YOLO_v3_Multiple_Class_Object_Detection_Final.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "YOLO v3 - Multiple Class Object Detection - Final.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "LLSknydxx7dZ",
        "ElvQqAPryF-a",
        "6ozmEdo0yeb4",
        "F5wPOYnmlkl6",
        "-yg__13Yy6yE",
        "ytWMr9MYx3b9",
        "RUqeLn4G1YMv"
      ],
      "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/ArpanSM/Object-Detection-and-Instance-Segmentaion/blob/master/YOLO_v3_Multiple_Class_Object_Detection_Final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLSknydxx7dZ",
        "colab_type": "text"
      },
      "source": [
        "## Clone DarkNet Repo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vydnW3FmUsik",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git clone https://github.com/pjreddie/darknet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uHQ-nwsdU2be",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# change makefile to have GPU and OPENCV enabled\n",
        "%cd darknet\n",
        "!sed -i 's/OPENCV=0/OPENCV=1/' Makefile\n",
        "!sed -i 's/GPU=0/GPU=1/' Makefile\n",
        "!sed -i 's/CUDNN=0/CUDNN=1/' Makefile\n",
        "!make"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ElvQqAPryF-a",
        "colab_type": "text"
      },
      "source": [
        "## Download Yolo weights and Darknet Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s36pwwRGpyvj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget https://pjreddie.com/media/files/yolov3.weights"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4uI4lXQTp9dU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget https://pjreddie.com/media/files/darknet53.conv.74"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ozmEdo0yeb4",
        "colab_type": "text"
      },
      "source": [
        "## Get Data from Drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uCVrY_LzhipY",
        "colab_type": "text"
      },
      "source": [
        "Format of Data\n",
        "\n",
        "\n",
        "*   Make a folder - `custom_data`\n",
        "*   Folder should contain : \n",
        "    *   `detector.data` - contains - classes, train, test, names and backup\n",
        "    *   `text.txt` \n",
        "    *   `train.txt`\n",
        "    *   `custom.names` - name of all classes.\n",
        "    *   `images` - folder to store all images and labels\n",
        "    *   `cfg` - contains the config file\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2g3nNwG074b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B6Ecmc0CqW9h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp -avr /content/drive/My\\ Drive/yolo_v31/custom_data /content/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ImUapbt7qalc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cd /content/custom_data/images "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spwN0tqfrdbU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip /content/drive/My\\ Drive/yolo_v31/images.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLvMQpeOy1UA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#make a label folder to store labels files separately\n",
        "!mkdir /content/custom_data/labels\n",
        "import os, shutil, glob\n",
        "src_fldr = r\"/content/custom_data/images/\"; ## Edit this\n",
        "dst_fldr = \"/content/custom_data/labels/\"; ## Edit this\n",
        "for txt_file in glob.glob(src_fldr+\"*.txt\"):\n",
        "    shutil.copy2(txt_file, dst_fldr);"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F5wPOYnmlkl6",
        "colab_type": "text"
      },
      "source": [
        "#### Configuration files setting alternate method"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gAOLtA_qI9vF",
        "colab_type": "text"
      },
      "source": [
        "**3) Configure Darknet network for training YOLO V3**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-RpscgU853t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp /content/darknet/cfg/yolov3.cfg /content/custom_data/cfg/yolov3_training.cfg"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZgVQRop_vwR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#edit the batch_size\n",
        "!sed -i 's/batch=1/batch=64/' /content/custom_data/cfg/yolov3_training.cfg\n",
        "!sed -i 's/subdivisions=1/subdivisions=16/' /content/custom_data/cfg/yolov3_training.cfg\n",
        "!sed -i 's/max_batches = 500200/max_batches = 4000/' /content/custom_data/cfg/yolov3_training.cfg\n",
        "#change the number of classes as required from 80 --> x\n",
        "!sed -i '610 s@classes=80@classes=1@' /content/custom_data/cfg/yolov3_training.cfg\n",
        "!sed -i '696 s@classes=80@classes=1@' /content/custom_data/cfg/yolov3_training.cfg\n",
        "!sed -i '783 s@classes=80@classes=1@' /content/custom_data/cfg/yolov3_training.cfg\n",
