def test_select_and_where_video_in_table(self):
select_query = "SELECT id,data FROM MyVideo WHERE id = 5;"
actual_batch = execute_query_fetch_all(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 = execute_query_fetch_all(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 = execute_query_fetch_all(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 = execute_query_fetch_all(select_query)
actual_batch.sort()
expected_batch = list(create_dummy_batches(filters=range(2, 5)))[0]
self.assertEqual(actual_batch, expected_batch)
def test_should_load_video_in_table(self):
query = """LOAD DATA INFILE 'dummy.avi' INTO MyVideo;"""
execute_query_fetch_all(query)
metadata = CatalogManager().get_dataset_metadata("", "MyVideo")
actual_batch = Batch(pd.DataFrame())
actual_batch = Batch.concat(StorageEngine.read(metadata), copy=False)
actual_batch.sort()
expected_batch = list(create_dummy_batches())
self.assertEqual([actual_batch], expected_batch)
def test_cnn_filtering(self):
create_udf_query = """CREATE UDF CNNFilter
INPUT (Frame_Array NDARRAY UINT8(3, 256, 256))
OUTPUT (hasCar BOOLEAN)
TYPE Filter
IMPL 'src/udfs/filters/cnn_filter.py';
"""
execute_query_fetch_all(create_udf_query)
select_query = """SELECT id FROM MyVideo WHERE CNNFilter(data);"""
execute_query_fetch_all(select_query)
def setUp(self):
CatalogManager().reset()
create_sample_video(NUM_FRAMES)
load_query = """LOAD DATA INFILE 'dummy.avi' INTO MyVideo;"""
execute_query_fetch_all(load_query)
create_udf_query = """CREATE UDF DummyObjectDetector
INPUT (Frame_Array NDARRAY UINT8(3, 256, 256))
OUTPUT (label NDARRAY STR(10))
TYPE Classification
IMPL 'test/util.py';
"""
execute_query_fetch_all(create_udf_query)
def test_should_load_and_select_in_table(self):
select_query = "SELECT id FROM MyVideo;"
actual_batch = execute_query_fetch_all(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 = execute_query_fetch_all(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 = execute_query_fetch_all(select_query)
select_query = "SELECT data FROM MyVideo"
expected_batch = execute_query_fetch_all(select_query)
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;"""
execute_query_fetch_all(query)
select_query = "SELECT id,data FROM MyVideo;"
actual_batch = execute_query_fetch_all(select_query)
actual_batch.sort()
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_and_select_using_udf_video(self):
# Equality test
select_query = "SELECT id,DummyObjectDetector(data) FROM MyVideo \
WHERE DummyObjectDetector(data).label = ['person'] ORDER BY id;"
actual_batch = execute_query_fetch_all(select_query)
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)
# Contain test
select_query = "SELECT id,DummyObjectDetector(data) FROM MyVideo \
WHERE DummyObjectDetector(data).label @> ['person'] ORDER BY id;"
actual_batch = execute_query_fetch_all(select_query)
self.assertEqual(actual_batch, expected_batch)
# Mutli element contain test
select_query = "SELECT id,DummyObjectDetector(data) FROM MyVideo \
WHERE DummyObjectDetector(data).label <@ ['person', 'bicycle'] \
ORDER BY id;"
actual_batch = execute_query_fetch_all(select_query)
expected = [{
'id': i * 2,
'label': ['person']
} for i in range(NUM_FRAMES // 2)]
expected += [{
'id': i,
'label': ['bicycle']
} for i in range(NUM_FRAMES) if i % 2 + 1 == 2]
expected_batch = Batch(frames=pd.DataFrame(expected))
expected_batch.sort()
self.assertEqual(actual_batch, expected_batch)
nested_select_query = """SELECT id, data FROM
(SELECT id, data, DummyObjectDetector(data) FROM MyVideo
WHERE id >= 2
)
WHERE ['person'] <@ label;
"""
actual_batch = execute_query_fetch_all(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 test_should_load_video_in_table(self):
query = """LOAD DATA INFILE 'dummy.avi' INTO MyVideo;"""
execute_query_fetch_all(query)
insert_query = """ INSERT INTO MyVideo (id, data) VALUES (40,
