LoFTR_evaluation2.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/davidenko2000/LoFTR_stereo/blob/master/LoFTR_evaluation2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1vcYjx3HFUcw"
      },
      "source": [
        "# LoFTR evaluation on KITTI dataset\n",
        "\n",
        "KITTI dataset consists of 200 stereo images (left and right).\n",
        "Below you can find results of LoFTR matcher on such dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWjHASahGCjv"
      },
      "source": [
        "# Configure environment and grab LoFTR code.\n",
        "!rm -rf sample_data\n",
        "!pip install torch einops yacs kornia\n",
        "!git clone https://github.com/zju3dv/LoFTR --depth 1\n",
        "!mv LoFTR/* . && rm -rf LoFTR\n",
        "\n",
        "# Download pretrained weights\n",
        "!mkdir weights \n",
        "%cd weights/\n",
        "#!gdown --id 1w1Qhea3WLRMS81Vod_k5rxS_GNRgIi-O  # indoor-ds\n",
        "!gdown --id 1M-VD35-qdB5Iw-AtbDBCKC7hPolFW9UY  # outdoor-ds\n",
        "%cd .."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RD9G7vCzFUc1",
        "outputId": "1233a4bc-dc11-40a2-ef47-6e4aac5b4bbb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import torch\n",
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib.cm as cm\n",
        "from matplotlib import pyplot as plt\n",
        "import matplotlib.colors as colors\n",
        "\n",
        "!pip install einops\n",
        "!pip install kornia\n",
        "!pip install yacs\n",
        "!pip install loguru\n",
        "import sys\n",
        "sys.path.append('/content/drive/MyDrive/LoFTR')\n",
        "from src.utils.plotting import make_matching_figure\n",
        "from src.loftr import LoFTR, default_cfg"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "\u001b[?25hInstalling collected packages: einops\n",
            "Successfully installed einops-0.6.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from kornia) (23.1)\n",
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            "Installing collected packages: kornia\n",
            "Successfully installed kornia-0.6.12\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "  Downloading yacs-0.1.8-py3-none-any.whl (14 kB)\n",
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            "Installing collected packages: yacs\n",
            "Successfully installed yacs-0.1.8\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting loguru\n",
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            "Successfully installed loguru-0.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "G04uRaAbHz5E",
        "outputId": "79ad86be-5e5f-4880-9a9e-0db6a1f3d73b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"Method to plot image using real disparity map and computed disparity map\"\"\"\n",
        "def plot_acc_byimage(idx, model):\n",
        "  real_disp = skimage.util.img_as_ubyte(mpimg.imread(f\"/content/drive/MyDrive/LoFTR/data/kitti/disparity/{str.zfill(str(idx), 6)}_10.png\"))\n",
        "  real_disp = cv2.resize(real_disp, (1240, 375))\n",
        "  predicted_disp = compute_disparity_map(idx, model)\n",
        "  acc = disparity_accuracy_byimage(real_disp=real_disp, predicted_disp=predicted_disp)\n",
        "\n",
        "  plt.figure(figsize=(20, 10))\n",
        "  color_map = colors.ListedColormap(['black', 'red', 'yellow'])\n",
        "  plt.imshow(acc, cmap=color_map)\n",
        " \n",
        "\n",
        "\"\"\"\n",
        "Function which uses real disparity, predicted disparity and pixel error to calcuate accuracy of prediction.\n",
        "Returns:\n",
        "        -1 -> if the real disparity is unknown\n",
        "        0  -> if the prediction in incorrect\n",
        "        1  -> if the prediction is correct (the prediction must be in the interval which uses allowed pixel error\n",
        "\"\"\"\n",
        "def disparity_accuracy_byimage(real_disp, predicted_disp, pxl_error=3):\n",
        "\tacc = np.zeros(predicted_disp.shape)\n",
        "\tacc[predicted_disp == 0] = -1\n",
        "\tacc[(predicted_disp != 0) & (np.abs(predicted_disp - real_disp) < pxl_error)] = 1\n",
        "\n",
        "\treturn acc\n",
        "\n",
        "\"\"\"\n",
        "Function which computes disparity map using model and index (idx) of an image.\n",
        "\"\"\"\n",
        "def compute_disparity_map(idx, model):\n",
        "  img0_raw = cv2.imread(f\"/content/drive/MyDrive/LoFTR/data/kitti/RGB/left/{str.zfill(str(idx), 6)}_10.png\", cv2.IMREAD_GRAYSCALE)\n",
        "  img1_raw = cv2.imread(f\"/content/drive/MyDrive/LoFTR/data/kitti/RGB/right/{str.zfill(str(idx), 6)}_10.png\", cv2.IMREAD_GRAYSCALE)\n",
        "  \n",
        "  if img0_raw is None:\n",
        "    print('Wrong path:')\n",
