add npp

.gitattributes

@@ -34,3 +34,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *last.ckpt* filter=lfs diff=lfs merge=lfs -text
 *.ckpt filter=lfs diff=lfs merge=lfs -text
 *events.out* filter=lfs diff=lfs merge=lfs -text
+.ckpt filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

add_caption.ipynb

@@ -0,0 +1,126 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os \n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import imageio\n",
+    "import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scene_dir = \"collage_shinyblender\"\n",
+    "imgorig_path = os.path.join(scene_dir, \"origimg\")\n",
+    "imgnew_path = os.path.join(scene_dir, \"newimg\")\n",
+    "imglist = sorted(os.listdir(imgorig_path))\n",
+    "imgpath = [os.path.join(imgorig_path, img) for img in imglist]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "200it [00:21,  9.46it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for (i, img) in tqdm.tqdm(enumerate(imgpath)):\n",
+    "    img_name = img.split(\"/\")[-1]\n",
+    "    img_file = Image.open(img)\n",
+    "    img_size = img_file.size\n",
+    "    \n",
+    "    if i < 40:\n",
+    "        model_name = \"NeRF\"\n",
+    "        psnr = 26.42\n",
+    "    elif i < 80:\n",
+    "        model_name = \"Mip-NeRF\"\n",
+    "        psnr = 26.72\n",
+    "    elif i < 120:\n",
+    "        model_name = \"DVGO\"\n",
+    "        psnr = 26.90\n",
+    "    elif i < 160:\n",
+    "        model_name = \"Plenoxels\"\n",
+    "        psnr = 26.11\n",
+    "    else:\n",
+    "        model_name = \"Ref-NeRF\"\n",
+    "        psnr = 30.70\n",
+    "\n",
+    "    box_str = f\"{model_name}\\n(PSNR: {psnr})\"\n",
+    "    props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
+    "    fig, (ax) = plt.subplots(ncols=1)\n",
+    "    ax.imshow(img_file)\n",
+    "    ax.axis(\"off\")\n",
+    "    ax.text(0.05, 0.95, box_str, fontsize=12,  verticalalignment='top', bbox=props, ha=\"center\", va=\"center\")\n",
+    "    fig.savefig(os.path.join(imgnew_path, img_name))\n",
+    "    plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "newimglist = [os.path.join(imgnew_path, imgname) for imgname in sorted(os.listdir(imgnew_path))]\n",
+    "imglist = [np.asarray(Image.open(img)) for img in newimglist]\n",
+    "imageio.mimwrite(os.path.join(scene_dir, \"video.mp4\"), imglist, fps=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.12 ('base')",
+   "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": "3.7.11"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}