hitswa/samplegeneration.ipynb

## samplegeneration.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/hitswa/7263f8540a0a87bb2bd859be576ad2e8/samplegeneration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "51Ex8BqHGi6z",
        "outputId": "24b60611-7746-4d88-b63d-a95c3ba92087"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting openmim\n",
            "  Downloading openmim-0.2.0-py2.py3-none-any.whl (49 kB)\n",
            "\u001b[K     |████████████████████████████████| 49 kB 2.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from openmim) (1.3.5)\n",
            "Collecting rich\n",
            "  Downloading rich-12.5.1-py3-none-any.whl (235 kB)\n",
            "\u001b[K     |████████████████████████████████| 235 kB 7.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from openmim) (0.8.10)\n",
            "Collecting colorama\n",
            "  Downloading colorama-0.4.5-py2.py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: Click in /usr/local/lib/python3.7/dist-packages (from openmim) (7.1.2)\n",
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            "Collecting model-index\n",
            "  Downloading model_index-0.1.11-py3-none-any.whl (34 kB)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from openmim) (2.23.0)\n",
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            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from model-index->openmim) (3.13)\n",
            "Collecting ordered-set\n",
            "  Downloading ordered_set-4.1.0-py3-none-any.whl (7.6 kB)\n",
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            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->openmim) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->openmim) (1.15.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->openmim) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->openmim) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->openmim) (2022.6.15)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->openmim) (2.10)\n",
            "Collecting commonmark<0.10.0,>=0.9.0\n",
            "  Downloading commonmark-0.9.1-py2.py3-none-any.whl (51 kB)\n",
            "\u001b[K     |████████████████████████████████| 51 kB 6.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich->openmim) (2.6.1)\n",
            "Installing collected packages: ordered-set, commonmark, rich, model-index, colorama, openmim\n",
            "Successfully installed colorama-0.4.5 commonmark-0.9.1 model-index-0.1.11 openmim-0.2.0 ordered-set-4.1.0 rich-12.5.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu113/torch1.12.0/index.html\n",
            "Collecting mmcv-full\n",
            "  Downloading https://download.openmmlab.com/mmcv/dist/cu113/torch1.12.0/mmcv_full-1.6.0-cp37-cp37m-manylinux1_x86_64.whl (40.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 40.1 MB 1.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (1.21.6)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (7.1.2)\n",
            "Collecting yapf\n",
            "  Downloading yapf-0.32.0-py2.py3-none-any.whl (190 kB)\n",
            "\u001b[K     |████████████████████████████████| 190 kB 4.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (3.13)\n",
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            "Collecting addict\n",
            "  Downloading addict-2.4.0-py3-none-any.whl (3.8 kB)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->mmcv-full) (3.0.9)\n",
            "Installing collected packages: yapf, addict, mmcv-full\n",
            "Successfully installed addict-2.4.0 mmcv-full-1.6.0 yapf-0.32.0\n"
          ]
        }
      ],
      "source": [
        "!pip3 install openmim\n",
        "!mim install mmcv-full"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting access of Google drive so that everything will remain saved even after session gets expired"
      ],
      "metadata": {
        "id": "T0Loh6hh5AIh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24PLycATHBMB",
        "outputId": "e1e58327-80fe-4237-d0d2-049b37a114af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install mmsegmentation from source"
      ],
      "metadata": {
        "id": "WAuC86q95YB8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/My\\ Drive/Wiley\\ Assignments/mmsegmentation\n",
        "%pwd\n",
        "!pip install -e ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5M1LxnuHbaW",
        "outputId": "c2f2dd92-74ab-4d20-ac9e-8d3115f2daca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/Wiley Assignments/mmsegmentation\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Obtaining file:///content/drive/My%20Drive/Wiley%20Assignments/mmsegmentation\n",
            "Installing collected packages: mmsegmentation\n",
            "  Running setup.py develop for mmsegmentation\n",
            "Successfully installed mmsegmentation-0.26.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check if installed"
      ],
      "metadata": {
        "id": "5qz7SjjL5f2r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import mmseg\n",
        "print(mmseg.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MiE3oMSQKCrN",
        "outputId": "b6873199-3b57-40ed-a379-495871a09aab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.26.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Grab the traiing checkpoints and configration"
