# Casting NN Concatenation

This example demonstrates how to concatenate frames from multiple cameras (RGB, left, and right) using a
[NeuralNetwork](https://docs.luxonis.com/software/depthai-components/nodes/neural_network.md) and the
[Cast](https://docs.luxonis.com/software/depthai-components/nodes/cast_node.md) node.

## Demo

## Setup

Please run the [install script](https://github.com/luxonis/depthai-python/blob/main/examples/install_requirements.py) to download
all required dependencies. Please note that this script must be ran from git context, so you have to download the
[depthai-python](https://github.com/luxonis/depthai-python) repository first and then run the script

```bash
git clone https://github.com/luxonis/depthai-python.git
cd depthai-python/examples
python3 install_requirements.py
```

For additional information, please follow the [installation guide](https://docs.luxonis.com/software/depthai/manual-install.md).

## Source code

#### Python

```python
#!/usr/bin/env python3

import numpy as np
import cv2
import depthai as dai
from pathlib import Path

SHAPE = 300

p = dai.Pipeline()

camRgb = p.create(dai.node.ColorCamera)
left = p.create(dai.node.MonoCamera)
right = p.create(dai.node.MonoCamera)
manipLeft = p.create(dai.node.ImageManip)
manipRight = p.create(dai.node.ImageManip)
nn = p.create(dai.node.NeuralNetwork)
cast = p.create(dai.node.Cast)
castXout = p.create(dai.node.XLinkOut)

camRgb.setPreviewSize(SHAPE, SHAPE)
camRgb.setInterleaved(False)
camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)

left.setCamera("left")
left.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
manipLeft.initialConfig.setResize(SHAPE, SHAPE)
manipLeft.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)

right.setCamera("right")
right.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
manipRight.initialConfig.setResize(SHAPE, SHAPE)
manipRight.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)

nnBlobPath = (Path(__file__).parent / Path('../models/concat_openvino_2021.4_6shave.blob')).resolve().absolute()
nn.setBlobPath(nnBlobPath)
nn.setNumInferenceThreads(2)

castXout.setStreamName("cast")
cast.setOutputFrameType(dai.ImgFrame.Type.BGR888p)

# Linking
left.out.link(manipLeft.inputImage)
right.out.link(manipRight.inputImage)
manipLeft.out.link(nn.inputs['img1'])
camRgb.preview.link(nn.inputs['img2'])
manipRight.out.link(nn.inputs['img3'])
nn.out.link(cast.input)
cast.output.link(castXout.input)

# Pipeline is defined, now we can connect to the device
with dai.Device(p) as device:
    qCast = device.getOutputQueue(name="cast", maxSize=4, blocking=False)

    while True:
        inCast = qCast.get()
        assert isinstance(inCast, dai.ImgFrame)
        cv2.imshow("Concated frames", inCast.getCvFrame())

        if cv2.waitKey(1) == ord('q'):
            break
```

#### C++

```cpp
#include <depthai/depthai.hpp>
#include <opencv2/opencv.hpp>

constexpr int SHAPE = 300;

int main() {
    dai::Pipeline p;

    auto camRgb = p.create<dai::node::ColorCamera>();
    auto left = p.create<dai::node::MonoCamera>();
    auto right = p.create<dai::node::MonoCamera>();
    auto manipLeft = p.create<dai::node::ImageManip>();
    auto manipRight = p.create<dai::node::ImageManip>();
    auto nn = p.create<dai::node::NeuralNetwork>();
    auto cast = p.create<dai::node::Cast>();
    auto castXout = p.create<dai::node::XLinkOut>();

    camRgb->setPreviewSize(SHAPE, SHAPE);
    camRgb->setInterleaved(false);
    camRgb->setColorOrder(dai::ColorCameraProperties::ColorOrder::BGR);

    left->setCamera("left");
    left->setResolution(dai::MonoCameraProperties::SensorResolution::THE_400_P);
    manipLeft->initialConfig.setResize(SHAPE, SHAPE);
    manipLeft->initialConfig.setFrameType(dai::ImgFrame::Type::BGR888p);

    right->setCamera("right");
    right->setResolution(dai::MonoCameraProperties::SensorResolution::THE_400_P);
    manipRight->initialConfig.setResize(SHAPE, SHAPE);
    manipRight->initialConfig.setFrameType(dai::ImgFrame::Type::BGR888p);

    nn->setBlobPath(BLOB_PATH);
    nn->setNumInferenceThreads(2);

    castXout->setStreamName("cast");
    cast->setOutputFrameType(dai::ImgFrame::Type::BGR888p);

    // Linking
    left->out.link(manipLeft->inputImage);
    right->out.link(manipRight->inputImage);
    manipLeft->out.link(nn->inputs["img1"]);
    camRgb->preview.link(nn->inputs["img2"]);
    manipRight->out.link(nn->inputs["img3"]);
    nn->out.link(cast->input);
    cast->output.link(castXout->input);

    // Pipeline is defined, now we can connect to the device
    dai::Device device(p);
    auto qCast = device.getOutputQueue("cast", 4, false);

    while(true) {
        auto inCast = qCast->get<dai::ImgFrame>();
        if(inCast) {
            cv::imshow("Concated frames", inCast->getCvFrame());
        }

        if(cv::waitKey(1) == 'q') {
            break;
        }
    }

    return 0;
}
```

