test/planercnn-dataset-generation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CustomData_Plane_RCNN.ipynb",
      "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/firekind/project-fox/blob/master/test/planercnn-dataset-generation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRSeXxuGDWQ0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b091509b-cb97-4758-a708-ef1ba3800c1a"
      },
      "source": [
        "! pip install git+https://github.com/longcw/RoIAlign.pytorch"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/longcw/RoIAlign.pytorch\n",
            "  Cloning https://github.com/longcw/RoIAlign.pytorch to /tmp/pip-req-build-u69l80zs\n",
            "  Running command git clone -q https://github.com/longcw/RoIAlign.pytorch /tmp/pip-req-build-u69l80zs\n",
            "Requirement already satisfied: torch>=1.2.0 in /usr/local/lib/python3.6/dist-packages (from roi-align==0.0.2) (1.7.0+cu101)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch>=1.2.0->roi-align==0.0.2) (0.16.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch>=1.2.0->roi-align==0.0.2) (3.7.4.3)\n",
            "Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from torch>=1.2.0->roi-align==0.0.2) (0.7)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch>=1.2.0->roi-align==0.0.2) (1.18.5)\n",
            "Building wheels for collected packages: roi-align\n",
            "  Building wheel for roi-align (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for roi-align: filename=roi_align-0.0.2-cp36-cp36m-linux_x86_64.whl size=3597018 sha256=a0c57147d59e80b600135a0527183ce87a3e9c48e9dc2f3317391097e8b81685\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-_mnqz3v0/wheels/8a/33/67/15dd04358ab544910b805eda00c435bd6de909f4db3e2391ce\n",
            "Successfully built roi-align\n",
            "Installing collected packages: roi-align\n",
            "Successfully installed roi-align-0.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kp5U2aF7W8l5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ccde4a8-44f8-4c87-9b7e-34e7ff9e4dc8"
      },
      "source": [
        "!git clone https://github.com/firekind/planercnn.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'planercnn'...\n",
            "remote: Enumerating objects: 114, done.\u001b[K\n",
            "remote: Counting objects: 100% (114/114), done.\u001b[K\n",
            "remote: Compressing objects: 100% (73/73), done.\u001b[K\n",
            "remote: Total 265 (delta 72), reused 76 (delta 40), pack-reused 151\u001b[K\n",
            "Receiving objects: 100% (265/265), 1.42 MiB | 30.84 MiB/s, done.\n",
            "Resolving deltas: 100% (124/124), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTgOFGFOXiES",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f395f4c6-1c4a-48cf-a166-09088fdbaf98"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thu Nov 19 14:44:06 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.38       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   38C    P8     9W /  70W |      0MiB / 15079MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0CU0BIzPguy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aab9139b-f602-444e-d9b8-165f48044f55"
      },
      "source": [
        "cd planercnn"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/planercnn\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "axfysrbmP6XG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3820fc9-1c76-49c8-e6f6-2f724b0f41de"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "anchors    evaluate.py\t      models\t      requirements.txt\n",
            "config.py  evaluate_utils.py  options.py      train_planercnn.py\n",
            "data_prep  example_images     plane_utils.py  utils.py\n",
            "datasets   LICENSE.md\t      README.md       visualize_utils.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rt6cQooHgJ3Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9192c258-54ee-454c-ba50-0fdb9a0cea83"
      },
      "source": [
        "!wget -O planercnn_checkpoint.zip https://www.dropbox.com/s/5exi5rg54746anx/checkpoint.zip?dl=0\n",