      ],
      "metadata": {
        "id": "YVD3VwEt5jwE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Segformer MIB-0\n",
        "# !wget https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20220617_162207-c00b9603.pth\n",
        "!mim download mmsegmentation --config segformer_mit-b0_512x512_160k_ade20k --dest .\n",
        "\n",
        "# Segformer MIT-B5\n",
        "# !wget https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20220617_203542-940a6bd8.pth\n",
        "# !mim download mmsegmentation --config segformer_mit-b5_640x640_160k_ade20k --dest .\n",
        "\n",
        "# Segmenter ViT-L_16\n",
        "# !wget https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706-ffcf7509.pth\n",
        "# !mim download mmsegmentation --config segmenter_vit-l_mask_8x1_640x640_160k_ade20k --dest .\n",
        "\n",
        "# Swin Transformer | UPerNet | Swin-L\n",
        "# !wget https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth\n",
        "# !mim download mmsegmentation --config upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k --dest ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pljBVZ2RLm2L",
        "outputId": "68649c19-2ad1-4020-dff3-a962516d96ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "processing segformer_mit-b0_512x512_160k_ade20k...\n",
            "\u001b[32msegformer_mit-b0_512x512_160k_ade20k_20220617_162207-c00b9603.pth exists in /content/drive/My Drive/Wiley Assignments/mmsegmentation\u001b[0m\n",
            "\u001b[32mSuccessfully dumped segformer_mit-b0_512x512_160k_ade20k.py to /content/drive/My Drive/Wiley Assignments/mmsegmentation\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check the presence of GPU"
      ],
      "metadata": {
        "id": "wUVpyRxj6Xxi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! nvidia-smi"
      ],
      "metadata": {
        "id": "z6wfbU1NM5Uu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d020ca7-492a-496d-c68f-71dc12b42d17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed Jul 20 15:29:13 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\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 P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   36C    P0    26W / 250W |      2MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Grabbing model"
      ],
      "metadata": {
        "id": "L2JQrFZa6kat"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mmseg.apis import inference_segmentor, init_segmentor\n",
        "import mmcv\n",
        "from os.path import exists\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Segformer MIB-0\n",
        "config_file = 'segformer_mit-b0_512x512_160k_ade20k.py'\n",
        "checkpoint_file = 'segformer_mit-b0_512x512_160k_ade20k_20220617_162207-c00b9603.pth'\n",
        "\n",
        "# Segformer MIT-B5\n",
        "# config_file = 'segformer_mit-b5_640x640_160k_ade20k.py'\n",
        "# checkpoint_file = 'segformer_mit-b5_640x640_160k_ade20k_20220617_203542-940a6bd8.pth'\n",
        "\n",
        "# Segmenter ViT-L_16\n",
        "# config_file = 'segmenter_vit-l_mask_8x1_640x640_160k_ade20k.py'\n",
        "# checkpoint_file = 'segmenter_vit-l_mask_8x1_640x640_160k_ade20k_20220614_024513-4783a347.pth'\n",
        "\n",
        "# Swin Transformer | UPerNet | Swin-L\n",
        "# config_file = 'upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k.py'\n",
        "# checkpoint_file = 'upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth'\n",
        "\n",
        "# build the model from a config file and a checkpoint file\n",
        "model = init_segmentor(config_file, checkpoint_file, device='cuda:0')\n"
      ],
      "metadata": {
        "id": "1BkDes59NAPS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63316bee-6788-4b71-a832-2544a1a899e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/drive/MyDrive/Wiley Assignments/mmsegmentation/mmseg/models/losses/cross_entropy_loss.py:236: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.\n",
            "  'Default ``avg_non_ignore`` is False, if you would like to '\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "load checkpoint from local path: segformer_mit-b0_512x512_160k_ade20k_20220617_162207-c00b9603.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Apply model on image"
      ],
      "metadata": {
        "id": "gMunQzjg6mgt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# test a single image and show the results\n",
        "img = '/content/drive/MyDrive/Wiley Assignments/Image Samples/img1.jpg'  # or img = mmcv.imread(img), which will only load it once\n",
        "\n",
        "result = inference_segmentor(model, img)\n",
        "\n",
        "# visualize the results in a new window\n",
        "# model.show_result(img, result, show=True)\n",
        "# or save the visualization results to image files\n",
        "# you can change the opacity of the painted segmentation map in (0, 1].\n",
        "model.show_result(img, result, out_file='/content/drive/MyDrive/Wiley Assignments/Image Samples/_img1.jpg', opacity=0.5)"
      ],
      "metadata": {
        "id": "VWvRWz9R0mSL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Apply model on video"
