Add ArUco PDF, update generator, remove old PDFs

README.md

@@ -1,58 +1,96 @@
+# Swarm Robotics Localization
+
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 
-# FYP-localization-robots
+Python tooling for ArUco-based localization, marker generation, and camera calibration.
 
-Python repository for address the Localization problem of Swarm Robotics
+## Project structure
 
-### Requirements
+- markers/ - Generate ArUco marker PNGs and a PDF.
+- scripts/ - Main localization pipeline (OpenCV + MQTT) and supporting tools.
+- scripts/board/ - Camera calibration workflow.
+- scripts/previousScripts/ - Legacy demos and prototypes.
+- docs/ - External documentation entry point.
 
-Please install following pip packages if they aren't pre-installed
+## Shared virtual environment
 
-```
-pip install numpy
+This repo uses a shared root .venv and a single global requirements.txt.
 
-pip install aruco
-pip install paho-mqtt
-pip3 install opencv-python
-pip3 install opencv-contrib-python
-pip3 install pyyaml
+Windows PowerShell:
+
+```powershell
+python -m venv .venv
+.\.venv\Scripts\Activate.ps1
+pip install -r requirements.txt
 ```
 
-or use following command
+macOS/Linux:
 
-```
-pip install -r requirement.txt
+```bash
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
 ```
 
-You need to copy and rename the following files in _scripts_ directory and fill the necessary configuration details before run the scripts.
+## Configuration
 
-- config-mapping_sample.yaml INTO config-mapping.yaml
-- config-mqtt_sample.yaml INTO config-mqtt.yaml
+If you run `./run.sh` (Unix-like shells), missing configs are auto-created from samples on first run.
+If you run `python scripts/script.py` directly, copy the sample configs in scripts/ first:
 
-### Run the scripts
+- scripts/config-mapping_sample.yaml -> scripts/config-mapping.yaml
+- scripts/config-mqtt_sample.yaml -> scripts/config-mqtt.yaml
 
-You can try scripts on the ./scripts/ directory
+## Entry points
 
-### Build a executable file (not working so far)
-
-Install PyInstaller from PyPI:
+Markers:
 
+```powershell
+cd markers
+python marker_generator.py
 ```
-pip install pyinstaller
+
+Localization pipeline:
+
+```powershell
+cd scripts
+python script.py
 ```
 
-Go to your program’s directory and run:
+Calibration:
 
+```powershell
+cd scripts/board
+python calibrate.py
 ```
-pyinstaller --onefile script.py
+
+Legacy demos:
+
+```powershell
+cd scripts/previousScripts
+python script2.py
 ```
 
-After, please make sure to copy the './board' folder into the directory which executes the exe file.
+## Outputs
+
+- markers/generated/ - PNG marker images
+- markers/aruco_markers.pdf - Generated marker PDF
+- scripts/board/calibration_data.txt - Saved calibration data
+
+## Troubleshooting
+
+- cv2.aruco missing: install opencv-contrib-python (not opencv-python only).
+- MyPy import-untyped: reportlab has no type hints; ignore or add a stub package.
+- Camera not opening: try a different camera index in scripts/script.py.
+- MQTT connection errors: verify scripts/config-mqtt.yaml values and broker reachability.
+
+## Notes
+
+- run.sh is a convenience script for Unix-like shells. On Windows, use the commands above.
+- Raspberry Pi camera script is documented in scripts/README.md.
 
-### Read More
+## Read more
 
-- [ar-markers 0.5.0](https://pypi.org/project/ar-markers/)
-- [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
-- [OpenCV: Detection of ArUco Markers](https://docs.opencv.org/trunk/d5/dae/tutorial_aruco_detection.html)
-- [OpenCV: Detection of ArUco Boards](https://docs.opencv.org/master/db/da9/tutorial_aruco_board_detection.html)
-- [Calibrating the board](https://mecaruco2.readthedocs.io/en/latest/notebooks_rst/Aruco/sandbox/ludovic/aruco_calibration_rotation.html)
+- [ar-markers](https://pypi.org/project/ar-markers/)
+- [Augmented Reality using ArUco Markers in OpenCV](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/)
+- [OpenCV ArUco detection](https://docs.opencv.org/master/d5/dae/tutorial_aruco_detection.html)
+- [OpenCV ArUco board detection](https://docs.opencv.org/master/db/da9/tutorial_aruco_board_detection.html)

markers/README.md

@@ -0,0 +1,29 @@
+# Markers
+
+Generate ArUco marker PNGs and a PDF sheet.
+
+## Setup
+
+Use the shared root .venv and the global requirements.txt:
+
+```powershell
+python -m venv .venv
+.\.venv\Scripts\Activate.ps1
+pip install -r ..\requirements.txt
+```
+
+## Run
+
+```powershell
+python marker_generator.py
+```
+
+## Outputs
+
+- generated/ - Marker PNG files
+- aruco_markers.pdf - PDF sheet
+
+## Troubleshooting
+
+- cv2.aruco missing: install opencv-contrib-python.
+- reportlab import warnings: ignore MyPy import-untyped warnings or add stubs.

