Python object_detection.utils.dataset_util.int64_feature() Examples
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Example #1
Source File: input_reader_builder_test.py From vehicle_counting_tensorflow with MIT License | 6 votes |
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 g-tensorflow-models with Apache License 2.0 | 6 votes |
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 multilabel-image-classification-tensorflow with MIT License | 6 votes |
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 Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
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_tf1_test.py From models with Apache License 2.0 | 6 votes |
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 Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
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: input_reader_builder_test.py From MAX-Object-Detector with Apache License 2.0 | 6 votes |
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 #8
Source File: create_kitti_tf_record.py From motion-rcnn with MIT License | 6 votes |
def _create_tfexample(label_map_dict, image_id, encoded_image, encoded_next_image, disparity_image, next_disparity_image, flow): #camera_intrinsics = np.array([982.529, 690.0, 233.1966]) camera_intrinsics = np.array([725.0, 620.5, 187.0], dtype=np.float32) f, x0, y0 = camera_intrinsics depth = _depth_from_disparity_image(disparity_image, f) next_depth = _depth_from_disparity_image(next_disparity_image, f) key = hashlib.sha256(encoded_image).hexdigest() 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(image_id.encode('utf8')), 'image/source_id': dataset_util.bytes_feature(image_id.encode('utf8')), 'image/encoded': dataset_util.bytes_feature(encoded_image), 'next_image/encoded': dataset_util.bytes_feature(encoded_next_image), 'image/format': dataset_util.bytes_feature('png'.encode('utf8')), 'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')), 'image/depth': dataset_util.float_list_feature(depth.ravel().tolist()), 'next_image/depth': dataset_util.float_list_feature(next_depth.ravel().tolist()), 'image/flow': dataset_util.float_list_feature(example_flow.ravel().tolist()), 'image/camera/intrinsics': dataset_util.float_list_feature(camera_intrinsics.tolist()) })) return example, num_instances
Example #9
Source File: dataset_builder_test.py From MAX-Object-Detector with Apache License 2.0 | 5 votes |
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 #10
Source File: gen_tfrecord.py From DeepLogo with MIT License | 5 votes |
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 #11
Source File: input_reader_builder_tf1_test.py From models with Apache License 2.0 | 5 votes |
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 #12
Source File: tf_example_decoder_test.py From MAX-Object-Detector with Apache License 2.0 | 5 votes |
def testDecodeEmptyPngInstanceMasks(self): image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8) encoded_jpeg = self._EncodeImage(image_tensor) encoded_masks = [] 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/mask': dataset_util.bytes_list_feature(encoded_masks), 'image/height': dataset_util.int64_feature(10), 'image/width': dataset_util.int64_feature(10), })).SerializeToString() example_decoder = tf_example_decoder.TfExampleDecoder( load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS) tensor_dict = example_decoder.decode(tf.convert_to_tensor(example)) with self.test_session() as sess: tensor_dict = sess.run(tensor_dict) self.assertAllEqual( tensor_dict[fields.InputDataFields.groundtruth_instance_masks].shape, [0, 10, 10])
Example #13
Source File: tf_example_decoder_test.py From vehicle_counting_tensorflow with MIT License | 5 votes |
def testDecodeEmptyPngInstanceMasks(self): image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8) encoded_jpeg = self._EncodeImage(image_tensor) encoded_masks = [] 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/mask': dataset_util.bytes_list_feature(encoded_masks), 'image/height': dataset_util.int64_feature(10), 'image/width': dataset_util.int64_feature(10), })).SerializeToString() example_decoder = tf_example_decoder.TfExampleDecoder( load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS) tensor_dict = example_decoder.decode(tf.convert_to_tensor(example)) with self.test_session() as sess: tensor_dict = sess.run(tensor_dict) self.assertAllEqual( tensor_dict[fields.InputDataFields.groundtruth_instance_masks].shape, [0, 10, 10])
Example #14
Source File: tf_example_decoder_test.py From MAX-Object-Detector with Apache License 2.0 | 5 votes |
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: dataset_builder_test.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
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 #16
Source File: tf_example_decoder_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
