create_csvs.ipynb

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    "colab": {
      "name": "create_csvs.ipynb",
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      "authorship_tag": "ABX9TyMfEYxGSHccgv8S2SuPKOVp",
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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/emailic/YOLOv5-Microplasticos/blob/main/create_csvs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
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        },
        "id": "gTiTvIhdDlzU",
        "outputId": "e393c2ee-f090-488a-f8ed-4a55014df3bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import os\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)\n",
        "img_dir = '/content/drive/My Drive/microplasticos/images/test_1152/' \n",
        "out_dir = '/content/drive/My Drive/microplasticos/microplasticos_1152/csvs/'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Model\n",
        "model = torch.hub.load('ultralytics/yolov5', 'custom', '/content/drive/My Drive/microplasticos/microplasticos_1152/feature_extraction16/weights/best.pt', trust_repo=True)  # yolov5n - yolov5x6 official model\n",
        "#                                            'custom', 'path/to/best.pt')  # custom model\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8gXYjOgEE6h",
        "outputId": "2b63a9c7-69e3-4676-a980-6447cccd6bca"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://github.com/ultralytics/yolov5/zipball/master\" to /root/.cache/torch/hub/master.zip\n",
            "INFO:yolov5:\u001b[31m\u001b[1mrequirements:\u001b[0m PyYAML>=5.3.1 not found and is required by YOLOv5, attempting auto-update...\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m PyYAML>=5.3.1 not found and is required by YOLOv5, attempting auto-update...\n",
            "INFO:yolov5:Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting PyYAML>=5.3.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "Installing collected packages: PyYAML\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed PyYAML-6.0\n",
            "\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting PyYAML>=5.3.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "Installing collected packages: PyYAML\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed PyYAML-6.0\n",
            "\n",
            "INFO:yolov5:\u001b[31m\u001b[1mrequirements:\u001b[0m 1 package updated per /root/.cache/torch/hub/ultralytics_yolov5_master/requirements.txt\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
            "\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m 1 package updated per /root/.cache/torch/hub/ultralytics_yolov5_master/requirements.txt\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
            "\n",
            "INFO:yolov5:YOLOv5 🚀 2022-7-18 Python-3.7.13 torch-1.12.0+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "YOLOv5 🚀 2022-7-18 Python-3.7.13 torch-1.12.0+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "INFO:yolov5:Fusing layers... \n",
            "Fusing layers... \n",
            "INFO:yolov5:Model summary: 280 layers, 12323608 parameters, 0 gradients\n",
            "Model summary: 280 layers, 12323608 parameters, 0 gradients\n",
            "INFO:yolov5:Adding AutoShape... \n",
            "Adding AutoShape... \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Images\n",
        "for f in os.listdir(img_dir):\n",
        "    im = img_dir+f  # Path\n",
        "    result = model(im, size=1152)\n",
        "    result.pandas().xyxy[0].to_csv(out_dir+f+'.csv')\n",
        "\n",
        "\n",
        "    #print(im)\n",
        "    #imgs.append(im)  # batch of images"
      ],
      "metadata": {
        "id": "aZ43WvmjEHkz"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
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        "result.pandas().xyxy[0] "
      ],
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        "outputId": "5afd98d1-23ec-4601-afc5-ba9cc5c0bb52"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         xmin        ymin        xmax        ymax  confidence  class     name\n",
              "0   32.193199  163.956635  151.642578  314.043488    0.651895      2  organic\n",
              "1  214.714294    1.391205  320.258789   97.320648    0.588192      2  organic"
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