        "!mkdir -p checkpoint"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-11-19 14:44:06--  https://www.dropbox.com/s/5exi5rg54746anx/checkpoint.zip?dl=0\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.80.1, 2620:100:601c:1::a27d:601\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.80.1|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/5exi5rg54746anx/checkpoint.zip [following]\n",
            "--2020-11-19 14:44:07--  https://www.dropbox.com/s/raw/5exi5rg54746anx/checkpoint.zip\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://ucac391055235f496c0f0c06bab5.dl.dropboxusercontent.com/cd/0/inline/BDfQ2-naGVepqQuCqmmsw548PAlcTlfliiiZtlEFxqi8xpGfIlXEJoTqLr94dOXRY7tk5IvQFq5iz9gcpS5-MEBIJMfNmbvkSVh4ioX5TmkSsyxuKexj8knEWeS8dBUA35I/file# [following]\n",
            "--2020-11-19 14:44:08--  https://ucac391055235f496c0f0c06bab5.dl.dropboxusercontent.com/cd/0/inline/BDfQ2-naGVepqQuCqmmsw548PAlcTlfliiiZtlEFxqi8xpGfIlXEJoTqLr94dOXRY7tk5IvQFq5iz9gcpS5-MEBIJMfNmbvkSVh4ioX5TmkSsyxuKexj8knEWeS8dBUA35I/file\n",
            "Resolving ucac391055235f496c0f0c06bab5.dl.dropboxusercontent.com (ucac391055235f496c0f0c06bab5.dl.dropboxusercontent.com)... 162.125.6.15, 2620:100:601b:15::a27d:80f\n",
            "Connecting to ucac391055235f496c0f0c06bab5.dl.dropboxusercontent.com (ucac391055235f496c0f0c06bab5.dl.dropboxusercontent.com)|162.125.6.15|:443... connected.\n",
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            "--2020-11-19 14:44:09--  https://ucac391055235f496c0f0c06bab5.dl.dropboxusercontent.com/cd/0/inline2/BDdbrS8aLiPw90B_LomS2aYMm4YuqnaMO9VQFtunsDcYPvjtyidEnmQs2uqd2T2DY1ulyzNAv0rK3WTKR1V6zmIKrWXn9w3EGnjZ7T3Gy4_hxDIol_qM16slZ1hTlSFzmA4rUTG-SsAHbQnNELnFFXRvrXT19grNpMAdMH6EuSaN9KqVT2Z7VUJ8ffuMqVHS6QSCrqTN38WllGSraPSKFJFIjvHxDc10h8P03l_qCDzaohpD372g6rgyGDdP6NR9pvGf7CREtSgWzEqUAToPWyn6dlU4VYZVczLOIOvUFYYz-aVDYdcidARUuhLu4mAaebljENsgxLgzwUEomhrNYo6-lcOiPfvUZtyX9e-MVbBaWw/file\n",
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            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 262259930 (250M) [application/zip]\n",
            "Saving to: ‘planercnn_checkpoint.zip’\n",
            "\n",
            "planercnn_checkpoin 100%[===================>] 250.11M   113MB/s    in 2.2s    \n",
            "\n",
            "2020-11-19 14:44:12 (113 MB/s) - ‘planercnn_checkpoint.zip’ saved [262259930/262259930]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MXUQe90d40T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8cca78f-fbd1-4d05-bc3b-16b0570c73ca"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "anchors     evaluate.py        options.py\t\t train_planercnn.py\n",
            "checkpoint  evaluate_utils.py  planercnn_checkpoint.zip  utils.py\n",
            "config.py   example_images     plane_utils.py\t\t visualize_utils.py\n",
            "data_prep   LICENSE.md\t       README.md\n",
            "datasets    models\t       requirements.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zIlcjnxbDph",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "058614e7-c07b-470f-c337-c2a975c72254"
      },
      "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": "code",
      "metadata": {
        "id": "0v9h9H4scxXC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80db5a84-39e3-4dca-f393-28da4942469d"
      },
      "source": [
        "## Loading Trained Model\n",
        "# !mv \"checkpoint.zip?dl=0\" planercnn_checkpoint.zip\n",
        "# !mv /content/gdrive/My\\ Drive/EVA5/planercnn_checkpoint.zip .\n",
        "!mv planercnn_checkpoint.zip checkpoint/\n",
        "%cd checkpoint/\n",
        "!unzip planercnn_checkpoint.zip\n",
        "!rm planercnn_checkpoint.zip\n",
        "%cd ..\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/planercnn/checkpoint\n",
            "Archive:  planercnn_checkpoint.zip\n",
            "   creating: planercnn_normal_warping_refine/\n",
            "  inflating: planercnn_normal_warping_refine/checkpoint_refine.pth  \n",
            "  inflating: planercnn_normal_warping_refine/checkpoint.pth  \n",
