Analyse_Input_Pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import numpy as np\n",
    "# import tensorflow as tf\n",
    "from glob import glob\n",
    "import cv2\n",
    "\n",
    "from config import Parameters\n",
    "# from processors import SimpleDataGenerator\n",
    "# from custom_processors import AnalyseCustomDataGenerator\n",
    "from point_pillars_custom_processors_v2 import AnalyseCustomDataGenerator\n",
    "from det3d.pc_kitti_dataset import PCKittiAugmentedDataset\n",
    "from tqdm.notebook import trange\n",
    "# print(dir(tqdm))\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# DATA_ROOT = \"/media/data3/tjtanaa/kitti_dataset/KITTI/object/training\"  # TODO make main arg\n",
    "DATA_ROOT = \"/media/data3/tjtanaa/kitti_dataset/\"  # TODO make main arg\n",
    "MODEL_ROOT = \"./logs_Car_Pedestrian_Custom_Dataset_single_process\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = Parameters()\n",
    "\n",
    "gt_database_dir = os.path.join(DATA_ROOT, \"gt_database\")\n",
    "\n",
    "# training_gen = AnalyseCustomDataGenerator(batch_size=params.batch_size,root_dir = DATA_ROOT,\n",
    "#                 npoints=20000, split='train',   classes=list(params.classes_map.keys()), \n",
    "#                 random_select=False, gt_database_dir=None, aug_hard_ratio=0.7)\n",
    "\n",
    "\n",
    "# validation_gen = PCKittiAugmentedDataset(root_dir=DATA_ROOT, \n",
    "#         npoints=20000, split='val',  random_select=False, classes=list(params.classes_map.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# # get the min and max (range) of every axis\n",
    "# x_max = np.array(-9999.0)\n",
    "# x_min = np.array(9999.0)\n",
    "# y_max = np.array(-9999.0)\n",
    "# y_min = np.array(9999.0)\n",
    "# z_max = np.array(-9999.0)\n",
    "# z_min = np.array(9999.0)\n",
    "# for i in trange(len(validation_gen)):\n",
    "#     sample = validation_gen[i]\n",
    "#     x_max = np.max([x_max, np.max(sample['pts_rect'][:,0])])\n",
    "#     x_min = np.min([x_min, np.min(sample['pts_rect'][:,0])])\n",
    "#     y_max = np.max([y_max, np.max(sample['pts_rect'][:,1])])\n",
    "#     y_min = np.min([y_min, np.min(sample['pts_rect'][:,1])])\n",
    "#     z_max = np.max([z_max, np.max(sample['pts_rect'][:,2])])\n",
    "#     z_min = np.min([z_min, np.min(sample['pts_rect'][:,2])])\n",
    "# print(x_min, x_max)\n",
    "# print(y_min, y_max)\n",
    "# print(z_min, z_max)\n",
    "# # 1.349664568901062 78.88325500488281\n",
    "# # -52.52265167236328 50.9976806640625\n",
    "# # -1.0496952533721924 3.1484153270721436\n",
    "    \n",
    "     \n",
    "# # 1.349664568901062 78.88325500488281\n",
    "# # -52.52265167236328 50.9976806640625\n",
    "# # -1.0496952533721924 3.1484153270721436"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      "(1, 12000, 100, 9) (1, 12000, 3)\n",
      "[ 6.1949997e+00 -1.4270003e+00 -1.6610003e+00 -2.9000002e-01\n",
      " -1.0499954e-02 -6.4125061e-02 -1.3750792e-03  1.1499977e-01\n",
      "  1.3002157e-02]\n",
      "[ 6.1959996e+00 -1.4070001e+00 -1.6590002e+00 -2.5000000e-01\n",
      " -9.5000267e-03 -4.4124842e-02  6.2501431e-04  1.1599970e-01\n",
      "  3.3002377e-02]\n",
      "[ 6.1980000e+00 -1.3870002e+00 -1.6590002e+00 -2.6999998e-01\n",
      " -7.4996948e-03 -2.4124980e-02  6.2501431e-04  1.1800003e-01\n",
      "  5.3002238e-02]\n",
      "[ 6.2029991e+00 -1.3670001e+00 -1.6590003e+00 -2.2000000e-01\n",
      " -2.5005341e-03 -4.1248798e-03  6.2489510e-04  1.2299919e-01\n",
      "  7.3002338e-02]\n",
      "[ 6.2049999e+00 -1.3580000e+00 -1.6590003e+00 -3.4000000e-01\n",
