{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8089,"databundleVersionId":44321,"sourceType":"competition"}],"dockerImageVersionId":30839,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"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\n\nimport os\nfor 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 20GB 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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport sys\nimport random\n\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\n\nimport tensorflow as tf\nfrom tensorflow import keras","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-30T05:17:41.13049Z","iopub.execute_input":"2025-01-30T05:17:41.13086Z","iopub.status.idle":"2025-01-30T05:17:59.129767Z","shell.execute_reply.started":"2025-01-30T05:17:41.130833Z","shell.execute_reply":"2025-01-30T05:17:59.12886Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"seed=2019\nrandom.seed=seed\nnp.random.seed=seed\ntf.seed = seed","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-30T05:18:58.638332Z","iopub.execute_input":"2025-01-30T05:18:58.638733Z","iopub.status.idle":"2025-01-30T05:18:58.643586Z","shell.execute_reply.started":"2025-01-30T05:18:58.638699Z","shell.execute_reply":"2025-01-30T05:18:58.64242Z"}},"outputs":[],"execution_count":5},{"cell_type":"markdown","source":"# Data Generator","metadata":{}},{"cell_type":"code","source":"class DataGen(keras.utils.Sequence):\n    def __init__(self, ids, path, batch_size=8, image_size=128):\n        self.ids = ids\n        self.path = path\n        self.batch_size = batch_size\n        self.image_size = image_size\n        self.on_epoch_end()\n        \n    def __load__(self, id_name):\n        ## Path\n        image_path = os.path.join(self.path, id_name, \"images\", id_name) + \".png\"\n        mask_path = os.path.join(self.path, id_name, \"masks/\")\n        all_masks = os.listdir(mask_path)\n        \n        ## Reading Image\n        image = cv2.imread(image_path, 1)\n        image = cv2.resize(image, (self.image_size, self.image_size))\n        \n        mask = np.zeros((self.image_size, self.image_size, 1))\n        \n        ## Reading Masks\n        for name in all_masks:\n            _mask_path = mask_path + name\n            _mask_image = cv2.imread(_mask_path, -1)\n            _mask_image = cv2.resize(_mask_image, (self.image_size, self.image_size)) #128x128\n            _mask_image = np.expand_dims(_mask_image, axis=-1)\n            mask = np.maximum(mask, _mask_image)\n            \n        ## Normalizaing \n        image = image/255.0\n        mask = mask/255.0\n        \n        return image, mask\n    \n    def __getitem__(self, index):\n        if(index+1)*self.batch_size > len(self.ids):\n            self.batch_size = len(self.ids) - index*self.batch_size\n        \n        files_batch = self.ids[index*self.batch_size : (index+1)*self.batch_size]\n        \n        image = []\n        mask  = []\n        \n        for id_name in files_batch:\n            _img, _mask = self.__load__(id_name)\n            image.append(_img)\n            mask.append(_mask)\n            \n        image = np.array(image)\n        mask  = np.array(mask)\n        \n        return image, mask\n    \n    def on_epoch_end(self):\n        pass\n    \n    def __len__(self):\n        return int(np.ceil(len(self.ids)/float(self.batch_size)))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-30T05:35:48.690662Z","iopub.execute_input":"2025-01-30T05:35:48.691299Z","iopub.status.idle":"2025-01-30T05:35:48.701917Z","shell.execute_reply.started":"2025-01-30T05:35:48.691246Z","shell.execute_reply":"2025-01-30T05:35:48.700828Z"}},"outputs":[],"execution_count":7},{"cell_type":"markdown","source":"# Hyperparameters","metadata":{}},{"cell_type":"code","source":"import os\nimport zipfile\n\n# Path to the ZIP file\ntrain_path = \"/kaggle/input/data-science-bowl-2018/stage1_train.zip\"\nextract_path = \"/kaggle/working/stage1_train\"  # Directory to extract the ZIP file\n\n# Extract the ZIP file\nwith zipfile.ZipFile(train_path, 'r') as zip_ref:\n    zip_ref.extractall(extract_path)\n\n# Training Ids (subdirectory names in the extracted folder)\ntrain_ids = next(os.walk(extract_path))[1]\n\n# Validation Data Size\nval_data_size = 10\n\n# Split into training and validation IDs\nvalid_ids = train_ids[:val_data_size]\ntrain_ids = train_ids[val_data_size:]\n\nprint(f\"Total training samples: {len(train_ids)}\")\nprint(f\"Total validation samples: {len(valid_ids)}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-30T05:39:50.693148Z","iopub.execute_input":"2025-01-30T05:39:50.693566Z","iopub.status.idle":"2025-01-30T05:39:56.190632Z","shell.execute_reply.started":"2025-01-30T05:39:50.69353Z","shell.execute_reply":"2025-01-30T05:39:56.18961Z"}},"outputs":[{"name":"stdout","text":"Total training samples: 660\nTotal validation samples: 10\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"gen = DataGen(train_ids, extract_path, batch_size=batch_size, image_size=image_size)\nx, y = gen.__getitem__(0)\nprint(x.shape, y.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-30T05:40:56.286697Z","iopub.execute_input":"2025-01-30T05:40:56.287174Z","iopub.status.idle":"2025-01-30T05:40:57.34998Z","shell.execute_reply.started":"2025-01-30T05:40:56.287124Z","shell.execute_reply":"2025-01-30T05:40:57.348947Z"}},"outputs":[{"name":"stdout","text":"(8, 128, 128, 3) (8, 128, 128, 1)\n","output_type":"stream"}],"execution_count":11},{"cell_type":"code","source":"\nr = random.randint(0, len(x)-1)\n\nfig = plt.figure()\nfig.subplots_adjust(hspace=0.4, wspace=0.4)\nax = fig.add_subplot(1, 2, 1)\nax.imshow(x[r])\nax = fig.add_subplot(1, 2, 2)\nax.imshow(np.reshape(y[r], (image_size, image_size)), cmap=\"gray\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-30T05:43:26.883923Z","iopub.execute_input":"2025-01-30T05:43:26.884313Z","iopub.status.idle":"2025-01-30T05:43:27.418506Z","shell.execute_reply.started":"2025-01-30T05:43:26.884284Z","shell.execute_reply":"2025-01-30T05:43:27.417241Z"}},"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7dc00c387580>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":13},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}