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        "author": "Lianfa Li",
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        "description": "# Library for Deep Residual Multiscale Segmenter (resmcseg)\n\n[![Build Status](https://travis-ci.org/pybind/cmake_example.svg?branch=master)](https://travis-ci.org/pybind/cmake_example)\n[![Build status](https://ci.appveyor.com/api/projects/status/57nnxfm4subeug43/branch/master?svg=true)](https://ci.appveyor.com/project/dean0x7d/cmake-example/branch/master)\n\nThe python library of Deep Residual Multiscale Segmenter (autonet). \nCurrent version just supports the KERAS package of deep learning and \nwill extend to the others in the future. \n\n## Major modules\n\n**model**\n\n* gResMCSeg: major class to obtain a deep extensive residual multiscale  \n      FCN. You can setup its aruments. See the class and its \n      member functions' help for details.  \n* gResMCSegPre: major class to make semantic segmentation for binary and multi class. \n* pretrainedmodel: function to download the pretrained models using the DSTL and \n      ZURICH datasets from the Google cloud \n\n**util**\n\n* segmetrics: main metrics including jaccard index, MIoU, and loss functions etc.\n* helper: helper functions including color mapping etc.   \n\n**data**\n\n* data: function to access one image for Zurich to test the model's prediction. \n       \n## Installation\n\nYou can directly install it using the following command for the latest version:\n\n```bash\nsudo pip install resmcseg\n```\n\n## Note for installation and use \n\n**Compiler requirements**\n\nresmcseg requires a C++11 compliant compiler to be available.\n\n**Runtime requirements**\n\nresmcseg requires installation of Keras with support of Tensorflow as the \nbackend system of deep learning (to support Keras). Also Pandas and Numpy should \nbe installed. \nSpecifics: Keras>=2.2.2; opencv2>=3.4.1,tensorflow,numpy,sklearn, pandas,\n          gdal,tifffile etc.  \n\n## Use case \nThe homepage of the github for the package, resmcseg provides specific \nexamples for use of the library:  \nhttps://github.com/lspatial/resmcsegpub \n\n## License\n\nThe resmcseg is provided under a MIT license that can be found in the LICENSE\nfile. By using, distributing, or contributing to this project, you agree to the\nterms and conditions of this license.\n\n## Test call\n\n```python\nimport os\nimport cv2\nfrom keras.models import load_model,model_from_json\nfrom resmcseg.util.segmetrics import compute_iou,jaccard,mean_iouC,miou,mean_iou\nfrom resmcseg.model.gresmcseg_pre import gResMCSegPre\nfrom resmcseg.model.resizelayer import ResizeLayer\nfrom resmcseg.model.pretrainedmodel import downloadPretrainedModel\nfrom resmcseg.util.helper import bce_dice_loss,jaccard_coef,jaccard_coef_int,jaccard_coef1\nfrom resmcseg.util.helper import onehot_to_rgb,color_dict\nfrom resmcseg.data import dload\n\nmodelFl='/tmp/model_strwei.h5'\nif not os.path.isfile(modelFl):\n    downloadPretrainedModel('ZURICH',destination=modelFl)\nmodel = load_model(modelFl,custom_objects={'ResizeLayer': ResizeLayer,'bce_dice_loss':bce_dice_loss,\n        'mean_iou':mean_iou,'jaccard_coef':jaccard_coef, 'jaccard_coef1':jaccard_coef1,'miou':miou,\n        'jaccard_coef_int':jaccard_coef_int,'mean_iouC': mean_iouC})\nppre=gResMCSegPre(patchsize=224,bordersize=16,overprop=0.3)\nimg, mask = dload()\nimgres = ppre.preAImgMulti(img, model, 9)\nmskImg = onehot_to_rgb(imgres, color_dict)\nfpath = \"/tmp/zurich1img_pre.jpg\"\ncv2.imwrite(fpath, cv2.cvtColor(mskImg, cv2.COLOR_RGB2BGR), [int(cv2.IMWRITE_JPEG_QUALITY), 100])\ny_pred = imgres.flatten()\ny_true = mask.flatten()\niou = compute_iou(imgres, mask)\njacard = jaccard(imgres, mask)\nprint(\"iou : \" + str(iou) + '; jacard is ', jacard)\n\n```\n## Collaboration\n\nWelcome to contact Dr. Lianfa Li (Email: lspatial@gmail.com or lilf@lreis.ac.cn).",
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