{"cells":[{"metadata":{},"cell_type":"markdown","source":"https://www.kaggle.com/pestipeti/pytorch-starter-fasterrcnn-train","execution_count":null},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport cv2\nimport os\nimport re\nimport shutil\nfrom tqdm import tqdm\nfrom zipfile import ZipFile\n\nfrom PIL import Image\n\nimport albumentations as A\nfrom albumentations.pytorch.transforms import ToTensorV2\n\nimport torch\nimport torchvision\n\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\nfrom torchvision.models.detection import FasterRCNN\nfrom torchvision.models.detection.rpn import AnchorGenerator\n\nfrom torch.utils.data import DataLoader, Dataset\nfrom torch.utils.data.sampler import SequentialSampler\n\nfrom matplotlib import pyplot as plt\n\nDIR_INPUT = '/kaggle/input/global-wheat-detection'\nDIR_TRAIN = f'{DIR_INPUT}/train'\nDIR_TEST = f'{DIR_INPUT}/test'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df = pd.read_csv(f'{DIR_INPUT}/train.csv')\ntrain_df.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df['x'] = -1\ntrain_df['y'] = -1\ntrain_df['w'] = -1\ntrain_df['h'] = -1","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def expand_bbox(x):\n    r = np.array(re.findall(\"([0-9]+[.]?[0-9]*)\", x))\n    if len(r) == 0:\n        r = [-1, -1, -1, -1]\n    return r","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df[['x', 'y', 'w', 'h']] = np.stack(train_df['bbox'].apply(lambda x: expand_bbox(x)))\ntrain_df.drop(columns=['bbox'], inplace=True)\ntrain_df['x'] = train_df['x'].astype(np.float)\ntrain_df['y'] = train_df['y'].astype(np.float)\ntrain_df['w'] = train_df['w'].astype(np.float)\ntrain_df['h'] = train_df['h'].astype(np.float)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df['x_center'] = train_df['x'] + train_df['w']/2.0","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df['y_center'] = train_df['y'] + train_df['h']/2.0","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"image_ids = train_df['image_id'].unique()\nvalid_ids = image_ids[-665:]\ntrain_ids = image_ids[:-665]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"valid_df = train_df[train_df['image_id'].isin(valid_ids)]\ntrain_df = train_df[train_df['image_id'].isin(train_ids)]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Create the Model","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"Generate train path list","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"def generate_txt_file(dataframe):\n    folder = \"Hello\"\n    DIR_PATH = folder + \".zip\"\n    zipObj = ZipFile(DIR_PATH, 'w')\n    for i in tqdm(range(len(dataframe))):\n        label = DIR_PATH + dataframe.iloc[i]['image_id'] + \".txt\"\n        f = open(label, \"w\")\n        line = \"{} {} {} {} {}\\n\".format(0, dataframe.iloc[i]['x_center'], dataframe.iloc[i]['y_center'], dataframe.iloc[i]['w'], dataframe.iloc[i]['h'])\n        f.write(line)\n        f.close()\n        zipObj.write(label)  \n    \n    zipObj.close()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"generate_txt_file(train_df)","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}