/width': dataset_util.int64_feature(5),
            'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
            'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
            'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
            'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
            'image/object/class/label': dataset_util.int64_list_feature([2]),
            'image/object/mask': dataset_util.float_list_feature(flat_mask),
        }
        if has_additional_channels:
          additional_channels_key = 'image/additional_channels/encoded'
          features[additional_channels_key] = dataset_util.bytes_list_feature(
              [encoded_additional_channels_jpeg] * 2)
        example = tf.train.Example(features=tf.train.Features(feature=features))
        writer.write(example.SerializeToString())
      writer.close()

    return path
Example #18
Source File: tf_example_decoder_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def testDecodeObjectLabel(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes = [0, 1]
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/object/class/label':
                    dataset_util.int64_list_feature(bbox_classes),
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
                         .get_shape().as_list()), [2])

    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(bbox_classes,
                        tensor_dict[fields.InputDataFields.groundtruth_classes])
Example #19
Source File: gen_tfrecord.py    From DeepLogo with MIT License 5 votes vote down vote up 
def create_tf_example(csv, img_dir):
    img_fname = csv[0]
    x1, y1, x2, y2 = list(map(int, csv[1:-1]))
    cls_idx = int(csv[-1])
    cls_text = config.CLASS_NAMES[cls_idx].encode('utf8')
    with tf.gfile.GFile(os.path.join(img_dir, img_fname), 'rb') as fid:
        encoded_jpg = fid.read()
    encoded_jpg_io = io.BytesIO(encoded_jpg)
    image = Image.open(encoded_jpg_io)
    width, height = image.size

    xmin = [x1 / width]
    xmax = [x2 / width]
    ymin = [y1 / height]
    ymax = [y2 / height]
    cls_text = [cls_text]
    cls_idx = [cls_idx]

    filename = img_fname.encode('utf8')
    image_format = b'jpg'

    tf_example = tf.train.Example(features=tf.train.Features(feature={
        'image/height': dataset_util.int64_feature(height),
        'image/width': dataset_util.int64_feature(width),
        'image/filename': dataset_util.bytes_feature(filename),
        'image/source_id': dataset_util.bytes_feature(filename),
        'image/encoded': dataset_util.bytes_feature(encoded_jpg),
        'image/format': dataset_util.bytes_feature(image_format),
        'image/object/bbox/xmin': dataset_util.float_list_feature(xmin),
        'image/object/bbox/xmax': dataset_util.float_list_feature(xmax),
        'image/object/bbox/ymin': dataset_util.float_list_feature(ymin),
        'image/object/bbox/ymax': dataset_util.float_list_feature(ymax),
        'image/object/class/text': dataset_util.bytes_list_feature(cls_text),
        'image/object/class/label': dataset_util.int64_list_feature(cls_idx),        
    }))

    return tf_example
Example #20
Source File: tf_example_decoder_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def testDecodeObjectLabel(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes = [0, 1]
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/object/class/label':
                    dataset_util.int64_list_feature(bbox_classes),
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
                         .get_shape().as_list()), [2])

    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(bbox_classes,
                        tensor_dict[fields.InputDataFields.groundtruth_classes])
Example #21
Source File: tf_example_decoder_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def testDecodeObjectIsCrowd(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    object_is_crowd = [0, 1]
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/object/is_crowd':
                    dataset_util.int64_list_feature(object_is_crowd),
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual(
        (tensor_dict[fields.InputDataFields.groundtruth_is_crowd].get_shape()
         .as_list()), [2])
    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(
        [bool(item) for item in object_is_crowd],
        tensor_dict[fields.InputDataFields.groundtruth_is_crowd])
Example #22
Source File: tf_example_decoder_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def testDecodeObjectDifficult(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    object_difficult = [0, 1]
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/object/difficult':
                    dataset_util.int64_list_feature(object_difficult),
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual(
        (tensor_dict[fields.InputDataFields.groundtruth_difficult].get_shape()
         .as_list()), [2])
    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(
        [bool(item) for item in object_difficult],
        tensor_dict[fields.InputDataFields.groundtruth_difficult])
Example #23
Source File: tf_example_decoder_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def testDecodeObjectGroupOf(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    object_group_of = [0, 1]
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/object/group_of':
                    dataset_util.int64_list_feature(object_group_of),
            })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual(
        (tensor_dict[fields.InputDataFields.groundtruth_group_of].get_shape()
         .as_list()), [2])
    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(
        [bool(item) for item in object_group_of],
        tensor_dict[fields.InputDataFields.groundtruth_group_of])
Example #24
Source File: tf_example_decoder_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def testInstancesNotAvailableByDefault(self):
    num_instances = 4
    image_height = 5
    image_width = 3
    # Randomly generate image.
    image_tensor = np.random.randint(
        256, size=(image_height, image_width, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)