def testDecodeEmptyPngInstanceMasks(self): image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8) encoded_jpeg = self._EncodeImage(image_tensor) encoded_masks = [] 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/mask': dataset_util.bytes_list_feature(encoded_masks), 'image/height': dataset_util.int64_feature(10), 'image/width': dataset_util.int64_feature(10), })).SerializeToString() example_decoder = tf_example_decoder.TfExampleDecoder( load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS) tensor_dict = example_decoder.decode(tf.convert_to_tensor(example)) with self.test_session() as sess: tensor_dict = sess.run(tensor_dict) self.assertAllEqual( tensor_dict[fields.InputDataFields.groundtruth_instance_masks].shape, [0, 10, 10])
Example #17
Source File: tf_example_decoder_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
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 #18
Source File: dataset_builder_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
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 #19
Source File: tf_example_decoder_test.py From models with Apache License 2.0 | 5 votes |
def testDecodeEmptyPngInstanceMasks(self): image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8) encoded_png, _ = self._create_encoded_and_decoded_data(image_tensor, 'png') encoded_masks = [] def graph_fn(): example = tf.train.Example( features=tf.train.Features( feature={ 'image/encoded': dataset_util.bytes_feature(encoded_png), 'image/format': dataset_util.bytes_feature(six.b('png')), 'image/object/mask': dataset_util.bytes_list_feature(encoded_masks), 'image/height': dataset_util.int64_feature(10), 'image/width': dataset_util.int64_feature(10), })).SerializeToString() example_decoder = tf_example_decoder.TfExampleDecoder( load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS) return example_decoder.decode(tf.convert_to_tensor(example)) tensor_dict = self.execute_cpu(graph_fn, []) self.assertAllEqual( tensor_dict[fields.InputDataFields.groundtruth_instance_masks].shape, [0, 10, 10])
Example #20
Source File: tf_example_decoder_test.py From models with Apache License 2.0 | 5 votes |
def testContextFeaturesNotAvailableByDefault(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_ymins = [0.0, 4.0] bbox_xmins = [1.0, 5.0] bbox_ymaxs = [2.0, 6.0] bbox_xmaxs = [3.0, 7.0] num_features = 10 context_feature_length = 10 context_features = np.random.random(num_features*context_feature_length) 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/context_features': dataset_util.float_list_feature(context_features), 'image/context_feature_length': dataset_util.int64_feature(context_feature_length), 'image/object/bbox/ymin': dataset_util.float_list_feature(bbox_ymins), 'image/object/bbox/xmin': dataset_util.float_list_feature(bbox_xmins), 'image/object/bbox/ymax': dataset_util.float_list_feature(bbox_ymaxs), 'image/object/bbox/xmax': dataset_util.float_list_feature(bbox_xmaxs), })).SerializeToString() example_decoder = tf_example_decoder.TfExampleDecoder() return example_decoder.decode(tf.convert_to_tensor(example)) tensor_dict = self.execute_cpu(graph_fn, []) self.assertNotIn(fields.InputDataFields.context_features, tensor_dict)
Example #21
Source File: gen_tfrecord_logos32plus.py From DeepLogo with MIT License | 5 votes |
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
Example #22
Source File: dataset_builder_test.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
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 #23
Source File: dataset_builder_test.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
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 #24
Source File: Generate_tfrecord.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
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 #25
Source File: generate_tfrecord.py From TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10 with Apache License 2.0 | 5 votes |
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 #26
Source File: tf_example_decoder_test.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
def testDecodeEmptyPngInstanceMasks(self): image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8) encoded_jpeg = self._EncodeImage(image_tensor) encoded_masks = [] 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/mask': dataset_util.bytes_list_feature(encoded_masks), 'image/height': dataset_util.int64_feature(10), 'image/width': dataset_util.int64_feature(10), })).SerializeToString() example_decoder = tf_example_decoder.TfExampleDecoder( load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS) tensor_dict = example_decoder.decode(tf.convert_to_tensor(example)) with self.test_session() as sess: tensor_dict = sess.run(tensor_dict) self.assertAllEqual( tensor_dict[fields.InputDataFields.groundtruth_instance_masks].shape, [0, 10, 10])
Example #27
Source File: generate_tfrecord.py From Tensorflow-Object-Detection-API-Train-Model with MIT License | 5 votes |
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 #28
Source File: tf_example_decoder_test.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
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 #29
Source File: generate_tf_records.py From Emojinator with MIT License | 5 votes |
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 #30
Source File: dataset_builder_test.py From vehicle_counting_tensorflow with MIT License | 5 votes |
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