      " -4.9972534e-04  4.8751831e-03  6.2489510e-04  1.2500000e-01\n",
      "  8.2002401e-02]\n",
      "[ 6.2149997e+00 -1.3390002e+00 -1.6610001e+00 -2.2999999e-01\n",
      "  9.5000267e-03  2.3874998e-02 -1.3749599e-03  1.3499975e-01\n",
      "  1.0100222e-01]\n",
      "[ 6.2089992e+00 -1.3180001e+00 -1.6580001e+00 -2.8000000e-01\n",
      "  3.4995079e-03  4.4875145e-02  1.6250610e-03  1.2899923e-01\n",
      "  1.2200236e-01]\n",
      "[ 6.2230000e+00 -1.3000002e+00 -1.6610000e+00 -2.9000002e-01\n",
      "  1.7500401e-02  6.2875032e-02 -1.3748407e-03  1.4300013e-01\n",
      "  1.4000225e-01]\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "\n"
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    {
     "ename": "NameError",
     "evalue": "name 'exit' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-613d9d8ced98>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m#     sample = validation_gen[i]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;34m[\u001b[0m\u001b[0mpillars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvoxels\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0moccupancy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mposition\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mangle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheading\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassification\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0;34m[\u001b[0m\u001b[0mpts_input\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgt_boxes3d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidation_gen\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpts_input\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mx_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx_max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpts_input\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tjtanaa/PointPillars/point_pillars_custom_processors_v2.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, batch_id)\u001b[0m\n\u001b[1;32m    379\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpillars_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m             \u001b[0;31m# print(np.sum(pillars_ > 0))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 381\u001b[0;31m             \u001b[0mexit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m             \u001b[0mpillars\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpillars_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'exit' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "validation_gen = AnalyseCustomDataGenerator(batch_size=params.batch_size,root_dir = DATA_ROOT,\n",
    "                npoints=20000, split='val',   classes=list(params.classes_map.keys()), \n",
    "                random_select=False, gt_database_dir=None, aug_hard_ratio=0.7)\n",
    "# get the min and max (range) of every axis\n",
    "x_max = np.array(-9999.0)\n",
    "x_min = np.array(9999.0)\n",
    "y_max = np.array(-9999.0)\n",
    "y_min = np.array(9999.0)\n",
    "z_max = np.array(-9999.0)\n",
    "z_min = np.array(9999.0)\n",
    "for i in trange(len(validation_gen)):\n",
    "#     sample = validation_gen[i]\n",
    "    [pillars, voxels], [occupancy, position, size, angle, heading, classification], \\\n",
    "    [pts_input, gt_boxes3d, sample] = validation_gen[i]\n",
    "    for j in range(len(pts_input)):\n",
    "        x_max = np.max([x_max, np.max(pts_input[j][:,0])])\n",
    "        x_min = np.min([x_min, np.min(pts_input[j][:,0])])\n",
    "        y_max = np.max([y_max, np.max(pts_input[j][:,1])])\n",
