simple_stim_gen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Simple generator\n",
    "\n",
    "- 1 frame : 1 second\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import cv2\n",
    "from skimage.draw import random_shapes, rectangle\n",
    "from skimage.util import img_as_ubyte\n",
    "import pandas as pd\n",
    "import datetime, dateutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "# which shapes to 'choose' from\n",
    "shape_arr = ['triangle', 'circle']\n",
    "frame_size = (1080,1920)\n",
    "\n",
    "# which shape is the conditioned stimulus\n",
    "other_stim = 'circle'\n",
    "cond_stim = 'triangle'\n",
    "\n",
    "# randomly selects shapes (must be even!)\n",
    "number_of_shapes = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create fixation stimulus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "h, w = frame_size\n",
    "blank_img = np.full((h, w), 255, dtype = np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "fix_img = np.full((h, w), 255, dtype = np.uint8)\n",
    "start = (540,960)\n",
    "extent = (590,1010)\n",
    "rr, cc = rectangle(start, end=extent, shape=fix_img.shape)\n",
    "fix_img[rr, cc] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create image arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "# each holds camera images \n",
    "cam1_images = []\n",
    "cam2_images = []\n",
    "cam3_images = []\n",
    "\n",
    "# order dfs for writing time keys\n",
    "time_order = []\n",
    "shape_order = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cam_appender(img_folder, img, n_repeats):\n",
    "    for _ in range(n_repeats):\n",
    "        img_folder.append(img)\n",
    "\n",
    "def cam3_fixation():\n",
    "    ''' Fixation pt for 10 seconds\n",
    "        followed by 15 second blank frame.\n",
    "    '''\n",
    "    cam_appender(cam3_images,fix_img, 10)\n",
    "    append_single(cam3_images, 15)\n",
    "\n",
    "def append_single(cam, num):\n",
    "    ''' for appending blank images to a single camera.\n",
    "            cam = name of camera image folder;\n",
    "            num = number of blank frames to append\n",
    "    '''\n",
    "    for _ in range(num):\n",
    "        cam.append(blank_img)\n",
    "\n",
    "def append_multiple(first_cam, second_cam, num):\n",
    "    ''' for appending blank images to a single camera.\n",
    "            first_cam = name of camera image folder;\n",
    "            second_cam = name of other camera image folder;\n",
    "            num = number of blank frames to append\n",
    "    '''\n",
    "    for _ in range(num):\n",
    "        first_cam.append(blank_img)\n",
    "        second_cam.append(blank_img)\n",
    "\n",
    "def get_results(shapes, camera_images, num):\n",
    "    result = random_shapes((140,140), intensity_range=(0, 1), max_shapes=1, min_size = 99, max_size=100, shape=shapes, multichannel=False)\n",
    "    result_pad = np.pad(result[0], ((470,470),(890,890)), mode='constant', constant_values=255)\n",
    "    cam_appender(camera_images,result_pad, num)\n",
    "    append_single(camera_images, int(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "shapes = np.random.choice(shape_arr, number_of_shapes, p=[0.5,0.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Blank into followed by fixation\n",
    "\n",
    "1. 1 minute (60 frames) blank\n",
    "2. 25 second fixation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generates first frame\n",
    "# video 3 will start with a 1 second blank\n",
    "# video 1 and 2 will begin with 6 second blank\n",
    "append_single(cam3_images, int(60))\n",
    "\n",
    "cam3_fixation()\n",
    "\n",
    "append_multiple(cam1_images, cam2_images, int(60+25))\n",
    "n_secs = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cam1_shapes, cam2_shapes in zip(shapes, shapes[::-1]):\n",
    "    # randomly chooses between camera 1 and 2 \n",
    "    cam_array = ['cam1', 'cam2']\n",
    "    which_array = np.random.choice(cam_array, 1, p=[0.5,0.5])\n",
    "\n",
    "    # if camera 1 is chosen\n",
    "    if which_array == 'cam1':\n",
    "\n",
    "        shape_order.append([cam1_shapes, 'fixation', cam2_shapes, 'fixation'])\n",
