def test_should_load_and_select_in_table(self):
query = """LOAD DATA INFILE 'dummy.avi' INTO MyVideo;"""
perform_query(query)
select_query = "SELECT id FROM MyVideo;"
actual_batch = perform_query(select_query)
expected_rows = [{"id": i} for i in range(NUM_FRAMES)]
expected_batch = Batch(frames=pd.DataFrame(expected_rows))
self.assertEqual(actual_batch, expected_batch)
select_query = "SELECT data FROM MyVideo;"
actual_batch = perform_query(select_query)
expected_rows = [{
"data":
np.array(np.ones((2, 2, 3)) * 0.1 * float(i + 1) * 255,
dtype=np.uint8)
} for i in range(NUM_FRAMES)]
expected_batch = Batch(frames=pd.DataFrame(expected_rows))
self.assertEqual(actual_batch, expected_batch)
# select * is not supported
select_query = "SELECT id,data FROM MyVideo;"
actual_batch = [perform_query(select_query)]
expected_batch = list(create_dummy_batches())
self.assertEqual(actual_batch, expected_batch)
def test_should_load_video_in_table(self):
query = """LOAD DATA INFILE 'dummy.avi' INTO MyVideo;"""
perform_query(query)
metadata = CatalogManager().get_dataset_metadata("", "MyVideo")
actual_batch = list(StorageEngine.read(metadata))
expected_batch = list(create_dummy_batches())
self.assertEqual(actual_batch, expected_batch)
def test_should_load_and_select_real_video_in_table(self):
query = """LOAD DATA INFILE 'data/ua_detrac/ua_detrac.mp4'
INTO MyVideo;"""
perform_query(query)
select_query = "SELECT id,data FROM MyVideo;"
actual_batch = perform_query(select_query)
video_reader = OpenCVReader('data/ua_detrac/ua_detrac/mp4')
expected_batch = Batch(frames=pd.DataFrame())
for batch in video_reader.read():
expected_batch += batch
self.assertTrue(actual_batch, expected_batch)
def test_should_load_video_in_table(self):
query = """LOAD DATA INFILE 'dummy.avi' INTO MyVideo;"""
perform_query(query)
metadata = CatalogManager().get_dataset_metadata("", "MyVideo")
actual_batch = Batch(pd.DataFrame())
for batch in StorageEngine.read(metadata):
actual_batch += batch
actual_batch.sort()
expected_batch = list(create_dummy_batches())
self.assertEqual([actual_batch], expected_batch)
def test_should_load_and_select_in_table(self):
select_query = "SELECT id FROM MyVideo;"
actual_batch = perform_query(select_query)
actual_batch.sort()
expected_rows = [{"id": i} for i in range(NUM_FRAMES)]
expected_batch = Batch(frames=pd.DataFrame(expected_rows))
self.assertEqual(actual_batch, expected_batch)
select_query = "SELECT id,data FROM MyVideo;"
actual_batch = perform_query(select_query)
actual_batch.sort()
expected_batch = list(create_dummy_batches())
self.assertEqual([actual_batch], expected_batch)
def test_sort_on_nonprojected_column(self):
""" This tests doing an order by on a column
that is not projected. The orderby_executor currently
catches the KeyError, passes, and returns the untouched
data
"""
select_query = "SELECT data FROM MyVideo ORDER BY id;"
actual_batch = perform_query(select_query)
select_query = "SELECT data FROM MyVideo"
expected_batch = perform_query(select_query)
self.assertEqual(actual_batch, expected_batch)
def test_should_load_and_sort_in_table(self):
select_query = "SELECT data, id FROM MyVideo ORDER BY id;"
actual_batch = perform_query(select_query)
expected_rows = [{
'id':
i,
'data':
np.array(np.ones((2, 2, 3)) * float(i + 1) * 25, dtype=np.uint8)
} for i in range(NUM_FRAMES)]
expected_batch = Batch(frames=pd.DataFrame(expected_rows))
