S14_MiDas.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled15.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOvCHFDtBlwLEoAUSmLr6xi",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/thamizhannal/eva5/blob/master/S14_MiDas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZYA24nKZc98",
        "outputId": "f62e31c5-100e-40f8-b064-93a19cf02d59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rrOiIJnsbjhl"
      },
      "source": [
        "#### S13 customer dataset annotated for PPE in gdrive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OnRv67WNaG6f",
        "outputId": "125f83f2-e764-495f-e163-d6557aa9abe0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls gdrive/'My Drive'/SchoolOfAI_EVA/YoloV3_S13/YoloV3/data/customdata/images | wc -l"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3491\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "35ISLc9hbv82"
      },
      "source": [
        "#### Create a diretory in gdrive to store S14 code & data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pvpN6SsPagPE"
      },
      "source": [
        "!mkdir -p gdrive/'My Drive'/SchoolOfAI_EVA/EVA5_S14"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9OBzdh4b1gW",
        "outputId": "6fbcc203-b2fc-4714-f3fb-41f0f789e40e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%cd gdrive/'My Drive'/SchoolOfAI_EVA/EVA5_S14"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/SchoolOfAI_EVA/EVA5_S14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aM4XDExocHQ2",
        "outputId": "f7ebeb88-f83e-4b61-b5a3-042f3826ab2d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!git clone https://github.com/intel-isl/MiDaS"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'MiDaS'...\n",
            "remote: Enumerating objects: 10, done.\u001b[K\n",
            "remote: Counting objects: 100% (10/10), done.\u001b[K\n",
            "remote: Compressing objects: 100% (9/9), done.\u001b[K\n",
            "remote: Total 154 (delta 2), reused 6 (delta 1), pack-reused 144\u001b[K\n",
            "Receiving objects: 100% (154/154), 42.50 KiB | 1.93 MiB/s, done.\n",
            "Resolving deltas: 100% (83/83), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PWFaPxFOcEbG",
        "outputId": "e1d563ba-2784-4a78-e450-be99bca179c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls /content/gdrive/'My Drive'/SchoolOfAI_EVA/EVA5_S14/\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MiDaS\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trTlu2jOcNC6",
        "outputId": "a9c1653e-4793-4b41-a324-94c872bca321",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%cd /content/gdrive/'My Drive'/SchoolOfAI_EVA/EVA5_S14/\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/SchoolOfAI_EVA/EVA5_S14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ob1ZhMPdPd0",
        "outputId": "a76967de-3d25-444f-b39d-e258ddd0f3c3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MiDaS\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTN7nuPgdnjH",
        "outputId": "ca7a4686-fef4-4db6-d07f-aa874841828c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%cd MiDaS/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/SchoolOfAI_EVA/EVA5_S14/MiDaS\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3AJdUNGh_EL",
        "outputId": "4e6cc448-6017-4391-b20a-bcce5b08c1d6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/SchoolOfAI_EVA/EVA5_S14/MiDaS\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_VTFhVuiAvR",
        "outputId": "a59a87b7-5d44-4d6c-abc1-f6eb825f586d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dockerfile  input    midas   README.md\ttf\n",
            "hubconf.py  LICENSE  output  run.py\tutils.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9v-r0BQMls7j"
      },
      "source": [
        "### 1. Place one or more input images in the folder input."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v713nP-XiD4W"
      },
      "source": [
        "!cp /content/gdrive/'My Drive'/SchoolOfAI_EVA//YoloV3_S13/YoloV3/data/customdata/images/* input/."
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEwbqrKtjZRL",
        "outputId": "965c97d6-16b2-46da-d59d-d458ff9ac046",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls input/* | wc -l"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3491\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ili9IW5lu5a"
      },
      "source": [
        "#### 2. Run the model:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSvoV7evm96P"
      },
      "source": [
        "#### 2.a Download model weight and place it in root dir"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEcTANfim83S",
        "outputId": "9c10b98e-e83f-43c7-dc3b-b3057e608eed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!wget https://github.com/intel-isl/MiDaS/releases/download/v2/model-f46da743.pt"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-10-30 17:44:27--  https://github.com/intel-isl/MiDaS/releases/download/v2/model-f46da743.pt\n",
            "Resolving github.com (github.com)... 140.82.112.3\n",
            "Connecting to github.com (github.com)|140.82.112.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-production-release-asset-2e65be.s3.amazonaws.com/193518067/cb0db580-a735-11ea-905c-4083069b9be1?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20201030%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201030T174427Z&X-Amz-Expires=300&X-Amz-Signature=931d101cbab369b0e6b5d54067ed6281bda21b191e79c71821bcdff17ece24a5&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=193518067&response-content-disposition=attachment%3B%20filename%3Dmodel-f46da743.pt&response-content-type=application%2Foctet-stream [following]\n",
            "--2020-10-30 17:44:27--  https://github-production-release-asset-2e65be.s3.amazonaws.com/193518067/cb0db580-a735-11ea-905c-4083069b9be1?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20201030%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201030T174427Z&X-Amz-Expires=300&X-Amz-Signature=931d101cbab369b0e6b5d54067ed6281bda21b191e79c71821bcdff17ece24a5&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=193518067&response-content-disposition=attachment%3B%20filename%3Dmodel-f46da743.pt&response-content-type=application%2Foctet-stream\n",
            "Resolving github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)... 52.217.1.236\n",
            "Connecting to github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)|52.217.1.236|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 422388453 (403M) [application/octet-stream]\n",
            "Saving to: ‘model-f46da743.pt’\n",
            "\n",
            "model-f46da743.pt   100%[===================>] 402.82M  58.3MB/s    in 6.3s    \n",
            "\n",
            "2020-10-30 17:44:34 (64.0 MB/s) - ‘model-f46da743.pt’ saved [422388453/422388453]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g4hi0h-xnGpP"
      },
      "source": [
        "### 2.b Run model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K7gDWSrllorY",
        "outputId": "4110aebb-06ef-42a8-a189-5f57ec0c7f24",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!python run.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "initialize\n",
            "device: cpu\n",
            "Loading weights:  model-f46da743.pt\n",
            "Using cache found in /root/.cache/torch/hub/facebookresearch_WSL-Images_master\n",
            "start processing\n",
            "  processing input/01-prince-boots - Copy.jpg (1/3491)\n",
            "  processing input/026f0ffddc.jpg (2/3491)\n",
            "  processing input/032620_LS_covid-masks_inline-680.jpg (3/3491)\n",
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            "  processing input/05a207d8-6378-43a2-9d1e-431ca7a944e4-unnamed - Copy.jpg (6/3491)\n",
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            "  processing input/148757020-female-engineer-wear-face-mask-with-safety-vest-and-yellow-helmet-operating-control-cnc-machinery-at.jpg (43/3491)\n",
            "  processing input/148789689-group-people-worker-is-wearing-protection-mask-face-and-safety-helmet-and-wearing-suit-safety-dress-.jpg (44/3491)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOZ2lTg1l1xZ"
      },
      "source": [
        "#### 3.The resulting inverse depth maps are written to the output folder.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "190PHePVl4Bh"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}