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test: add unit tests and fix error

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This commit is contained in:
Cyrille Nofficial
2022-10-21 11:01:38 +02:00
committed by Cyrille Nofficial
parent 9b0b772786
commit 0c5e8e93ac
6 changed files with 206 additions and 16 deletions
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49
camera/depthai.py
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@@ -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

0
camera/tests/__init__.py Normal file
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150
camera/tests/test_depthai.py Normal file
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@@ -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"