            "/content/planercnn\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MKkX9YwjP19",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba655136-771f-41d0-b866-9eec25ab0263"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "anchors     evaluate.py        options.py\t   utils.py\n",
            "checkpoint  evaluate_utils.py  plane_utils.py\t   visualize_utils.py\n",
            "config.py   example_images     README.md\n",
            "data_prep   LICENSE.md\t       requirements.txt\n",
            "datasets    models\t       train_planercnn.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJIeSLzVzidT"
      },
      "source": [
        "# !cp -a /content/gdrive/'My Drive'/EVA5/assignment-13/YoloV3_Dataset ."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "POBIQA1VziT_"
      },
      "source": [
        "# !unzip ./YOLOV3_customdata.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bliyfRnbz821"
      },
      "source": [
        "!cp ./example_images/camera.txt /content/gdrive/'My Drive'/EVA5/assignment-13/YoloV3_Dataset/images"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xFGwyeMoKyg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "db3960ad-2694-4280-ab93-42dda12bfe8b"
      },
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "imgs = os.listdir(\"/content/gdrive/My Drive/EVA5/assignment-13/YoloV3_Dataset/images\")\n",
        "imgs.remove(\"camera.txt\")\n",
        "train_mini = [f\"/content/gdrive/My Drive/EVA5/assignment-13/YoloV3_Dataset/images/{x}\" for x in np.random.choice(imgs, size=(64,))]\n",
        "\n",
        "ds_folder = \"/content/train-mini\"\n",
        "\n",
        "if os.path.exists(ds_folder):\n",
        "    shutil.rmtree(ds_folder)\n",
        "os.mkdir(ds_folder)\n",
        "\n",
        "for p in train_mini:\n",
        "    shutil.copy(p, ds_folder)\n",
        "shutil.copy(\"/content/planercnn/example_images/camera.txt\", ds_folder)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/train-mini/camera.txt'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ah5I8VYXXWVO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "faa23a04-f473-4eeb-b40d-b3bea4fb6442"
      },
      "source": [
        "!python evaluate.py --methods=f --suffix=warping_refine --dataset=inference --customDataFolder=/content/gdrive/'My Drive'/EVA5/assignment-13/YoloV3_Dataset/images --numTestingImages=4000 --maskMode=png --noVisualize\n",
        "# !python evaluate.py --methods=f --suffix=warping_refine --dataset=inference --customDataFolder=/content/train-mini --numTestingImages=4000 --maskMode=png --noVisualize"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "the number of images 3545\n",
            "\r  0% 0/3545 [00:00<?, ?it/s]rm: cannot remove 'test/inference/*_final.png': No such file or directory\n",
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2952: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.\n",
            "  warnings.warn(\"nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.\")\n",
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:3063: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
            "  \"See the documentation of nn.Upsample for details.\".format(mode))\n",
            "100% 3545/3545 [18:15<00:00,  3.24it/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGepkOB62lP_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96c6df73-abfb-473e-e95d-19000d994c49"
      },
      "source": [
        "!zip -r ../planercnn_data.zip test/inference/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  adding: test/inference/african-american-woman-wearing-hardhat-and-safety-vest-picture-id475144426.jpg_parameters.npy (deflated 20%)\n",
            "  adding: test/inference/boots_11.jpg_masks.png (deflated 35%)\n",
            "  adding: test/inference/8c7d91f7.jpg_parameters.npy (deflated 21%)\n",
            "  adding: test/inference/LImage_109.jpg_parameters.npy (deflated 20%)\n",
            "  adding: test/inference/Mimg_021.jpg_masks.png (deflated 31%)\n",
            "  adding: test/inference/Himage_092.jpg_parameters.npy (deflated 20%)\n",