      ],
      "metadata": {
        "id": "zNXTIwPr6vCe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# test a video and show the results\n",
        "video = mmcv.VideoReader('/content/drive/MyDrive/Wiley Assignments/Video Samples/video.mp4')\n",
        "\n",
        "i = 0\n",
        "for frame in video:\n",
        "   result = inference_segmentor(model, frame)\n",
        "   model.show_result(frame, result, out_file='/content/drive/MyDrive/Wiley Assignments/Video Samples/segmenter/_img_' + str(i) +'.jpg', opacity=0.5)\n",
        "   i = i + 1\n",
        "  #  model.show_result(frame, result, wait_time=1)"
      ],
      "metadata": {
        "id": "Oy5U1Frl0sU9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Apply model on ive image captured from camera"
      ],
      "metadata": {
        "id": "FPuaYNI06yye"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display, Javascript\n",
        "from google.colab.output import eval_js\n",
        "from base64 import b64decode\n",
        "\n",
        "def take_photo(filename='photo.jpg', quality=0.8):\n",
        "  js = Javascript('''\n",
        "    async function takePhoto(quality) {\n",
        "      const div = document.createElement('div');\n",
        "      const capture = document.createElement('button');\n",
        "      capture.textContent = 'Capture';\n",
        "      div.appendChild(capture);\n",
        "\n",
        "      const video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
        "\n",
        "      document.body.appendChild(div);\n",
        "      div.appendChild(video);\n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      // Resize the output to fit the video element.\n",
        "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
        "\n",
        "      // Wait for Capture to be clicked.\n",
        "      await new Promise((resolve) => capture.onclick = resolve);\n",
        "\n",
        "      const canvas = document.createElement('canvas');\n",
        "      canvas.width = video.videoWidth;\n",
        "      canvas.height = video.videoHeight;\n",
        "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
        "      stream.getVideoTracks()[0].stop();\n",
        "      div.remove();\n",
        "      return canvas.toDataURL('image/jpeg', quality);\n",
        "    }\n",
        "    ''')\n",
        "  display(js)\n",
        "  data = eval_js('takePhoto({})'.format(quality))\n",
        "  binary = b64decode(data.split(',')[1])\n",
        "  with open(filename, 'wb') as f:\n",
        "    f.write(binary)\n",
        "  return filename"
      ],
      "metadata": {
        "id": "7UdBEupmRMiJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image\n",
        "try:\n",
        "  filename = take_photo()\n",
        "  # print('Saved to {}'.format(filename))\n",
        "\n",
        "  # Show the image which was just taken.\n",
        "  # display(Image(filename))\n",
        "\n",
        "  result = inference_segmentor(model, format(filename))\n",
        "\n",
        "  model.show_result(format(filename), result, out_file='_'+format(filename), opacity=0.5)\n",
        "\n",
        "  display(Image('_'+format(filename)))\n",
        "\n",
        "except Exception as err:\n",
        "  # Errors will be thrown if the user does not have a webcam or if they do not\n",
        "  # grant the page permission to access it.\n",
        "  print(str(err))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "l83DuajTSHhG",
        "outputId": "39211198-7507-43be-ca9a-1eee3a5d7a25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function takePhoto(quality) {\n",
              "      const div = document.createElement('div');\n",
              "      const capture = document.createElement('button');\n",
              "      capture.textContent = 'Capture';\n",
              "      div.appendChild(capture);\n",
              "\n",
              "      const video = document.createElement('video');\n",
              "      video.style.display = 'block';\n",
              "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
              "\n",
              "      document.body.appendChild(div);\n",
              "      div.appendChild(video);\n",
              "      video.srcObject = stream;\n",
              "      await video.play();\n",
              "\n",
              "      // Resize the output to fit the video element.\n",
              "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
              "\n",
              "      // Wait for Capture to be clicked.\n",
              "      await new Promise((resolve) => capture.onclick = resolve);\n",
              "\n",
              "      const canvas = document.createElement('canvas');\n",
              "      canvas.width = video.videoWidth;\n",
              "      canvas.height = video.videoHeight;\n",
              "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
              "      stream.getVideoTracks()[0].stop();\n",
              "      div.remove();\n",
              "      return canvas.toDataURL('image/jpeg', quality);\n",
              "    }\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/jpeg": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}