test: add unit tests and fix error

.pylintrc

@@ -53,7 +53,7 @@ ignore-paths=
 # Files or directories matching the regular expression patterns are skipped.
 # The regex matches against base names, not paths. The default value ignores
 # Emacs file locks
-ignore-patterns=^\.#
+ignore-patterns=^\.#,test_.*?py
 
 # List of module names for which member attributes should not be checked
 # (useful for modules/projects where namespaces are manipulated during runtime

camera/depthai.py

@@ -29,12 +29,11 @@ class ObjectProcessor:
         self._objects_topic = objects_topic
         self._objects_threshold = objects_threshold
 
-    def process(self, in_nn: dai.NNData, frame_ref, frame_date: datetime.datetime) -> None:
+    def process(self, in_nn: dai.NNData, frame_ref) -> None:
         """
         Parse and publish result of NeuralNetwork result
         :param in_nn: NeuralNetwork result read from device
         :param frame_ref: Id of the frame where objects are been detected
-        :param frame_date: Datetime of the frame used for detection
         :return:
         """
         detection_boxes = np.array(in_nn.getLayerFp16("ExpandDims")).reshape((100, 4))
@@ -45,9 +44,10 @@ class ObjectProcessor:
         scores = detection_scores[mask]
 
         if boxes.shape[0] > 0:
-            self._publish_objects(boxes, frame_ref, frame_date, scores)
+            self._publish_objects(boxes, frame_ref, scores)
+
+    def _publish_objects(self, boxes: np.array, frame_ref, scores: np.array) -> None:
 
-    def _publish_objects(self, boxes: np.array, frame_ref, now: datetime.datetime, scores: np.array) -> None:
         objects_msg = events.events_pb2.ObjectsMessage()
         objs = []
         for i in range(boxes.shape[0]):
@@ -56,7 +56,7 @@ class ObjectProcessor:
         objects_msg.objects.extend(objs)
         objects_msg.frame_ref.name = frame_ref.name
         objects_msg.frame_ref.id = frame_ref.id
-        objects_msg.frame_ref.created_at.FromDatetime(now)
+        objects_msg.frame_ref.created_at.FromDatetime(frame_ref.created_at.ToDatetime())
         logger.debug("publish object event to %s", self._objects_topic)
         self._mqtt_client.publish(topic=self._objects_topic,
                                   payload=objects_msg.SerializeToString(),
@@ -64,6 +64,21 @@ class ObjectProcessor:
                                   retain=False)
 
 
+class FrameProcessError(Exception):
+    """
+    Error base for invalid frame processing
+
+    Attributes:
+        message -- explanation of the error
+    """
+
+    def __init__(self, message: str):
+        """
+        :param message: explanation of the error
+        """
+        self.message = message
+
+
 class FrameProcessor:
     """
     Processor for camera frames
@@ -73,16 +88,19 @@ class FrameProcessor:
         self._mqtt_client = mqtt_client
         self._frame_topic = frame_topic
 
-    def process(self, img: dai.ImgFrame) -> (typing.Any, datetime.datetime):
+    def process(self, img: dai.ImgFrame) -> typing.Any:
         """
         Publish camera frames
         :param img:
         :return:
-            id frame
-            frame creation datetime
+            id frame reference
+        :raise:
+            FrameProcessError if frame can't be processed
         """
         im_resize = img.getCvFrame()
         is_success, im_buf_arr = cv2.imencode(".jpg", im_resize)
+        if not is_success:
+            raise FrameProcessError("unable to process to encode frame to jpg")
         byte_im = im_buf_arr.tobytes()
 
         now = datetime.datetime.now()
@@ -96,7 +114,7 @@ class FrameProcessor:
                                   payload=frame_msg.SerializeToString(),
                                   qos=0,
                                   retain=False)
-        return frame_msg.id, now
+        return frame_msg.id
 
 
 class PipelineController:
@@ -124,7 +142,7 @@ class PipelineController:
 
         # Resize image
         manip = pipeline.create(dai.node.ImageManip)
-        manip.initialConfig.setResize(NN_WIDTH, NN_HEIGHT)
+        manip.initialConfig.setResize(_NN_WIDTH, _NN_HEIGHT)
         manip.initialConfig.setFrameType(dai.ImgFrame.Type.RGB888p)
         manip.initialConfig.setKeepAspectRatio(False)
 
@@ -161,7 +179,7 @@ class PipelineController:
     def _configure_detection_nn(pipeline: dai.Pipeline) -> dai.node.NeuralNetwork:
         # Define a neural network that will make predictions based on the source frames
         detection_nn = pipeline.create(dai.node.NeuralNetwork)
-        detection_nn.setBlobPath(NN_PATH)
+        detection_nn.setBlobPath(_NN_PATH)
         detection_nn.setNumPoolFrames(4)
         detection_nn.input.setBlocking(False)
         detection_nn.setNumInferenceThreads(2)
@@ -201,11 +219,13 @@ class PipelineController:
 
