finishing touches, documentation mostly

camera/raspberry/client.py

@@ -1,10 +1,14 @@
+#Pavol Debnar 
+#Thesis 2022/23
+
+#for use of this script, TCP_IP address must be changed
 import socket
 import subprocess
 #from picamera2 import Picamera2
 import numpy
 import time
 
-TCP_IP = '192.168.1.2'
+TCP_IP = '192.168.1.2'  #needs to be BBX-mini address
 TCP_PORT = 5001
 
 sock = socket.socket()

camera/server.py

@@ -1,3 +1,7 @@
+#Pavol Debnar 
+#Thesis 2022/23
+
+#for use of this script, TCP_IP address must be changed and the folders in writeString and fileString
 import socket
 import cv2
 import numpy as np
@@ -22,11 +26,11 @@ def recvall(sock, count):
         count -= len(newbuf)
     return buf
 
-TCP_IP = '192.168.1.2'
+TCP_IP = '192.168.1.2' #this address needs to be BBX-mini
 TCP_PORT = 5001
 
 s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-s.bind(('192.168.1.2', TCP_PORT))
+s.bind(( TCP_IP, TCP_PORT))
 s.listen(True)
 conn, addr = s.accept()
 

camera/server2.py

@@ -1,3 +1,8 @@
+#Pavol Debnar 
+#Thesis 2022/23
+
+#for use of this script, TCP_IP address must be changed and the folders in writeString and fileString
+
 import socket
 import cv2
 import numpy as np
@@ -22,11 +27,11 @@ def recvall(sock, count):
         count -= len(newbuf)
     return buf
 
-TCP_IP = '192.168.1.2'
+TCP_IP = '192.168.1.2' #needs to be BBX-mini address
 TCP_PORT = 5001
 
 s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-s.bind(('192.168.1.2', TCP_PORT))
+s.bind((TCP_IP, TCP_PORT))
 s.listen(True)
 conn, addr = s.accept()
 

src/pointbase.cpp

@@ -5,7 +5,56 @@
  * 2022/23
  */
 
-
+/*! \mainpage Stitching Barrel Surface Images and Correcting Their Brightness
+ *
+ * \section intro_sec Introduction
+ *
+ * This is the documentation for the thesis
+ * 
+ * There are two .cpp files: test.cpp and pointbase.cpp
+ * 
+ * test.cpp contains sample use cases of the implemented library
+ * 
+ * pointbase.cpp is the implemented library
+ * 
+ * in the camera folder are scripts for dataset capture, method of operation is mentioned below
+ * 
+ * \section prereq Prerequisites
+ * OpenCV version: 4.5.2 https://docs.opencv.org/4.x/d7/d9f/tutorial_linux_install.html
+ *
+ * C++17
+ * jsoncpp:
+ * 
+ *     sudo apt install libjsoncpp-dev
+ * 
+ *     sudo ln -s /usr/include/jsoncpp/json/ /usr/include/json
+ *
+ * \section install_sec Installation
+ *
+ *  Install using make
+ * 
+ * \section cam_op Dataset capture
+ * 
+ * The scripts in the camera folder are used for image capture from BBX-mini
+ * 
+ * The mode of operation is following:
+ * 
+ * 0. (If you want to stitch images automatically, make the project and run online stitching)
+ *
+ * 1. Make sure, that you are connected to bbx-mini and that you have addresses that can ping each other
+ * 
+ * 2. Copy the contents of camera/raspberry to BBX-mini and make
+ * 
+ * 3. Edit the addresses in the server and client scripts
+ * 
+ * 4. Run a GStreamer pipeline on BBX-mini (sample pipeline is in runThisOnRpi.txt)
+ * 
+ * 5. server.py provides manual stitching with the 'v' button is pressed, server2.py saves the sharpest image each second 
+ * 
+ * 6. run a chosen server.py on your workstation
+ * 
+ * 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)
+ */
 
 
 

src/test.cpp

@@ -1,5 +1,5 @@
 /**
- * @file pointbase.cpp
+ * @file test.cpp
  * @author Bc. Pavol Debnár
  * This is the test file with sample usage of the pointBase class
  * 2022/23
@@ -7,6 +7,58 @@
 
 
 
+/*! \mainpage Stitching Barrel Surface Images and Correcting Their Brightness
+ *
+ * \section intro_sec Introduction
+ *
+ * This is the documentation for the thesis
+ * 
+ * There are two files: test.cpp and pointbase.cpp
+ * 
+ * test.cpp contains sample use cases of the implemented library
+ * 
+ * pointbase.cpp is the implemented library
+ * 
+ * in the camera folder are scripts for dataset capture, method of operation is mentioned below
+ * 
+ * \section prereq Prerequisites
+ * OpenCV version: 4.5.2 https://docs.opencv.org/4.x/d7/d9f/tutorial_linux_install.html
+ *
+ * C++17
+ * jsoncpp:
+ * 
+ *     sudo apt install libjsoncpp-dev
+ * 
+ *     sudo ln -s /usr/include/jsoncpp/json/ /usr/include/json
+ *
+ * \section install_sec Installation
+ *
+ *  Install using make
+ * 
+ * \section cam_op Dataset capture
+ * 
+ * The scripts in the camera folder are used for image capture from BBX-mini
+ * 
+ * The mode of operation is following:
+ * 
+ * 0. (If you want to stitch images automatically, make the project and run online stitching)
+ *
+ * 1. Make sure, that you are connected to bbx-mini and that you have addresses that can ping each other
+ * 
+ * 2. Copy the contents of camera/raspberry to BBX-mini and make
+ * 
+ * 3. Edit the addresses in the server and client scripts
+ * 
+ * 4. Run a GStreamer pipeline on BBX-mini (sample pipeline is in runThisOnRpi.txt)
+ * 
+ * 4.5. server.py provides manual stitching with the 'v' button is pressed, server2.py saves the sharpest image each second 
+ * 
+ * 5. run a chosen server.py on your workstation
+ * 
+ * 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)
+ */
+
+
 #include "opencv2/highgui.hpp"
 #include "opencv2/core.hpp"
 #include "opencv2/imgproc.hpp"