coco-map-dubplicate-pred

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from pycocotools.coco import COCO\n",
    "from pycocotools.cocoeval import COCOeval\n",
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "orig_gt_path = \"/home/botoscs/data/MS-COCO/annotations/instances_minival2014.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt = json.load(open(orig_gt_path))\n",
    "\n",
    "gt[\"annotations\"][:] = gt[\"annotations\"][:1]\n",
    "del gt[\"annotations\"][0][\"segmentation\"]\n",
    "\n",
    "pred = deepcopy(gt)\n",
    "pred[\"annotations\"][0][\"score\"] = 1.0\n",
    "\n",
    "# Duplicate the detections for the very same object\n",
    "pred[\"annotations\"] += pred[\"annotations\"]\n",
    "pred = pred[\"annotations\"]\n",
    "\n",
    "json.dump(gt, open(\"gt.json\", \"w\"))\n",
    "json.dump(pred, open(\"pred.json\", \"w\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'area': 702.10575,\n",
       "  'iscrowd': 0,\n",
       "  'image_id': 289343,\n",
       "  'bbox': [473.07, 395.93, 38.65, 28.67],\n",
       "  'category_id': 18,\n",
       "  'id': 1768}]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt[\"annotations\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'area': 702.10575,\n",
       "  'iscrowd': 0,\n",
       "  'image_id': 289343,\n",
       "  'bbox': [473.07, 395.93, 38.65, 28.67],\n",
       "  'category_id': 18,\n",
       "  'id': 1768,\n",
       "  'score': 1.0},\n",
       " {'area': 702.10575,\n",
       "  'iscrowd': 0,\n",
       "  'image_id': 289343,\n",
       "  'bbox': [473.07, 395.93, 38.65, 28.67],\n",
       "  'category_id': 18,\n",
       "  'id': 1768,\n",
       "  'score': 1.0}]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=0.02s)\n",
      "creating index...\n",
      "index created!\n",
      "Loading and preparing results...\n",
      "DONE (t=0.00s)\n",
      "creating index...\n",
      "index created!\n"
     ]
    }
   ],
   "source": [
    "cocoGt = COCO(\"gt.json\")\n",
    "cocoDt = cocoGt.loadRes(\"pred.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=3.14s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.52s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 1.000\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000\n"
     ]
    }
   ],
   "source": [
    "cocoEval = COCOeval(cocoGt,cocoDt,\"bbox\")\n",
    "cocoEval.evaluate()\n",
    "cocoEval.accumulate()\n",
    "cocoEval.summarize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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