Yolo_park.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Yolo_park.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ankit1khare/Smart-Park-with-YOLO-V3/blob/master/Yolo_park.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "LDWYg0DGvHqN",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "'''Detecting number of occupied parking spots. Code written keeping in mind the resources provided\n",
        "   under google colab \n",
        "'''\n",
        "\n",
        "from tensorflow.python.client import device_lib\n",
        "device_lib.list_local_devices()\n",
        "\n",
        "import torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "LNRzAVNy-izy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "outputId": "ea677f0b-6bdb-4504-88ba-f95000cec234"
      },
      "cell_type": "code",
      "source": [
        "#mount your google drive to avoid losing data on colab when session expires \n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LXOJ-sOyiSqs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import glob\n",
        "import os\n",
        "\n",
        "os.chdir(\"/content/drive/My Drive/AK49/Detecting_occupied_spaces\")\n",
        "# Directory of images to run detection on\n",
        "ROOT_DIR = os.getcwd()\n",
        "print(ROOT_DIR)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "11sRDwtbiPBT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install Cython\n",
        "!git clone https://github.com/waleedka/coco\n",
        "!pip install -U setuptools\n",
        "!pip install -U wheel\n",
        "\n",
        "os.getcwd()\n",
        "!mkdir ak"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "hLVvkwHlsiRu",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from __future__ import division\n",
        "import time\n",
        "import torch \n",
        "import torch.nn as nn\n",
        "from torch.autograd import Variable\n",
        "import numpy as np\n",
        "import cv2 \n",
        "from util import *\n",
        "from darknet import Darknet\n",
        "from preprocess import prep_image, inp_to_image, letterbox_image\n",
        "import pandas as pd\n",
        "import random \n",
        "import pickle as pkl\n",
        "import argparse\n",
        "\n",
        "\n",
        "def get_test_input(input_dim, CUDA):\n",
        "    img = cv2.imread(\"Khare_frame_02.png\")\n",
        "    img = cv2.resize(img, (input_dim, input_dim)) \n",
        "    img_ =  img[:,:,::-1].transpose((2,0,1))\n",
        "    img_ = img_[np.newaxis,:,:,:]/255.0\n",
        "    img_ = torch.from_numpy(img_).float()\n",
        "    img_ = Variable(img_)\n",
        "    \n",
        "    if CUDA:\n",
        "        img_ = img_.cuda()\n",
        "    \n",
        "    return img_\n",
        "\n",
        "def prep_image(img, inp_dim):\n",
        "    \"\"\"\n",
        "    Prepare image for inputting to the neural network. \n",
        "    \n",
        "    Returns a Variable \n",
        "    \"\"\"\n",
        "\n",
        "    orig_im = img\n",
        "    dim = orig_im.shape[1], orig_im.shape[0]\n",
        "    img = (letterbox_image(orig_im, (inp_dim, inp_dim)))\n",
        "    img_ = img[:,:,::-1].transpose((2,0,1)).copy()\n",
        "    img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)\n",
        "    return img_, orig_im, dim\n",
        "\n",
        "def write(x, img, classes):\n",
        "    c1 = tuple(x[1:3].int())\n",
        "    c2 = tuple(x[3:5].int())\n",
        "    cls = int(x[-1])\n",
        "    label = \"{0}\".format(classes[cls])\n",
        "    color = (0,0,255)\n",
        "    cv2.rectangle(img, c1, c2,color, 1)\n",
        "    t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]\n",
        "    c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4\n",
        "    cv2.rectangle(img, c1, c2,color, -1)\n",
        "    cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);\n",
        "    return img\n",
        "\n",
        "def arg_parse():\n",
        "    \"\"\"\n",
        "    Parse arguements to the detect module\n",
        "    \n",
        "    \"\"\"\n",
        "    \n",
        "    \n",
        "    parser = argparse.ArgumentParser(description='YOLO v3 Video Detection Module')\n",
        "   \n",
        "    parser.add_argument(\"--video\", dest = 'video', help = \n",
        "                        \"Video to run detection upon\",\n",
        "                        default = \"ff.mp4\", type = str)\n",
        "    parser.add_argument(\"--dataset\", dest = \"dataset\", help = \"Dataset on which the network has been trained\", default = \"pascal\")\n",