        "#change the number of filters as (classes*3)+1\n",
        "!sed -i '603 s@filters=255@filters=18@' /content/custom_data/cfg/yolov3_training.cfg\n",
        "!sed -i '689 s@filters=255@filters=18@' /content/custom_data/cfg/yolov3_training.cfg\n",
        "!sed -i '776 s@filters=255@filters=18@' /content/custom_data/cfg/yolov3_training.cfg"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-yg__13Yy6yE",
        "colab_type": "text"
      },
      "source": [
        "## Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mhHC_wir7yj",
        "colab_type": "code",
        "outputId": "975858b7-5528-40e4-9211-6676db1b5e70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "cd /content/darknet/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/darknet\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEPHzHv9rg5m",
        "colab_type": "code",
        "outputId": "56d8164c-cbbf-4bd0-a387-4da5d5290ab7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!./darknet detector train /content/custom_data/detector.data /content/custom_data/cfg/yolov3-custom.cfg darknet53.conv.74"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Region 106 Avg IOU: 0.434686, Class: 0.635082, Obj: 0.662354, No Obj: 0.000420, .5R: 0.333333, .75R: 0.000000,  count: 3\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000016, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.613037, Class: 0.913363, Obj: 0.429606, No Obj: 0.000786, .5R: 0.833333, .75R: 0.000000,  count: 6\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000009, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000006, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.627668, Class: 0.910077, Obj: 0.640707, No Obj: 0.000559, .5R: 0.666667, .75R: 0.333333,  count: 3\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000007, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.472569, Class: 0.456906, Obj: 0.498475, No Obj: 0.000595, .5R: 0.333333, .75R: 0.000000,  count: 6\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000014, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.427526, Class: 0.800528, Obj: 0.525746, No Obj: 0.000396, .5R: 0.333333, .75R: 0.000000,  count: 3\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000006, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.489315, Class: 0.845802, Obj: 0.921420, No Obj: 0.000856, .5R: 0.500000, .75R: 0.166667,  count: 6\n",
            "1156: 1.119930, 0.709596 avg, 0.001000 rate, 1.580370 seconds, 36992 images\n",
            "Loaded: 0.000049 seconds\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.639104, Class: 0.579247, Obj: 0.264555, No Obj: 0.000351, .5R: 1.000000, .75R: 0.000000,  count: 4\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000006, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.730524, Class: 0.973168, Obj: 0.993585, No Obj: 0.000245, .5R: 1.000000, .75R: 0.000000,  count: 2\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.616004, Class: 0.998478, Obj: 0.853723, No Obj: 0.000280, .5R: 0.500000, .75R: 0.000000,  count: 2\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000009, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.472245, Class: 0.640162, Obj: 0.424790, No Obj: 0.000422, .5R: 0.600000, .75R: 0.200000,  count: 5\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000009, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.593899, Class: 0.743726, Obj: 0.942682, No Obj: 0.000378, .5R: 0.666667, .75R: 0.000000,  count: 3\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000004, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.537751, Class: 0.593479, Obj: 0.618452, No Obj: 0.000559, .5R: 0.666667, .75R: 0.000000,  count: 6\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.454048, Class: 0.731333, Obj: 0.497415, No Obj: 0.000418, .5R: 0.200000, .75R: 0.000000,  count: 5\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000009, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.722843, Class: 0.969711, Obj: 0.969582, No Obj: 0.000305, .5R: 1.000000, .75R: 0.500000,  count: 2\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.533699, Class: 0.900852, Obj: 0.663901, No Obj: 0.000392, .5R: 0.500000, .75R: 0.000000,  count: 2\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.624827, Class: 0.670049, Obj: 0.386457, No Obj: 0.000328, .5R: 0.500000, .75R: 0.500000,  count: 2\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.250429, Class: 0.683034, Obj: 0.246757, No Obj: 0.000389, .5R: 0.250000, .75R: 0.000000,  count: 4\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000017, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.545620, Class: 0.941308, Obj: 0.936375, No Obj: 0.000504, .5R: 0.750000, .75R: 0.000000,  count: 4\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000007, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.630384, Class: 0.884784, Obj: 0.762700, No Obj: 0.000361, .5R: 1.000000, .75R: 0.000000,  count: 4\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.662929, Class: 0.967927, Obj: 0.721644, No Obj: 0.000438, .5R: 0.500000, .75R: 0.500000,  count: 2\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000009, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.721258, Class: 0.991188, Obj: 0.982949, No Obj: 0.000289, .5R: 1.000000, .75R: 0.500000,  count: 2\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.468240, Class: 0.801311, Obj: 0.982760, No Obj: 0.000656, .5R: 0.500000, .75R: 0.166667,  count: 6\n",
