{"cells":[{"metadata":{},"cell_type":"markdown","source":"

Detect diabetic retinopathy to stop blindness before it's too late

\n
\n##### Image source: http://cceyemd.com/diabetes-and-eye-exams/"},{"metadata":{"trusted":true},"cell_type":"markdown","source":"Keras EfficientNet Trainer v34"},{"metadata":{},"cell_type":"markdown","source":"## Load a model"},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nimport sys\nfrom keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU\nfrom keras.models import Model, Sequential\n\nsys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))\nfrom efficientnet import EfficientNetB5\n\ndef create_effnetB5_model(input_shape, n_out):\n model = Sequential()\n base_model = EfficientNetB5(weights = None, \n include_top = False,\n input_shape = input_shape)\n base_model.name = 'base_model'\n model.add(base_model)\n model.add(Dropout(0.25))\n model.add(Dense(1024))\n model.add(LeakyReLU())\n model.add(GlobalAveragePooling2D())\n model.add(Dropout(0.5)) \n model.add(Dense(n_out, activation = 'sigmoid'))\n return model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"PRETRAINED_MODEL = '../input/efficientnetb5-blindness-detector/blindness_detector_best_qwk.h5'\nIMAGE_HEIGHT = 340\nIMAGE_WIDTH = 340\nnum_classes = 5\nclass_text = ['Normal', 'Mild', 'Moderate', 'Severe', 'Proliferative']\n\nprint('Creating model...')\nmodel = create_effnetB5_model(input_shape = (IMAGE_HEIGHT, IMAGE_WIDTH, 3), n_out = num_classes)\nprint('Restoring model from ' + PRETRAINED_MODEL + '...')\nmodel.load_weights(PRETRAINED_MODEL)\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Make some predictions "},{"metadata":{"trusted":true},"cell_type":"code","source":"def crop_image_from_gray(img, tol = 7):\n if img.ndim ==2:\n mask = img>tol\n return img[np.ix_(mask.any(1),mask.any(0))]\n elif img.ndim==3:\n gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n mask = gray_img>tol\n \n check_shape = img[:,:,0][np.ix_(mask.any(1),mask.any(0))].shape[0]\n if (check_shape == 0): # image is too dark so that we crop out everything,\n return img # return original image\n else:\n img1=img[:,:,0][np.ix_(mask.any(1),mask.any(0))]\n img2=img[:,:,1][np.ix_(mask.any(1),mask.any(0))]\n img3=img[:,:,2][np.ix_(mask.any(1),mask.any(0))]\n # print(img1.shape,img2.shape,img3.shape)\n img = np.stack([img1,img2,img3],axis = -1)\n # print(img.shape)\n return img\n \ndef process_image(image):\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image = crop_image_from_gray(image)\n image = cv2.resize(image, (IMAGE_HEIGHT, IMAGE_WIDTH))\n image=cv2.addWeighted ( image,4, cv2.GaussianBlur( image , (0,0) , 10) ,-4 ,128)\n return image","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport cv2\nfrom tqdm import tqdm_notebook as tqdm\n\nsubmit = pd.read_csv('../input/aptos2019-blindness-detection/sample_submission.csv')\npredicted = []\n\nprint(\"Making predictions...\")\nfor i, name in tqdm(enumerate(submit['id_code'])):\n path = os.path.join('../input/aptos2019-blindness-detection/test_images/', name + '.png')\n image = cv2.imread(path)\n# image = cv2.resize(image, (IMAGE_HEIGHT, IMAGE_WIDTH))\n image = process_image(image)\n X = np.array((image[np.newaxis]) / 255)\n raw_prediction = model.predict(X) > 0.5\n prediction = raw_prediction.astype(int).sum(axis = 1) - 1\n predicted.append(prediction[0])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Generate a submission"},{"metadata":{"trusted":true},"cell_type":"code","source":"submit['diagnosis'] = predicted\nsubmit.to_csv('submission.csv', index = False)\nsubmit.head(10)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 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