    "        time_order.append(['camera 1', 'fixation', 'camera 2', 'fixation'])\n",
    "        get_results(cam1_shapes,cam1_images, int(n_secs))\n",
    "        \n",
    "        append_multiple(cam2_images, cam3_images, int(n_secs + 5))\n",
    "\n",
    "        # camera 3 fixation\n",
    "        \n",
    "        cam3_fixation()\n",
    "\n",
    "        # for camera 1 and 2 (delay from fixation point)\n",
    "        append_multiple(cam1_images, cam2_images, int(25))\n",
    "\n",
    "        get_results(cam2_shapes,cam2_images, int(n_secs))\n",
    "        append_multiple(cam1_images, cam3_images, int(n_secs + 5))\n",
    "\n",
    "        # camera 3 only\n",
    "        cam3_fixation()\n",
    "\n",
    "        append_multiple(cam1_images, cam2_images, int(25))\n",
    "\n",
    "    # if camera 2 is chosen,\n",
    "    else:\n",
    "        shape_order.append([cam2_shapes, 'fixation', cam1_shapes, 'fixation'])\n",
    "        time_order.append(['camera 2', 'fixation', 'camera 1', 'fixation'])\n",
    "\n",
    "        get_results(cam2_shapes,cam2_images, int(n_secs))\n",
    "\n",
    "        append_multiple(cam1_images, cam3_images, int(n_secs + 5))\n",
    "\n",
    "        # camera 3 only\n",
    "        cam3_fixation()\n",
    "\n",
    "        # for camera 1 and 2 (delay from fixation point)\n",
    "        append_multiple(cam1_images, cam2_images, int(25))\n",
    "\n",
    "        # append camera 2\n",
    "        get_results(cam1_shapes,cam1_images, int(n_secs))\n",
    "        append_multiple(cam2_images, cam3_images, int(n_secs + 5))\n",
    "\n",
    "        # camera 3 only\n",
    "        cam3_fixation()\n",
    "        append_multiple(cam1_images, cam2_images, int(25))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Time Key\n",
    "\n",
    "Generate a key to videos to know when particular stimuli occur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_order = np.array(time_order).flatten()\n",
    "time_order = np.insert(time_order,0,'fixation')\n",
    "shape_order =  np.array(shape_order).flatten()\n",
    "shape_order = np.insert(shape_order,0,'fixation')\n",
    "# generating timekey \n",
    "\n",
    "time_key = pd.DataFrame({'camera_order':time_order, 'shape_order':shape_order})\n",
    "time_key['duration'] = np.nan\n",
    "time_key.loc[time_key['shape_order']=='fixation', 'duration']= 25\n",
    "time_key.loc[time_key['shape_order']!='fixation', 'duration']= 15\n",
    "\n",
    "# shift since first frame begins with blank frame (1sec) \n",
    "time_key.at[0,'duration'] = time_key.at[0,'duration'] + 60\n",
    "time_key['times'] = time_key['duration'].cumsum()\n",
    "time_key['times'] = pd.to_timedelta(time_key['times'], unit='s')\n",
    "time_key['times'] = time_key['times'].shift(1)\n",
    "time_key.at[0,'times'] = 60\n",
    "\n",
    "# Save times\n",
    "today = datetime.datetime.now()\n",
    "today = today.strftime(\"%m-%d-%Y-%H-%M-%S\")\n",
    "time_key_df_name = '{}_{}'.format(today, 'time_key.csv')\n",
    "time_key.to_csv(time_key_df_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing videos to folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "# writing videos\n",
    "fourcc = cv2.VideoWriter_fourcc(*'avc1')\n",
    "fps=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_name = '{}{}{}'.format('cam_01_', today, '.mp4' )\n",
    "video = cv2.VideoWriter(video_name, fourcc, fps , (w,h), 0)\n",
    "\n",
    "for frame in cam1_images:\n",
    "    frame = img_as_ubyte(frame)\n",
    "    video.write(frame)\n",
    "video.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_name = '{}{}{}'.format('cam_02_', today, '.mp4' )\n",
    "video = cv2.VideoWriter(video_name, fourcc, fps , (w,h), 0)\n",
    "\n",
    "for frame in cam2_images:\n",
    "    frame = img_as_ubyte(frame)\n",
    "    video.write(frame)\n",
    "video.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_name = '{}{}{}'.format('cam_03_', today, '.mp4' )\n",
    "video = cv2.VideoWriter(video_name, fourcc, fps , (w,h), 0)\n",
    "\n",
    "for frame in cam3_images:\n",
    "    frame = img_as_ubyte(frame)\n",
    "    video.write(frame)\n",
    "video.release()"
   ]
  }
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