inference-no-polygon.ipynb

{"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":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nfrom tqdm.notebook import tqdm\n\nimport os\nimport rasterio\nfrom rasterio.windows import Window\nimport cv2\nimport glob\nimport gc\n\nimport json\nfrom skimage import data, io\nfrom matplotlib import pyplot as plt","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"! pip install ../input/segmentation-models/keras-applications-master/keras-applications-master/ -f ./ --no-index -q\n\n! pip install ../input/segmentation-models/efficientnet-1.0.0/efficientnet-1.0.0/ -f ./ --no-index -q\n\nimport tensorflow as tf\nimport efficientnet as efn\nimport efficientnet.tfkeras","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def rle_encode_less_memory(img):\n    pixels = img.T.flatten()\n    # pixels = img.flatten()\n    pixels[0] = 0\n    pixels[-1] = 0\n    runs = np.where(pixels[1:] != pixels[:-1])[0] + 2\n    runs[1::2] -= runs[::2]\n    return ' '.join(str(x) for x in runs)\n\n    \ndef make_grid(shape, window=256, min_overlap=32):\n    \"\"\"\n        Return Array of size (N,4), where N - number of tiles,\n        2nd axis represente slices: x1,x2,y1,y2 \n    \"\"\"\n    x, y = shape\n    nx = x // (window - min_overlap) + 1\n    x1 = np.linspace(0, x, num=nx, endpoint=False, dtype=np.int64)\n    x1[-1] = x - window\n    x2 = (x1 + window).clip(0, x)\n    ny = y // (window - min_overlap) + 1\n    y1 = np.linspace(0, y, num=ny, endpoint=False, dtype=np.int64)\n    y1[-1] = y - window\n    y2 = (y1 + window).clip(0, y)\n    slices = np.zeros((nx,ny, 4), dtype=np.int64)\n    \n    #print(nx,ny)\n    \n    for i in range(nx):\n        for j in range(ny):\n            slices[i,j] = x1[i], x2[i], y1[j], y2[j]\n            # print(x1[i]-x2[i], y1[j]-y2[j])\n    return slices.reshape(nx*ny,4)\n\nfold_models = []\nfor fold_model_path in glob.glob(\"/kaggle/input/hubmap-weights/*.h5\"):\n    print(fold_model_path)\n    fold_models.append(tf.keras.models.load_model(fold_model_path,compile=False))\n\nprint(len(fold_models))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"identity = rasterio.Affine(1, 0, 0, 0, 1, 0)\nNEW_SIZE = 256\nWINDOW = 1024\nMIN_OVERLAP = 300\nsubm = {}\nTHRESHOLD = 0.3\nCHECKSUM = False\np_th = 1000*(NEW_SIZE//256)**2\ns_th = 40\n\ndf_sample = pd.read_csv('../input/hubmap-kidney-segmentation/sample_submission.csv')\nfor i, (idx,row) in tqdm(enumerate(df_sample.iterrows()), total=5):\n    print(i,\"Predicting \",row['id'])\n\n    img_path = \"../input/hubmap-kidney-segmentation/test/\"+row['id']+\".tiff\"\n    \n    \n    dataset = rasterio.open(img_path, transform = identity)\n    preds = np.zeros(dataset.shape, dtype=np.uint8) \n    \n    slices = make_grid(dataset.shape, window=WINDOW, min_overlap=MIN_OVERLAP)\n    \n    if dataset.count != 3:\n        print('Image file with subdatasets as channels')\n        layers = [rasterio.open(subd) for subd in dataset.subdatasets]\n\n    inference_count = 0\n    noinference_count = 0\n    for (x1,x2,y1,y2) in slices:\n        if dataset.count == 3:\n            image = dataset.read([1,2,3],\n                        window=Window.from_slices((x1,x2),(y1,y2)))\n            image = np.moveaxis(image, 0, -1)\n        else:\n            image = np.zeros((WINDOW, WINDOW, 3), dtype=np.uint8)\n            for fl in range(3):\n                image[:,:,fl] = layers[fl].read(window=Window.from_slices((x1,x2),(y1,y2)))\n        image = cv2.resize(image, (NEW_SIZE, NEW_SIZE),interpolation = cv2.INTER_AREA)\n        \n        \n        \n        hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n        h,s,v = cv2.split(hsv)\n        \n        if (s>s_th).sum() <= p_th or image.sum() <= p_th:\n            continue \n        \n        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n            \n        image = np.expand_dims(image, 0)\n        \n\n        pred = None\n\n        for fold_model in fold_models:\n            image_aug = image /255.0\n            if pred is None:\n                pred = np.squeeze(fold_model.predict(image_aug))\n            else:\n                pred += np.squeeze(fold_model.predict(image_aug))\n\n        pred = pred/len(fold_models)\n\n        pred = cv2.resize(pred, (WINDOW, WINDOW))\n        preds[x1:x2,y1:y2] += (pred > THRESHOLD).astype(np.uint8)           \n\n        \n    preds = (preds > 0.5).astype(np.uint8)\n    \n    subm[i] = {'id':row['id'], 'predicted': rle_encode_less_memory(preds)}\n    \n    if CHECKSUM:\n        print('Checksum: '+ str(np.sum(preds)))\n    \n    del preds, dataset\n    gc.collect();","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission = pd.DataFrame.from_dict(subm, orient='index')\nsubmission.to_csv('submission.csv', index=False)\nsubmission.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### ","metadata":{}}]}