{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!git clone https://github.com/yaq007/Autofocus-Layer.git\n!wget http://cseweb.ucsd.edu/~yaq007/models.tar.gz\n!tar -xzvf models.tar.gz\n!rm models.tar.gz\n!wget http://cseweb.ucsd.edu/~yaq007/dataset.zip\n!unzip dataset.zip\n!rm dataset.zip\n!pip install nibabel\n!pip install SimpleITK\n!cp -r ./Autofocus-Layer/* ./","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-07-19T14:03:17.622616Z","iopub.execute_input":"2021-07-19T14:03:17.622912Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import imageio\nfrom IPython.display import Image\nimport numpy as np\nimport SimpleITK as sitk \nimport nibabel as nib","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!python test.py --num_gpus 1 --id AFN1 --test_epoch 390  --visualize","metadata":{"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def norm(img):\n    img-=img.min()\n    return img/img.max()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred = sitk.ReadImage(\"result/AFN1/VSD.brats_tcia_pat111_0001.42287.mha\")\nnda = sitk.GetArrayFromImage(pred).transpose(0,2,1)\np = 'HGG/brats_tcia_pat111_0001/VSD.Brain.XX.O.MR_Flair.40827/VSD.Brain.XX.O.MR_Flair.40827.nii.gz'\nimage = nib.load(p).get_data().transpose(2,0,1)\nimage = np.expand_dims(image,-1)\nimage = norm(np.concatenate([image,image,image],axis=-1))\nmask = image.copy()\nfor row in range(1,nda.max()):\n    nda2 = nda.copy()\n    nda2[nda2!=row] = 0\n    nda2*=1\n    if row <3:\n        mask[:,:,:,row] +=nda2/4.0\n    else:\n        mask[:,:,:,0] +=nda2/4.0\n        mask[:,:,:,1] +=nda2/4.0\nnda = np.expand_dims(nda,-1)\nnda = norm(np.concatenate([nda,nda,nda],axis=-1))\nimageio.mimsave(\"/tmp/gif.gif\", np.concatenate([image,mask,norm(nda)],axis=-2), duration=0.0001)\nImage(filename=\"/tmp/gif.gif\", format='png')","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}