self.assertEqual(actual_batch, expected_batch)
select_query = "SELECT data, id FROM MyVideo ORDER BY id DESC;"
actual_batch = perform_query(select_query)
expected_batch.reverse()
self.assertEqual(actual_batch, expected_batch)
def test_should_run_pytorch_and_fastrcnn(self):
query = """LOAD DATA INFILE 'data/ua_detrac/ua_detrac.mp4'
INTO MyVideo;"""
perform_query(query)
create_udf_query = """CREATE UDF FastRCNNObjectDetector
INPUT (Frame_Array NDARRAY (3, 256, 256))
OUTPUT (label TEXT(10))
TYPE Classification
IMPL 'src/udfs/fastrcnn_object_detector.py';
"""
perform_query(create_udf_query)
select_query = """SELECT FastRCNNObjectDetector(data) FROM MyVideo
WHERE id < 5;"""
actual_batch = perform_query(select_query)
print(actual_batch)
self.assertEqual(actual_batch.batch_size, 5)
def test_select_and_limit(self):
select_query = "SELECT id,data FROM MyVideo LIMIT 5;"
actual_batch = perform_query(select_query)
actual_batch.sort()
expected_batch = list(create_dummy_batches(num_frames=10,
batch_size=5))
self.assertEqual(actual_batch.batch_size, expected_batch[0].batch_size)
self.assertEqual(actual_batch, expected_batch[0])
def test_select_and_union_video_in_table(self):
select_query = """SELECT id, data FROM MyVideo WHERE id < 3
UNION ALL SELECT id, data FROM MyVideo WHERE id > 7;"""
actual_batch = perform_query(select_query)
actual_batch.sort()
expected_batch = list(
create_dummy_batches(
filters=[i for i in range(NUM_FRAMES) if i < 3 or i > 7]))[0]
self.assertEqual(actual_batch, expected_batch)
select_query = """SELECT id, data FROM MyVideo WHERE id < 2
UNION ALL SELECT id, data FROM MyVideo WHERE id > 4 AND id < 6
UNION ALL SELECT id, data FROM MyVideo WHERE id > 7;"""
actual_batch = perform_query(select_query)
actual_batch.sort()
expected_batch = list(
create_dummy_batches(filters=[
i for i in range(NUM_FRAMES) if i < 2 or i == 5 or i > 7
]))[0]
self.assertEqual(actual_batch, expected_batch)
def test_should_load_and_select_using_udf_video_in_table(self):
load_query = """LOAD DATA INFILE 'dummy.avi' INTO MyVideo;"""
perform_query(load_query)
create_udf_query = """CREATE UDF DummyObjectDetector
INPUT (Frame_Array NDARRAY (3, 256, 256))
OUTPUT (label TEXT(10))
TYPE Classification
IMPL 'test/util.py';
"""
perform_query(create_udf_query)
select_query = "SELECT id,DummyObjectDetector(data) FROM MyVideo;"
actual_batch = perform_query(select_query)
labels = DummyObjectDetector().labels
expected = [{
'id': i,
'label': labels[1 + i % 2]
} for i in range(NUM_FRAMES)]
expected_batch = Batch(frames=pd.DataFrame(expected))
self.assertEqual(actual_batch, expected_batch)
def test_should_run_pytorch_and_ssd(self):
query = """LOAD DATA INFILE 'data/ua_detrac/ua_detrac.mp4'
INTO MyVideo;"""
perform_query(query)
create_udf_query = """CREATE UDF SSDObjectDetector
INPUT (Frame_Array NDARRAY (3, 256, 256))
OUTPUT (label TEXT(10))
TYPE Classification
IMPL 'src/udfs/ssd_object_detector.py';
"""
perform_query(create_udf_query)
select_query = """SELECT SSDObjectDetector(data) FROM MyVideo
WHERE id < 5;"""
actual_batch = perform_query(select_query)
self.assertEqual(actual_batch.batch_size, 5)
# non-trivial test case
res = actual_batch.frames
for idx in res.index:
self.assertTrue('car' in res['label'][idx])
def test_should_load_and_select_in_table(self):
select_query = "SELECT id FROM MyVideo;"
