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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np, os, json, pandas as pd, matplotlib.pyplot as plt, seaborn as sns\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "import cv2\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Creating Synthetic Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from synthetic import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Running the Classifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import IPython.display as ipd\n",
+ "class Vid:\n",
+ " \n",
+ " def __init__(self, video_path):\n",
+ " self.video_path = video_path\n",
+ " self.frames = self.read(video_path)\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.frames)\n",
+ " \n",
+ " def __getitem__(self, idx):\n",
+ " return self.frames[idx]\n",
+ " \n",
+ " def __iter__(self):\n",
+ " for frame in self.frames:\n",
+ " yield frame\n",
+ " \n",
+ " @staticmethod\n",
+ " def read(video_path):\n",
+ " cap = cv2.VideoCapture(video_path)\n",
+ " frames = []\n",
+ " while True:\n",
+ " ret, frame = cap.read()\n",
+ " if not ret:\n",
+ " break\n",
+ " frames.append(frame)\n",
+ " cap.release()\n",
+ " return np.array(frames)\n",
+ "\n",
+ " def show(self):\n",
+ " return VideoPlayer(self.video_path)\n",
+ " \n",
+ "from IPython.display import HTML\n",
+ "\n",
+ "HTML(\"\"\"