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  • ImageManip Tiling
  • Demo
  • Setup
  • Source code
  • Pipeline

ImageManip Tiling

Frame tiling could be useful for eg. feeding large frame into a NeuralNetwork whose input size isn't as large. In such case, you can tile the large frame into multiple smaller ones and feed smaller frames to the NeuralNetwork.In this example we use 2 ImageManip for splitting the original 1000x500 preview frame into two 500x500 frames.

Demo

Setup

Please run the install script to download all required dependencies. Please note that this script must be ran from git context, so you have to download the depthai-python repository first and then run the script
Command Line
1git clone https://github.com/luxonis/depthai-python.git
2cd depthai-python/examples
3python3 install_requirements.py
For additional information, please follow the installation guide.

Source code

Python
C++

Python

Python
GitHub
1#!/usr/bin/env python3
2
3import cv2
4import depthai as dai
5
6# Create pipeline
7pipeline = dai.Pipeline()
8
9camRgb = pipeline.create(dai.node.ColorCamera)
10camRgb.setPreviewSize(1000, 500)
11camRgb.setInterleaved(False)
12maxFrameSize = camRgb.getPreviewHeight() * camRgb.getPreviewWidth() * 3
13
14# In this example we use 2 imageManips for splitting the original 1000x500
15# preview frame into 2 500x500 frames
16manip1 = pipeline.create(dai.node.ImageManip)
17manip1.initialConfig.setCropRect(0, 0, 0.5, 1)
18manip1.setMaxOutputFrameSize(maxFrameSize)
19camRgb.preview.link(manip1.inputImage)
20
21manip2 = pipeline.create(dai.node.ImageManip)
22manip2.initialConfig.setCropRect(0.5, 0, 1, 1)
23manip2.setMaxOutputFrameSize(maxFrameSize)
24camRgb.preview.link(manip2.inputImage)
25
26xout1 = pipeline.create(dai.node.XLinkOut)
27xout1.setStreamName('out1')
28manip1.out.link(xout1.input)
29
30xout2 = pipeline.create(dai.node.XLinkOut)
31xout2.setStreamName('out2')
32manip2.out.link(xout2.input)
33
34# Connect to device and start pipeline
35with dai.Device(pipeline) as device:
36    # Output queue will be used to get the rgb frames from the output defined above
37    q1 = device.getOutputQueue(name="out1", maxSize=4, blocking=False)
38    q2 = device.getOutputQueue(name="out2", maxSize=4, blocking=False)
39
40    while True:
41        if q1.has():
42            cv2.imshow("Tile 1", q1.get().getCvFrame())
43
44        if q2.has():
45            cv2.imshow("Tile 2", q2.get().getCvFrame())
46
47        if cv2.waitKey(1) == ord('q'):
48            break

Pipeline

React Flow

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