keypoint_visualize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import IPython.display\n",
    "from utils import pc_util\n",
    "import PIL\n",
    "from PIL import Image\n",
    "import io\n",
    "import skimage.io\n",
    "from scipy.io import loadmat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import data_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset name: keypoint\n",
      "X_train: (846, 2048, 3)\n",
      "X_test: (212, 2048, 3)\n",
      "y_train: (846, 2048)\n",
      "y_test: (212, 2048)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = data_loader.get_pointcloud('keypoint')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pc_util import *\n",
    "def point_cloud_three_views_with_keypoint(points, keypoints):\n",
    "    print(points.shape)\n",
    "    print(keypoints.shape)\n",
    "    img1 = draw_point_cloud(points, normalize=False, zrot=110/180.0*np.pi, xrot=45/180.0*np.pi, yrot=0/180.0*np.pi)\n",
    "    img2 = draw_point_cloud(points, normalize=False, zrot=70/180.0*np.pi, xrot=135/180.0*np.pi, yrot=0/180.0*np.pi)\n",
    "    img3 = draw_point_cloud(points, normalize=False, zrot=180.0/180.0*np.pi, xrot=90/180.0*np.pi, yrot=0/180.0*np.pi)\n",
    "    image_large = np.concatenate([img1, img2, img3], 1)\n",
    "\n",
    "    keypts_img1 = draw_point_cloud(points[keypoints==1], normalize=False, zrot=110/180.0*np.pi, xrot=45/180.0*np.pi, yrot=0/180.0*np.pi)\n",
    "    keypts_img2 = draw_point_cloud(points[keypoints==1], normalize=False, zrot=70/180.0*np.pi, xrot=135/180.0*np.pi, yrot=0/180.0*np.pi)\n",
    "    keypts_img3 = draw_point_cloud(points[keypoints==1], normalize=False, zrot=180.0/180.0*np.pi, xrot=90/180.0*np.pi, yrot=0/180.0*np.pi)\n",
    "    keypts_image_large = np.concatenate([keypts_img1, keypts_img2, keypts_img3], 1)\n",
    "\n",
    "    image = np.stack([image_large, image_large, image_large], axis=-1)\n",
    "    image[:,:,0] += keypts_image_large\n",
    "    np.clip(image, 0, 1, image)\n",
    "\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2048, 3)\n",
      "(2048,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f287ed25f98>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = np.random.randint(X_train.shape[0])\n",
    "img = point_cloud_three_views_with_keypoint(X_train[idx], y_train[idx])\n",
    "skimage.io.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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