{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nimport tifffile as tiff\nimport cv2\nimport os\nfrom tqdm.notebook import tqdm\nimport zipfile","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sz = 512   #the size of tiles\nreduce = 4 #reduce the original images by 4 times \nMASKS = '../input/hubmap-kidney-segmentation/train.csv'\nDATA = '../input/hubmap-kidney-segmentation/train/'\nOUT_TRAIN = 'train.zip'\nOUT_MASKS = 'masks.zip'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#functions to convert encoding to mask and mask to encoding\ndef enc2mask(encs, shape):\n    img = np.zeros(shape[0]*shape[1], dtype=np.uint8)\n    for m,enc in enumerate(encs):\n        if isinstance(enc,np.float) and np.isnan(enc): continue\n        s = enc.split()\n        for i in range(len(s)//2):\n            start = int(s[2*i]) - 1\n            length = int(s[2*i+1])\n            img[start:start+length] = 1 + m\n    return img.reshape(shape).T\n\ndef mask2enc(mask, n=1):\n    pixels = mask.T.flatten()\n    encs = []\n    for i in range(1,n+1):\n        p = (pixels == i).astype(np.int8)\n        if p.sum() == 0: encs.append(np.nan)\n        else:\n            p = np.concatenate([[0], p, [0]])\n            runs = np.where(p[1:] != p[:-1])[0] + 1\n            runs[1::2] -= runs[::2]\n            encs.append(' '.join(str(x) for x in runs))\n    return encs\n\ndf_masks = pd.read_csv(MASKS).set_index('id')\ndf_masks.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"s_th = 40  #saturation blancking threshold\np_th = 200*sz//sz #threshold for the minimum number of pixels\n\nx_tot,x2_tot = [],[]\nwith zipfile.ZipFile(OUT_TRAIN, 'w') as img_out,\\\n zipfile.ZipFile(OUT_MASKS, 'w') as mask_out:\n    for index, encs in tqdm(df_masks.iterrows(),total=len(df_masks)):\n        #read image and generate the mask\n        img = tiff.imread(os.path.join(DATA,index+'.tiff'))\n        if len(img.shape) == 5: img = np.transpose(img.squeeze(), (1,2,0))\n        mask = enc2mask(encs,(img.shape[1],img.shape[0]))\n\n        #add padding to make the image dividable into tiles\n        shape = img.shape\n        pad0 = (reduce*sz - shape[0]%(reduce*sz))%(reduce*sz)\n        pad1 = (reduce*sz - shape[1]%(reduce*sz))%(reduce*sz)\n        img = np.pad(img,[[pad0//2,pad0-pad0//2],[pad1//2,pad1-pad1//2],[0,0]],\n                    constant_values=0)\n        mask = np.pad(mask,[[pad0//2,pad0-pad0//2],[pad1//2,pad1-pad1//2]],\n                    constant_values=0)\n\n        #split image and mask into tiles using the reshape+transpose trick\n        img = cv2.resize(img,(img.shape[1]//reduce,img.shape[0]//reduce),\n                         interpolation = cv2.INTER_AREA)\n        img = img.reshape(img.shape[0]//sz,sz,img.shape[1]//sz,sz,3)\n        img = img.transpose(0,2,1,3,4).reshape(-1,sz,sz,3)\n\n        mask = cv2.resize(mask,(mask.shape[1]//reduce,mask.shape[0]//reduce),\n                          interpolation = cv2.INTER_NEAREST)\n        mask = mask.reshape(mask.shape[0]//sz,sz,mask.shape[1]//sz,sz)\n        mask = mask.transpose(0,2,1,3).reshape(-1,sz,sz)\n\n        #write data\n        for i,(im,m) in enumerate(zip(img,mask)):\n            #remove black or gray images based on saturation check\n            hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)\n            h, s, v = cv2.split(hsv)\n            if (s>s_th).sum() <= p_th or im.sum() <= p_th: continue\n            \n            x_tot.append((im/255.0).reshape(-1,3).mean(0))\n            x2_tot.append(((im/255.0)**2).reshape(-1,3).mean(0))\n            \n            im = cv2.imencode('.png',cv2.cvtColor(im, cv2.COLOR_RGB2BGR))[1]\n            img_out.writestr(f'{index}_{i}.png', im)\n            m = cv2.imencode('.png',m)[1]\n            mask_out.writestr(f'{index}_{i}.png', m)\n\n#image stats\nimg_avr =  np.array(x_tot).mean(0)\nimg_std =  np.sqrt(np.array(x2_tot).mean(0) - img_avr**2)\nprint('mean:',img_avr, ', std:', img_std)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"columns, rows = 4,4\nidx0 = 20\nfig=plt.figure(figsize=(columns*4, rows*4))\nwith zipfile.ZipFile(OUT_TRAIN, 'r') as img_arch, \\\n     zipfile.ZipFile(OUT_MASKS, 'r') as msk_arch:\n    fnames = sorted(img_arch.namelist())[8:]\n    for i in range(rows):\n        for j in range(columns):\n            idx = i+j*columns\n            img = cv2.imdecode(np.frombuffer(img_arch.read(fnames[idx0+idx]), \n                                             np.uint8), cv2.IMREAD_COLOR)\n            img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n            mask = cv2.imdecode(np.frombuffer(msk_arch.read(fnames[idx0+idx]), \n                                              np.uint8), cv2.IMREAD_GRAYSCALE)\n    \n            fig.add_subplot(rows, columns, idx+1)\n            plt.axis('off')\n            plt.imshow(Image.fromarray(img))\n            plt.imshow(Image.fromarray(mask), alpha=0.2)\nplt.show()","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}