            "1157: 0.732966, 0.711933 avg, 0.001000 rate, 1.616165 seconds, 37024 images\n",
            "Loaded: 0.000067 seconds\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.510005, Class: 0.491835, Obj: 0.086616, No Obj: 0.000114, .5R: 0.500000, .75R: 0.000000,  count: 2\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000006, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.632267, Class: 0.950401, Obj: 0.717891, No Obj: 0.000592, .5R: 1.000000, .75R: 0.166667,  count: 6\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000004, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.452584, Class: 0.785898, Obj: 0.343769, No Obj: 0.000362, .5R: 0.333333, .75R: 0.000000,  count: 3\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000008, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000005, .5R: -nan, .75R: -nan,  count: 0\n",
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            "Saving weights to /content/yolov3-custom.backup\n",
            "Resizing\n",
            "544\n",
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            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000006, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000004, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.725780, Class: 0.986164, Obj: 0.833296, No Obj: 0.000356, .5R: 1.000000, .75R: 0.333333,  count: 3\n",
            "1255: 0.428801, 0.496321 avg, 0.001000 rate, 2.126661 seconds, 40160 images\n",
            "Loaded: 0.000059 seconds\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000006, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000004, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.747659, Class: 0.891572, Obj: 0.650148, No Obj: 0.000160, .5R: 1.000000, .75R: 0.000000,  count: 2\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000006, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000004, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.687338, Class: 0.998987, Obj: 0.976218, No Obj: 0.000307, .5R: 1.000000, .75R: 0.333333,  count: 3\n",
            "Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000007, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.000010, .5R: -nan, .75R: -nan,  count: 0\n",
            "Region 106 Avg IOU: 0.661461, Class: 0.978085, Obj: 0.733700, No Obj: 0.000231, .5R: 1.000000, .75R: 0.000000,  count: 2\n",
            "^C\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_mvGRRP9llB",
        "colab_type": "code",
        "outputId": "77d5601e-43ca-49f3-c1c8-700ec3164645",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "!cp -avr /content/yolov3-custom_900.weights /content/drive/My\\ Drive/yolo_v31/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'/content/yolov3-custom_900.weights' -> '/content/drive/My Drive/yolo_v31/yolov3-custom_900.weights'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ytWMr9MYx3b9",
        "colab_type": "text"
      },
      "source": [
        "##Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pSok8QyWx4wk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import argparse\n",
        "\n",
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "parser = argparse.ArgumentParser(add_help=False)\n",
        "parser.add_argument(\"--image\", default='samples/image.jpg', help=\"image for prediction\")\n",
        "parser.add_argument(\"--config\", default='cfg/yolov3.cfg', help=\"YOLO config path\")\n",
        "parser.add_argument(\"--weights\", default='yolov3.weights', help=\"YOLO weights path\")\n",
        "parser.add_argument(\"--names\", default='data/coco.names', help=\"class names path\")\n",
        "args = parser.parse_args()\n",
        "\n",
        "CONF_THRESH, NMS_THRESH = 0.5, 0.5\n",
        "\n",
        "# Load the network\n",
        "net = cv2.dnn.readNetFromDarknet(args.config, args.weights)\n",
        "net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)\n",
        "net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)\n",
        "\n",
        "# Get the output layer from YOLO\n",
        "layers = net.getLayerNames()\n",
        "output_layers = [layers[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n",
        "\n",
        "# Read and convert the image to blob and perform forward pass to get the bounding boxes with their confidence scores\n",
        "img = cv2.imread(args.image)\n",