markers/marker_generator.py

@@ -1,17 +1,69 @@
+# This is the updated version of the aruco marker generater.
+# Using this you can generate pdf at once
+
 import cv2 as cv
-import numpy as np
+import os
+from reportlab.platypus import SimpleDocTemplate, Image, Table  # type: ignore
+from reportlab.lib import colors  # type: ignore
+from reportlab.lib.pagesizes import A4  # type: ignore
+from reportlab.lib.units import mm  # type: ignore
+
+# Output PDF file
+pdf_path = "aruco_markers.pdf"
 
-# Load the predefined dictionary
-dictionary = cv.aruco.Dictionary_get(cv.aruco.DICT_6X6_250)
+# Create temporary folder for images
+temp_dir = "./generated"
+os.makedirs(temp_dir, exist_ok=True)
 
-# Generate the marker
+# Load predefined dictionary
+dictionary = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_6X6_250)
 
-markers = [int(x) for x in range(0, 10)]
+# Marker settings
+markers = list(range(0, 10))
 image_size = 600
 
-for m in markers:
-    markerImage = np.zeros((image_size, image_size), dtype=np.uint8)
+# Generate marker images
+image_paths = []
+for marker_id in markers:
+    marker_image = cv.aruco.generateImageMarker(
+        dictionary,
+        marker_id,
+        image_size
+    )
+
+    path = os.path.join(temp_dir, f"marker{marker_id}.png")
+    cv.imwrite(path, marker_image)
+    image_paths.append(path)
+
+print("Markers generated.")
+
+# Create PDF
+doc = SimpleDocTemplate(pdf_path, pagesize=A4)
+elements = []
+
+# Prepare images in grid (2 columns)
+table_data = []
+row = []
+
+for i, img_path in enumerate(image_paths):
+    img = Image(img_path, width=70*mm, height=70*mm)
+    row.append(img)
+
+    if len(row) == 2:
+        table_data.append(row)
+        row = []
+
+# Add remaining images if odd count
+if row:
+    table_data.append(row)
+
+table = Table(table_data)
+table.setStyle([
+    ('GRID', (0, 0), (-1, -1), 0.5, colors.grey),
+    ('ALIGN', (0, 0), (-1, -1), 'CENTER')
+])
+
+elements.append(table)
+doc.build(elements)
 
-    # drawMarkers(dict, marker_id, pixel_size, output_image, border_width)
-    markerImage = cv.aruco.drawMarker(dictionary, m, image_size, markerImage, 1);
-    cv.imwrite("./generated/marker" + str(m) + ".png", markerImage);
+print(f"PDF created successfully: {pdf_path}")

requirements.txt

@@ -1,6 +1,5 @@
-opencv-python==4.3.0.36
-numpy==1.22.0
-ar-markers==0.5.0
-paho-mqtt==1.5.0
-opencv-contrib-python==4.5.1.48
-pyyaml==5.4
+numpy
+opencv-contrib-python
+paho-mqtt
+pyyaml
+reportlab

run.sh

@@ -1,4 +1,5 @@
 #!/bin/sh
 
 cd ./scripts
+
 python3  ./script.py

scripts/README.md

@@ -0,0 +1,49 @@
+# Localization Scripts
+
+Main OpenCV + MQTT localization pipeline and support scripts.
+
+## Setup
+
+Use the shared root .venv and the global requirements.txt:
+
+```powershell
+python -m venv .venv
+.\.venv\Scripts\Activate.ps1
+pip install -r ..\requirements.txt
+```
+
+## Configuration
+
+If you run `../run.sh`, missing configs are auto-created from samples on first run.
+If you run `python script.py` directly, copy the sample configs before running:
+
+- config-mapping_sample.yaml -> config-mapping.yaml
+- config-mqtt_sample.yaml -> config-mqtt.yaml
+
+## Run
+
+Main localization pipeline:
+
+```powershell
+python script.py
+```
+
+Raspberry Pi camera demo (Raspberry Pi OS only):
+
+```powershell
+python pycamera.py
+```
+
+## Outputs
+
+- No files are written by default. Results are shown in the OpenCV window and published to MQTT topics.
+
+## OS notes
+
+- pycamera.py requires the Raspberry Pi camera stack and the picamera package.
+
+## Troubleshooting
+
+- cv2.aruco missing: install opencv-contrib-python.
+- Camera not opening: change camera index in script.py.
+- MQTT errors: verify broker settings in config-mqtt.yaml.

scripts/board/README.md

@@ -0,0 +1,32 @@
+# Camera Calibration
+
+Calibrate the camera from chessboard images and write calibration_data.txt.
+
+## Setup
+
+Use the shared root .venv and the global requirements.txt:
+
+```powershell
+python -m venv .venv
+.\.venv\Scripts\Activate.ps1
+pip install -r ..\..\requirements.txt
+```
+
+## Inputs
+
+- sample/\*.jpg - Chessboard images (7x6 corners) for calibration.
+
+## Run
+
+```powershell
+python calibrate.py
+```
+
+## Outputs
+
+- calibration_data.txt - Camera matrix and distortion coefficients.
+
+## Troubleshooting
+
+- No corners detected: ensure the chessboard has 7x6 inner corners and good lighting.
+- cv2 missing: install opencv-contrib-python.