    # Randomly generate instance segmentation masks.
    instance_masks = (
        np.random.randint(2, size=(num_instances, image_height,
                                   image_width)).astype(np.float32))
    instance_masks_flattened = np.reshape(instance_masks, [-1])

    # Randomly generate class labels for each instance.
    object_classes = np.random.randint(
        100, size=(num_instances)).astype(np.int64)

    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'),
                'image/height':
                    dataset_util.int64_feature(image_height),
                'image/width':
                    dataset_util.int64_feature(image_width),
                'image/object/mask':
                    dataset_util.float_list_feature(instance_masks_flattened),
                'image/object/class/label':
                    dataset_util.int64_list_feature(object_classes)
            })).SerializeToString()
    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
    self.assertTrue(
        fields.InputDataFields.groundtruth_instance_masks not in tensor_dict)
Example #25
Source File: input_reader_builder_tf1_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def create_tf_record_with_context(self):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    context_features = (10 * 3) * [1.0]
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    dataset_util.bytes_feature(encoded_jpeg),
                'image/format':
                    dataset_util.bytes_feature('jpeg'.encode('utf8')),
                'image/height':
                    dataset_util.int64_feature(4),
                'image/width':
                    dataset_util.int64_feature(5),
                'image/object/bbox/xmin':
                    dataset_util.float_list_feature([0.0]),
                'image/object/bbox/xmax':
                    dataset_util.float_list_feature([1.0]),
                'image/object/bbox/ymin':
                    dataset_util.float_list_feature([0.0]),
                'image/object/bbox/ymax':
                    dataset_util.float_list_feature([1.0]),
                'image/object/class/label':
                    dataset_util.int64_list_feature([2]),
                'image/object/mask':
                    dataset_util.float_list_feature(flat_mask),
                'image/context_features':
                    dataset_util.float_list_feature(context_features),
                'image/context_feature_length':
                    dataset_util.int64_list_feature([10]),
            }))
    writer.write(example.SerializeToString())
    writer.close()

    return path
Example #26
Source File: dataset_builder_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def create_tf_record(self, has_additional_channels=False, num_examples=1):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    additional_channels_tensor = np.random.randint(
        255, size=(4, 5, 1)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
      encoded_additional_channels_jpeg = tf.image.encode_jpeg(
          tf.constant(additional_channels_tensor)).eval()
      for i in range(num_examples):
        features = {
            'image/source_id': dataset_util.bytes_feature(str(i)),
            'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
            'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
            'image/height': dataset_util.int64_feature(4),
            'image/width': dataset_util.int64_feature(5),
            'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
            'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
            'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
            'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
            'image/object/class/label': dataset_util.int64_list_feature([2]),
            'image/object/mask': dataset_util.float_list_feature(flat_mask),
        }
        if has_additional_channels:
          additional_channels_key = 'image/additional_channels/encoded'
          features[additional_channels_key] = dataset_util.bytes_list_feature(
              [encoded_additional_channels_jpeg] * 2)
        example = tf.train.Example(features=tf.train.Features(feature=features))
        writer.write(example.SerializeToString())
      writer.close()

    return path
Example #27
Source File: tf_example_decoder_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def testDecodeObjectLabel(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg, _ = self._create_encoded_and_decoded_data(
        image_tensor, 'jpeg')
    bbox_classes = [0, 1]