    "        y_min = np.min([y_min, np.min(pts_input[j][:,1])])\n",
    "        z_max = np.max([z_max, np.max(pts_input[j][:,2])])\n",
    "        z_min = np.min([z_min, np.min(pts_input[j][:,2])])\n",
    "print(x_min, x_max)\n",
    "print(y_min, y_max)\n",
    "print(z_min, z_max)\n",
    "# 1.8940000534057617 70.76299285888672\n",
    "# -39.99699783325195 40.195003509521484\n",
    "# -3.204000234603882 2.0290002822875977"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# import numba\n",
    "\n",
    "# [pillars, voxels], \\\n",
    "# [occupancy, position, size, angle, heading, classification], \\\n",
    "# [pts_input, gt_boxes3d, sample] = validation_gen[8]\n",
    "\n",
    "\n",
    "# @numba.jit(nopython=True)\n",
    "# def _points_to_bevmap_reverse_kernel(\n",
    "#         points,\n",
    "#         voxel_size,\n",
    "#         coors_range,\n",
    "#         coor_to_voxelidx,\n",
    "#         # coors_2d,\n",
    "#         bev_map,\n",
    "#         height_lowers,\n",
    "#         # density_norm_num=16,\n",
    "#         with_reflectivity=False,\n",
    "#         max_voxels=40000):\n",
    "#     # put all computations to one loop.\n",
    "#     # we shouldn't create large array in main jit code, otherwise\n",
    "#     # reduce performance\n",
    "#     N = points.shape[0]\n",
    "#     ndim = 3\n",
    "#     ndim_minus_1 = ndim - 1\n",
    "#     grid_size = (coors_range[3:] - coors_range[:3]) / voxel_size\n",
    "#     # np.round(grid_size)\n",
    "#     # grid_size = np.round(grid_size).astype(np.int64)(np.int32)\n",
    "#     grid_size = np.round(grid_size, 0, grid_size).astype(np.int32)\n",
    "#     height_slice_size = voxel_size[-1]\n",
    "#     coor = np.zeros(shape=(3, ), dtype=np.int32)  # DHW\n",
    "#     voxel_num = 0\n",
    "#     failed = False\n",
    "#     for i in range(N):\n",
    "#         failed = False\n",
    "#         for j in range(ndim):\n",
    "#             c = np.floor((points[i, j] - coors_range[j]) / voxel_size[j])\n",
    "#             if c < 0 or c >= grid_size[j]:\n",
    "#                 failed = True\n",
    "#                 break\n",
    "#             coor[ndim_minus_1 - j] = c\n",
    "#         if failed:\n",
    "#             continue\n",
    "#         voxelidx = coor_to_voxelidx[coor[0], coor[1], coor[2]]\n",
    "#         if voxelidx == -1:\n",
    "#             voxelidx = voxel_num\n",
    "#             if voxel_num >= max_voxels:\n",
    "#                 break\n",
    "#             voxel_num += 1\n",
    "#             coor_to_voxelidx[coor[0], coor[1], coor[2]] = voxelidx\n",
    "#             # coors_2d[voxelidx] = coor[1:]\n",
    "#         bev_map[-1, coor[1], coor[2]] += 1\n",
    "#         height_norm = bev_map[coor[0], coor[1], coor[2]]\n",
    "#         incomimg_height_norm = (\n",
    "#             points[i, 2] - height_lowers[coor[0]]) / height_slice_size\n",
    "#         if incomimg_height_norm > height_norm:\n",
    "#             bev_map[coor[0], coor[1], coor[2]] = incomimg_height_norm\n",
    "#             if with_reflectivity:\n",
    "#                 bev_map[-2, coor[1], coor[2]] = points[i, 3]\n",
    "#     # return voxel_num\n",
    "\n",
    "# def points_to_bev(points,\n",
    "#                   voxel_size,\n",
    "#                   coors_range,\n",
    "#                   with_reflectivity=False,\n",
    "#                   density_norm_num=16,\n",
    "#                   max_voxels=40000):\n",
    "#     \"\"\"convert kitti points(N, 4) to a bev map. return [C, H, W] map.\n",