[[[40, 40, 40] , [40, 40, 40]],
[[40, 40, 40], [40, 40, 40]]]);"""
execute_query_fetch_all(insert_query)
insert_query_2 = """ INSERT INTO MyVideo (id, data) VALUES (41,
[[[41, 41, 41] , [41, 41, 41]],
[[41, 41, 41], [41, 41, 41]]]);"""
execute_query_fetch_all(insert_query_2)
query = 'SELECT id, data FROM MyVideo WHERE id = 40'
batch = execute_query_fetch_all(query)
self.assertIsNone(
np.testing.assert_array_equal(
batch.frames['data'][0],
np.array([[[40, 40, 40], [40, 40, 40]],
[[40, 40, 40], [40, 40, 40]]])))
query = 'SELECT id, data FROM MyVideo WHERE id = 41;'
batch = execute_query_fetch_all(query)
self.assertIsNone(
np.testing.assert_array_equal(
batch.frames['data'][0],
np.array([[[41, 41, 41], [41, 41, 41]],
[[41, 41, 41], [41, 41, 41]]])))
def test_should_load_and_sort_in_table(self):
select_query = "SELECT data, id FROM MyVideo ORDER BY id;"
actual_batch = execute_query_fetch_all(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 = execute_query_fetch_all(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;"""
execute_query_fetch_all(query)
create_udf_query = """CREATE UDF FastRCNNObjectDetector
INPUT (Frame_Array NDARRAY UINT8(3, 256, 256))
OUTPUT (label NDARRAY STR(10))
TYPE Classification
IMPL 'src/udfs/fastrcnn_object_detector.py';
"""
execute_query_fetch_all(create_udf_query)
select_query = """SELECT FastRCNNObjectDetector(data) FROM MyVideo
WHERE id < 5;"""
actual_batch = execute_query_fetch_all(select_query)
self.assertEqual(actual_batch.batch_size, 5)
def test_nested_select_video_in_table(self):
nested_select_query = """SELECT id, data FROM
(SELECT id, data FROM MyVideo WHERE id >= 2 AND id < 5)
WHERE id >= 3;"""
actual_batch = execute_query_fetch_all(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_select_and_limit(self):
select_query = "SELECT id,data FROM MyVideo LIMIT 5;"
actual_batch = execute_query_fetch_all(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_sample(self):
select_query = "SELECT id,data FROM MyVideo SAMPLE 7;"
actual_batch = execute_query_fetch_all(select_query)
actual_batch.sort()
expected_batch = list(
create_dummy_batches(filters=range(0, NUM_FRAMES, 7)))
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 = execute_query_fetch_all(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 = execute_query_fetch_all(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):
select_query = "SELECT id,DummyObjectDetector(data) FROM MyVideo \
ORDER BY id;"
actual_batch = execute_query_fetch_all(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;"""
execute_query_fetch_all(query)
create_udf_query = """CREATE UDF SSDObjectDetector
INPUT (Frame_Array NDARRAY UINT8(3, 256, 256))
OUTPUT (label NDARRAY STR(10))
TYPE Classification
IMPL 'src/udfs/ssd_object_detector.py';
"""
execute_query_fetch_all(create_udf_query)
select_query = """SELECT SSDObjectDetector(data) FROM MyVideo
WHERE id < 5;"""
actual_batch = execute_query_fetch_all(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_logical_to_physical_udf(self):
load_query = """LOAD DATA INFILE 'dummy.avi' INTO MyVideo;"""
execute_query_fetch_all(load_query)
create_udf_query = """CREATE UDF DummyObjectDetector
INPUT (Frame_Array NDARRAY UINT8(3, 256, 256))
OUTPUT (label NDARRAY STR(10))
TYPE Classification
IMPL 'test/util.py';
"""
execute_query_fetch_all(create_udf_query)
select_query = """SELECT id, DummyObjectDetector(data)
FROM MyVideo
WHERE DummyObjectDetector(data).label = ['person'];
"""
actual_batch = execute_query_fetch_all(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)
def setUp(self):
CatalogManager().reset()
load_query = """LOAD DATA INFILE 'data/m30.mp4' INTO MyVideo;"""
execute_query_fetch_all(load_query)
def setUpClass(cls):
CatalogManager().reset()
create_sample_video(NUM_FRAMES)
load_query = """LOAD DATA INFILE 'dummy.avi' INTO MyVideo;"""
execute_query_fetch_all(load_query)