Computer_Vision/Chapter07/Understanding_selectivesearch.ipynb

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    "<a href=\"https://colab.research.google.com/github/PacktPublishing/Hands-On-Computer-Vision-with-PyTorch/blob/master/Chapter07/Understanding_selectivesearch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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     "text": [
      "Collecting selectivesearch\n",
      "  Downloading selectivesearch-0.4.tar.gz (3.8 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
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      "Building wheels for collected packages: selectivesearch\n",
      "  Building wheel for selectivesearch (setup.py): started\n",
      "  Building wheel for selectivesearch (setup.py): finished with status 'done'\n",
      "  Created wheel for selectivesearch: filename=selectivesearch-0.4-py3-none-any.whl size=4336 sha256=61ab0b66466fb21b6924de3810cb5ff89f7ccef477899ded62f27c63343e2413\n",
      "  Stored in directory: c:\\users\\frakt\\appdata\\local\\pip\\cache\\wheels\\dd\\5d\\c1\\1f9a5d313a93983d5d76cd3f1a4adb286a17176e65c6f704cb\n",
      "Successfully built selectivesearch\n",
      "Installing collected packages: selectivesearch\n",
      "Successfully installed selectivesearch-0.4\n",
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     ]
    }
   ],
   "source": [
    "!pip install selectivesearch\n",
    "!pip install torch_snippets\n",
    "from torch_snippets import *\n",
    "import selectivesearch\n",
    "from skimage.segmentation import felzenszwalb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 377
    },
    "id": "389eKddctfzb",
    "outputId": "2fc383ab-411e-41fd-df40-bd315d73e002"
   },
   "outputs": [],
   "source": [
    "img = read('Hemanvi.jpeg', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "g5bl_d_4thZU"
   },
   "outputs": [],
   "source": [
    "segments_fz = felzenszwalb(img, scale=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 670
    },
    "id": "zG1rT26sti8X",
    "outputId": "c4307487-7a44-4cc7-b26b-0bfdf5cb39b6"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplots([img, segments_fz], titles=['Original Image','Image post \\nfelzenszwalb segmentation'], figsize=(10,10), nc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8f_k7FKctklU"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "6D0nJylhtviy"
   },
   "outputs": [],
   "source": [
    "def extract_candidates(img):\n",
    "    img_lbl, regions = selectivesearch.selective_search(img, scale=200, min_size=100)\n",
    "    img_area = np.prod(img.shape[:2])\n",
    "    candidates = []\n",
    "    for r in regions:\n",
    "        if r['rect'] in candidates: continue\n",
    "        if r['size'] < (0.05*img_area): continue\n",
    "        if r['size'] > (1*img_area): continue\n",
    "        x, y, w, h = r['rect']\n",
    "        candidates.append(list(r['rect']))\n",
    "    return candidates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 938
    },
    "id": "qYahUedyuIEN",
    "outputId": "fb262843-cbfa-434c-8f91-37d16c50f47e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: PyWavelets>=1.1.1 in c:\\users\\frakt\\anaconda3\\lib\\site-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->torch_snippets) (1.3.0)\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in c:\\users\\frakt\\anaconda3\\lib\\site-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->torch_snippets) (2021.7.2)\n",
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      "Requirement already satisfied: platformdirs<5,>=3.9.1 in c:\\users\\frakt\\anaconda3\\lib\\site-packages (from virtualenv>=20.10.0->pre-commit->torch_snippets) (4.2.0)\n",
      "Requirement already satisfied: distlib<1,>=0.3.7 in c:\\users\\frakt\\anaconda3\\lib\\site-packages (from virtualenv>=20.10.0->pre-commit->torch_snippets) (0.3.8)\n",
      "Requirement already satisfied: filelock<4,>=3.12.2 in c:\\users\\frakt\\anaconda3\\lib\\site-packages (from virtualenv>=20.10.0->pre-commit->torch_snippets) (3.13.1)\n",
      "Requirement already satisfied: soupsieve>1.2 in c:\\users\\frakt\\anaconda3\\lib\\site-packages (from beautifulsoup4->nbconvert->torch_snippets) (2.3.1)\n",
      "Requirement already satisfied: webencodings in c:\\users\\frakt\\anaconda3\\lib\\site-packages (from bleach->nbconvert->torch_snippets) (0.5.1)\n",
      "Requirement already satisfied: pywin32>=1.0 in c:\\users\\frakt\\anaconda3\\lib\\site-packages (from jupyter-core->nbconvert->torch_snippets) (302)\n",
      "Requirement already satisfied: pyzmq>=13 in c:\\users\\frakt\\anaconda3\\lib\\site-packages (from jupyter-client>=6.1.5->nbclient<0.6.0,>=0.5.0->nbconvert->torch_snippets) (23.2.0)\n",
      "Requirement already satisfied: tornado>=4.1 in c:\\users\\frakt\\anaconda3\\lib\\site-packages (from jupyter-client>=6.1.5->nbclient<0.6.0,>=0.5.0->nbconvert->torch_snippets) (6.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install selectivesearch\n",
    "!pip install torch_snippets\n",
    "from torch_snippets import *\n",
    "import selectivesearch\n",
    "img = read('Hemanvi.jpeg', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 575
    },
    "id": "r0TcZNeguIUs",
    "outputId": "d74a2237-6a98-468a-c043-e6279cde470f"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "candidates = extract_candidates(img)\n",
    "show(img, bbs = candidates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_cRa7wtUuKAt"
   },
   "outputs": [],
   "source": []
  }
 ],
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