Python tensorflow.core.example.feature_pb2.FloatList() Examples
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code examples of tensorflow.core.example.feature_pb2.FloatList().
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Example #1
| 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 #2
| Source File: tfexample_decoder_test.py From keras-lambda with MIT License | 5 votes |
|
def _EncodedFloatFeature(self, ndarray):
return feature_pb2.Feature(float_list=feature_pb2.FloatList(
value=ndarray.flatten().tolist()))
Example #3
| Source File: input_reader_builder_test.py From mtl-ssl 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 #4
| Source File: input_reader_builder_test.py From motion-rcnn 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: 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 #6
| 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 #7
| 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 #8
| 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 #9
| 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 #10
| 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 #11
| 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 #12
| 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 #13
| Source File: input_reader_builder_test.py From BMW-TensorFlow-Training-GUI 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 ros_tensorflow 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 #15
| Source File: input_reader_builder_test.py From Gun-Detector 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 #16
| Source File: input_reader_builder_test.py From DOTA_models 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 #17
| 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 #18
| Source File: input_reader_builder_test.py From Person-Detection-and-Tracking 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 #19
| Source File: input_reader_builder_test.py From cartoonify 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 #20
| Source File: input_reader_builder_test.py From garbage-object-detection-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)
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 #21
| Source File: input_reader_builder_test.py From HereIsWally 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 #22
| 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
Example #23
| 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 #24
| Source File: input_reader_builder_test.py From ros_people_object_detection_tensorflow 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 #25
| Source File: tfexample_decoder_test.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def _EncodedFloatFeature(self, ndarray):
return feature_pb2.Feature(float_list=feature_pb2.FloatList(
value=ndarray.flatten().tolist()))
Example #26
| 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 #27
| Source File: dnn_test_fc_v2.py From estimator with Apache License 2.0 | 4 votes |
|
def test_input_fn_from_parse_example(self):
"""Tests complete flow with input_fn constructed from parse_example."""
label_dimension = 2
batch_size = 10
data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
data = data.reshape(batch_size, label_dimension)
serialized_examples = []
for datum in data:
example = example_pb2.Example(
features=feature_pb2.Features(
feature={
'x':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=datum)),
'y':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=datum)),
}))
serialized_examples.append(example.SerializeToString())
feature_spec = {
'x': tf.io.FixedLenFeature([label_dimension], tf.dtypes.float32),
'y': tf.io.FixedLenFeature([label_dimension], tf.dtypes.float32),
}
def _train_input_fn():
feature_map = tf.compat.v1.io.parse_example(serialized_examples,
feature_spec)
features = _queue_parsed_features(feature_map)
labels = features.pop('y')
return features, labels
def _eval_input_fn():
feature_map = tf.compat.v1.io.parse_example(
tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features = _queue_parsed_features(feature_map)
labels = features.pop('y')
return features, labels
def _predict_input_fn():
feature_map = tf.compat.v1.io.parse_example(
tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features = _queue_parsed_features(feature_map)
features.pop('y')
return features, None
self._test_complete_flow(
train_input_fn=_train_input_fn,
eval_input_fn=_eval_input_fn,
predict_input_fn=_predict_input_fn,
input_dimension=label_dimension,
label_dimension=label_dimension,
batch_size=batch_size)
Example #28
| Source File: baseline_test_v1.py From estimator with Apache License 2.0 | 4 votes |
|
def _test_input_fn_from_parse_example(self, n_classes):
"""Tests complete flow with input_fn constructed from parse_example."""
