Custom Visualizations

1#!/usr/bin/env python3
2
3import depthai as dai
4
5class ImgDetectionsExtended(dai.ImgDetections):
6    def __init__(self, detections: dai.ImgDetections):
7        dai.ImgDetections.__init__(self)
8        self.detections = detections.detections
9
10    # The function can return dai.ImgAnnotations or dai.ImgFrame
11    def getVisualizationMessage(self):
12        detections = self.detections
13        imgAnnt = dai.ImgAnnotations()
14        # Setting the timestamp is important, as the visualizer uses it to synchronize the data
15        imgAnnt.setTimestamp(self.getTimestamp())
16        annotation = dai.ImgAnnotation()
17        for detection in detections:
18            pointsAnnotation = dai.PointsAnnotation()
19            pointsAnnotation.type = dai.PointsAnnotationType.LINE_STRIP
20            pointsAnnotation.points = dai.VectorPoint2f([
21                dai.Point2f(detection.xmin, detection.ymin),
22                dai.Point2f(detection.xmax, detection.ymin),
23                dai.Point2f(detection.xmax, detection.ymax),
24                dai.Point2f(detection.xmin, detection.ymax),
25            ])
26            outlineColor = dai.Color(1.0, 0.5, 0.5, 1.0)
27            pointsAnnotation.outlineColor = outlineColor
28            fillColor = dai.Color(0.5, 1.0, 0.5, 0.5)
29            pointsAnnotation.fillColor = fillColor
30            pointsAnnotation.thickness = 2.0
31            text = dai.TextAnnotation()
32            text.position = dai.Point2f(detection.xmin, detection.ymin)
33            text.text = f"Test annotation"
34            text.fontSize = 50.5
35            textColor = dai.Color(0.5, 0.5, 1.0, 1.0)
36            text.textColor = textColor
37            backgroundColor = dai.Color(1.0, 1.0, 0.5, 1.0)
38            text.backgroundColor = backgroundColor
39            annotation.points.append(pointsAnnotation)
40            annotation.texts.append(text)
41
42        imgAnnt.annotations.append(annotation)
43        return imgAnnt
44
45class ImgAnnotationsGenerator(dai.node.ThreadedHostNode):
46    def __init__(self):
47        super().__init__()
48        self.inputDet = self.createInput()
49        self.output = self.createOutput()
50
51    def run(self):
52        while self.isRunning():
53            nnData = self.inputDet.get()
54            extended = ImgDetectionsExtended(nnData)
55            # Setting the timestamp is important, as the visualizer uses it to synchronize the data
56            extended.setTimestamp(nnData.getTimestamp())
57            self.output.send(extended)
58
59remoteConnector = dai.RemoteConnection()
60
61# Create pipeline
62with dai.Pipeline() as pipeline:
63    cameraNode = pipeline.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_A)
64    detectionNetwork = pipeline.create(dai.node.DetectionNetwork).build(
65        cameraNode, dai.NNModelDescription("yolov6-nano")
66    )
67    imageAnnotationsGenerator = pipeline.create(ImgAnnotationsGenerator)
68    outputToVisualize = cameraNode.requestOutput((640,480), type=dai.ImgFrame.Type.NV12)
69    detectionNetwork.out.link(imageAnnotationsGenerator.inputDet)
70
71    # Add the remote connector topics
72    remoteConnector.addTopic("encoded", outputToVisualize, "images")
73    remoteConnector.addTopic("detections", detectionNetwork.out, "images")
74    remoteConnector.addTopic("annotations", imageAnnotationsGenerator.output, "images")
75
76    pipeline.start()
77
78    # Register the pipeline with the remote connector
79    remoteConnector.registerPipeline(pipeline)
80
81    while pipeline.isRunning():
82        if remoteConnector.waitKey(1) == ord("q"):
83            pipeline.stop()
84            break