actual_batch = perform_query(select_query)
actual_batch.sort()
expected_rows = [{"id": i} for i in range(NUM_FRAMES)]
expected_batch = Batch(frames=pd.DataFrame(expected_rows))
self.assertEqual(actual_batch, expected_batch)
# Need Order by
# select_query = "SELECT data FROM MyVideo;"
# actual_batch = perform_query(select_query)
# expected_rows = [{"data": np.array(np.ones((2, 2, 3))
# * 0.1 * float(i + 1) * 255,
# dtype=np.uint8)}
# for i in range(NUM_FRAMES)]
# expected_batch = Batch(frames=pd.DataFrame(expected_rows))
# self.assertEqual(actual_batch, expected_batch)
select_query = "SELECT id,data FROM MyVideo;"
actual_batch = perform_query(select_query)
actual_batch.sort()
expected_batch = list(create_dummy_batches())
self.assertEqual([actual_batch], expected_batch)
def test_select_and_where_video_in_table(self):
select_query = "SELECT id,data FROM MyVideo WHERE id = 5;"
actual_batch = perform_query(select_query)
expected_batch = list(create_dummy_batches(filters=[5]))[0]
self.assertEqual(actual_batch, expected_batch)
select_query = "SELECT data FROM MyVideo WHERE id = 5;"
actual_batch = perform_query(select_query)
expected_rows = [{
"data":
np.array(np.ones((2, 2, 3)) * float(5 + 1) * 25, dtype=np.uint8)
}]
expected_batch = Batch(frames=pd.DataFrame(expected_rows))
self.assertEqual(actual_batch, expected_batch)
select_query = "SELECT id, data FROM MyVideo WHERE id >= 2;"
actual_batch = perform_query(select_query)
actual_batch.sort()
expected_batch = list(
create_dummy_batches(filters=range(2, NUM_FRAMES)))[0]
self.assertEqual(actual_batch, expected_batch)
select_query = "SELECT id, data FROM MyVideo WHERE id >= 2 AND id < 5;"
actual_batch = perform_query(select_query)
actual_batch.sort()
expected_batch = list(create_dummy_batches(filters=range(2, 5)))[0]
self.assertEqual(actual_batch, expected_batch)
nested_select_query = """SELECT id, data FROM
(SELECT id, data FROM MyVideo WHERE id >= 2 AND id < 5)
WHERE id >= 3;"""
actual_batch = perform_query(nested_select_query)
actual_batch.sort()
expected_batch = list(create_dummy_batches(filters=range(3, 5)))[0]
self.assertEqual(actual_batch, expected_batch)
def test_should_load_and_select_using_udf_video(self):
load_query = """LOAD DATA INFILE 'dummy.avi' INTO MyVideo;"""
perform_query(load_query)
create_udf_query = """CREATE UDF DummyObjectDetector
INPUT (Frame_Array NDARRAY (3, 256, 256))
OUTPUT (label TEXT(10))
TYPE Classification
IMPL 'test/util.py';
"""
perform_query(create_udf_query)
select_query = "SELECT id,DummyObjectDetector(data) FROM MyVideo \
WHERE DummyObjectDetector(data).label = 'person';"
actual_batch = perform_query(select_query)
actual_batch.sort()
expected = [{
'id': i * 2,
'label': 'person'
} for i in range(NUM_FRAMES // 2)]
expected_batch = Batch(frames=pd.DataFrame(expected))
self.assertEqual(actual_batch, expected_batch)
nested_select_query = """SELECT id, data FROM
(SELECT id, data, DummyObjectDetector(data) FROM MyVideo
WHERE id >= 2
)
WHERE label = 'person';
"""
actual_batch = perform_query(nested_select_query)
actual_batch.sort()
expected_batch = list(
create_dummy_batches(
filters=[i for i in range(2, NUM_FRAMES) if i % 2 == 0]))[0]
self.assertEqual(actual_batch, expected_batch)
def setUpClass(cls):
CatalogManager().reset()
create_sample_video(NUM_FRAMES)
load_query = """LOAD DATA INFILE 'dummy.avi' INTO MyVideo;"""
perform_query(load_query)