         # Wait for frame
         in_rgb: dai.ImgFrame = q_rgb.get()  # blocking call, will wait until a new data has arrived
-        frame_msg, now = self._frame_processor.process(in_rgb)
-
+        try:
+            frame_ref = self._frame_processor.process(in_rgb)
+        except FrameProcessError as ex:
+            logger.error("unable to process frame: %s", str(ex))
         # Read NN result
         in_nn: dai.NNData = q_nn.get()
-        self._object_processor.process(in_nn, frame_msg.id, now)
+        self._object_processor.process(in_nn, frame_ref)
 
     def stop(self):
         """
@@ -214,6 +234,7 @@ class PipelineController:
         """
         self._stop = True
 
+
 def _bbox_to_object(bbox: np.array, score: float) -> events.events_pb2.Object:
     obj = events.events_pb2.Object()
     obj.type = events.events_pb2.TypeObject.ANY

camera/tests/test_depthai.py

@@ -0,0 +1,150 @@
+import datetime
+import unittest.mock
+
+import depthai as dai
+import numpy as np
+import paho.mqtt.client as mqtt
+import pytest
+import pytest_mock
+
+import camera.depthai
+import events.events_pb2
+
+Object = dict[str, float]
+
+
+@pytest.fixture
+def mqtt_client(mocker: pytest_mock.MockerFixture) -> mqtt.Client:
+    return mocker.MagicMock()
+
+
+class TestObjectProcessor:
+    @pytest.fixture
+    def frame_ref(self):
+        now = datetime.datetime.now()
+        frame_msg = events.events_pb2.FrameMessage()
+        frame_msg.id.name = "robocar-oak-camera-oak"
+        frame_msg.id.id = str(int(now.timestamp() * 1000))
+        frame_msg.id.created_at.FromDatetime(now)
+        return frame_msg.id
+
+    @pytest.fixture
+    def object1(self) -> Object:
+        return {
+            "left": 0.3,
+            "right": 0.7,
+            "top": 0.1,
+            "bottom": 0.6,
+            "score": 0.8,
+        }
+
+    @pytest.fixture
+    def raw_objects_empty(self, mocker: pytest_mock.MockerFixture) -> dai.NNData:
+        raw_objects = mocker.MagicMock()
+
+        def mock_return(name):
+            if name == "ExpandDims":
+                return [[0] * 4] * 100
+            elif name == "ExpandDims_2":
+                return [0] * 100
+            else:
+                raise ValueError(f"{name} is not a valid arg")
+
+        m = mocker.patch(target='depthai.NNData.getLayerFp16', autospec=True)
+        m.getLayerFp16 = mock_return
+        return m
+
+    @pytest.fixture
+    def raw_objects_one(self, mocker: pytest_mock.MockerFixture, object1: Object) -> dai.NNData:
+        def mock_return(name):
+            if name == "ExpandDims":  # Detection boxes
+                boxes = [[0] * 4] * 100
+                boxes[0] = [object1["top"], object1["left"], object1["bottom"], object1["right"]]
+                return np.array(boxes)
+
+            elif name == "ExpandDims_2":  # Detection scores
+                scores = [0] * 100
+                scores[0] = object1["score"]
+                return scores
+            else:
+                raise ValueError(f"{name} is not a valid arg")
+
+        m = mocker.patch(target='depthai.NNData.getLayerFp16', autospec=True)
+        m.getLayerFp16 = mock_return
+        return m
+
+    @pytest.fixture
+    def object_processor(self, mqtt_client: mqtt.Client) -> camera.depthai.ObjectProcessor:
+        return camera.depthai.ObjectProcessor(mqtt_client, "topic/object", 0.2)
+
+    def test_process_without_object(self, object_processor: camera.depthai.ObjectProcessor, mqtt_client,
+                                    raw_objects_empty, frame_ref):
+        object_processor.process(raw_objects_empty, frame_ref)
+        mqtt_client.publish.assert_not_called()
+
+    def test_process_with_object_with_low_score(self, object_processor: camera.depthai.ObjectProcessor, mqtt_client,
+                                                raw_objects_one, frame_ref):
+        object_processor._objects_threshold = 0.9
+        object_processor.process(raw_objects_one, frame_ref)
+        mqtt_client.publish.assert_not_called()
+
+    def test_process_with_one_object(self,
+                                     object_processor: camera.depthai.ObjectProcessor, mqtt_client,
+                                     raw_objects_one, frame_ref, object1: Object):
+        object_processor.process(raw_objects_one, frame_ref)
+        left = object1["left"]
+        right = object1["right"]
+        top = object1["top"]
+        bottom = object1["bottom"]
+        score = object1["score"]
+
+        pub_mock: unittest.mock.MagicMock = mqtt_client.publish
+        pub_mock.assert_called_once_with(payload=unittest.mock.ANY, qos=0, retain=False, topic="topic/object")
+        payload = pub_mock.call_args.kwargs['payload']
+        objects_msg = events.events_pb2.ObjectsMessage()
+        objects_msg.ParseFromString(payload)
+        assert len(objects_msg.objects) == 1
+        assert left - 0.0001 < objects_msg.objects[0].left < left + 0.0001
+        assert right - 0.0001 < objects_msg.objects[0].right < right + 0.0001
+        assert top - 0.0001 < objects_msg.objects[0].top < top + 0.0001
+        assert bottom - 0.0001 < objects_msg.objects[0].bottom < bottom + 0.0001
+        assert score - 0.0001 < objects_msg.objects[0].confidence < score + 0.0001
+        assert objects_msg.frame_ref == frame_ref
+
+
+class TestFrameProcessor:
+    @pytest.fixture
+    def frame_processor(self, mqtt_client: mqtt.Client) -> camera.depthai.FrameProcessor:
+        return camera.depthai.FrameProcessor(mqtt_client, "topic/frame")
+
+    def test_process(self, frame_processor: camera.depthai.FrameProcessor, mocker: pytest_mock.MockerFixture,
+                     mqtt_client: mqtt.Client):
+        img: dai.ImgFrame = mocker.MagicMock()
+        mocker.patch(target="cv2.imencode").return_value = (True, np.array(b"img content"))
+
+        frame_ref = frame_processor.process(img)
+
+        pub_mock: unittest.mock.MagicMock = mqtt_client.publish
+        pub_mock.assert_called_once_with(payload=unittest.mock.ANY, qos=0, retain=False, topic="topic/frame")
+        payload = pub_mock.call_args.kwargs['payload']
+        frame_msg = events.events_pb2.FrameMessage()
+        frame_msg.ParseFromString(payload)
+
+        assert frame_msg.id == frame_ref
+        assert frame_msg.frame == b"img content"
+
+        assert frame_msg.id.name == "robocar-oak-camera-oak"
+        assert len(frame_msg.id.id) is 13
+        now = datetime.datetime.now()
+        assert now - datetime.timedelta(
+            milliseconds=10) < frame_msg.id.created_at.ToDatetime() < now + datetime.timedelta(milliseconds=10)
+
+    def test_process_error(self, frame_processor: camera.depthai.FrameProcessor, mocker: pytest_mock.MockerFixture,
+                           mqtt_client: mqtt.Client):
+        img: dai.ImgFrame = mocker.MagicMock()
+        mocker.patch(target="cv2.imencode").return_value = (False, None)
+
+        with pytest.raises(camera.depthai.FrameProcessError) as ex:
+            _ = frame_processor.process(img)
+        exception_raised = ex.value
+        assert exception_raised.message == "unable to process to encode frame to jpg"

