Des cours de formation sur la vision par ordinateur en direct, organisés localement, démontrent par des discussions interactives et la pratique manuelle les bases de la vision par ordinateur alors que les participants progressent dans la création de simples applications de vision par ordinateur La formation en vision par ordinateur est disponible en tant que «formation en direct sur site» ou «formation en direct à distance» La formation en direct sur site peut être effectuée localement dans les locaux du client France ou dans les centres de formation d'entreprise NobleProg à France La formation en ligne à distance est réalisée au moyen d'un ordinateur de bureau interactif et distant NobleProg Votre fournisseur de formation local.
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Nos Clients témoignent
J'ai vraiment apprécié l'approche pratique.
Kevin De Cuyper
Formation: Computer Vision with OpenCV
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L’utilisation facile de la fonctionnalité VideoCapture pour acquérir des images vidéo à partir d’un appareil photo pour ordinateur portable.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Formation: Computer Vision with OpenCV
Machine Translated
J'ai apprécié les conseils du formateur sur l'utilisation des outils. C'est quelque chose qui ne peut pas être obtenu à partir d'Internet et qui est très utile.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Formation: Computer Vision with OpenCV
Machine Translated
J'ai apprécié les conseils du formateur sur l'utilisation des outils. C'est quelque chose qui ne peut pas être obtenu à partir d'Internet et qui est très utile.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Formation: Computer Vision with OpenCV
Machine Translated
C'était facile à suivre.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Formation: Computer Vision with OpenCV
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WesCEF
Formation: Computer Vision with Python
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WesCEF
Formation: Computer Vision with Python
Machine Translated
WesCEF
Formation: Computer Vision with Python
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WesCEF
Formation: Computer Vision with Python
Machine Translated
Formateur était très knowlegable et très ouvert à la rétroaction sur le rythme à suivre par le contenu et les sujets que nous avons abordés. J’ai beaucoup gagné de la formation et j’ai l’impression d’avoir maintenant une bonne compréhension de la manipulation de l’image et quelques techniques pour construire un bon ensemble de formation pour un problème de classification d’image.
Anthea King - WesCEF
Formation: Computer Vision with Python
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Sous-catégories Computer Vision
Plans de cours Computer Vision
Nom du Cours
Durée
Aperçu
Nom du Cours
Durée
Aperçu
14 hours
Aperçu
SimpleCV is an open source framework — meaning that it is a collection of libraries and software that you can use to develop vision applications. It lets you work with the images or video streams that come from webcams, Kinects, FireWire and IP cameras, or mobile phones. It’s helps you build software to make your various technologies not only see the world, but understand it too.
Audience
This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV.
Audience
This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV.
21 hours
Aperçu
Caffe est un cadre d'apprentissage en profondeur conçu pour l'expression, la rapidité et la modularité.
Ce cours explore l’application de Caffe tant que cadre d’apprentissage approfondi pour la reconnaissance d’images en prenant comme exemple le MNIST.
Public
Ce cours convient aux chercheurs et ingénieurs Deep Learning intéressés par l'utilisation de Caffe tant que cadre.
Une fois ce cours terminé, les délégués seront en mesure de:
- comprendre la structure et les mécanismes de déploiement de Caffe
- effectuer des tâches d'installation / environnement de production / architecture et configuration
- évaluer la qualité du code, effectuer le débogage, la surveillance
- implémenter une production avancée telle que des modèles d'entraînement, implémenter des couches et se connecter
Ce cours explore l’application de Caffe tant que cadre d’apprentissage approfondi pour la reconnaissance d’images en prenant comme exemple le MNIST.
Public
Ce cours convient aux chercheurs et ingénieurs Deep Learning intéressés par l'utilisation de Caffe tant que cadre.
Une fois ce cours terminé, les délégués seront en mesure de:
- comprendre la structure et les mécanismes de déploiement de Caffe
- effectuer des tâches d'installation / environnement de production / architecture et configuration
- évaluer la qualité du code, effectuer le débogage, la surveillance
- implémenter une production avancée telle que des modèles d'entraînement, implémenter des couches et se connecter
14 hours
Aperçu
Marvin is an extensible, cross-platform, open-source image and video processing framework developed in Java. Developers can use Marvin to manipulate images, extract features from images for classification tasks, generate figures algorithmically, process video file datasets, and set up unit test automation.
Some of Marvin's video applications include filtering, augmented reality, object tracking and motion detection.
In this instructor-led, live course participants will learn the principles of image and video analysis and utilize the Marvin Framework and its image processing algorithms to construct their own application.
Format of the Course
- The basic principles of image analysis, video analysis and the Marvin Framework are first introduced. Students are given project-based tasks which allow them to practice the concepts learned. By the end of the class, participants will have developed their own application using the Marvin Framework and libraries.
Some of Marvin's video applications include filtering, augmented reality, object tracking and motion detection.
In this instructor-led, live course participants will learn the principles of image and video analysis and utilize the Marvin Framework and its image processing algorithms to construct their own application.
