def test_valid_input_data_type_proto(self):
text_config = """
input_stream: 'input_detections'
output_stream: 'output_detections'
node {
calculator: 'DetectionUniqueIdCalculator'
input_stream: 'DETECTION_LIST:input_detections'
output_stream: 'DETECTION_LIST:output_detections'
}
"""
config_proto = text_format.Parse(text_config,
calculator_pb2.CalculatorGraphConfig())
with solution_base.SolutionBase(graph_config=config_proto) as solution:
input_detections = detection_pb2.DetectionList()
detection_1 = input_detections.detection.add()
text_format.Parse('score: 0.5', detection_1)
detection_2 = input_detections.detection.add()
text_format.Parse('score: 0.8', detection_2)
results = solution.process({'input_detections': input_detections})
self.assertTrue(hasattr(results, 'output_detections'))
self.assertLen(results.output_detections.detection, 2)
expected_detection_1 = detection_pb2.Detection()
text_format.Parse('score: 0.5, detection_id: 1', expected_detection_1)
expected_detection_2 = detection_pb2.Detection()
text_format.Parse('score: 0.8, detection_id: 2', expected_detection_2)
self.assertEqual(results.output_detections.detection[0],
expected_detection_1)
self.assertEqual(results.output_detections.detection[1],
expected_detection_2)
def test_unqualified_detection(self):
detection = text_format.Parse('location_data {format: GLOBAL}',
detection_pb2.Detection())
image = np.arange(27, dtype=np.uint8).reshape(3, 3, 3)
with self.assertRaisesRegex(ValueError,
'LocationData must be relative'):
drawing_utils.draw_detection(image, detection)
def test_invalid_input_image(self):
image = np.arange(18, dtype=np.uint8).reshape(3, 3, 2)
with self.assertRaisesRegex(
ValueError,
'Input image must contain three channel rgb data.'):
drawing_utils.draw_landmarks(image,
landmark_pb2.NormalizedLandmarkList())
with self.assertRaisesRegex(
ValueError,
'Input image must contain three channel rgb data.'):
drawing_utils.draw_detection(image, detection_pb2.Detection())
def test_draw_bboxs_only(self):
detection = text_format.Parse(
'location_data {'
' format: RELATIVE_BOUNDING_BOX'
' relative_bounding_box {xmin: 0 ymin: 0 width: 1 height: 1}}',
detection_pb2.Detection())
image = np.zeros((100, 100, 3), np.uint8)
expected_result = np.copy(image)
cv2.rectangle(expected_result, (0, 0), (99, 99),
DEFAULT_BBOX_DRAWING_SPEC.color,
DEFAULT_BBOX_DRAWING_SPEC.thickness)
drawing_utils.draw_detection(image, detection)
np.testing.assert_array_equal(image, expected_result)
def test_draw_keypoints_only(self):
detection = text_format.Parse(
'location_data {'
' format: RELATIVE_BOUNDING_BOX'
' relative_keypoints {x: 0 y: 1}'
' relative_keypoints {x: 1 y: 0}}', detection_pb2.Detection())
image = np.zeros((100, 100, 3), np.uint8)
expected_result = np.copy(image)
cv2.circle(expected_result, (0, 99),
DEFAULT_CIRCLE_DRAWING_SPEC.circle_radius,
DEFAULT_CIRCLE_DRAWING_SPEC.color,
DEFAULT_CIRCLE_DRAWING_SPEC.thickness)
cv2.circle(expected_result, (99, 0),
DEFAULT_CIRCLE_DRAWING_SPEC.circle_radius,
DEFAULT_CIRCLE_DRAWING_SPEC.color,
DEFAULT_CIRCLE_DRAWING_SPEC.thickness)
drawing_utils.draw_detection(image, detection)
np.testing.assert_array_equal(image, expected_result)
def test_invalid_input_data_type(self):
text_config = """
input_stream: 'input_detections'
output_stream: 'output_detections'
node {
calculator: 'DetectionUniqueIdCalculator'
input_stream: 'DETECTIONS:input_detections'
output_stream: 'DETECTIONS:output_detections'
}
"""
config_proto = text_format.Parse(
text_config, calculator_pb2.CalculatorGraphConfig())
with solution_base.SolutionBase(graph_config=config_proto) as solution:
detection = detection_pb2.Detection()
text_format.Parse('score: 0.5', detection)
with self.assertRaisesRegex(
NotImplementedError,
'SolutionBase can only process image data. PROTO_LIST type is not supported.'
):
solution.process({'input_detections': detection})
def testDetectionProtoPacket(self):
detection = detection_pb2.Detection()
text_format.Parse('score: 0.5', detection)
p = mp.packet_creator.create_proto(detection).at(100)