            "  adding: test/inference/E13.jpg_masks.png (deflated 32%)\n",
            "  adding: test/inference/ImageYolo (5).jpg_masks.png (deflated 35%)\n",
            "  adding: test/inference/majdoor_99.jpg_masks.png (deflated 37%)\n",
            "  adding: test/inference/z035 .jpg_parameters.npy (deflated 23%)\n",
            "  adding: test/inference/Dimg066.jpg_parameters.npy (deflated 27%)\n",
            "  adding: test/inference/manual-worker-in-hard-hat-and-safety-glasses-picture-id184279657.jpg_parameters.npy (deflated 21%)\n",
            "  adding: test/inference/images55.jpg_masks.png (deflated 44%)\n",
            "  adding: test/inference/majdoor_9.jpg_parameters.npy (deflated 21%)\n",
            "  adding: test/inference/R40.jpg_masks.png (deflated 31%)\n",
            "  adding: test/inference/image027.jpg_parameters.npy (deflated 17%)\n",
            "  adding: test/inference/img64.jpg_parameters.npy (deflated 18%)\n",
            "  adding: test/inference/img58.jpg_parameters.npy (deflated 26%)\n",
            "  adding: test/inference/Kee.jpg_parameters.npy (deflated 20%)\n",
            "  adding: test/inference/airborne-virus-and-man-in-suit-wearing-face-mask-picture-id1204480186.jpg_masks.png (deflated 29%)\n",
            "  adding: test/inference/Bimg_95.jpg_masks.png (deflated 26%)\n",
            "  adding: test/inference/img14.jpg_parameters.npy (deflated 30%)\n",
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            "  adding: test/inference/8c7db8cc.jpg_parameters.npy (deflated 21%)\n",
            "  adding: test/inference/majdoor_102.jpg_masks.png (deflated 32%)\n",
            "  adding: test/inference/H_Img_007.jpg_parameters.npy (deflated 22%)\n",
            "  adding: test/inference/85409ae2bc.jpg_masks.png (deflated 36%)\n",
            "  adding: test/inference/a88.jpg_masks.png (deflated 34%)\n",
            "  adding: test/inference/majdoor_51.jpg_parameters.npy (deflated 22%)\n",
            "  adding: test/inference/Jimg122.jpg_parameters.npy (deflated 23%)\n",
            "  adding: test/inference/d83e4d58b67bcd74bec77260d36dcfa3.jpg_masks.png (deflated 38%)\n",
            "  adding: test/inference/27bb806c8c.jpg_parameters.npy (deflated 18%)\n",
            "  adding: test/inference/Jimg058.jpg_parameters.npy (deflated 28%)\n",
            "  adding: test/inference/Himage_095.jpg_masks.png (deflated 35%)\n",
            "  adding: test/inference/Bimg_30.jpg_masks.png (deflated 39%)\n",
            "  adding: test/inference/Bimg_18.jpg_masks.png (deflated 42%)\n",
            "  adding: test/inference/Jimg025.jpg_masks.png (deflated 32%)\n",
            "  adding: test/inference/majdoor_37.jpg_parameters.npy (deflated 17%)\n",
            "  adding: test/inference/R98.jpg_masks.png (deflated 35%)\n",
            "  adding: test/inference/image088.jpg_masks.png (deflated 37%)\n",
            "  adding: test/inference/image030.jpg_parameters.npy (deflated 27%)\n",
            "  adding: test/inference/Himage_026.jpg_masks.png (deflated 48%)\n",
            "  adding: test/inference/C56.jpg_parameters.npy (deflated 22%)\n",
            "  adding: test/inference/Aimg_036.jpg_parameters.npy (deflated 25%)\n",
            "  adding: test/inference/Gimage_79.jpg_parameters.npy (deflated 24%)\n",
            "  adding: test/inference/V_Img_007.jpg_masks.png (deflated 26%)\n",
            "  adding: test/inference/mature-businessman-with-mask-using-digital-tablet-at-work-picture-id1208383243.jpg_parameters.npy (deflated 18%)\n",
            "  adding: test/inference/Himage_098.jpg_parameters.npy (deflated 27%)\n",
            "  adding: test/inference/LImage_102.jpg_parameters.npy (deflated 27%)\n",
            "  adding: test/inference/Himage_057.jpg_parameters.npy (deflated 28%)\n",
            "  adding: test/inference/image037(1).jpg_parameters.npy (deflated 20%)\n",
            "  adding: test/inference/20.jpg_masks.png (deflated 33%)\n",
            "  adding: test/inference/A_21.jpg_masks.png (deflated 36%)\n",
            "  adding: test/inference/R67.jpg_masks.png (deflated 31%)\n",
