Pigs_herd_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Pigs_:).ipynb",
      "private_outputs": true,
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM0Kk7TJVb2MdAJA1E0yHn1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rohanjha04/IITI_SoC_2022_AUTO_2_AerialSurveyingWithDrone/blob/master/Pigs_herd_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "qsxk5RIcAhyr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/ultralytics/yolov5  \n",
        "%cd yolov5"
      ],
      "metadata": {
        "id": "5gNDSpldZHgq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qr requirements.txt"
      ],
      "metadata": {
        "id": "QnJr5c5pZsrg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q roboflow"
      ],
      "metadata": {
        "id": "-bTZHz2wCuEz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from roboflow import Roboflow\n",
        "rf= Roboflow(model_format=\"yolov5\", notebook= \"Pigs_:)\")"
      ],
      "metadata": {
        "id": "MI2HcRdqC8iB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"KFImbK0bKcJbFEdhKs5B\")\n",
        "project = rf.workspace(\"jhar\").project(\"pigs-p5ltl\")\n",
        "dataset = project.version(2).download(\"yolov5\")"
      ],
      "metadata": {
        "id": "Z5rUOKjXDd9E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cat {dataset.location}/data.yaml"
      ],
      "metadata": {
        "id": "qFq-1bMfEiJp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import yaml\n",
        "with open(dataset.location + \"/data.yaml\", 'r') as stream:\n",
        "    num_classes = str(yaml.safe_load(stream)['nc'])"
      ],
      "metadata": {
        "id": "cJeuus0cEQmt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd yolov5\n",
        "import torch\n",
        "from yolov5 import utils\n",
        "import torch\n",
        "import utils\n",
        "from IPython import display\n",
        "from IPython.display import clear_output\n",
        "from pathlib import Path\n",
        "import yaml\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import glob"
      ],
      "metadata": {
        "id": "zb7dTTKraOMk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cat /content/yolov5/models/yolov5s.yaml"
      ],
      "metadata": {
        "id": "8WWuMGREbjR8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.core.magic import register_line_cell_magic\n",
        "\n",
        "@register_line_cell_magic\n",
        "def writetemplate(line, cell):\n",
        "    with open(line, 'w') as f:\n",
        "        f.write(cell.format(**globals()))"
      ],
      "metadata": {
        "id": "Nepe7DTwhbN4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%writetemplate /content/yolov5/models/custom_yolov5s.yaml\n",
        "\n",
        "# parameters\n",
        "nc: 1 # number of classes\n",
        "depth_multiple: 0.33  # model depth multiple\n",
        "width_multiple: 0.50  # layer channel multiple\n",
        "\n",
        "# anchors\n",
        "anchors:\n",
        "  - [10,13, 16,30, 33,23]  # P3/8\n",
        "  - [30,61, 62,45, 59,119]  # P4/16\n",
        "  - [116,90, 156,198, 373,326]  # P5/32\n",
        "\n",
        "# YOLOv5 backbone\n",
        "backbone:\n",
        "  # [from, number, module, args]\n",
        "  [[-1, 1, Focus, [64, 3]],  # 0-P1/2\n",
        "   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4\n",
        "   [-1, 3, BottleneckCSP, [128]],\n",
        "   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8\n",
        "   [-1, 9, BottleneckCSP, [256]],\n",
        "   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16\n",
        "   [-1, 9, BottleneckCSP, [512]],\n",
        "   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32\n",
        "   [-1, 1, SPP, [1024, [5, 9, 13]]],\n",
        "   [-1, 3, BottleneckCSP, [1024, False]],  # 9\n",
        "  ]\n",
        "\n",
        "# YOLOv5 head\n",
        "head:\n",
        "  [[-1, 1, Conv, [512, 1, 1]],\n",
        "   [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
        "   [[-1, 6], 1, Concat, [1]],  # cat backbone P4\n",
        "   [-1, 3, BottleneckCSP, [512, False]],  # 13\n",
        "\n",
        "   [-1, 1, Conv, [256, 1, 1]],\n",
        "   [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
        "   [[-1, 4], 1, Concat, [1]],  # cat backbone P3\n",
        "   [-1, 3, BottleneckCSP, [256, False]],  # 17 (P3/8-small)\n",
        "\n",
        "   [-1, 1, Conv, [256, 3, 2]],\n",
        "   [[-1, 14], 1, Concat, [1]],  # cat head P4\n",
        "   [-1, 3, BottleneckCSP, [512, False]],  # 20 (P4/16-medium)\n",
        "\n",
        "   [-1, 1, Conv, [512, 3, 2]],\n",
        "   [[-1, 10], 1, Concat, [1]],  # cat head P5\n",
        "   [-1, 3, BottleneckCSP, [1024, False]],  # 23 (P5/32-large)\n",
        "\n",
        "   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)\n",
        "  ]"
      ],
      "metadata": {
        "id": "j_WFhT6EheSx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "%cd /content/yolov5/\n",
        "!python train.py --img 64 --batch 50 --epochs 400 --data {dataset.location}/data.yaml --cfg ./models/custom_yolov5s.yaml --weights '' --name yolov5_results --cache --patience 0"
      ],
      "metadata": {
        "id": "4tpHP2nbbxz3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image"
      ],
      "metadata": {
        "id": "iGpygX7sd4ZQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%ls runs"
      ],
      "metadata": {
        "id": "1cjWxYWoexRD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%ls runs/train/yolov5_results3/weights"
      ],
      "metadata": {
        "id": "fb6ZxhJKe0O6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/yolov5/\n",
        "!python detect.py --img 416 --conf 0.4 --source /content/yolov5/Pigs-2/test/images --weights runs/train/yolov5_results3/weights/last.pt\n"
      ],
      "metadata": {
        "id": "96v-EH3de7WM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import glob\n",
        "from IPython.display import Image, display\n",
        "\n",
        "for imageName in glob.glob('/content/yolov5/runs/detect/exp3/*.jpg'): #assuming JPG\n",
        "    display(Image(filename=imageName))\n",
        "    print(\"\\n\")"
      ],
      "metadata": {
        "id": "-EO6zhmXgmQ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Image(filename='/content/yolov5/runs/train/yolov5_results/train_batch0.jpg', width=500)"
      ],
      "metadata": {
        "id": "jQjqAnvihPnv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/yolov5/\n",
        "!python detect.py --img 32 --conf 0.4 --source https://www.youtube.com/watch?v=u1emPEApVlo --weights runs/train/yolov5_reults3/weights/best.pt"
      ],
      "metadata": {
        "id": "RIwPrM4LMMF4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}