        "    parser.add_argument(\"--confidence\", dest = \"confidence\", help = \"Object Confidence to filter predictions\", default = 0.5)\n",
        "    parser.add_argument(\"--nms_thresh\", dest = \"nms_thresh\", help = \"NMS Threshhold\", default = 0.4)\n",
        "    parser.add_argument(\"--cfg\", dest = 'cfgfile', help = \n",
        "                        \"Config file\",\n",
        "                        default = \"./cfg/yolov3.cfg\", type = str)\n",
        "    parser.add_argument(\"--weights\", dest = 'weightsfile', help = \n",
        "                        \"weightsfile\",\n",
        "                        default = \"yolov3.weights\", type = str)\n",
        "    parser.add_argument(\"--reso\", dest = 'reso', help = \n",
        "                        \"Input resolution of the network. Increase to increase accuracy. Decrease to increase speed\",\n",
        "                        default = \"704\", type = str)\n",
        "    \n",
        "    args = parser.parse_args(args=[])\n",
        "    \n",
        "    return args"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "6zWdYhCZzQAB",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def yolo_videoprocess():\n",
        "    batch_size = 15\n",
        "    args = arg_parse()\n",
        "    confidence = float(args.confidence)\n",
        "    nms_thesh = float(args.nms_thresh)\n",
        "    start = 0\n",
        "\n",
        "    CUDA = torch.cuda.is_available()\n",
        "\n",
        "    num_classes = 1\n",
        "\n",
        "    CUDA = torch.cuda.is_available()\n",
        "    \n",
        "    bbox_attrs = 5 + num_classes\n",
        "    \n",
        "    print(\"Loading network.....\")\n",
        "    model = Darknet(args.cfgfile)\n",
        "    model.load_weights(args.weightsfile)\n",
        "    print(\"Network successfully loaded\")\n",
        "\n",
        "    model.net_info[\"height\"] = args.reso\n",
        "    inp_dim = int(model.net_info[\"height\"])\n",
        "    #uncomment this if error occurs due to frame size\n",
        "#     assert inp_dim % 32 == 0 \n",
        "#     assert inp_dim > 32\n",
        "\n",
        "    if CUDA:\n",
        "        model.cuda()\n",
        "        \n",
        "    model(get_test_input(inp_dim, CUDA), CUDA)\n",
        "\n",
        "    model.eval()\n",
        "    \n",
        "    videofile = args.video\n",
        "    \n",
        "    cap = cv2.VideoCapture(videofile)\n",
        "    \n",
        "    assert cap.isOpened(), 'Cannot capture source'\n",
        "    frame_count = 0\n",
        "    frames = []\n",
        "    start = time.time()    \n",
        "    while True:\n",
        "        ret, frame = cap.read()\n",
        "        if ret:\n",
        "            frame_count += 1\n",
        "            frames.append(frame)\n",
        "#             print(\"appended\")\n",
        "            if len(frames) == batch_size:\n",
        "              for i, item in enumerate(frames):\n",
        "                frame = item\n",
        "                img, orig_im, dim = prep_image(frame, inp_dim)\n",
        "\n",
        "                im_dim = torch.FloatTensor(dim).repeat(1,2)                        \n",
        "\n",
        "\n",
        "                if CUDA:\n",
        "                    im_dim = im_dim.cuda()\n",
        "                    img = img.cuda()\n",
        "\n",
        "                with torch.no_grad():   \n",
        "                    output = model(Variable(img), CUDA)\n",
        "                output = write_results(output, confidence, num_classes, nms = True, nms_conf = nms_thesh)\n",
        "                print(\"count cars {}\".format(output.size(0)))\n",
        "\n",
        "                if type(output) == int:\n",
        "                    frame_count+=1\n",
        "                    print(\"FPS of the video is {:5.2f}\".format( frame_count / (time.time() - start)))\n",
        "                    cv2.imshow(\"frame\", orig_im)\n",
        "                    key = cv2.waitKey(1)\n",
        "                    if key & 0xFF == ord('q'):\n",
        "                        break\n",
        "                    continue\n",
        "\n",
        "\n",
        "                im_dim = im_dim.repeat(output.size(0), 1)\n",
        "                scaling_factor = torch.min(inp_dim/im_dim,1)[0].view(-1,1)\n",
        "\n",
        "                output[:,[1,3]] -= (inp_dim - scaling_factor*im_dim[:,0].view(-1,1))/2\n",
