AdaptativeFastDensityVisDrone.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/imenebak/VisDrone_CC/blob/master/AdaptativeFastDensityVisDrone.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bSZKl9HA3ha",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2WQs5c-DmJB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip \"/content/drive/My Drive/VisdroneCrowdCounting/Visdrone.zip\" -d \"/content/\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jDHVkmiDDyoo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#set the root to the Shanghai dataset you download\n",
        "root = '/content/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Do4LmqZyEfHF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import cv2\n",
        "import glob\n",
        "import h5py\n",
        "import scipy\n",
        "import pickle\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "import scipy.io as io\n",
        "from scipy import spatial\n",
        "from itertools import islice\n",
        "from tqdm import tqdm_notebook\n",
        "from matplotlib import pyplot as plt\n",
        "from sortedcontainers import SortedDict\n",
        "from scipy.ndimage.filters import gaussian_filter \n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x02MvszyGdjl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_img_pathes(path_sets):\n",
        "    \"\"\"\n",
        "    Return all images from all pathes in 'path_sets'\n",
        "    \"\"\"\n",
        "    img_pathes = []\n",
        "    for path in path_sets:\n",
        "        for img_path in glob.glob(os.path.join(path, '*.jpg')):\n",
        "            img_pathes.append(img_path)\n",
        "    return img_pathes\n",
        "\n",
        "\n",
        "def save_computed_density(density_map, out_path):\n",
        "    \"\"\"\n",
        "    Save density map to h5py format\n",
        "    \"\"\"\n",
        "    with h5py.File(out_path, 'w') as hf:\n",
        "        hf['density'] = density_map\n",
        "        \n",
        "\n",
        "def compute_sigma(gt_count, distance=None, min_sigma=1, method=1, fixed_sigma=15):\n",
        "    \"\"\"\n",
        "    Compute sigma for gaussian kernel with different methods :\n",
        "    * method = 1 : sigma = (sum of distance to 3 nearest neighbors) / 10\n",
        "    * method = 2 : sigma = distance to nearest neighbor\n",
        "    * method = 3 : sigma = fixed value\n",
        "    ** if sigma lower than threshold 'min_sigma', then 'min_sigma' will be used\n",
        "    ** in case of one point on the image sigma = 'fixed_sigma'\n",
        "    \"\"\"    \n",
        "    if gt_count > 1 and distance is not None:\n",
        "        if   method == 1:\n",
        "            sigma = np.mean(distance[1:4])*0.1\n",
        "        elif method == 2:\n",
        "            sigma = distance[1]\n",
        "        elif method == 3:\n",
        "            sigma = fixed_sigma\n",
        "    else:\n",
        "        sigma = fixed_sigma\n",
        "    if sigma < min_sigma:\n",
        "        sigma = min_sigma\n",
        "    return sigma\n",
        "def find_closest_key(sorted_dict, key):\n",
        "    \"\"\"\n",
        "    Find closest key in sorted_dict to 'key'\n",
        "    \"\"\"\n",
        "    keys = list(islice(sorted_dict.irange(minimum=key), 1))\n",
        "    keys.extend(islice(sorted_dict.irange(maximum=key, reverse=True), 1))\n",
        "    return min(keys, key=lambda k: abs(key - k))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HdyzG9FoGnF3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def gaussian_filter_density(non_zero_points, map_h, map_w, distances=None, kernels_dict=None, min_sigma=2, method=1, const_sigma=15):\n",
        "    \"\"\"\n",
        "    Fast gaussian filter implementation : using precomputed distances and kernels\n",
        "    \"\"\"\n",
        "    gt_count = non_zero_points.shape[0]\n",
        "    density_map = np.zeros((map_h, map_w), dtype=np.float32)\n",
        "    print(map_h, map_w)\n",
        "    for i in range(gt_count):\n",
        "        point_y, point_x = non_zero_points[i]\n",
        "        sigma = compute_sigma(gt_count, distances[i], min_sigma=min_sigma, method=method, fixed_sigma=const_sigma)\n",
