py-cbir:python中基于内容的图像检索演示

Demo of Content Based Image Retrieval, implemented by Python and Tornado. ## Image descriptors * perceptual hash * Otsu's method * gray/RGB/YUV/HSV histograms * GIST * HoG and LSH (built by Kmeans clustering) * SIFT and LSH (built by Kmeans clustering) * Dense SIFT ## Distance functions * Hamming distance, or norm0 distance (L0) * abs distance (L1) * Eculidean distance (L2) ## Simple re-ranking * blending: mix results * ensembling: weighted sum ## Code structure * util/: feature descriptors, feature and LSH preparation * app/: http server, matching and retrieval * templates/: html templates * static/: datasets, js, css * conf/: log.conf, and for feature data * logs/: for log data * settings.py: http port, common setting * urls.py: server url path ## Dependencies * Tornado * Image * numpy, scipy ## Run (Linux or Mac) * `cd util/pyleargist-2.0.5/lear_gist/ && make && cp compute_gist ../../ && cd -` * `cd util && python prepare.py && cd -` * `python main.py` * access http://localhost:19999/cbir ## How to change dataset * add a new image folder in static/dataset/ * in util/prepare.py, change dataset to the folder name, like `dataset = 'ferrari'` * run as previous section ## Author Any question, please contact: Zuotao Liu(zuotaoliu@126.com)