{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'DepthRepresentation' from 'dronescapes_reader' (/scratch/sdc/datasets/dronescapes/dronescapes_reader/__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [4], line 7\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrandom\u001b[39;00m\n\u001b[1;32m      6\u001b[0m sys\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mappend(Path\u001b[38;5;241m.\u001b[39mcwd()\u001b[38;5;241m.\u001b[39mparent\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__str__\u001b[39m())\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdronescapes_reader\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MultiTaskDataset, DepthRepresentation, OpticalFlowRepresentation, SemanticRepresentation\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DataLoader\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'DepthRepresentation' from 'dronescapes_reader' (/scratch/sdc/datasets/dronescapes/dronescapes_reader/__init__.py)"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "from functools import partial\n",
    "from pprint import pprint\n",
    "import random\n",
    "sys.path.append(Path.cwd().parent.__str__())\n",
    "from dronescapes_reader import MultiTaskDataset, DepthRepresentation, OpticalFlowRepresentation, SemanticRepresentation\n",
    "from torch.utils.data import DataLoader\n",
    "import numpy as np\n",
    "import torch as tr\n",
    "from media_processing_lib.collage_maker import collage_fn\n",
    "from media_processing_lib.image import image_add_title\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[24-05-13 14:30:43 DEBUG]\u001b[0m Building dataset from: '/scratch/sdc/datasets/dronescapes/scripts/../data/train_set' (multitask_dataset.py:186)\n",
      "\u001b[32m[24-05-13 14:30:44 INFO]\u001b[0m Found 11664 data points as union of all nodes' data (8 nodes). (multitask_dataset.py:174)\n",
      "\u001b[32m[24-05-13 14:30:44 DEBUG]\u001b[0m No explicit tasks provided. Using all of them as read from the paths (8). (multitask_dataset.py:86)\n",
      "\u001b[32m[24-05-13 14:30:44 INFO]\u001b[0m Tasks used in this dataset: ['depth_dpt', 'depth_sfm_manual202204', 'edges_dexined', 'normals_sfm_manual202204', 'opticalflow_rife', 'rgb', 'semantic_mask2former_swin_mapillary_converted', 'semantic_segprop8'] (multitask_dataset.py:93)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MultiTaskDataset]\n",
      " - Path: '/scratch/sdc/datasets/dronescapes/scripts/../data/train_set'\n",
      " - Only full data: False\n",
      " - Representations (8): [DepthRepresentation(depth_dpt), DepthRepresentation(depth_sfm_manual202204), NpzRepresentation(edges_dexined), NpzRepresentation(normals_sfm_manual202204), OpticalFlowRepresentation(opticalflow_rife), NpzRepresentation(rgb), SemanticRepresentation(semantic_mask2former_swin_mapillary_converted), SemanticRepresentation(semantic_segprop8)]\n",
      " - Length: 11664\n",
      "== Shapes ==\n",
      "{'depth_dpt': torch.Size([540, 960]),\n",
      " 'depth_sfm_manual202204': torch.Size([540, 960]),\n",
      " 'edges_dexined': torch.Size([540, 960]),\n",
      " 'normals_sfm_manual202204': torch.Size([540, 960, 3]),\n",
      " 'opticalflow_rife': torch.Size([540, 960, 2]),\n",
      " 'rgb': torch.Size([540, 960, 3]),\n",
      " 'semantic_mask2former_swin_mapillary_converted': torch.Size([540, 960]),\n",
      " 'semantic_segprop8': torch.Size([540, 960])}\n"
     ]
    }
   ],
   "source": [
    "sema_repr = partial(SemanticRepresentation, classes=8, color_map=[[0, 255, 0], [0, 127, 0], [255, 255, 0],\n",
    "                                                                  [255, 255, 255], [255, 0, 0], [0, 0, 255],\n",
    "                                                                  [0, 255, 255], [127, 127, 63]])\n",
    "reader = MultiTaskDataset(\"../data/train_set\", handle_missing_data=\"fill_none\",\n",
    "                          task_types={\"depth_dpt\": DepthRepresentation(\"depth_dpt\", min_depth=0, max_depth=0.999),\n",
    "                                      \"depth_sfm_manual202204\": DepthRepresentation(\"depth_sfm_manual202204\",\n",
    "                                                                                    min_depth=0, max_depth=300),\n",
    "                                      \"opticalflow_rife\": OpticalFlowRepresentation,\n",
    "                                      \"semantic_segprop8\": sema_repr,\n",
    "                                      \"semantic_mask2former_swin_mapillary_converted\": sema_repr})\n",
    "print(reader)\n",
    "print(\"== Shapes ==\")\n",
    "pprint(reader.data_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== Random loaded item ==\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'reader' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [2], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m== Random loaded item ==\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m rand_ix \u001b[38;5;241m=\u001b[39m random\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mlen\u001b[39m(\u001b[43mreader\u001b[49m))\n\u001b[1;32m      3\u001b[0m data, name, repr_names \u001b[38;5;241m=\u001b[39m reader[rand_ix] \u001b[38;5;66;03m# get a random item\u001b[39;00m\n\u001b[1;32m      4\u001b[0m img_data \u001b[38;5;241m=\u001b[39m {}\n",
      "\u001b[0;31mNameError\u001b[0m: name 'reader' is not defined"
     ]
    }
   ],
   "source": [
    "print(\"== Random loaded item ==\")\n",
    "rand_ix = random.randint(0, len(reader))\n",
    "data, name, repr_names = reader[rand_ix] # get a random item\n",
    "img_data = {}\n",
    "for k, v in data.items():\n",
    "    img_data[k] = reader.name_to_task[k].plot_fn(v) if v is not None else np.zeros((*reader.data_shape[k][0:2], 3))\n",
    "if \"rgb\" in img_data: # move rgb as 1st item in the collage\n",
    "    img_data = {\"rgb\": img_data[\"rgb\"], **{k: v for k, v in img_data.items() if k != \"rgb\"}}\n",
    "pprint({k: v.shape for k, v in img_data.items()})\n",
    "collage = collage_fn(list(img_data.values()), titles=img_data.keys(), size_px=55)\n",
    "collage = image_add_title(collage, name, size_px=55, top_padding=110)\n",
    "plt.imshow(collage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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