poetry.lock

@@ -345,6 +345,20 @@ tomli = ">=1.0.0"
 [package.extras]
 testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "xmlschema"]
 
+[[package]]
+name = "pytest-mock"
+version = "3.10.0"
+description = "Thin-wrapper around the mock package for easier use with pytest"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+pytest = ">=5.0"
+
+[package.extras]
+dev = ["pre-commit", "pytest-asyncio", "tox"]
+
 [[package]]
 name = "python-dateutil"
 version = "2.8.2"
@@ -452,7 +466,7 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.10"
-content-hash = "c241f1088945e1b451684386b6aac16ba85a85394c94c8a19099b5ebea05b53f"
+content-hash = "d062fb11b00c63a20b69560322be0723850a7fe3a4363bfe0339f1f75ffd0e2e"
 
 [metadata.files]
 astroid = [
@@ -679,6 +693,10 @@ pytest = [
     {file = "pytest-7.1.3-py3-none-any.whl", hash = "sha256:1377bda3466d70b55e3f5cecfa55bb7cfcf219c7964629b967c37cf0bda818b7"},
     {file = "pytest-7.1.3.tar.gz", hash = "sha256:4f365fec2dff9c1162f834d9f18af1ba13062db0c708bf7b946f8a5c76180c39"},
 ]
+pytest-mock = [
+    {file = "pytest-mock-3.10.0.tar.gz", hash = "sha256:fbbdb085ef7c252a326fd8cdcac0aa3b1333d8811f131bdcc701002e1be7ed4f"},
+    {file = "pytest_mock-3.10.0-py3-none-any.whl", hash = "sha256:f4c973eeae0282963eb293eb173ce91b091a79c1334455acfac9ddee8a1c784b"},
+]
 python-dateutil = [
     {file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
     {file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"},

pyproject.toml

@@ -22,6 +22,7 @@ protobuf = "^4.21.8"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^7.1.3"
+pytest-mock = "^3.10.0"
 
 
 [tool.poetry.group.dev.dependencies]