{"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 tensorflow as tf\nfrom tensorflow import keras\nfrom keras.models import Sequential\nfrom keras.layers import Activation, Dense, Flatten, BatchNormalization, Conv2D, MaxPool2D, Dropout\nfrom keras.optimizers import Adam, SGD\nfrom keras.metrics import categorical_crossentropy\nfrom keras.preprocessing.image import ImageDataGenerator\n\nimport warnings\nimport numpy as np\nimport cv2\nfrom keras.callbacks import ReduceLROnPlateau\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\n\nbackground = None\naccumulated_weight = 0.5\n\n#Creating the dimensions for the ROI...\nROI_top = 100\nROI_bottom = 300\nROI_right = 150\nROI_left = 350\n\n\ndef cal_accum_avg(frame, accumulated_weight):\n\n    global background\n    \n    if background is None:\n        background = frame.copy().astype(\"float\")\n        return None\n\n    cv2.accumulateWeighted(frame, background, accumulated_weight)","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-02-24T08:57:21.327005Z","iopub.execute_input":"2023-02-24T08:57:21.328381Z","iopub.status.idle":"2023-02-24T08:57:32.515570Z","shell.execute_reply.started":"2023-02-24T08:57:21.328194Z","shell.execute_reply":"2023-02-24T08:57:32.513968Z"},"trusted":true},"execution_count":null,"outputs":[]}]}