        "        closest_sigma = find_closest_key(kernels_dict, sigma)\n",
        "        kernel = kernels_dict[closest_sigma]\n",
        "        full_kernel_size = kernel.shape[0]\n",
        "        kernel_size = full_kernel_size // 2\n",
        "\n",
        "        min_img_x = max(0, point_x-kernel_size)\n",
        "        min_img_y = max(0, point_y-kernel_size)\n",
        "        max_img_x = min(point_x+kernel_size+1, map_h - 1)\n",
        "        max_img_y = min(point_y+kernel_size+1, map_w - 1)\n",
        "\n",
        "        kernel_x_min = kernel_size - point_x if point_x <= kernel_size else 0\n",
        "        kernel_y_min = kernel_size - point_y if point_y <= kernel_size else 0\n",
        "        kernel_x_max = kernel_x_min + max_img_x - min_img_x\n",
        "        kernel_y_max = kernel_y_min + max_img_y - min_img_y\n",
        "        #print(min_img_x,max_img_x, min_img_y,max_img_y)\n",
        "        if(max_img_y - min_img_y) == (kernel_y_max-kernel_y_min):\n",
        "          density_map[min_img_x:max_img_x, min_img_y:max_img_y] += kernel[kernel_x_min:kernel_x_max, kernel_y_min:kernel_y_max]\n",
        "          print(density_map)\n",
        "    return density_map"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQXxXVdLGsA5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_gt_dots(mat_path, annotation, img_height, img_width):\n",
        "    \"\"\"\n",
        "    Load ground truth coordinates and save it to numpy array\n",
        "    \"\"\"\n",
        "    gt =[]\n",
        "    file_annota = open(mat_path)\n",
        "    lines = file_annota.readlines()\n",
        "    file_annota.close()\n",
        "    for ligne in lines:\n",
        "        line = ligne.strip()\n",
        "        line = line.split(\",\")\n",
        "        line = list(map(int, line))\n",
        "        if line[0] == int(annotation):\n",
        "          #print(\"get_gt_dots :\", line)\n",
        "          gt.append([line[2], line[1]])\n",
        "    #print(gt)\n",
        "    gt = np.array(gt)\n",
        "    gt[:,0] = gt[:,0].clip(0, img_width - 1)\n",
        "    gt[:,1] = gt[:,1].clip(0, img_height - 1)\n",
        "    #print(gt)\n",
        "    return gt\n",
        "\n",
        "def set_circles_on_img(image, bbox_list, circle_size=2):\n",
        "    \"\"\"\n",
        "    Set circles on images at centers of bboxes in bbox_list\n",
        "    \"\"\"\n",
        "    for bbox in bbox_list:\n",
        "        cv2.circle(image, (bbox[0], bbox[1]), circle_size, (255, 0, 0), -1)\n",
        "    return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5jOdPeWrGtbj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_gaussian_kernels(out_kernels_path='gaussian_kernels.pkl', round_decimals = 3, sigma_threshold = 4, sigma_min=0, sigma_max=20, num_sigmas=2460):\n",
        "    \"\"\"\n",
        "    Computing gaussian filter kernel for sigmas in linspace(sigma_min, sigma_max, num_sigmas) and saving \n",
        "    them to dict.     \n",
        "    \"\"\"\n",
        "    kernels_dict = dict()\n",
        "    sigma_space = np.linspace(sigma_min, sigma_max, num_sigmas)\n",
        "    for sigma in tqdm_notebook(sigma_space):\n",
        "        sigma = np.round(sigma, decimals=round_decimals) \n",
        "        kernel_size = np.ceil(sigma*sigma_threshold).astype(np.int)\n",
        "\n",
        "        img_shape  = (kernel_size*2+1, kernel_size*2+1)\n",
        "        img_center = (img_shape[0]//2, img_shape[1]//2)\n",
        "\n",
        "        arr = np.zeros(img_shape)\n",
        "        arr[img_center] = 1\n",
        "\n",
        "        arr = scipy.ndimage.filters.gaussian_filter(arr, sigma, mode='constant') \n",
        "        kernel = arr / arr.sum()\n",
        "        kernels_dict[sigma] = kernel\n",
        "        \n",
        "    print(f'Computed {len(sigma_space)} gaussian kernels. Saving them to {out_kernels_path}')\n",
        "\n",
        "    with open(out_kernels_path, 'wb') as f:\n",
        "        pickle.dump(kernels_dict, f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYSeKqaITbEi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re\n",
        "\n",