Format of the Course
- The basic principles of image analysis, video analysis and the Marvin Framework are first introduced. Students are given project-based tasks which allow them to practice the concepts learned. By the end of the class, participants will have developed their own application using the Marvin Framework and libraries.
14 hours
Aperçu
Computer Vision is a field that involves automatically extracting, analyzing, and understanding useful information from digital media. Python is a high-level programming language famous for its clear syntax and code readibility.
In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of set of simple Computer Vision application using Python.
By the end of this training, participants will be able to:
- Understand the basics of Computer Vision
- Use Python to implement Computer Vision tasks
- Build their own face, object, and motion detection systems
Audience
- Python programmers interested in Computer Vision
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of set of simple Computer Vision application using Python.
By the end of this training, participants will be able to:
- Understand the basics of Computer Vision
- Use Python to implement Computer Vision tasks
- Build their own face, object, and motion detection systems
Audience
- Python programmers interested in Computer Vision
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Aperçu
This instructor-led, live training in France (online or onsite) is aimed at developers who wish to build a self-driving car using deep learning techniques.
By the end of this training, participants will be able to:
- Use Keras to build and train a convolutional neural network.
- Use computer vision techniques to identify lanes in an autonomos driving project.
- Train a deep learning model to differentiate traffic signs.
- Simulate a fully autonomous car.
By the end of this training, participants will be able to:
- Use Keras to build and train a convolutional neural network.
- Use computer vision techniques to identify lanes in an autonomos driving project.
- Train a deep learning model to differentiate traffic signs.
- Simulate a fully autonomous car.
14 hours
Aperçu
This instructor-led, live training in France (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.
By the end of this training, participants will be able to:
- Install and configure the necessary development environment, software and libraries to begin developing.
- Build, train, and deploy deep learning models to analyze live video feeds.
- Identify, track, segment and predict different objects within video frames.
- Optimize object detection and tracking models.
- Deploy an intelligent video analytics (IVA) application.
By the end of this training, participants will be able to:
- Install and configure the necessary development environment, software and libraries to begin developing.
- Build, train, and deploy deep learning models to analyze live video feeds.
- Identify, track, segment and predict different objects within video frames.
- Optimize object detection and tracking models.
- Deploy an intelligent video analytics (IVA) application.
7 hours
Aperçu
This instructor-led, live training in France (online or onsite) is aimed at back-end developers and data scientists who wish to incorporate pre-trained YOLO models into their enterprise-driven programs and implement cost-effective components for object-detection.
By the end of this training, participants will be able to:
- Install and configure the necessary tools and libraries required in object detection using YOLO.
- Customize Python command-line applications that operate based on YOLO pre-trained models.
- Implement the framework of pre-trained YOLO models for various computer vision projects.
- Convert existing datasets for object detection into YOLO format.
- Understand the fundamental concepts of the YOLO algorithm for computer vision and/or deep learning.
By the end of this training, participants will be able to:
- Install and configure the necessary tools and libraries required in object detection using YOLO.
- Customize Python command-line applications that operate based on YOLO pre-trained models.
- Implement the framework of pre-trained YOLO models for various computer vision projects.
- Convert existing datasets for object detection into YOLO format.
- Understand the fundamental concepts of the YOLO algorithm for computer vision and/or deep learning.
14 hours
Aperçu
Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.
Format of the Course
- This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
Format of the Course
- This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
28 hours
Aperçu
OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms.
Audience
This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
Audience
This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
21 hours
Aperçu
This instructor-led, live training introduces the software, hardware, and step-by-step process needed to build a facial recognition system from scratch. Facial Recognition is also known as Face Recognition.
The hardware used in this lab includes Rasberry Pi, a camera module, servos (optional), etc. Participants are responsible for purchasing these components themselves. The software used includes OpenCV, Linux, Python, etc.
By the end of this training, participants will be able to:
- Install Linux, OpenCV and other software utilities and libraries on a Rasberry Pi.
- Configure OpenCV to capture and detect facial images.
- Understand the various options for packaging a Rasberry Pi system for use in real-world environments.
- Adapt the system for a variety of use cases, including surveillance, identity verification, etc.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- Other hardware and software options include: Arduino, OpenFace, Windows, etc. If you wish to use any of these, please contact us to arrange.
The hardware used in this lab includes Rasberry Pi, a camera module, servos (optional), etc. Participants are responsible for purchasing these components themselves. The software used includes OpenCV, Linux, Python, etc.
By the end of this training, participants will be able to:
- Install Linux, OpenCV and other software utilities and libraries on a Rasberry Pi.
- Configure OpenCV to capture and detect facial images.
- Understand the various options for packaging a Rasberry Pi system for use in real-world environments.
- Adapt the system for a variety of use cases, including surveillance, identity verification, etc.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- Other hardware and software options include: Arduino, OpenFace, Windows, etc. If you wish to use any of these, please contact us to arrange.
14 hours
Aperçu
This instructor-led, live training in France (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
Prochains cours Computer Vision
15-11-2021 09:30
21 heures
24-11-2021 09:30
14 heures
30-11-2021 09:30
14 heures
13-12-2021 09:30
21 heures
13-12-2021 09:30
14 heures
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