Python tensorflow.core.example.feature_pb2.Feature() Examples
The following are 30
code examples of tensorflow.core.example.feature_pb2.Feature().
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Example #1
| Source File: dataset_test.py From spotify-tensorflow with Apache License 2.0 | 6 votes |
|
def _write_test_data():
schema = feature_spec_to_schema({"f0": tf.VarLenFeature(dtype=tf.int64),
"f1": tf.VarLenFeature(dtype=tf.int64),
"f2": tf.VarLenFeature(dtype=tf.int64)})
batches = [
[1, 4, None],
[2, None, None],
[3, 5, None],
[None, None, None],
]
example_proto = [example_pb2.Example(features=feature_pb2.Features(feature={
"f" + str(i): feature_pb2.Feature(int64_list=feature_pb2.Int64List(value=[f]))
for i, f in enumerate(batch) if f is not None
})) for batch in batches]
return DataUtil.write_test_data(example_proto, schema)
Example #2
| Source File: input_reader_builder_test.py From aster 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)
with self.test_session():
encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
example = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/transcript': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[
'hello'.encode('utf-8')]))
}))
writer.write(example.SerializeToString())
writer.close()
return path
Example #3
| Source File: tf_example_decoder_test.py From ros_tensorflow with Apache License 2.0 | 5 votes |
|
def _Int64FeatureFromList(self, ndarray):
return feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=ndarray.flatten().tolist()))
Example #4
| Source File: input_reader_builder_test.py From moveo_ros with MIT License | 5 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)
with self.test_session():
encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
example = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/object/bbox/xmin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
}))
writer.write(example.SerializeToString())
writer.close()
return path
Example #5
| Source File: tf_example_decoder_test.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
def _BytesFeatureFromList(self, ndarray):
values = ndarray.flatten().tolist()
return feature_pb2.Feature(bytes_list=feature_pb2.BytesList(value=values))
Example #6
| Source File: tf_example_decoder_test.py From ros_tensorflow with Apache License 2.0 | 5 votes |
|
def _BytesFeatureFromList(self, ndarray):
values = ndarray.flatten().tolist()
for i in range(len(values)):
values[i] = values[i].encode('utf-8')
return feature_pb2.Feature(bytes_list=feature_pb2.BytesList(value=values))
Example #7
| Source File: tf_example_decoder_test.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
def _BytesFeature(self, value):
if isinstance(value, list):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
Example #8
| Source File: tf_example_decoder_test.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
def _FloatFeature(self, value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
Example #9
| Source File: input_reader_builder_test.py From object_detector_app with MIT License | 5 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)
with self.test_session():
encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
example = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/object/bbox/xmin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
}))
writer.write(example.SerializeToString())
writer.close()
return path
Example #10
| Source File: tf_example_decoder_test.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
def _Int64FeatureFromList(self, ndarray):
return feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=ndarray.flatten().tolist()))
Example #11
| Source File: input_reader_builder_test.py From object_detection_kitti with Apache License 2.0 | 5 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)
with self.test_session():
encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
example = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/object/bbox/xmin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
}))
writer.write(example.SerializeToString())
writer.close()
return path
Example #12
| Source File: input_reader_builder_test.py From MBMD with MIT License | 5 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)
with self.test_session():
encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
example = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/object/bbox/xmin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
}))
writer.write(example.SerializeToString())
writer.close()
return path
Example #13
| Source File: input_reader_builder_test.py From Elphas with Apache License 2.0 | 5 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 = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/height': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[4])),
'image/width': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[5])),
'image/object/bbox/xmin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
'image/object/mask': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=flat_mask)),
}))
writer.write(example.SerializeToString())
writer.close()
return path
Example #14
| Source File: input_reader_builder_test.py From object_detection_with_tensorflow with MIT License | 5 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 = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/height': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[4])),
'image/width': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[5])),
'image/object/bbox/xmin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
'image/object/mask': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=flat_mask)),
}))
writer.write(example.SerializeToString())
writer.close()
return path
Example #15
| Source File: input_reader_builder_test.py From object_detection_with_tensorflow with MIT License | 5 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 = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/height': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[4])),
'image/width': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[5])),
'image/object/bbox/xmin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
'image/object/mask': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=flat_mask)),
}))
writer.write(example.SerializeToString())
writer.close()
return path
Example #16
| Source File: input_reader_builder_test.py From AniSeg with Apache License 2.0 | 5 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 = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/height': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[4])),
'image/width': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[5])),
'image/object/bbox/xmin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
'image/object/mask': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=flat_mask)),
}))
writer.write(example.SerializeToString())
writer.close()
return path
Example #17
| Source File: input_reader_builder_test.py From hands-detection with MIT License | 5 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)
with self.test_session():
encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
example = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/object/bbox/xmin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
}))
writer.write(example.SerializeToString())
writer.close()
return path
Example #18
| Source File: tf_example_decoder_test.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
def _Int64Feature(self, value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
Example #19
| Source File: tf_example_decoder_test.py From ros_tensorflow with Apache License 2.0 | 5 votes |
|
def _BytesFeature(self, value):
if isinstance(value, list):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
Example #20
| Source File: tf_example_decoder_test.py From ros_tensorflow with Apache License 2.0 | 5 votes |
|
def _FloatFeature(self, value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
Example #21
| Source File: tf_example_decoder_test.py From ros_tensorflow with Apache License 2.0 | 5 votes |
|
def _Int64Feature(self, value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
Example #22
| Source File: tf_example_decoder_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def _BytesFeatureFromList(self, ndarray):
values = ndarray.flatten().tolist()
for i in range(len(values)):
values[i] = values[i].encode('utf-8')
return feature_pb2.Feature(bytes_list=feature_pb2.BytesList(value=values))
Example #23
| Source File: tf_example_decoder_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def _Int64FeatureFromList(self, ndarray):
return feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=ndarray.flatten().tolist()))
Example #24
| Source File: tf_example_decoder_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def _BytesFeature(self, value):
if isinstance(value, list):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
Example #25
| Source File: tf_example_decoder_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def _FloatFeature(self, value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
Example #26
| Source File: tf_example_decoder_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def _Int64Feature(self, value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
Example #27
| Source File: input_reader_builder_test.py From tensorflow with BSD 2-Clause "Simplified" License | 5 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)
with self.test_session():
encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
example = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/object/bbox/xmin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
}))
writer.write(example.SerializeToString())
writer.close()
return path
Example #28
| Source File: input_reader_builder_test.py From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License | 5 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)
with self.test_session():
encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
example = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/object/bbox/xmin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
}))
writer.write(example.SerializeToString())
writer.close()
return path
Example #29
| Source File: input_reader_builder_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 5 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 = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/height': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[4])),
'image/width': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[5])),
'image/object/bbox/xmin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
'image/object/mask': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=flat_mask)),
}))
writer.write(example.SerializeToString())
writer.close()
return path
Example #30
| Source File: input_reader_builder_test.py From yolo_v2 with Apache License 2.0 | 5 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 = example_pb2.Example(features=feature_pb2.Features(feature={
'image/encoded': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format': feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
'image/height': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[4])),
'image/width': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[5])),
'image/object/bbox/xmin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label': feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
'image/object/mask': feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=flat_mask)),
}))
writer.write(example.SerializeToString())
writer.close()
return path
