{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. _example_blank:\n",
    "\n",
    "Rendering blank image\n",
    "==========================\n",
    "\n",
    "Let's start to use Lightmetrica by rendering a blank image.\n",
    "We first import the ``lightmetrica`` module, where we use ``lm`` as an alias of ``lightmetrica`` for simplicity.\n",
    "\n",
    ".. note::\n",
    "    Although we recommend to use Python API to organize the experiments, similar APIs can be accessible from C++. See `example directory`_ for the detail.\n",
    "\n",
    "    .. _example directory: https://github.com/hi2p-perim/lightmetrica-v3/tree/master/example\n",
    "\n",
    ".. note::\n",
    "    ``lmfunctest`` is a simple module to configure Lightmetrica envrionment from a file named ``.lmenv``. You want to create your own ``.lmenv`` file if you want to execute examples or tests by yourself. For detail, see todo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lmfunctest as ft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightmetrica as lm"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Lightmetrica offers an extension for the Jupyter notebook to support logging or interactive progress reporting inside the notebook. The extension can be loaded by a line magic command as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext lightmetrica_jupyter"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "After importing the module, you can initialize the framwork by calling :cpp:func:`lm::init` function. You can pass various arguments to configure the framework to the function, but here we keep it empty so that everything is configured to be default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm.init()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Logging and progress reporting in Jupyter notebook can be enabled by :cpp:func:`lm::log::init` and :cpp:func:`lm::progress::init` functions with corresponding types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm.log.init('logger::jupyter')\n",
    "lm.progress.init('progress::jupyter')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Once the framework has been initialized properly, you can get an splash message using :cpp:func:`lm::info()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I|0.007|114@user  ] Lightmetrica -- Version 3.0.0 (rev. fe30e7c) Linux x64\n"
     ]
    }
   ],
   "source": [
    "lm.info()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Next we define `assets` necessary to dispatch renderer, like materials, meshes, etc. In this example, we only need a `film` to which the renderer outputs the image. We can define assets by :cpp:func:`lm::asset` function. The first argument (``film``) specifies the name of the asset to be referenced. The second argument (``film::bitmap``) is given as the type of the assets formatted as ``<type of asset>::<implementation>`` where the last argument (``{...}``) specifies the parameters passed to the instance. ``film::bitmap`` takes two parameters ``w`` and ``h`` which respectively specify width and height of the film."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I|0.017|48@assets ] Loading asset [name='film']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'$.assets.film'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.asset('film', 'film::bitmap', {\n",
    "    'w': 1920,\n",
    "    'h': 1080\n",
    "})"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Now we are ready for rendering. :cpp:func:`lm::render` function dispatches rendering where we speficy type of the renderer and the parameters as arguments. ``renderer::blank`` is a toy renderer that only produces a blank image to the film specifies by ``film`` parameter where we can use predefined ID of the asset. Also, we can specify the background color by ``color`` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I|0.151|151@user  ] Starting render [name='renderer::blank']\n"
     ]
    }
   ],
   "source": [
    "lm.render('renderer::blank', {\n",
    "    'output': lm.asset('film'),\n",
    "    'color': [1,1,1]\n",
    "})"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "After rendering, the generated image is kept in ``film``. :cpp:func:`lm::save` function outputs this film to the disk as an image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I|0.330|97@film_bi] Saving image [file='blank.png']\n"
     ]
    }
   ],
   "source": [
    "lm.save(lm.asset('film'), 'blank.png')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "You can also visualize the film directly in Jupyter notebook. :cpp:func:`lm::buffer` gets the internal image data as numpy array which you can visualize using matplotlib. We rendered a white blank image thus the following image is as we expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = np.copy(lm.buffer(lm.asset('film')))\n",
    "f = plt.figure(figsize=(15,15))\n",
    "ax = f.add_subplot(111)\n",
    "ax.imshow(np.clip(np.power(img,1/2.2),0,1), origin='lower')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Finally, we gracefully shutdown the framework with :cpp:func:`lm::shutdown` function. Usually you don't want to explicitly call shutdown function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm.shutdown()"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:light"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}