convert_coco_images.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycocotools.coco import COCO\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import os\n",
    "import cv2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=15.05s)\n",
      "creating index...\n",
      "index created!\n"
     ]
    }
   ],
   "source": [
    "dataDir = 'datasets/coco'\n",
    "dataType = 'train2017'\n",
    "\n",
    "annFile = '{}/annotations/instances_{}.json'.format(dataDir, dataType)\n",
    "coco = COCO(annFile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'name': 'person', 'id': 1, 'supercategory': 'person'}] [{'name': 'backpack', 'id': 27, 'supercategory': 'accessory'}]\n",
      "1 27\n",
      "People: 33670\n",
      "Backpacks: 2180\n"
     ]
    }
   ],
   "source": [
    "cats = coco.loadCats(coco.getCatIds())\n",
    "\n",
    "pcat = [cat for cat in cats if cat['supercategory'] == \"person\"]\n",
    "bcat = [cat for cat in cats if cat['name'] == \"backpack\"]\n",
    "\n",
    "print(pcat, bcat)\n",
    "\n",
    "p_id, b_id = coco.getCatIds(catNms=[\"person\", \"backpack\"])\n",
    "print(p_id, b_id)\n",
    " \n",
    "p_img_ids = coco.getImgIds(catIds=p_id)\n",
    "b_img_ids = coco.getImgIds(catIds=b_id)\n",
    "\n",
    "def size_filter(i):\n",
    "    # Only filter people, there's not enough backpacks\n",
    "    ann_ids = coco.getAnnIds(imgIds=i, catIds=p_id)\n",
    "    anns = coco.loadAnns(ann_ids)\n",
    "    bboxes = [ann['bbox'] for ann in anns]\n",
    "    size = 50\n",
    "    return all([True if (bbox[2] > size and bbox[3] > size) else False for bbox in bboxes])\n",
    "    \n",
    "p_img_ids = filter(size_filter, p_img_ids)\n",
    "b_img_ids = filter(size_filter, b_img_ids)\n",
    "\n",
    "person_img_ids = set(p_img_ids)\n",
    "backpack_img_ids = set(b_img_ids)\n",
    "\n",
    "print(\"People:\", len(person_img_ids))\n",
    "print(\"Backpacks:\", len(backpack_img_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Just people: 31984\n",
      "Just backpacks: 494\n",
      "Combined: 1686\n"
     ]
    }
   ],
   "source": [
    "just_people = list(person_img_ids - backpack_img_ids)\n",
    "just_backpacks = list(backpack_img_ids - person_img_ids)\n",
    "combined = list(person_img_ids & backpack_img_ids)\n",
    "\n",
    "print(\"Just people:\", len(just_people))\n",
    "print(\"Just backpacks:\", len(just_backpacks))\n",
    "print(\"Combined:\", len(combined))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(42)\n",
    "\n",
    "random.shuffle(just_people)\n",
    "random.shuffle(just_backpacks)\n",
    "random.shuffle(combined)\n",
    "\n",
    "chosen_people = just_people[:3000]\n",
    "chosen_backpacks = just_backpacks[:1000]\n",
    "chosen_both = combined[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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     ]
    }
   ],
   "source": [
    "all_images = chosen_backpacks + chosen_people + chosen_both\n",
    "\n",
    "out_dir = \"datasets/coco_filtered\"\n",
    "os.makedirs(out_dir, exist_ok=True)\n",
    "for i, img_id in enumerate(all_images):\n",
    "    ann_ids = coco.getAnnIds(imgIds = img_id, catIds = [p_id, b_id])\n",
    "    anns = coco.loadAnns(ann_ids)\n",
    "    \n",
    "    # Bbox annotations given as [x, y, w, h]\n",
    "    out_anns = []\n",
    "    for ann in anns:\n",
    "        p0 = np.array(ann['bbox'][:2])\n",
    "        size = np.array(ann['bbox'][2:])\n",
    "        p1 = p0 + size\n",
    "        cat = \"person\" if ann['category_id'] == 1 else \"backpack\"\n",
    "        out_anns.append(\"%s,%s,%s,%s,%s\" % (*p0, *p1, cat))\n",
    "    \n",
    "    img = coco.loadImgs(img_id)[0]\n",
    "    img_path = \"%s/images/%s/%s\" % (dataDir, dataType, img['file_name'])\n",
    "    \n",
    "    im = cv2.imread(img_path)\n",
    "    \n",
    "    out_im_path = \"%s/%05d.png\" % (out_dir, i)\n",
    "    out_ann_path = \"%s/%05d.txt\" % (out_dir, i)\n",
    "    \n",
    "    with open(out_ann_path, \"w\") as f:\n",
    "        f.write(\"\\n\".join(out_anns))\n",
    "    \n",
    "    cv2.imwrite(out_im_path, im)\n",
    "    print(out_im_path)\n",
    "    \n",
    "#    plt.axis('off')\n",
    "#    plt.imshow(im)\n",
    "#    coco.showAnns(anns)\n",
    "#    plt.show()"
   ]
  }
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