input_dimension = 2
batch_size = 10
prediction_length = batch_size
data = np.linspace(0., 2., batch_size * input_dimension, dtype=np.float32)
data = data.reshape(batch_size, input_dimension)
target = np.array([1] * batch_size, dtype=np.int64)
serialized_examples = []
for x, y in zip(data, target):
example = example_pb2.Example(
features=feature_pb2.Features(
feature={
'x':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=x)),
'y':
feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[y])),
}))
serialized_examples.append(example.SerializeToString())
feature_spec = {
'x': tf.io.FixedLenFeature([input_dimension], tf.dtypes.float32),
'y': tf.io.FixedLenFeature([1], tf.dtypes.int64),
}
def _train_input_fn():
feature_map = tf.compat.v1.io.parse_example(serialized_examples,
feature_spec)
features = queue_parsed_features(feature_map)
labels = features.pop('y')
return features, labels
def _eval_input_fn():
feature_map = tf.compat.v1.io.parse_example(
tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features = queue_parsed_features(feature_map)
labels = features.pop('y')
return features, labels
def _predict_input_fn():
feature_map = tf.compat.v1.io.parse_example(
tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features = queue_parsed_features(feature_map)
features.pop('y')
return features, None
self._test_complete_flow(
n_classes=n_classes,
train_input_fn=_train_input_fn,
eval_input_fn=_eval_input_fn,
predict_input_fn=_predict_input_fn,
input_dimension=input_dimension,
prediction_length=prediction_length)
Example #29
| Source File: linear_testing_utils_v1.py From estimator with Apache License 2.0 | 4 votes |
|
def test_input_fn_from_parse_example(self):
"""Tests complete flow with input_fn constructed from parse_example."""
label_dimension = 2
input_dimension = label_dimension
batch_size = 10
prediction_length = batch_size
data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
data = data.reshape(batch_size, label_dimension)
serialized_examples = []
for datum in data:
example = example_pb2.Example(
features=feature_pb2.Features(
feature={
'x':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=datum)),
'y':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(
value=datum[:label_dimension])),
}))
serialized_examples.append(example.SerializeToString())
feature_spec = {
'x': tf.io.FixedLenFeature([input_dimension], tf.dtypes.float32),
'y': tf.io.FixedLenFeature([label_dimension], tf.dtypes.float32),
}
def _train_input_fn():
feature_map = tf.compat.v1.io.parse_example(serialized_examples,
feature_spec)
features = queue_parsed_features(feature_map)
labels = features.pop('y')
return features, labels
def _eval_input_fn():
feature_map = tf.compat.v1.io.parse_example(
tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features = queue_parsed_features(feature_map)
labels = features.pop('y')
return features, labels
def _predict_input_fn():
feature_map = tf.compat.v1.io.parse_example(
tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features = queue_parsed_features(feature_map)
features.pop('y')
return features, None
self._test_complete_flow(
train_input_fn=_train_input_fn,
eval_input_fn=_eval_input_fn,
predict_input_fn=_predict_input_fn,
input_dimension=input_dimension,
label_dimension=label_dimension,
prediction_length=prediction_length)
Example #30
| Source File: linear_testing_utils_v1.py From estimator with Apache License 2.0 | 4 votes |
|
def _test_input_fn_from_parse_example(self, n_classes):
"""Tests complete flow with input_fn constructed from parse_example."""
input_dimension = 2
batch_size = 10
prediction_length = batch_size
data = np.linspace(0., 2., batch_size * input_dimension, dtype=np.float32)
data = data.reshape(batch_size, input_dimension)
target = np.array([1] * batch_size, dtype=np.int64)
serialized_examples = []
for x, y in zip(data, target):
example = example_pb2.Example(
features=feature_pb2.Features(
feature={
'x':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=x)),
'y':
feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[y])),
}))
serialized_examples.append(example.SerializeToString())
feature_spec = {
'x': tf.io.FixedLenFeature([input_dimension], tf.dtypes.float32),
'y': tf.io.FixedLenFeature([1], tf.dtypes.int64),
}
def _train_input_fn():
feature_map = tf.compat.v1.io.parse_example(serialized_examples,
feature_spec)
features = queue_parsed_features(feature_map)
labels = features.pop('y')
return features, labels
def _eval_input_fn():
feature_map = tf.compat.v1.io.parse_example(
tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features = queue_parsed_features(feature_map)
labels = features.pop('y')
return features, labels
def _predict_input_fn():
feature_map = tf.compat.v1.io.parse_example(
tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features = queue_parsed_features(feature_map)
features.pop('y')
return features, None
self._test_complete_flow(
n_classes=n_classes,
train_input_fn=_train_input_fn,
eval_input_fn=_eval_input_fn,
predict_input_fn=_predict_input_fn,
input_dimension=input_dimension,
prediction_length=prediction_length)
