{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from glob import glob\n",
    "from os.path import join\n",
    "from pathlib import Path\n",
    "sys.path.append('/work/megapixels_dev/megapixels')\n",
    "from app.models.bbox import BBox\n",
    "#from app.utils import im_utils\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_ims = '/data_store_ssd/apps/megapixels/datasets/umd_faces/faces/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "fp_ims = glob(join(dir_ims, '*.png'))\n",
    "print(len(fp_ims))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function choice in module random:\n",
      "\n",
      "choice(self, seq)\n",
      "    Choose a random element from a non-empty sequence.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(random.sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 8, 0, 6, 3] True\n"
     ]
    }
   ],
   "source": [
    "a = list(range(0,10))\n",
    "b = random.sample(a, 5)\n",
    "print(b, len(set(b))==5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import randint\n",
    "imu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import face_alignment\n",
    "from skimage import io\n",
    "\n",
    "fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, flip_input=False,  device='cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp_im = np.random.choice(fp_ims)\n",
    "im = io.imread(fp_im)\n",
    "preds = fa.get_landmarks(im)\n",
    "print(preds[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(preds[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('test.json', 'w') as fp:\n",
    "  json.dump(preds[0].tolist(), fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python [conda env:megapixels]",
   "language": "python",
   "name": "conda-env-megapixels-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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