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juancarlosmiranda/README.md

About me...

I am a computer engineer with experience in academia, the software industry and the financial sector. Throughout my professional career, I have held various positions, including infrastructure support technician, software developer, database administrator, and information security specialist. My doctoral research has focused on the characterization and classification of fruits through the application of artificial intelligence techniques, digital image processing and computer vision. I have developed AI tools for fruit identification and classification, having worked with oranges and apples.

Welcome to my site! https://juancarlosmiranda.github.io/

Here I try to publish what I do, that includes: notes, investigations, works, code. I hope this is useful to you, and if it is, please let me know.

Academic profiles:

Connect with me:

Guides / How To's

Some notes.

Resources Description
Azure Kinect camera setup (automated scripts for Linux and Windows) Ready-to-use installation scripts (https://github.com/juancarlosmiranda/azure_kinect_notes.
ResearchGate - Azure Kinect camera information resources Question open in ResearchGate about Azure Kinect DK camera.

Publications/ articles

Resources Description
Fruit sizing using AI: A review of methods and challenges https://doi.org/10.1016/j.postharvbio.2023.112587 Artificial intelligence Fruit detection Fruit measure Image processing Deep learning Fruit quality
Assessing automatic data processing algorithms for RGB-D cameras to predict fruit size and weight in apples https://doi.org/10.1016/j.compag.2023.108302 Azure Kinect Fruit sizing Allometric weight models Apple tree Digital fruit growing
AKFruitData: A dual software application for Azure Kinect cameras to acquire and extract informative data in yield tests performed in fruit orchard environments https://doi.org/10.1016/j.softx.2022.101231 RGB-D camera Data acquisition Data extraction Fruit yield trials Precision fructiculture
AKFruitYield: Modular benchmarking and video analysis software for Azure Kinect cameras for fruit size and fruit yield estimation in apple orchards https://doi.org/10.1016/j.softx.2023.101548 RGB-D camera Fruit detection Apple fruit sizing Yield prediction Allometry
CLASIFICACIÓN AUTOMÁTICA DE NARANJAS POR TAMAÑO Y POR DEFECTOS UTILIZANDO TÉCNICAS DE VISIÓN POR COMPUTADORA https://repositorio.conacyt.gov.py/handle/20.500.14066/3172 Orange classification by computer vision in controled environments. Github repository: https://github.com/juancarlosmiranda/orange_classification/
Open source software and benchmarking of computer vision algorithms for apple fruit detection, fruit sizing and yield prediction using RGB-D cameras Doctoral thesis URI: https://hdl.handle.net/10803/690455, https://repositori.udl.cat/handle/10459.1/465456, https://dx.doi.org/10.13140/RG.2.2.27765.18406
. . .

Open Source Software Packages

Currently maintaining software packages at (Pypi.org)[https://pypi.org].

Package Description
AK_ACQS_100 AK_ACQS Azure Kinect Acquisition System (https://github.com/GRAP-UdL-AT/ak_acquisition_system) AK_ACQS is a software solution for data acquisition in fruit orchards using a sensor system boarded on a terrestrial vehicle. It allows the coordination of computers and sensors through the sending of remote commands via a GUI. At the same time, it adds an abstraction layer on library stack of each sensor, facilitating its integration. This software solution is supported by a local area network (LAN), which connects computers and sensors from different manufacturers ( cameras of different technologies, GNSS receiver) for in-field fruit yield testing.
AK_SM_RECORDER_100 AK_SM_RECORDER - Azure Kinect Standalone Mode (https://pypi.org/project/ak-sm-recorder/) A simple GUI recorder based on Python to manage Azure Kinect camera devices in a standalone mode.
AK_FRAEX_100 AK_FRAEX - Azure Kinect Frame Extractor (https://pypi.org/project/ak-frame-extractor/) AK_FRAEX is a desktop tool created for post-processing tasks after field acquisition. It enables the extraction of information from videos recorded in MKV format with the Azure Kinect camera. Through a GUI, the user can configure initial parameters to extract frames and automatically create the necessary metadata for a set of images.
AK_SW_BENCHMARKER_100 AK_SW_BENCHMARKER - Azure Kinect Size Estimation & Weight Prediction Benchmarker (https://pypi.org/project/ak-sw-benchmarker/) Python based GUI tool for fruit size estimation and weight prediction.
AK_VIDEO_ANALYSER_100 AK_VIDEO_ANALYSER - Azure Kinect Video Analyser (https://pypi.org/project/ak-video-analyser/) Python based GUI tool for fruit size estimation and weight prediction from videos.

Azure Kinect DK dDta and Information - Demo Videos

Currently maintaining software packages at (Pypi.org)[https://pypi.org].

Resource Description
AK_FRAEX - Azure Kinect Frame Extractor demo videos Video samples recorded in the field using the Azure Kinect DK. These videos are part of the https://pypi.org/project/ak-frame-extractor/ software to demonstrate the use of frame extraction tasks.

Pinned Loading

  1. azure_kinect_notes azure_kinect_notes Public

    Give me a star please. This document contains instructions/notes on how to install the Azure Kinect camera. Here I collected experiences that have arisen during the development of the following sof…

    Shell 34 5

  2. packaging_tutorial packaging_tutorial Public

    Packaging tutorial, adapted to Tkinter app

    Python

  3. shell_scripts_notes shell_scripts_notes Public

    This repository contains notes and examples related to programming with Bash and Shell script in Unix like systems.

    Shell

  4. python_code_recipes python_code_recipes Public

    Python code recipes

    Python

  5. object_detector_tutorial object_detector_tutorial Public

    This folder contains data and various code samples related to using object detectors and object segmentation.

    Python 1

  6. orange_classification orange_classification Public

    In this work, an automatic and reproducible methodology is proposed using computer vision techniques for sorting oranges by size and defects. Master thesis written in Spanish.

    MATLAB