## Pipeline

### examples/cast_concat.pipeline.json

```json
{
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        "node1OutputGroup": "",
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      {
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        "node1Output": "out",
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        "node2Id": 4,
        "node2Input": "inputImage",
        "node2InputGroup": ""
      },
      {
        "node1Id": 3,
        "node1Output": "out",
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      },
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      },
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    "globalProperties": {
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      "cameraTuningBlobUri": "",
      "leonCssFrequencyHz": 700000000.0,
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            ],
            [
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                "img1"
              ],
              {
                "blocking": false,
                "group": "inputs",
                "id": 24,
                "name": "img1",
                "queueSize": 1,
                "type": 3,
                "waitForMessage": true
              }
            ],
            [
              [
                "inputs",
                "img2"
              ],
              {
                "blocking": false,
                "group": "inputs",
                "id": 25,
                "name": "img2",
                "queueSize": 1,
                "type": 3,
                "waitForMessage": true
              }
            ],
            [
              [
                "",
                "out"
              ],
              {
                "blocking": false,
                "group": "",
                "id": 27,
                "name": "out",
                "queueSize": 8,
                "type": 0,
                "waitForMessage": false
              }
            ],
            [
              [
                "inputs",
                "img3"
              ],
              {
                "blocking": false,
                "group": "inputs",
                "id": 26,
                "name": "img3",
                "queueSize": 1,
                "type": 3,
                "waitForMessage": true
              }
            ],
            [
              [
                "",
                "passthrough"
              ],
              {
                "blocking": false,
                "group": "",
                "id": 28,
                "name": "passthrough",
                "queueSize": 8,
                "type": 0,
                "waitForMessage": false
              }
            ]
          ],
          "name": "NeuralNetwork",
          "properties": {
            "blobSize": 1024,
            "blobUri": "asset:__blob",
            "numFrames": 8,
            "numNCEPerThread": 0,
            "numThreads": 2
          }
        }
      ],
      [
        6,
        {
          "id": 6,
          "ioInfo": [
            [
              [
                "",
                "input"
              ],
              {
                "blocking": false,
                "group": "",
                "id": 29,
                "name": "input",
                "queueSize": 4,
                "type": 3,
                "waitForMessage": true
              }
            ],
            [
              [
                "",
                "output"
              ],
              {
                "blocking": false,
                "group": "",
                "id": 30,
                "name": "output",
                "queueSize": 8,
                "type": 0,
                "waitForMessage": false
              }
            ],
            [
              [
                "",
                "passthroughInput"
              ],
              {
                "blocking": false,
                "group": "",
                "id": 31,
                "name": "passthroughInput",
                "queueSize": 8,
                "type": 0,
                "waitForMessage": false
              }
            ]
          ],
          "name": "Cast",
          "properties": {
            "numFramesPool": 4,
            "offset": null,
            "outputType": 8,
            "scale": null
          }
        }
      ],
      [
        7,
        {
          "id": 7,
          "ioInfo": [
            [
              [
                "",
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              ],
              {
                "blocking": true,
                "group": "",
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                "name": "in",
                "queueSize": 8,
                "type": 3,
                "waitForMessage": true
              }
            ]
          ],
          "name": "XLinkOut",
          "properties": {
            "maxFpsLimit": -1.0,
            "metadataOnly": false,
            "streamName": "cast"
          }
        }
      ]
    ]
  }
}
```

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