example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "import glob\n",
    "import os\n",
    "import shutil\n",
    "import sys\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Object_Detection_Metrics.metrics import calculate_metrics, draw_bounding_boxes_truth_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_name = {0 : 'bouchon_casse', 1 : 'trou', 2 : 'deforme', 3 : 'delamine'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Save folder /home/psycle/PsycleResearch/Babynov/results\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Save folder /home/psycle/PsycleResearch/Babynov/results\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Save folder /home/psycle/PsycleResearch/Babynov/results\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AP: 100.00% (0)\n",
      "AP: 60.00% (2)\n",
      "AP: 50.00% (3)\n",
      "mAP: 70.00%\n"
     ]
    }
   ],
   "source": [
    "calculate_metrics(dict_name, gtFolder='/home/psycle/PsycleResearch/Babynov/groundtruths', \n",
    "                  detFolder='/home/psycle/PsycleResearch/Babynov/detections', iouThreshold=0.3, showPlot=True, gtCoordinates='rel', detCoordinates='rel',\n",
    "                      imgSize='640,640', savePath='/home/psycle/PsycleResearch/Babynov/results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/home/psycle/PsycleResearch/Babynov/results/images/008d9d6a-94a1-4ff4-b344-9ac9fcb8e4d8.jpg'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-963c55834a3e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m                                \u001b[0;34m'/home/psycle/PsycleResearch/Babynov/groundtruths'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                                \u001b[0;34m'/home/psycle/PsycleResearch/Babynov/results/images'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m                                '/home/psycle/PsycleResearch/Babynov/detections')\n\u001b[0m",
      "\u001b[0;32m~/PsycleResearch/env_object_detect/lib/python3.6/site-packages/Object_Detection_Metrics/metrics.py\u001b[0m in \u001b[0;36mdraw_bounding_boxes_truth_pred\u001b[0;34m(dict_name, gt_annotations, img_folder, results, prediction_annotations)\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[0mresults\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mfolder\u001b[0m \u001b[0mwhere\u001b[0m \u001b[0mresults\u001b[0m \u001b[0mare\u001b[0m \u001b[0mto\u001b[0m \u001b[0mbe\u001b[0m \u001b[0msaved\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m     \"\"\"\n\u001b[0;32m--> 236\u001b[0;31m     \u001b[0mdraw_boxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgt_annotations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0misGT\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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    237\u001b[0m     \u001b[0mdraw_boxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprediction_annotations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0misGT\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0madd_legend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/PsycleResearch/env_object_detect/lib/python3.6/site-packages/Object_Detection_Metrics/metrics.py\u001b[0m in \u001b[0;36mdraw_boxes\u001b[0;34m(dict_name, annotations, img_folder, results, isGT)\u001b[0m\n\u001b[1;32m    204\u001b[0m                 img = add_bb_into_image(image, [x, y, w, h], color=(0, 0, 255), thickness=2,\n\u001b[1;32m    205\u001b[0m                                         label=dict_name[int(idClass)])\n\u001b[0;32m--> 206\u001b[0;31m         \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{results}/{nameOfImage}.jpg'\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    207\u001b[0m         \u001b[0mfh1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/PsycleResearch/env_object_detect/lib/python3.6/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m   2146\u001b[0m                 \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r+b\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2147\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2148\u001b[0;31m                 \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"w+b\"\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   2149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2150\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/psycle/PsycleResearch/Babynov/results/images/008d9d6a-94a1-4ff4-b344-9ac9fcb8e4d8.jpg'"
     ]
    }
   ],
   "source": [
    "draw_bounding_boxes_truth_pred(dict_name, '/home/psycle/PsycleResearch/Babynov/groundtruths',\n",
    "                               '/home/psycle/PsycleResearch/Babynov/groundtruths', \n",
    "                               '/home/psycle/PsycleResearch/Babynov/results/images', \n",
    "                               '/home/psycle/PsycleResearch/Babynov/detections')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################################################"
   ]
  }
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