        "def recup_annotation_img(img_path):\n",
        "          img = plt.imread(img_path)\n",
        "          #print(img_path)\n",
        "          #annotation = img_path.replace('.jpg','')\n",
        "          annotation = img_path\n",
        "          result = re.search('sequences/(.*).jpg', annotation)\n",
        "          result = result.group(1)\n",
        "          folder, imgg = result.split('/')\n",
        "          #print(folder, imgg)\n",
        "          #print(img_path.split(folder))\n",
        "          folderPath = img_path.split(folder)\n",
        "          img_folder_path = folderPath[0]+folder\n",
        "          pos = 0\n",
        "          #nombre de l'image\n",
        "          for nbr in imgg:\n",
        "            if nbr != '0':\n",
        "              imgg = imgg[pos:]\n",
        "              break\n",
        "            pos+=1\n",
        "          file_annota = img_folder_path.replace('sequences','annotations')+\".txt\"\n",
        "          #img: frame,  file_annota: path of annotation, imgg: frame name\n",
        "          return(img, file_annota, imgg)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V2UPqdZbGyfg",
        "colab_type": "code",
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      "source": [
        "def compute_distances(out_dist_path, VisDrone_root_path, n_neighbors = 15, leafsize=1024):\n",
        "    distances_dict = dict()\n",
        "\n",
        "    for img_path in VisDrone_root_path:\n",
        "          #mat_path = full_img_path.replace('.jpg','.mat').replace('images','ground-truth').replace('IMG_','GT_IMG_')\n",
        "          \n",
        "          #img = plt.imread(full_img_path)\n",
        "          #print(img_path)\n",
        "          img, file_annota, annotation = recup_annotation_img(img_path)\n",
        "\n",
        "          non_zero_points = get_gt_dots(file_annota, annotation, *img.shape[0:2])\n",
        "\n",
        "          tree = spatial.KDTree(non_zero_points.copy(), leafsize=leafsize)  # build kdtree\n",
        "          distances, _ = tree.query(non_zero_points, k=n_neighbors)  # query kdtree\n",
        "\n",
        "          distances_dict[img_path] = distances\n",
        "        \n",
        "    print(f'Distances computed for {len(VisDrone_root_path)}. Saving them to {out_dist_path}')\n",
        "    with open(out_dist_path, 'wb') as f:\n",
        "      pickle.dump(distances_dict, f)"
      ],
      "execution_count": 0,
      "outputs": []
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            "90936c1cbc1e443c83a0ebebf0dce7dc"
          ]
        },
        "outputId": "5347e3be-5018-4e3a-a81c-c794eea500a4"
      },
      "source": [
        "precomputed_kernels_path = 'gaussian_kernels.pkl'\n",
        "\n",
        "# uncomment to generate and save dict with kernel sizes\n",
        "generate_gaussian_kernels(precomputed_kernels_path, round_decimals=3, sigma_threshold=4, sigma_min=0, sigma_max=20, num_sigmas=2460)\n",
        "    \n",
        "with open(precomputed_kernels_path, 'rb') as f:\n",
        "    kernels_dict = pickle.load(f)\n",
        "    kernels_dict = SortedDict(kernels_dict)"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ad17daed67d84a8d929c2202e3c884e2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2460), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Computed 2460 gaussian kernels. Saving them to gaussian_kernels.pkl\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQAaBcC6QQVO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "#now generate the ShanghaiA's ground truth\n",
        "train = os.path.join(root,'train/sequences')\n",
        "test = os.path.join(root,'test/sequences')\n",
        "path_sets = [train,test]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1-331W2QtEN",
        "colab_type": "text"
      },
      "source": [
        "train Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHIOH8xsQql2",
        "colab_type": "code",
        "outputId": "8a8ecc4e-0dfb-4d9a-8cbe-99c162ff674a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "img_folder_paths=[]\n",
        "for path, dirs, files in os.walk(path_sets[0]):\n",
        "    for d in dirs:\n",