            "  adding: test/inference/Aimg_053.jpg_masks.png (deflated 38%)\n",
            "  adding: test/inference/img38.jpg_masks.png (deflated 35%)\n",
            "  adding: test/inference/N42.jpg_masks.png (deflated 36%)\n",
            "  adding: test/inference/Bimg_5.jpg_parameters.npy (deflated 37%)\n",
            "  adding: test/inference/img54.jpg_parameters.npy (deflated 16%)\n",
            "  adding: test/inference/construction-workers-looking-at-papers-on-construction-site-picture-id112156603.jpg_masks.png (deflated 31%)\n",
            "  adding: test/inference/Mimg_059.jpg_parameters.npy (deflated 21%)\n",
            "  adding: test/inference/R120.jpg_parameters.npy (deflated 20%)\n",
            "  adding: test/inference/Jimg043.jpg_parameters.npy (deflated 25%)\n",
            "  adding: test/inference/HVM_Img_013.jpg_parameters.npy (deflated 25%)\n",
            "  adding: test/inference/ImageYolo (45).jpg_masks.png (deflated 29%)\n",
            "  adding: test/inference/Kz.jpg_masks.png (deflated 36%)\n",
            "  adding: test/inference/Bimg_51.jpg_parameters.npy (deflated 22%)\n",
            "  adding: test/inference/Aimg_040.jpg_masks.png (deflated 32%)\n",
            "  adding: test/inference/Jimg073.jpg_parameters.npy (deflated 27%)\n",
            "  adding: test/inference/images (24).jpg_parameters.npy (deflated 25%)\n",
            "  adding: test/inference/majdoor_81.jpg_masks.png (deflated 30%)\n",
            "  adding: test/inference/Q23.jpg_masks.png (deflated 22%)\n",
            "  adding: test/inference/R95.jpg_masks.png (deflated 43%)\n",
            "  adding: test/inference/71.jpg_parameters.npy (deflated 21%)\n",
            "  adding: test/inference/Dimg064.jpg_parameters.npy (deflated 17%)\n",
            "  adding: test/inference/african-black-warehouse-worker-with-face-mask-checking-goods-use-digital-tablet-barcode-scanner-she-wear-face-mask-prevent-from-covid-19-after-factory-reopen_63253-8364.jpg_masks.png (deflated 37%)\n",
            "  adding: test/inference/Mimg_100.jpg_masks.png (deflated 38%)\n",
            "  adding: test/inference/img_016.jpg_masks.png (deflated 37%)\n",
            "  adding: test/inference/img_052.jpg_masks.png (deflated 35%)\n",
            "  adding: test/inference/PImage1.jpg_masks.png (deflated 29%)\n",
            "  adding: test/inference/Jimg131.jpg_parameters.npy (deflated 14%)\n",
            "  adding: test/inference/Jimg057.jpg_masks.png (deflated 34%)\n",
            "  adding: test/inference/8c7bcc02.jpg_parameters.npy (deflated 24%)\n",
            "  adding: test/inference/2Q__ (2).jpg_parameters.npy (deflated 18%)\n",
            "  adding: test/inference/VB_Img_002.jpg_masks.png (deflated 42%)\n",
            "  adding: test/inference/9db22c99927a96031b3d8b4ed00084cb.jpg_masks.png (deflated 31%)\n",
            "  adding: test/inference/majdoor_257.jpg_masks.png (deflated 35%)\n",
            "  adding: test/inference/majdoor_216.jpg_masks.png (deflated 35%)\n",
            "  adding: test/inference/portrait-of-boy-wearing-mask-gesturing-outdoors-picture-id1208316363.jpg_masks.png (deflated 25%)\n",
            "  adding: test/inference/R118.jpg_parameters.npy (deflated 23%)\n",
            "  adding: test/inference/PImage65.jpg_masks.png (deflated 35%)\n",
            "  adding: test/inference/Himage_190.jpg_parameters.npy (deflated 15%)\n",
            "  adding: test/inference/img55.jpg_parameters.npy (deflated 15%)\n",
            "  adding: test/inference/image012.jpg_masks.png (deflated 40%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F4GSJh5V9Tug"
      },
      "source": [
        "!cp ../planercnn_data.zip /content/gdrive/'My Drive'/EVA5/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LolEkO4UjeC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "3d1271a6-40c3-4ee3-c7c1-d4517d373470"
      },
      "source": [
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "img = cv2.imread('test/inference/10_plane_masks_0.png')\n",
        "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t3zNDSUQUqli"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}