{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\nfrom zipfile import ZipFile\nimport os\nprint(os.listdir(\"../input\"))\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n#     for filename in filenames:\n#         print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import os, sys\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport skimage.io\nfrom skimage.transform import resize\nfrom imgaug import augmenters as iaa\nfrom tqdm import tqdm\nimport PIL\nfrom PIL import Image, ImageOps\nimport cv2\nfrom sklearn.utils import class_weight, shuffle\nfrom keras.losses import binary_crossentropy\nfrom keras.applications.resnet50 import preprocess_input\nimport keras.backend as K\nimport tensorflow as tf\nfrom sklearn.metrics import f1_score, fbeta_score\nfrom keras.utils import Sequence\nfrom keras.utils import to_categorical\nfrom sklearn.model_selection import train_test_split\n\nWORKERS = 2\nCHANNEL = 3\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nIMG_SIZE = 512\nNUM_CLASSES = 5\nSEED = 77\nTRAIN_NUM = 1000 # use 1000 when you just want to explore new idea, use -1 for full train","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train = pd.read_csv('../input/aptos2019-blindness-detection/train.csv')\ndf_test = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')\n\nprint(df_train.head())\nprint(df_test.head())\n\n\nx = df_train['id_code']\ny = df_train['diagnosis']\n\nx, y = shuffle(x, y, random_state=SEED)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_x, valid_x, train_y, valid_y = train_test_split(x, y, test_size=0.15,\n                                                      stratify=y, random_state=SEED)\nprint(train_x.shape, train_y.shape, valid_x.shape, valid_y.shape)\n# train_y.hist()\nvalid_y.hist()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_y.unique()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nfig = plt.figure(figsize=(25, 16))\n# display 10 images from each class\nfor class_id in sorted(train_y.unique()):\n    for i, (idx, row) in enumerate(df_train.loc[df_train['diagnosis'] == class_id].sample(5, random_state=SEED).iterrows()):\n        ax = fig.add_subplot(5, 5, class_id * 5 + i + 1, xticks=[], yticks=[])\n        path=f\"../input/aptos2019-blindness-detection/train_images/{row['id_code']}.png\"\n        image = cv2.imread(path)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n        image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))\n\n        plt.imshow(image)\n        ax.set_title('Label: %d-%d-%s' % (class_id, idx, row['id_code']) )\n        ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nfig = plt.figure(figsize=(25, 16))\nfor class_id in sorted(train_y.unique()):\n    for i, (idx, row) in enumerate(df_train.loc[df_train['diagnosis'] == class_id].sample(5, random_state=SEED).iterrows()):\n        ax = fig.add_subplot(5, 5, class_id * 5 + i + 1, xticks=[], yticks=[])\n        path=f\"../input/aptos2019-blindness-detection/train_images/{row['id_code']}.png\"\n        image = cv2.imread(path)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n#         image=cv2.addWeighted ( image, 0 , cv2.GaussianBlur( image , (0 ,0 ) , 10) ,-4 ,128)\n        image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))\n\n        plt.imshow(image, cmap='gray')\n        ax.set_title('Label: %d-%d-%s' % (class_id, idx, row['id_code']) )","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dpi = 80 #inch\n\n# path=f\"../input/aptos2019-blindness-detection/train_images/5c7ab966a3ee.png\" # notice upper part\npath=f\"../input/aptos2019-blindness-detection/train_images/cd54d022e37d.png\" # lower-right, this still looks not so severe, can be class3\nimage = cv2.imread(path)\nimage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nheight, width = image.shape\nprint(height, width)\n\nSCALE=2\nfigsize = (width / float(dpi))/SCALE, (height / float(dpi))/SCALE\n\nfig = plt.figure(figsize=figsize)\nplt.imshow(image, cmap='gray')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nfig = plt.figure(figsize=(25, 16))\nfor class_id in sorted(train_y.unique()):\n    for i, (idx, row) in enumerate(df_train.loc[df_train['diagnosis'] == class_id].sample(5, random_state=SEED).iterrows()):\n        ax = fig.add_subplot(5, 5, class_id * 5 + i + 1, xticks=[], yticks=[])\n        path=f\"../input/aptos2019-blindness-detection/train_images/{row['id_code']}.png\"\n        image = cv2.imread(path)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n#         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n        image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))\n        image=cv2.addWeighted ( image,4, cv2.GaussianBlur( image , (0,0) , IMG_SIZE/10) ,-4 ,128) # the trick is to add this line\n\n        plt.imshow(image, cmap='gray')\n        ax.set_title('Label: %d-%d-%s' % (class_id, idx, row['id_code']) )","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}