    def graph_fn():
      example = tf.train.Example(
          features=tf.train.Features(
              feature={
                  'image/encoded':
                      dataset_util.bytes_feature(encoded_jpeg),
                  'image/format':
                      dataset_util.bytes_feature(six.b('jpeg')),
                  'image/object/class/label':
                      dataset_util.int64_list_feature(bbox_classes),
              })).SerializeToString()

      example_decoder = tf_example_decoder.TfExampleDecoder()
      output = example_decoder.decode(tf.convert_to_tensor(example))

      self.assertAllEqual((output[
          fields.InputDataFields.groundtruth_classes].get_shape().as_list()),
                          [2])
      return output

    tensor_dict = self.execute_cpu(graph_fn, [])

    self.assertAllEqual(bbox_classes,
                        tensor_dict[fields.InputDataFields.groundtruth_classes])
Example #28
Source File: decoder_builder_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def _make_serialized_tf_example(self, has_additional_channels=False):
    image_tensor_np = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    additional_channels_tensor_np = np.random.randint(
        255, size=(4, 5, 1)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    def graph_fn(image_tensor):
      encoded_jpeg = tf.image.encode_jpeg(image_tensor)
      return encoded_jpeg
    encoded_jpeg = self.execute_cpu(graph_fn, [image_tensor_np])
    encoded_additional_channels_jpeg = self.execute_cpu(
        graph_fn, [additional_channels_tensor_np])

    features = {
        'image/source_id': dataset_util.bytes_feature('0'.encode()),
        'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
        'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
        'image/height': dataset_util.int64_feature(4),
        'image/width': dataset_util.int64_feature(5),
        'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
        'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
        'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
        'image/object/class/label': dataset_util.int64_list_feature([2]),
        'image/object/mask': dataset_util.float_list_feature(flat_mask),
    }
    if has_additional_channels:
      additional_channels_key = 'image/additional_channels/encoded'
      features[additional_channels_key] = dataset_util.bytes_list_feature(
          [encoded_additional_channels_jpeg] * 2)
    example = tf.train.Example(features=tf.train.Features(feature=features))
    return example.SerializeToString()
Example #29
Source File: tf_example_decoder_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def testDecodeObjectIsCrowd(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg, _ = self._create_encoded_and_decoded_data(
        image_tensor, 'jpeg')
    object_is_crowd = [0, 1]

    def graph_fn():
      example = tf.train.Example(
          features=tf.train.Features(
              feature={
                  'image/encoded':
                      dataset_util.bytes_feature(encoded_jpeg),
                  'image/format':
                      dataset_util.bytes_feature(six.b('jpeg')),
                  'image/object/is_crowd':
                      dataset_util.int64_list_feature(object_is_crowd),
              })).SerializeToString()

      example_decoder = tf_example_decoder.TfExampleDecoder()
      output = example_decoder.decode(tf.convert_to_tensor(example))

      self.assertAllEqual((output[
          fields.InputDataFields.groundtruth_is_crowd].get_shape().as_list()),
                          [2])
      return output

    tensor_dict = self.execute_cpu(graph_fn, [])
    self.assertAllEqual(
        [bool(item) for item in object_is_crowd],
        tensor_dict[fields.InputDataFields.groundtruth_is_crowd])
Example #30
Source File: tf_example_decoder_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def testDecodeObjectGroupOf(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg, _ = self._create_encoded_and_decoded_data(
        image_tensor, 'jpeg')
    object_group_of = [0, 1]

    def graph_fn():
      example = tf.train.Example(
          features=tf.train.Features(
              feature={
                  'image/encoded':
                      dataset_util.bytes_feature(encoded_jpeg),
                  'image/format':
                      dataset_util.bytes_feature(six.b('jpeg')),
                  'image/object/group_of':
                      dataset_util.int64_list_feature(object_group_of),
              })).SerializeToString()

      example_decoder = tf_example_decoder.TfExampleDecoder()
      output = example_decoder.decode(tf.convert_to_tensor(example))

      self.assertAllEqual((output[
          fields.InputDataFields.groundtruth_group_of].get_shape().as_list()),
                          [2])
      return output

    tensor_dict = self.execute_cpu(graph_fn, [])
    self.assertAllEqual(
        [bool(item) for item in object_group_of],
        tensor_dict[fields.InputDataFields.groundtruth_group_of])