mean_std.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "with open('lvis_v1_train.json') as f:\n",
    "    a=json.load(f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "d=defaultdict(list)\n",
    "img_area={}\n",
    "for i in a['images']:\n",
    "    img_area[i['id']]=i['width']*i['height']\n",
    "\n",
    "    \n",
    "for i in a['annotations']:\n",
    "    d[i['category_id']].append(i['bbox'][2]*i['bbox'][3]/img_area[i['image_id']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "x=np.sqrt(np.array(d[5]))\n",
    "mu,std=np.mean(x),np.std(x)\n",
    "x2=np.linspace(0,mu+3*std,100)\n",
    "y=norm.pdf(x2,mu,std)\n",
    "plt.hist(x, bins=100, density=True, alpha=0.6, color='g')\n",
    "plt.plot(x2,y,color='r')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "p={}\n",
    "for i in d:\n",
    "    x=np.sqrt(np.array(d[i]))\n",
    "    mu,std=np.mean(x),np.std(x)\n",
    "    p[i]=[mu,std]\n",
    "\n",
    "with open('area_mean_std2.json','w') as f:\n",
    "    json.dump(p,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3x",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}