    "#     this function based on algorithm in points_to_voxel.\n",
    "#     takes 5ms in a reduced pointcloud with voxel_size=[0.1, 0.1, 0.8]\n",
    "\n",
    "#     Args:\n",
    "#         points: [N, ndim] float tensor. points[:, :3] contain xyz points and\n",
    "#             points[:, 3] contain reflectivity.\n",
    "#         voxel_size: [3] list/tuple or array, float. xyz, indicate voxel size\n",
    "#         coors_range: [6] list/tuple or array, float. indicate voxel range.\n",
    "#             format: xyzxyz, minmax\n",
    "#         with_reflectivity: bool. if True, will add a intensity map to bev map.\n",
    "#     Returns:\n",
    "#         bev_map: [num_height_maps + 1(2), H, W] float tensor. \n",
    "#             `WARNING`: bev_map[-1] is num_points map, NOT density map, \n",
    "#             because calculate density map need more time in cpu rather than gpu. \n",
    "#             if with_reflectivity is True, bev_map[-2] is intensity map. \n",
    "#     \"\"\"\n",
    "#     if not isinstance(voxel_size, np.ndarray):\n",
    "#         voxel_size = np.array(voxel_size, dtype=points.dtype)\n",
    "#     if not isinstance(coors_range, np.ndarray):\n",
    "#         coors_range = np.array(coors_range, dtype=points.dtype)\n",
    "#     voxelmap_shape = (coors_range[3:] - coors_range[:3]) / voxel_size\n",
    "#     voxelmap_shape = tuple(np.round(voxelmap_shape).astype(np.int32).tolist())\n",
    "#     voxelmap_shape = voxelmap_shape[::-1]  # DHW format\n",
    "#     coor_to_voxelidx = -np.ones(shape=voxelmap_shape, dtype=np.int32)\n",
    "#     # coors_2d = np.zeros(shape=(max_voxels, 2), dtype=np.int32)\n",
    "#     bev_map_shape = list(voxelmap_shape)\n",
    "#     bev_map_shape[0] += 1\n",
    "#     height_lowers = np.linspace(\n",
    "#         coors_range[2], coors_range[5], voxelmap_shape[0], endpoint=False)\n",
    "#     if with_reflectivity:\n",
    "#         bev_map_shape[0] += 1\n",
    "#     bev_map = np.zeros(shape=bev_map_shape, dtype=points.dtype)\n",
    "#     _points_to_bevmap_reverse_kernel(points, voxel_size, coors_range,\n",
    "#                                      coor_to_voxelidx, bev_map, height_lowers,\n",
    "#                                      with_reflectivity, max_voxels)\n",
    "#     # print(voxel_num)\n",
    "#     return bev_map\n",
    "\n",
    "# def point_to_vis_bev(points,\n",
    "#                      voxel_size=None,\n",
    "#                      coors_range=None,\n",
    "#                      max_voxels=80000):\n",
    "#     if voxel_size is None:\n",
    "#         voxel_size = [0.1, 0.1, 0.1]\n",
    "#     if coors_range is None:\n",
    "#         coors_range = [-50, -50, -3, 50, 50, 1]\n",
    "#     voxel_size[2] = coors_range[5] - coors_range[2]\n",
    "#     bev_map = points_to_bev(\n",
    "#         points, voxel_size, coors_range, max_voxels=max_voxels)\n",
    "#     height_map = (bev_map[0] * 255).astype(np.uint8)\n",
    "#     return cv2.cvtColor(height_map, cv2.COLOR_GRAY2RGB)\n",
    "\n",
    "# img = point_to_vis_bev(pts_input[0], voxel_size=[params.x_step/2, params.y_step/2, 0.01],\n",
    "#                        coors_range=[params.x_min, params.y_min+20.32, params.z_min, \n",
    "#                                     params.x_max-40.32, params.y_max-20.32, params.z_max] )\n",
    "                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig1 = plt.figure(figsize=(10,10))\n",
    "# ax = fig1.add_subplot(111)\n",
    "# plt.imshow(img)"
   ]
  }
 ],
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