        "                output[:,[2,4]] -= (inp_dim - scaling_factor*im_dim[:,1].view(-1,1))/2\n",
        "\n",
        "                output[:,1:5] /= scaling_factor\n",
        "\n",
        "                for i in range(output.shape[0]):\n",
        "                    output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim[i,0])\n",
        "                    output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim[i,1])\n",
        "\n",
        "                classes = load_classes('data/coco.names')\n",
        "                colors = pkl.load(open(\"pallete\", \"rb\"))\n",
        "\n",
        "                list(map(lambda x: write(x, orig_im, classes), output))\n",
        "                empty = 6 - output.size(0)\n",
        "                cv2.putText(orig_im, \"Total empty spots: \" + str(empty), (5,30), cv2.FONT_HERSHEY_SIMPLEX, 2, [255,255,255], 9, cv2.LINE_AA)\n",
        "\n",
        "    #             cv2.imshow(\"Parking 1\", orig_im)\n",
        "    #             frames += 1\n",
        "                name = '{0}.jpg'.format(frame_count + i - batch_size)\n",
        "\n",
        "                name = os.path.join('./ak', name)\n",
        "                cv2.imwrite(name, frame)\n",
        "\n",
        "                \n",
        "                key = cv2.waitKey(1)\n",
        "                if key & 0xFF == ord('q'):\n",
        "                    break\n",
        "              print(\"FPS of the video is {:5.2f}\".format( frame_count / (time.time() - start)))\n",
        "              \n",
        "              frames = []\n",
        "        else:\n",
        "            break\n",
        "            \n",
        "    images = list(glob.iglob(os.path.join('./ak', '*.*')))\n",
        "    images = sorted(images, key=lambda x: float(os.path.split(x)[1][:-3]))\n",
        "    \n",
        "    make_video('./out.mp4', images, fps=5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "7ToeBIlEdPwY",
        "outputId": "7116beef-d790-4577-f0ed-3bf7e0457f8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "args = arg_parse()\n",
        "videofile = args.video\n",
        "video = cv2.VideoCapture(videofile)\n",
        "\n",
        "# Find OpenCV version\n",
        "(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')\n",
        "\n",
        "if int(major_ver)  < 3 :\n",
        "    fps = video.get(cv2.cv.CV_CAP_PROP_FPS)\n",
        "    print(\"Frames per second using video.get(cv2.cv.CV_CAP_PROP_FPS): {0}\".format(fps))\n",
        "else :\n",
        "    fps = video.get(cv2.CAP_PROP_FPS)\n",
        "    print(\"Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}\".format(fps))\n",
        "\n",
        "video.release();"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Frames per second using video.get(cv2.CAP_PROP_FPS) : 25.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "E-ndlFIy7bXk",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Get all image file paths to a list.\n",
        "# Sort the images by name index.\n",
        "# images = sorted(images, key=lambda x: float(os.path.split(x)[1][:-3]))\n",
        "\n",
        "def make_video(outvid, images=None, fps=25, size=None,\n",
        "               is_color=True, format=\"FMP4\"):\n",
        "    \"\"\"\n",
        "    Create a video from a list of images.\n",
        " \n",
        "    @param      outvid      output video\n",
        "    @param      images      list of images to use in the video\n",
        "    @param      fps         frame per second\n",
        "    @param      size        size of each frame\n",
        "    @param      is_color    color\n",
        "    @param      format      see http://www.fourcc.org/codecs.php\n",
        "    @return                 see http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_gui/py_video_display/py_video_display.html\n",
        "    \"\"\"\n",
        "    from cv2 import VideoWriter, VideoWriter_fourcc, imread, resize\n",
        "    fourcc = VideoWriter_fourcc(*format)\n",
        "    vid = None\n",
        "    for image in images:\n",
        "        if not os.path.exists(image):\n",
        "            raise FileNotFoundError(image)\n",
        "        img = imread(image)\n",
        "        if vid is None:\n",
        "            if size is None:\n",
        "                size = img.shape[1], img.shape[0]\n",
        "            vid = VideoWriter(outvid, fourcc, float(fps), size, is_color)\n",
        "        if size[0] != img.shape[1] and size[1] != img.shape[0]:\n",
