A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images深度监督影像融合网络DSIFN用于高分辨率双时相遥感影像变化检测Official implement of the Paper：A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. If you find this work helps in your research, please consider citing: 论文《A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images》的官方模型代码。如果该代码对你的研究有所帮助，烦请引用： Zhang, C., Yue, P., Tapete, D., Jiang, L., Shangguan, B., Huang, L., & Liu, G. (2020). […]

Photographic Image Synthesis with Cascaded Refinement Networks-Pytorch This is a Pytorch implementation of cascaded refinement networks to synthesize photographic images from semantic layouts. Now the pretrained model and codes for training the network from scratch are available for 256×512 resolution. Thanks to Qifeng Chen for his tensorflow implementation which helped a lot in developing this pytorch version. Testing Download this package and keep all the subsequent mentioned files in the same folder. Download the pretrained VGG19 Net from VGG19 Download […]

NIMA: Neural IMage Assessment This is a PyTorch implementation of the paper NIMA: Neural IMage Assessment (accepted at IEEE Transactions on Image Processing) by Hossein Talebi and Peyman Milanfar. You can learn more from this post at Google Research Blog. Implementation Details The model was trained on the AVA (Aesthetic Visual Analysis) dataset containing 255,500+ images. You can get it from here. Note: there may be some corrupted images in the dataset, remove them first before you start training. Use […]

FastPhotoStyle Given a content photo and a style photo, the code can transfer the style of the style photo to the content photo. The details of the algorithm behind the code is documented in our arxiv paper. Please cite the paper if this code repository is used in your publications. GitHub https://github.com/NVIDIA/FastPhotoStyle    

Deep-Image-Analogy This project is a python implementation of Deep Image Analogy. Some results Requirements python 3 opencv3 If you use anaconda, you can install opencv3 by conda install opencv pytorch See pytorch for installation Codes in branch “master” works with pytorch 0.4 Codes in branch “pytorch0.3” works with pytorch 0.3 cuda (CPU version is not implemented yet) Usage (demo) python main.py –resize_ratio 0.5 –weight 2 –img_A_path data/demo/ava.png –img_BP_path data/demo/mona.png –use_cuda True GitHub https://github.com/Ben-Louis/Deep-Image-Analogy-PyTorch    

PyTorch NIMA: Neural IMage Assessment PyTorch implementation of Neural IMage Assessment by Hossein Talebi and Peyman Milanfar. You can learn more from this post at Google Research Blog. Installing Docker docker run -it truskovskiyk/nima:latest /bin/bash PYPI package (In Progress) pip install nima VirtualEnv git clone https://github.com/truskovskiyk/nima.pytorch.git cd nima.pytorch virtualenv -p python3.7 env source ./env/bin/activate Dataset The model was trained on the AVA (Aesthetic Visual Analysis) datasetYou can get it from hereHere are some examples of images with theire scores Pre-train […]

Topo-boundary This is the official github repo of paper Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving. Project page. Topo-boundary is a publicly available benchmark dataset for topological road-boundary detection in aerial images. With an aerial image as the input, the evaluated method should predict the topological structure of road boundaries in the form of a graph. This dataset is based on NYC Planimetric Database. Topo-boundary consists of 25,297 4-channel aerial images, and each […]

image2excel A program which converts images and video into Excel spreadsheets. Videos can take a long time to save. Example conversion Original image: Result as viewed in Excel: Video is the same except each frame is it’s own spreadsheet. Prerequisites Main libraries can be installed with: pip install -r requirements.txt Web UI libraries can be installed with: pip install -r web_requirements.txt Command syntax image2excel-cmd.py [-h] [–scale SCALE] [–filter FILTER] [–frameskip FRAMESKIP] [–forceframeskip] [–videocut VIDEOCUT] type file_path output_path mode Use image2excel-cmd.py […]

impy Speed up image analysis in Python with efficient reading, batch-processing, viewing functions and easily extend your own function for batch processing. Image analysis programatically is sometimes troublesome like … for multi-dimensional images, you need to check which is time-axis and which is channel axis and so on. you need to consider the output data types and shapes for every batch image processing. you need to care about all the images’ information such as the names and directories of original […]

Fill Voids Fill holes in binary 2D & 3D images fast. # PYTHON import fill_voids img = … # 2d or 3d binary image filled_image = fill_voids.fill(img, in_place=False) # in_place allows editing of original image filled_image, N = fill_voids.fill(img, return_fill_count=True) # returns number of voxels filled in // C++ #include “fill_voids.hpp” size_t sx, sy, sz; sx = sy = sz = 512; uint8_t* labels = …; // 512x512x512 binary image // modifies labels as a side effect, returns number of […]