mirror of
https://github.com/debnarpavol/spajanie_snimkov_uprava_jasu.git
synced 2025-07-01 15:37:20 +02:00
finishing touches, documentation mostly
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@ -1,10 +1,14 @@
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#Pavol Debnar
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#Thesis 2022/23
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#for use of this script, TCP_IP address must be changed
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import socket
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import subprocess
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#from picamera2 import Picamera2
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import numpy
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import time
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TCP_IP = '192.168.1.2'
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TCP_IP = '192.168.1.2' #needs to be BBX-mini address
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TCP_PORT = 5001
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sock = socket.socket()
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@ -1,3 +1,7 @@
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#Pavol Debnar
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#Thesis 2022/23
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#for use of this script, TCP_IP address must be changed and the folders in writeString and fileString
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import socket
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import cv2
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import numpy as np
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@ -22,11 +26,11 @@ def recvall(sock, count):
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count -= len(newbuf)
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return buf
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TCP_IP = '192.168.1.2'
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TCP_IP = '192.168.1.2' #this address needs to be BBX-mini
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TCP_PORT = 5001
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s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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s.bind(('192.168.1.2', TCP_PORT))
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s.bind(( TCP_IP, TCP_PORT))
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s.listen(True)
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conn, addr = s.accept()
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@ -1,3 +1,8 @@
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#Pavol Debnar
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#Thesis 2022/23
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#for use of this script, TCP_IP address must be changed and the folders in writeString and fileString
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import socket
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import cv2
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import numpy as np
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@ -22,11 +27,11 @@ def recvall(sock, count):
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count -= len(newbuf)
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return buf
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TCP_IP = '192.168.1.2'
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TCP_IP = '192.168.1.2' #needs to be BBX-mini address
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TCP_PORT = 5001
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s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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s.bind(('192.168.1.2', TCP_PORT))
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s.bind((TCP_IP, TCP_PORT))
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s.listen(True)
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conn, addr = s.accept()
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2579
src/Doxyfile
Normal file
2579
src/Doxyfile
Normal file
File diff suppressed because it is too large
Load Diff
@ -5,7 +5,56 @@
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* 2022/23
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*/
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/*! \mainpage Stitching Barrel Surface Images and Correcting Their Brightness
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*
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* \section intro_sec Introduction
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*
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* This is the documentation for the thesis
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*
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* There are two .cpp files: test.cpp and pointbase.cpp
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*
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* test.cpp contains sample use cases of the implemented library
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*
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* pointbase.cpp is the implemented library
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*
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* in the camera folder are scripts for dataset capture, method of operation is mentioned below
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*
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* \section prereq Prerequisites
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* OpenCV version: 4.5.2 https://docs.opencv.org/4.x/d7/d9f/tutorial_linux_install.html
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*
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* C++17
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* jsoncpp:
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*
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* sudo apt install libjsoncpp-dev
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*
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* sudo ln -s /usr/include/jsoncpp/json/ /usr/include/json
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*
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* \section install_sec Installation
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*
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* Install using make
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*
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* \section cam_op Dataset capture
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*
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* The scripts in the camera folder are used for image capture from BBX-mini
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*
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* The mode of operation is following:
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*
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* 0. (If you want to stitch images automatically, make the project and run online stitching)
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*
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* 1. Make sure, that you are connected to bbx-mini and that you have addresses that can ping each other
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*
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* 2. Copy the contents of camera/raspberry to BBX-mini and make
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*
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* 3. Edit the addresses in the server and client scripts
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*
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* 4. Run a GStreamer pipeline on BBX-mini (sample pipeline is in runThisOnRpi.txt)
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*
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* 5. server.py provides manual stitching with the 'v' button is pressed, server2.py saves the sharpest image each second
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*
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* 6. run a chosen server.py on your workstation
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*
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* 7. run the client.py script on bbx-mini (lighting is activated by running /lights/lights - needs to be created by make as in step 2)
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*/
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54
src/test.cpp
54
src/test.cpp
@ -1,5 +1,5 @@
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/**
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* @file pointbase.cpp
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* @file test.cpp
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* @author Bc. Pavol Debnár
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* This is the test file with sample usage of the pointBase class
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* 2022/23
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@ -7,6 +7,58 @@
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/*! \mainpage Stitching Barrel Surface Images and Correcting Their Brightness
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*
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* \section intro_sec Introduction
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*
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* This is the documentation for the thesis
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*
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* There are two files: test.cpp and pointbase.cpp
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*
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* test.cpp contains sample use cases of the implemented library
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*
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* pointbase.cpp is the implemented library
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*
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* in the camera folder are scripts for dataset capture, method of operation is mentioned below
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*
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* \section prereq Prerequisites
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* OpenCV version: 4.5.2 https://docs.opencv.org/4.x/d7/d9f/tutorial_linux_install.html
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*
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* C++17
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* jsoncpp:
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*
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* sudo apt install libjsoncpp-dev
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*
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* sudo ln -s /usr/include/jsoncpp/json/ /usr/include/json
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*
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* \section install_sec Installation
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*
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* Install using make
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*
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* \section cam_op Dataset capture
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*
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* The scripts in the camera folder are used for image capture from BBX-mini
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*
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* The mode of operation is following:
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*
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* 0. (If you want to stitch images automatically, make the project and run online stitching)
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*
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* 1. Make sure, that you are connected to bbx-mini and that you have addresses that can ping each other
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*
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* 2. Copy the contents of camera/raspberry to BBX-mini and make
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*
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* 3. Edit the addresses in the server and client scripts
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*
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* 4. Run a GStreamer pipeline on BBX-mini (sample pipeline is in runThisOnRpi.txt)
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*
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* 4.5. server.py provides manual stitching with the 'v' button is pressed, server2.py saves the sharpest image each second
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*
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* 5. run a chosen server.py on your workstation
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*
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* 6. run the client.py script on bbx-mini (lighting is activated by running /lights/lights - needs to be created by make as in step 2)
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*/
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#include "opencv2/highgui.hpp"
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#include "opencv2/core.hpp"
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#include "opencv2/imgproc.hpp"
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