        "            img = resize(img, size)\n",
        "        vid.write(img)\n",
        "    vid.release()\n",
        "    return vid"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "O3Z0FbJ_zuOo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 11321
        },
        "outputId": "168b94e8-0ee9-477e-833f-48bb7b2718b6"
      },
      "cell_type": "code",
      "source": [
        "yolo_videoprocess() #main program for detecting number of cars in the frames"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading network.....\n",
            "Network successfully loaded\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/upsampling.py:129: UserWarning: nn.Upsample is deprecated. Use nn.functional.interpolate instead.\n",
            "  warnings.warn(\"nn.{} is deprecated. Use nn.functional.interpolate instead.\".format(self.name))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.38\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.44\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.46\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.47\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.48\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.49\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.49\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.49\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.49\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.49\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.49\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.49\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 6\n",
            "count cars 5\n",
            "FPS of the video is  4.50\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 6\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 6\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.50\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "FPS of the video is  4.51\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 6\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "FPS of the video is  4.51\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "FPS of the video is  4.51\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "FPS of the video is  4.51\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "FPS of the video is  4.51\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "FPS of the video is  4.51\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "FPS of the video is  4.51\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "FPS of the video is  4.51\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "FPS of the video is  4.51\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "count cars 5\n",
            "FPS of the video is  4.51\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "gsUQlMrCHOWD",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!rm -r ak"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "MXVPdrk9qsf0",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir ak #don't execute remove and make dir in the same cell on colab, it creates error"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "0dwZOANvuT22",
        "outputId": "22b051a5-c6ee-4fc5-bb4e-24d6b379504a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "#optional code to create video again with different fps\n",
        "images = list(glob.iglob(os.path.join('./ak/', '*.*')))\n",
        "images = sorted(images, key=lambda x: float(os.path.split(x)[1][:-3]))\n",
        "make_video('out1.mp4', images, fps=5)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<VideoWriter 0x7f4de627ce70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "T-PFuE8SJkAY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "a94eb135-33ee-4027-c85f-95cb5bcca17d"
      },
      "cell_type": "code",
      "source": [
        "!wget https://pjreddie.com/media/files/yolov3.weights"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-02-11 03:10:01--  https://pjreddie.com/media/files/yolov3.weights\n",
            "Resolving pjreddie.com (pjreddie.com)... 128.208.3.39\n",
            "Connecting to pjreddie.com (pjreddie.com)|128.208.3.39|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 248007048 (237M) [application/octet-stream]\n",
            "Saving to: ‘yolov3.weights.2’\n",
            "\n",
            "yolov3.weights.2    100%[===================>] 236.52M   345KB/s    in 6m 34s  \n",
            "\n",
            "2019-02-11 03:16:36 (614 KB/s) - ‘yolov3.weights.2’ saved [248007048/248007048]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "b3jsrBWnhXOQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}