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• C++ framework for offline/real-time visualization and processing of multi-sensor data. • Thermavip is used for firm real-time machine control and offline physical studies. • Thermavip provides a SDK and a plugin mechanism for porting to any fusion devices. • Widely used on WEST and EAST tokamaks, W7-X stellarator and HADES facility. • Strong emphasis on video data (IR/VIS), provides a full-featured video annotation tool. In current tokamaks, imaging diagnostics (visible/infrared) have become increasingly used for real-time control of the temperature of the Plasma Facing Components (PFC) and for off-line physical analysis. Developing a full acquisition and control system based on a network of multiple cameras is a complex task requiring advanced software tools. Furthermore, such systems produce a large quantity of data encouraging end users to share tools and codes for data access and analysis. In this paper, we introduce a software platform, named Thermavip, dedicated to these tasks. The main contribution of this software is to gather under the same framework different functionalities for (1) firm real-time video acquisition and PFC monitoring, (2) offline data access and display, (3) signal and video processing applied to plasma physics, (4) video annotation and thermal events database management. This software platform is widely used on the WEST (W Environment in S teady-state Tokamak) tokamak (France) for offline study of infrared (IR) and almost all WEST diagnostics data, and online display of IR videos and temperature time traces for critical components. Thermavip platform is also used on the HADES (H igh he A t Loa D tESt) facility (France) and the EAST (Experimental Advanced Superconducting Tokamak) tokamak (China) for offline signal analysis, and on the Wendelstein 7-X (W7-X) stellarator (Germany) for the IR diagnostic acquisition, PFC real-time (RT) monitoring, online display and offline video analysis. In addition, Thermavip provides a full featured video annotation tool used on both WEST and W7-X to build and manage thermal events datasets. These annotations are used as input dataset to train deep-learning models, like Region Based Convolutional Neural Networks, for automatic detection and classification of thermal events based on IR movies. The Thermavip framework is composed of a unique C++ Software Development Kit (SDK) providing high-level classes for offline multi-sensor data analysis, firm real-time processing and online visualization of these sensor data. Its strength comes from its unique component block architecture, allowing to build multicore and distributed processing pipelines for both offline and firm real-time applications. Thermavip architecture relies on a versatile plugin mechanism to extend its functionalities using C++ code or Python scripts, facilitating the integration on new machines. [ABSTRACT FROM AUTHOR]