Pinned Repositories
365-Days-Computer-Vision-Learning-Linkedin-Post
365 Days Computer Vision Learning Linkedin Post
all_sky_cloud_detection
find clouds on allsky images using skimage and astropy
awesome-semantic-segmentation
:metal: awesome-semantic-segmentation
basemap
bifacialvf_mismatch
Bifacial PV View Factor model for system performance calculation
Biomedical-Image-Segmentation-via-CMM-Net
Biomedical Image Segmentation via RMSPP-UNet
BrightSolarModel
The Bright Solar Model (SIG, and SDSIG) as detailed in the Journal of Solar Energy
FishEyeModel
Python project based on OpenCV module to model FishEye camera and undistort acquired images
python-control
The Python Control Systems Library is a Python module that implements basic operations for analysis and design of feedback control systems.
SUNSET
Stanford University Neural-network for Solar Electricity Trend
lmartinp's Repositories
lmartinp/python-control
The Python Control Systems Library is a Python module that implements basic operations for analysis and design of feedback control systems.
lmartinp/365-Days-Computer-Vision-Learning-Linkedin-Post
365 Days Computer Vision Learning Linkedin Post
lmartinp/awesome-semantic-segmentation
:metal: awesome-semantic-segmentation
lmartinp/Biomedical-Image-Segmentation-via-CMM-Net
Biomedical Image Segmentation via RMSPP-UNet
lmartinp/building-machine-learning-pipelines
Code repository for the O'Reilly publication "Building Machine Learning Pipelines" by Hannes Hapke & Catherine Nelson
lmartinp/cloud-rain-monitor
Device to measure sky conditions (rain and cloud) for an astronomical observatory
lmartinp/csd-library
Clear-sky irradiance detection methodologies from literature
lmartinp/deq-flow
[CVPR 2022] Deep Equilibrium Optical Flow Estimation
lmartinp/flownet2-pytorch
Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
lmartinp/GMA
Learning to Estimate Hidden Motions with Global Motion Aggregation (ICCV 2021)
lmartinp/kubric
A data generation pipeline for creating semi-realistic synthetic multi-object videos with rich annotations such as instance segmentation masks, depth maps, and optical flow.
lmartinp/NN-SVG
Publication-ready NN-architecture schematics.
lmartinp/orac
Optimal Retrieval of Aerosol and Cloud
lmartinp/PyBaMM
Fast and flexible physics-based battery models in Python
lmartinp/rcc-run-demo
Demonstration for continuous benchmarking on Google Cloud
lmartinp/rigid_transform_3D
lmartinp/sahi
A lightweight vision library for performing large scale object detection/ instance segmentation.
lmartinp/SAM
System Advisor Model (SAM)
lmartinp/satellite-image-deep-learning
Resources for deep learning with satellite & aerial imagery
lmartinp/seviri_ml
SEVIRI_ML: A machine learning based module to derive: (1) A cloud mask, (2) the cloud phase, (3) the cloud top pressure, (4) the cloud top temperature, (4) a multilayer flag and (5) a cloud base height from SEVIRI measurements using its full spectral capabilities. Also to be used as external module with ORAC (https://github.com/ORAC-CC/orac).
lmartinp/SGDenBT
lmartinp/SkyCam
lmartinp/skysat_stereo
Tools and libraries for processing Planet SkySat imagery, including camera model refinement, stereo reconstruction, and orthomosaic production
lmartinp/solar-data-tools
Some data analysis tools for working with historical PV solar time-series data sets.
lmartinp/solar-fleet-forecast-probability-tool
A tool for making a probabilistic power forecast for a fleet of solar plants using an existing deterministic forecast
lmartinp/t81_558_deep_learning
Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks
lmartinp/trinityX
TrinityX is the new generation of ClusterVision's open-source HPC platform. It is designed from the ground up to provide all services required in a modern HPC system, and to allow full customization of the installation.
lmartinp/Unsuprevised_Seg_via_CNN
An unsupervised (or self-supervised) loss function for binary image segmentation.
lmartinp/Video-Interpolation-using-Deep-Optical-Flow
In this repository, we deal with the task of video frame interpolation with estimated optical flow. To estimate the optical flow we use pre-trained FlowNet2 deep learning model and experiment by fine-tuning it. We explore the interpolation performance on Spheres dataset and Corridor dataset.
lmartinp/voxel_space_carving
Space Carving of Voxels for Building 3D Models