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Qt-based project allows the interface between the Flir Lepton 2.5 camera and Raspicam to be part of my thesis project for the Master's Degree in Mechatronics, by title Enanching UAV capabilities with machine learning on board.

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frank1789/Flir-Lepton

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Flir-Lepton

This Qt-based project allows the interface between the Flir Lepton 2.5 camera and Raspicam to be part of my thesis project for the Master's Degree in Mechatronics, by title Enanching UAV capabilities with machine learning on board.

Prerequisites

  • Raspbian Buster
  • Qt (5.12 or above)
  • gcc 8.3 (c++17 or above)
  • cmake (3.7 or above)
  • Flir Lepton Guide product page
  • RaspiCam: C++ API for using Raspberry

Configuration

Connections:

  1. match your female to female jumpers wires to the images below (note: your jumper wires may differ in color), otherwise you can also use the list below.
Raspberry GPIO Breakout Board
(Pin 1) +3.3V VIN
(Pin 3) SDA SDA
(Pin5) SCL SCL
(Pin6) GND GND
(Pin 19)MOSI MOSI
(Pin 21) MISO MISO
(Pin 23) SCLK CLK
(Pin 24) CE0 CS

  1. Now install the Raspberry Pi Camera, the camera should face towards the IO ports.

This configuration works well and avoids the red rectangle on the screen most of the time.

  • I preferred a method that would automatically decide whether to use: "/dev/spidev0.1" or "/dev/spidev0.1"

Run

To run the project in release mode:

git clone https://github.com/frank1789/Flir-Lepton.git
cd Flir-Lepton
./build.sh

The software has a single main window in which the images acquired by the thermal camera and Raspicam are displayed.
You can edit the color map for the thermal image from the main widget and save photos. It is possible to modify the fusion filter between the images, these however require that the cameras are perfectly aligned with a support structure.
Inside there is a TCP server to send images to clients connected using the default port 52693, while the address is shown in the main widget.

About

Qt-based project allows the interface between the Flir Lepton 2.5 camera and Raspicam to be part of my thesis project for the Master's Degree in Mechatronics, by title Enanching UAV capabilities with machine learning on board.

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