        "      img_folder_paths.append(path_sets[0]+\"/\"+d)\n",
        "print(img_folder_paths)"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['/content/train/sequences/00094', '/content/train/sequences/00084', '/content/train/sequences/00067', '/content/train/sequences/00102', '/content/train/sequences/00027', '/content/train/sequences/00019', '/content/train/sequences/00054', '/content/train/sequences/00109', '/content/train/sequences/00003', '/content/train/sequences/00052', '/content/train/sequences/00086', '/content/train/sequences/00039', '/content/train/sequences/00071', '/content/train/sequences/00009', '/content/train/sequences/00078', '/content/train/sequences/00059', '/content/train/sequences/00037', '/content/train/sequences/00083', '/content/train/sequences/00033', '/content/train/sequences/00080', '/content/train/sequences/00047', '/content/train/sequences/00041', '/content/train/sequences/00008', '/content/train/sequences/00040', '/content/train/sequences/00085', '/content/train/sequences/00089', '/content/train/sequences/00064', '/content/train/sequences/00092', '/content/train/sequences/00051', '/content/train/sequences/00030', '/content/train/sequences/00060', '/content/train/sequences/00091', '/content/train/sequences/00057', '/content/train/sequences/00050', '/content/train/sequences/00004', '/content/train/sequences/00093', '/content/train/sequences/00032', '/content/train/sequences/00096', '/content/train/sequences/00020', '/content/train/sequences/00110', '/content/train/sequences/00090', '/content/train/sequences/00007', '/content/train/sequences/00031', '/content/train/sequences/00046', '/content/train/sequences/00066', '/content/train/sequences/00072', '/content/train/sequences/00081', '/content/train/sequences/00025', '/content/train/sequences/00106', '/content/train/sequences/00005', '/content/train/sequences/00073', '/content/train/sequences/00029', '/content/train/sequences/00021', '/content/train/sequences/00098', '/content/train/sequences/00087', '/content/train/sequences/00077', '/content/train/sequences/00038', '/content/train/sequences/00045', '/content/train/sequences/00036', '/content/train/sequences/00002', '/content/train/sequences/00055', '/content/train/sequences/00068', '/content/train/sequences/00058', '/content/train/sequences/00076', '/content/train/sequences/00026', '/content/train/sequences/00099', '/content/train/sequences/00013', '/content/train/sequences/00014', '/content/train/sequences/00104', '/content/train/sequences/00001', '/content/train/sequences/00049', '/content/train/sequences/00012', '/content/train/sequences/00053', '/content/train/sequences/00028', '/content/train/sequences/00010', '/content/train/sequences/00097', '/content/train/sequences/00056', '/content/train/sequences/00048', '/content/train/sequences/00006', '/content/train/sequences/00107', '/content/train/sequences/00079', '/content/train/sequences/00100', '/content/train/sequences/.ipynb_checkpoints']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MilTsBu7-uEs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f8ef21c5-5535-45c6-f09f-ce930c2009c4"
      },
      "source": [
        "fully = []\n",
        "for img_folder_path in img_folder_paths:\n",
        "      for path, dirs, files in os.walk(img_folder_path):\n",
        "        for img_path in files:\n",
        "          fully.append(img_folder_path+\"/\"+img_path)\n",
        "print(len(fully))"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2460\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4-OdJg-LNOF",
        "colab_type": "code",
        "outputId": "cd0af97f-951c-4fcb-dccd-3e66fc3debe8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "precomputed_distances_path = 'distances_dict.pkl'\n",
        "\n",
        "# uncomment to generate and save dict with distances \n",
        "compute_distances(precomputed_distances_path,fully)\n",
        "    \n",
        "with open(precomputed_distances_path, 'rb') as f:\n",
        "    distances_dict = pickle.load(f)"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Distances computed for 2460. Saving them to distances_dict.pkl\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TG0SFQpCLT05",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# generate GT for part A\n",
