{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6afcec5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fastbook\n",
    "fastbook.setup_book()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4e4f004b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision.all import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c27d35cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1698b023",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Load a MAT file\n",
    "mat_p = os.path.join(os.getcwd(),'data','cars_annos.mat')\n",
    "mat = scipy.io.loadmat(mat_p)\n",
    "annotations = mat['annotations']\n",
    "class_names = mat['class_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b4402e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotations = np.array(annotations[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ef6b02ad",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (7, 1) + inhomogeneous part.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[31], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m annotations \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvectorize(np\u001b[38;5;241m.\u001b[39marray)(annotations)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\numpy\\lib\\function_base.py:2372\u001b[0m, in \u001b[0;36mvectorize.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2369\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_stage_2(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   2370\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[1;32m-> 2372\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_as_normal(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\numpy\\lib\\function_base.py:2365\u001b[0m, in \u001b[0;36mvectorize._call_as_normal\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2362\u001b[0m     vargs \u001b[38;5;241m=\u001b[39m [args[_i] \u001b[38;5;28;01mfor\u001b[39;00m _i \u001b[38;5;129;01min\u001b[39;00m inds]\n\u001b[0;32m   2363\u001b[0m     vargs\u001b[38;5;241m.\u001b[39mextend([kwargs[_n] \u001b[38;5;28;01mfor\u001b[39;00m _n \u001b[38;5;129;01min\u001b[39;00m names])\n\u001b[1;32m-> 2365\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_vectorize_call(func\u001b[38;5;241m=\u001b[39mfunc, args\u001b[38;5;241m=\u001b[39mvargs)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\numpy\\lib\\function_base.py:2455\u001b[0m, in \u001b[0;36mvectorize._vectorize_call\u001b[1;34m(self, func, args)\u001b[0m\n\u001b[0;32m   2452\u001b[0m \u001b[38;5;66;03m# Convert args to object arrays first\u001b[39;00m\n\u001b[0;32m   2453\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [asanyarray(a, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mobject\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m args]\n\u001b[1;32m-> 2455\u001b[0m outputs \u001b[38;5;241m=\u001b[39m ufunc(\u001b[38;5;241m*\u001b[39minputs)\n\u001b[0;32m   2457\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ufunc\u001b[38;5;241m.\u001b[39mnout \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   2458\u001b[0m     res \u001b[38;5;241m=\u001b[39m asanyarray(outputs, dtype\u001b[38;5;241m=\u001b[39motypes[\u001b[38;5;241m0\u001b[39m])\n",
      "\u001b[1;31mValueError\u001b[0m: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (7, 1) + inhomogeneous part."
     ]
    }
   ],
   "source": [
    "annotations = np.where(annotations, np.arary(annotations), np.arary(annotations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73630f27",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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