        "height, width = img.shape[:2]\n",
        "\n",
        "blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), swapRB=True, crop=False)\n",
        "net.setInput(blob)\n",
        "layer_outputs = net.forward(output_layers)\n",
        "\n",
        "class_ids, confidences, b_boxes = [], [], []\n",
        "for output in layer_outputs:\n",
        "    for detection in output:\n",
        "        scores = detection[5:]\n",
        "        class_id = np.argmax(scores)\n",
        "        confidence = scores[class_id]\n",
        "\n",
        "        if confidence > CONF_THRESH:\n",
        "            center_x, center_y, w, h = (detection[0:4] * np.array([width, height, width, height])).astype('int')\n",
        "\n",
        "            x = int(center_x - w / 2)\n",
        "            y = int(center_y - h / 2)\n",
        "\n",
        "            b_boxes.append([x, y, int(w), int(h)])\n",
        "            confidences.append(float(confidence))\n",
        "            class_ids.append(int(class_id))\n",
        "\n",
        "# Perform non maximum suppression for the bounding boxes to filter overlapping and low confident bounding boxes\n",
        "indices = cv2.dnn.NMSBoxes(b_boxes, confidences, CONF_THRESH, NMS_THRESH).flatten().tolist()\n",
        "\n",
        "# Draw the filtered bounding boxes with their class to the image\n",
        "with open(args.names, \"r\") as f:\n",
        "    classes = [line.strip() for line in f.readlines()]\n",
        "colors = np.random.uniform(0, 255, size=(len(classes), 3))\n",
        "\n",
        "for index in indices:\n",
        "    x, y, w, h = b_boxes[index]\n",
        "    cv2.rectangle(img, (x, y), (x + w, y + h), colors[index], 2)\n",
        "    cv2.putText(img, classes[class_ids[index]], (x + 5, y + 20), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, colors[index], 2)\n",
        "\n",
        "cv2.imshow(\"image\", img)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vhep8frP1Vr1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RUqeLn4G1YMv",
        "colab_type": "text"
      },
      "source": [
        "## Kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "25gbSSBc1jL8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import cv2\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Reading Image 00002.jpg and getting its spatial dimension\n",
        "image_BGR = cv2.imread('/content/custom_data/images/00002.jpg')\n",
        "print('Image shape:', image_BGR.shape)\n",
        "h, w = image_BGR.shape[:2]  \n",
        "print('Image height={0} and width={1}'.format(h, w))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vV-JI7r_1WRi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Drawing Bounding Boxes of Traffic Signs on Image\n",
        "\n",
        "with open('/content/custom_data/images/00002.txt') as f:\n",
        "    # Preparing list for annotation of BB (bounding boxes)\n",
        "    lst = []\n",
        "    for line in f:\n",
        "        lst += [line.rstrip()]\n",
        "        print(line)\n",
        "\n",
        "# Going through all BB\n",
        "for i in range(len(lst)):\n",
        "    # Getting current bounding box coordinates, its width and height\n",
        "    bb_current = lst[i].split()\n",
        "    x_center, y_center = int(float(bb_current[1]) * w), int(float(bb_current[2]) * h)\n",
        "    box_width, box_height = int(float(bb_current[3]) * w), int(float(bb_current[4]) * h)\n",
        "    \n",
        "    # Now, from YOLO data format, we can get top left corner coordinates\n",
        "    # that are x_min and y_min\n",
        "    x_min = int(x_center - (box_width / 2))\n",
        "    y_min = int(y_center - (box_height / 2))\n",
        "\n",
        "    # Drawing bounding box on the original image\n",
        "    cv2.rectangle(image_BGR, (x_min, y_min), (x_min + box_width, y_min + box_height), [172 , 10, 127], 2)\n",
        "\n",
        "    # Preparing text with label and confidence for current bounding box\n",
        "    class_current = 'Class: {}'.format(bb_current[0])\n",
        "\n",
        "    # Putting text with label and confidence on the original image\n",
        "    cv2.putText(image_BGR, class_current, (x_min, y_min - 5), cv2.FONT_HERSHEY_COMPLEX, 0.7, [172 , 10, 127], 2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZPqd8Uy2UPo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Showing Image 00002.jpg with Detected Traffic Signs\n",
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = (15, 15)\n",
        "fig = plt.figure()\n",
        "plt.imshow(cv2.cvtColor(image_BGR, cv2.COLOR_BGR2RGB))\n",
        "plt.axis('off')\n",
        "plt.title('Image 00003.jpg with Traffic Signs', fontsize=18)\n",
        "plt.show()\n",
        "fig.savefig('example.png')\n",
        "plt.close()"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}