Casting NN Concatenation

1#!/usr/bin/env python3
2
3import numpy as np
4import cv2
5import depthai as dai
6from pathlib import Path
7
8SHAPE = 300
9
10p = dai.Pipeline()
11
12camRgb = p.create(dai.node.ColorCamera)
13left = p.create(dai.node.MonoCamera)
14right = p.create(dai.node.MonoCamera)
15manipLeft = p.create(dai.node.ImageManip)
16manipRight = p.create(dai.node.ImageManip)
17nn = p.create(dai.node.NeuralNetwork)
18cast = p.create(dai.node.Cast)
19castXout = p.create(dai.node.XLinkOut)
20
21camRgb.setPreviewSize(SHAPE, SHAPE)
22camRgb.setInterleaved(False)
23camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
24
25left.setCamera("left")
26left.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
27manipLeft.initialConfig.setResize(SHAPE, SHAPE)
28manipLeft.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)
29
30right.setCamera("right")
31right.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
32manipRight.initialConfig.setResize(SHAPE, SHAPE)
33manipRight.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)
34
35nnBlobPath = (Path(__file__).parent / Path('../models/concat_openvino_2021.4_6shave.blob')).resolve().absolute()
36nn.setBlobPath(nnBlobPath)
37nn.setNumInferenceThreads(2)
38
39castXout.setStreamName("cast")
40cast.setOutputFrameType(dai.ImgFrame.Type.BGR888p)
41
42# Linking
43left.out.link(manipLeft.inputImage)
44right.out.link(manipRight.inputImage)
45manipLeft.out.link(nn.inputs['img1'])
46camRgb.preview.link(nn.inputs['img2'])
47manipRight.out.link(nn.inputs['img3'])
48nn.out.link(cast.input)
49cast.output.link(castXout.input)
50
51# Pipeline is defined, now we can connect to the device
52with dai.Device(p) as device:
53    qCast = device.getOutputQueue(name="cast", maxSize=4, blocking=False)
54
55    while True:
56        inCast = qCast.get()
57        assert isinstance(inCast, dai.ImgFrame)
58        cv2.imshow("Concated frames", inCast.getCvFrame())
59
60        if cv2.waitKey(1) == ord('q'):
61            break