        "data_root  = root+'train/'\n",
        "map_out_folder = 'maps_adaptive_kernel/'\n",
        "min_sigma = 2  ## can be set 0\n",
        "method = 1\n",
        "import time\n",
        "\n",
        "for img_path in fully[70:]:\n",
        "          _, file_annota, annotation = recup_annotation_img(img_path)\n",
        "          print(file_annota)\n",
        "          #data_folder, img_path = full_img_path.split('images')\n",
        "          #mat_path = full_img_path.replace('.jpg','.mat').replace('images','ground-truth').replace('IMG_','GT_IMG_')\n",
        "          print(img_path)\n",
        "          # load img and map\n",
        "          img = Image.open(img_path)\n",
        "          width, height = img.size\n",
        "          gt_points = get_gt_dots(file_annota,annotation, height, width)\n",
        "    \n",
        "          distances = distances_dict[img_path]\n",
        "          #print(\"size : \",img.size,\"gt_points\", gt_points,\"distance\", distances)\n",
        "          start_time = time.time()\n",
        "          density_map = gaussian_filter_density(gt_points, height, width, distances, kernels_dict, min_sigma=min_sigma, method=method)\n",
        "          print(\"Temps d execution : %s secondes ---\" % (time.time() - start_time))\n",
        "          #curr_map_out_folder = data_folder + map_out_folder\n",
        "          #gt_out_path = curr_map_out_folder + img_path.strip('/').replace('.jpg', '.h5')\n",
        "          #curr_map_out_folder=img_path.replace('.jpg','.h5')\n",
        "          #with h5py.File(img_path.replace('.jpg','.h5'), 'w') as hf:\n",
        "            #hf['density'] = density_map\n",
        "          curr_map_out_folder = \"/content/train/\" + map_out_folder\n",
        "\n",
        "          annotation = img_path\n",
        "          result = re.search('sequences/(.*).jpg', annotation)\n",
        "          result = result.group(1)\n",
        "          folder, imgg = result.split('/')\n",
        "          #print(folder, imgg)\n",
        "          #print(img_path.split(folder))\n",
        "          folderPath = img_path.split(folder)\n",
        "          img_folder_path = folderPath[0]+folder\n",
        "\n",
        "          gt_out_path = curr_map_out_folder + result+\".h5\"\n",
        "    \n",
        "#     #plt.imshow(img)\n",
        "#     plt.imshow(density_map, alpha=1)\n",
        "#     plt.show()\n",
        "#     break\n",
        "#guvutxtcgv\n",
        "#khgc    \n",
        "\n",
        "          if not os.path.isdir(curr_map_out_folder):\n",
        "            print('creating ' + curr_map_out_folder)\n",
        "            #os.makedirs(curr_map_out_folder)\n",
        "          try:\n",
        "              os.mkdir(curr_map_out_folder)\n",
        "          except OSError:\n",
        "                print (\"Creation of the directory %s failed\" % path)\n",
        "          else:\n",
        "                print (\"Successfully created the directory %s \" % path)\n",
        "          try:\n",
        "              os.mkdir(curr_map_out_folder+folder)\n",
        "          except OSError:\n",
        "                print (\"Creation of the directory %s failed\" % path)\n",
        "          else:\n",
        "                print (\"Successfully created the directory %s \" % path)\n",
        "          save_computed_density(density_map, gt_out_path)\n",
        "          break"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3nE-ZkFsZIB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " plt.imshow(img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5F40HTJZLcMY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "outputId": "e08f3151-b255-43aa-9634-2e4cb67ce533"
      },
      "source": [
        "  plt.imshow(img)\n",
        "  plt.imshow(density_map, alpha=1)\n",
        "  plt.show()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5vLTURx2hu8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "af95e723-34be-4e04-edc3-29ee0be2c5fa"
      },
      "source": [
        "np.sum(density_map)"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "188.64032"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    }
  ]
}