        "  else:\n",
        "    img0_raw = cv2.resize(img0_raw, (1240, 375))\n",
        "    img1_raw = cv2.resize(img1_raw, (1240, 375))\n",
        "\n",
        "  img0 = torch.from_numpy(img0_raw)[None][None].cuda() / 255.\n",
        "  img1 = torch.from_numpy(img1_raw)[None][None].cuda() / 255.\n",
        "  batch = {'image0': img0, 'image1': img1}\n",
        "  \n",
        "  predicted_disp = np.zeros(img0_raw.shape)\n",
        "\n",
        "  # Inference with LoFTR and get prediction\n",
        "  with torch.no_grad():\n",
        "      matcher(batch)\n",
        "      mkpts0 = batch['mkpts0_f'].cpu().numpy()\n",
        "      mkpts1 = batch['mkpts1_f'].cpu().numpy()\n",
        "      mconf = batch['mconf'].cpu().numpy()\n",
        "\n",
        "  for i in range(mkpts0.shape[0]):\n",
        "    disp = mkpts0[i][0] - mkpts1[i][0] #check only on epipolar line\n",
        "    predicted_disp[int(mkpts0[i][1])][int(mkpts0[i][0])] = disp\n",
        "\n",
        "  return predicted_disp\n"
      ],
      "metadata": {
        "id": "PdFhbTOGBfvU"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdJ-G1CiFUc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "outputId": "bec6a6a5-5a02-42a0-c59e-b56203cb5996"
      },
      "source": [
        "from re import M\n",
        "import skimage\n",
        "from matplotlib import image as mpimg\n",
        "\n",
        "matcher = LoFTR(config=default_cfg)\n",
        "ckpts = [\n",
        "  \"/content/drive/MyDrive/LoFTR/data/kitti/outdoor_ds.ckpt\",\n",
        "  \"/content/drive/MyDrive/LoFTR/logs/tb_logs/outdoor-kitti-1248x376/version_13/checkpoints/epoch=92-auc@5=0.883-auc@10=0.942-auc@20=0.971.ckpt\"\n",
        "]\n",
        "\n",
        "metrics = []\n",
        "for ckpt in ckpts:\n",
        "  matcher.load_state_dict(torch.load(ckpt)['state_dict'])\n",
        "  matcher = matcher.eval().cuda()\n",
        "\n",
        "  \"\"\"max_N = 200\n",
        "  counter_correct = 0\n",
        "  counter_total = 0\n",
        "  pxl_error = 3\n",
        "  by_error=[]\n",
        "  for pxl_error in [3]:\n",
        "\n",
        "    for idx in range(140, 200):\n",
        "      real_disp = skimage.util.img_as_ubyte(mpimg.imread(f\"/content/drive/MyDrive/LoFTR/data/kitti/disparity/{str.zfill(str(idx), 6)}_10.png\"))\n",
        "      real_disp = cv2.resize(real_disp, (1240, 375))\n",
        "\n",
        "      predicted_disp = compute_disparity_map(idx, matcher)\n",
        "      counter_correct += np.count_nonzero((predicted_disp != 0) & (np.abs(predicted_disp - real_disp) < pxl_error))\n",
        "      counter_total += np.count_nonzero(predicted_disp)\n",
        "\n",
        "    by_error.append(counter_correct/counter_total)\n",
        "  \n",
        "  metrics.append(by_error)\"\"\"\n",
        "  plot_acc_byimage(100, matcher)\n",
        "\n",
        "#np.savetxt(\"/content/drive/MyDrive/LoFTR/data/kitti/metrics_outdoor_test.csv\", np.array(metrics), delimiter=\",\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m2023-05-29 20:53:37.927\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36msrc.loftr.utils.coarse_matching\u001b[0m:\u001b[36mget_coarse_match\u001b[0m:\u001b[36m246\u001b[0m - \u001b[33m\u001b[1mNumber of matches:5361\u001b[0m\n",
            "\u001b[32m2023-05-29 20:53:38.679\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36msrc.loftr.utils.coarse_matching\u001b[0m:\u001b[36mget_coarse_match\u001b[0m:\u001b[36m246\u001b[0m - \u001b[33m\u001b[1mNumber of matches:2905\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"Najboljih 5 modela u 2. eksperimentu\")\n",
        "plt.xlabel(\"Epoha\")\n",
        "plt.ylabel(\"Rekonstrukcijska točnost (3 px error)\")\n",
        "plt.scatter([60, 78, 85, 86, 92], [20.29, 20.70, 20.88, 20.83, 21.66], label=\"skup za učenje\")\n",
        "plt.scatter([60, 78, 85, 86, 92], [16.68, 17.35, 17.54, 17.56, 18.42], label=\"skup za testiranje\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "kCKXa29GXwTG",
        "outputId": "0efdd0c2-d356-42a0-8801-9fe49500c7f0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f62b47fd090>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"Najboljih 5 modela u 1. eksperimentu\")\n",
        "plt.xlabel(\"Epoha\")\n",
        "plt.ylabel(\"Rekonstrukcijska točnost (3 px error)\")\n",
        "plt.scatter([42, 45, 49, 53, 58], [16.71, 11.03, 7.41, 3.25, 0.50], label=\"skup za učenje\")\n",
        "plt.scatter([42, 45, 49, 53, 58], [12.50, 7.81, 4.29, 1.28, 0.00], label=\"skup za testiranje\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "66ODK204ZPgE",
        "outputId": "bcf1a8e7-88ef-4f5c-fe65-dace4f08966f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f62b47fecb0>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}