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py_pansharpening

Rewrite some pansharpening methods with python

pytorch-implementation-for-relationnet

the implementation of CVPR2018 paper : https://arxiv.org/abs/1711.06025

pytorch-maml

PyTorch implementation of MAML: https://arxiv.org/abs/1703.03400

pytorch-maml-rl

Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch

pytorch-nce

The Noise Contrastive Estimation for softmax output written in Pytorch

pytorch-sentiment-analysis

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

pytorch-unet

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

pytorch_danet

pytorch_deeplab_large_fov

Pytorch implementation of Deeplab Large-FOV

pyxvis

Official Python implementation for XVis Toolbox release with the book Computer Vision for X-Ray Testing.

qgis-otb-plugin

radi-ossim

random_forest_landsat

RandomForest classification of satellite imagery (Landsat 5 TM / Landat 8 OLI) and validation

randomforestexplainer

A set of tools to understand what is happening inside a Random Forest

raster-vision

An open source framework for deep learning on satellite and aerial imagery.

rasterio

Rasterio reads and writes geospatial raster datasets

rcf

Richer Convolutional Features for Edge Detection

rcf-pytorch

Richer Convolutional Features for Edge Detection model in pytorch

reccs

SfM Photogrammetry Research

recursive-cascaded-networks

[ICCV 2019] Recursive Cascaded Networks for Unsupervised Medical Image Registration

regnet

Nonrigid image registration using multi-scale 3D convolutional neural networks

reimplement-hed

reimplement hed using keras

remote-sensing-image

Remote sensing image scene classification

remote-sensing-indices-derivation-tool

Calculate spectral remote sensing indices from satellite imagery

remote_sensing_change_detection

an unoffice reimpliment for main remote sensing change detection network. including CDNet FC-EF FC-EF-conc FC-EF-diff FC-EF-Res FCN-PP UNet_ASPP UNet++ and some work of main

reproduceadaptsegnet

retinopathy-glaucoma-anemia-detection-app

An android app that detects eye diseases by image of retina.

retinopathy_project_thesis

Diabetic Retinopathy (DR) is one of the eye-related disease that reduces the integrity of the blood vessels in the retinal layers which leads to retinal blood vessel leakage [2]. Sodium Fluorescein Angiography (FA) is widely used to monitor the leakage or the permeability of the vessel by imaging the back of the eyes as an important diagnostic value. Gamez [2] which is a PhD student in University of Bristol started FA on mice. Gamez [2] manually extracted fluorescent intensity data from the resulting FA videos and a graph of the fluorescence intensity ratio (FIR) versus time was plotted to obtain the gradient which is the solute flux (ΔIf/ Δt). These data was then used to assist the development of the Fick's Law adapted equation P=ΔIf/ Δt /(ΔC × A) to obtain the permeability of the vessel. The obstacle of this method was the manual data capturing process was too time consuming. This method also requires a lot of manual adjustments due to the movement of the camera caused by the heartbeat of the mice and their eyeball motion. The movement of the camera also caused blurry and unsharp images in the FA videos which led to inaccurate fluorescent intensity. A more intelligent way of data capturing was developed in this project using openCV with Python. This project firstly experimented on using K-means clustering to segment the exchange vessel groups and the large vessel group out of the FA frames to obtain the FIR for the FIR vs time graph. This project experimented on the two settings of K-mean clustering. One used random initial centers and the other one used the previous frame’s centers found by K-means clustering as the initial centers of the K-means clustering for the current frame (Reuse center K-means clustering). The experiment found that the random initial centers K-means clustering output stable FIR when the maximum iteration was 7 or above and the best epsilon (specific accuracy) was 0.1. Maximum iteration below 7 cannot be used due to FIR vs time graph showed large amount of noise and severe deformation. Conversely, the reuse center K-means clustering showed no deformation and noise on the FIR vs time graph when the maximum iteration was 7 or below and a much shorter execution time than the random initial centers K-means clustering. Then, the difference on the gradient of the FIR vs time graph was further examine between the two K-means clustering setting. The random initial centers Kmeans clustering showed fluctuation on the gradient value when the maximum iteration was between 7 and 15. The reuse center K-means clustering showed either an ascending or descending trend on the gradient value when the maximum iteration was below 7 and the gradient value stabilized when the maximum iteration was between 7 and 15. Reuse center K-means clustering was decided to implement in the final software and maximum iteration 7 was set as default to prioritize gradient accuracy over the execution time, and allow user to lower the maximum iteration to reduce execution time. This project then experimented on blurry frame classification by using Sobel edge detection. Convolution was performed with Sobel derivative operator on each FA frame to obtain an edge sharpness value. The edge sharpness versus frame number graphs were examined for all video and discovered a great separation between sharp and blurry frames in edge sharpness value. Sharp frames had higher edge sharpness and blurry frames had lower edge sharpness. A piece of code was created to loop along all the data points in edge sharpness vs frame number graph to classify sharp and blurry frames. The code firstly checked if the range of several neighboring data point (PtPbox) is larger than a specific value (tolerance value), then the data point needed a sharpness check, which take the mean of several neighboring data point (meanBox) and check is the current data point edge sharpness is lower or higher than the mean value. Lower means blurry frame and higher means sharp frames. A series of experiments were performed and the optimal value for PtPbox is 20, the meanBox is 10, the tolerance value is 0.1 and no histogram equalization is required. The sharp frames identification accuracy was above 80% and the blurry frames identification accuracy was above 96% for all the tested FA videos. All these experimental codes were then connected by a graphical user interface based on python with PyQT4. Finally, the PyInstaller was used to package these Python codes into a stand-alone Microsoft Window executable for Gamez [2] to use.

rgbt-ped-detection

KAIST Multispectral Pedestrian Detection Benchmark [CVPR '15]

rice_dataset

A Remote Sensing Image Dataset for Cloud Removal