Python dataset_util.int64_list_feature方法代码示例

# 需要导入模块: from object_detection.utils import dataset_util [as 别名]
# 或者: from object_detection.utils.dataset_util import int64_list_feature [as 别名]
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 

# 需要导入模块: from object_detection.utils import dataset_util [as 别名]
# 或者: from object_detection.utils.dataset_util import int64_list_feature [as 别名]
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]) 

# 需要导入模块: from object_detection.utils import dataset_util [as 别名]
# 或者: from object_detection.utils.dataset_util import int64_list_feature [as 别名]
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]) 

# 需要导入模块: from object_detection.utils import dataset_util [as 别名]
# 或者: from object_detection.utils.dataset_util import int64_list_feature [as 别名]
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]) 

# 需要导入模块: from object_detection.utils import dataset_util [as 别名]
# 或者: from object_detection.utils.dataset_util import int64_list_feature [as 别名]
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]) 

# 需要导入模块: from object_detection.utils import dataset_util [as 别名]
# 或者: from object_detection.utils.dataset_util import int64_list_feature [as 别名]
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) 

# 需要导入模块: from object_detection.utils import dataset_util [as 别名]
# 或者: from object_detection.utils.dataset_util import int64_list_feature [as 别名]
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 

# 需要导入模块: from object_detection.utils import dataset_util [as 别名]
# 或者: from object_detection.utils.dataset_util import int64_list_feature [as 别名]
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 

# 需要导入模块: from object_detection.utils import dataset_util [as 别名]
# 或者: from object_detection.utils.dataset_util import int64_list_feature [as 别名]
def create_tf_example(img_fname, logo_name, bbox, img_dir, logo_names):
    x1, y1, w, h = list(map(int, bbox))
    x2, y2 = x1 + w, y1 + h
    cls_idx = logo_names[logo_name]
    cls_text = logo_name.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 

# 需要导入模块: from object_detection.utils import dataset_util [as 别名]
# 或者: from object_detection.utils.dataset_util import int64_list_feature [as 别名]
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