perspectiveTranslation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "perspectiveTranslation.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyP8jA5YFk6PW7MfGDRKDH9f",
      "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/Pmilivojevic/Perspective-translation/blob/master/perspectiveTranslation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1JB64b43Vws",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-hlBs9pmhyGV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "1cdd0fe8-ab0c-4914-89f2-e4048046fe8d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zKP8cy-NotM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "MAX_FEATURES = 500\n",
        "GOOD_MATCH_PERCENT = 0.15"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ROA-U62yh4NL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "imgPersp= cv2.cvtColor(cv2.imread('/content/drive/My Drive/Colab Notebooks/s4.jpg'), cv2.COLOR_BGR2GRAY)\n",
        "imgDest= cv2.cvtColor(cv2.imread('/content/drive/My Drive/Colab Notebooks/s5.jpg'), cv2.COLOR_BGR2GRAY)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m8QVsAi2NVo9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "orb = cv2.ORB_create(MAX_FEATURES)\n",
        "keypoints1, descriptors1 = orb.detectAndCompute(imgPersp, None)\n",
        "keypoints2, descriptors2 = orb.detectAndCompute(imgDest, None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJpUQnH0N6bC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)\n",
        "matches = matcher.match(descriptors1, descriptors2, None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCIq2ebfOEFa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "matches.sort(key=lambda x: x.distance, reverse=False)\n",
        "numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)\n",
        "matches = matches[:numGoodMatches]\n",
        "imMatches = cv2.drawMatches(imgPersp, keypoints1, imgDest, keypoints2, matches, None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TVU396hGOZCd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "38d5e42a-cd3e-41ff-d02b-206c151e1a73"
      },
      "source": [
        "points1 = np.zeros((len(matches), 2), dtype=np.float32)\n",
        "points2 = np.zeros((len(matches), 2), dtype=np.float32)\n",
        "\n",
        "for i, match in enumerate(matches):\n",
        "  points1[i, :] = keypoints1[match.queryIdx].pt\n",
        "  points2[i, :] = keypoints2[match.trainIdx].pt\n",
        "\n",
        "cv2.drawChessboardCorners(imgPersp, (3, 3), points1, True)\n",
        "cv2.drawChessboardCorners(imgDest, (3, 3), points2, True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0,   0,   0, ..., 255, 255, 255],\n",
              "       [  0,   0,   0, ..., 255, 255, 255],\n",
              "       [  0,   0,   0, ..., 255, 255, 255],\n",
              "       ...,\n",
              "       [255, 255, 255, ...,   0,   0,   0],\n",
              "       [255, 255, 255, ...,   0,   0,   0],\n",
              "       [255, 255, 255, ...,   0,   0,   0]], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5L3dd6htRTHb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)\n",
        "imgTrans= cv2.warpPerspective(imgPersp, h, (imgDest.shape[1], imgDest.shape[0]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gElCtAeolSip",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ret1, cornersPersp = cv2.findChessboardCorners(imgPersp, (3, 3), None)\n",
        "if ret1:\n",
        "    cv2.drawChessboardCorners(imgPersp, (3, 3), cornersPersp, ret1)\n",
        "ret2, cornersDist = cv2.findChessboardCorners(imgDest, (3, 3), None)\n",
        "if ret2:\n",
        "    cv2.drawChessboardCorners(imgDest, (3, 3), cornersDist, ret2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLWbXDKm25M8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "outputId": "623e3f9e-a19d-476c-9723-7ab15bbceb17"
      },
      "source": [
        "ptsPersp= np.array([[cornersPersp[6][0][0], cornersPersp[6][0][1]], [cornersPersp[3][0][0], cornersPersp[3][0][1]], [cornersPersp[0][0][0], cornersPersp[0][0][1]],\n",
        "                    [cornersPersp[7][0][0], cornersPersp[7][0][1]], [cornersPersp[4][0][0], cornersPersp[4][0][1]], [cornersPersp[1][0][0], cornersPersp[1][0][1]],\n",
        "                    [cornersPersp[8][0][0], cornersPersp[8][0][1]], [cornersPersp[5][0][0], cornersPersp[5][0][1]], [cornersPersp[2][0][0], cornersPersp[2][0][1]]])\n",
        "\n",
        "ptsDest= np.array([[cornersDist[0][0][0], cornersDist[0][0][1]], [cornersDist[1][0][0], cornersDist[1][0][1]], [cornersDist[2][0][0], cornersDist[2][0][1]],\n",
        "                   [cornersDist[3][0][0], cornersDist[3][0][1]], [cornersDist[4][0][0], cornersDist[4][0][1]], [cornersDist[5][0][0], cornersDist[5][0][1]], \n",
        "                   [cornersDist[6][0][0], cornersDist[6][0][1]], [cornersDist[7][0][0], cornersDist[7][0][1]], [cornersDist[8][0][0], cornersDist[8][0][1]]])\n",
        "\n",
        "h, status = cv2.findHomography(ptsPersp, ptsDest)\n",
        "imgTrans= cv2.warpPerspective(imgPersp, h, (imgDest.shape[1], imgDest.shape[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-a9b2f5b72e1e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m ptsPersp= np.array([[cornersPersp[6][0][0], cornersPersp[6][0][1]], [cornersPersp[3][0][0], cornersPersp[3][0][1]], [cornersPersp[0][0][0], cornersPersp[0][0][1]],\n\u001b[0m\u001b[1;32m      2\u001b[0m                     \u001b[0;34m[\u001b[0m\u001b[0mcornersPersp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcornersPersp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcornersPersp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcornersPersp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcornersPersp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcornersPersp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                     [cornersPersp[8][0][0], cornersPersp[8][0][1]], [cornersPersp[5][0][0], cornersPersp[5][0][1]], [cornersPersp[2][0][0], cornersPersp[2][0][1]]])\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m ptsDest= np.array([[cornersDist[0][0][0], cornersDist[0][0][1]], [cornersDist[1][0][0], cornersDist[1][0][1]], [cornersDist[2][0][0], cornersDist[2][0][1]],\n",
            "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L51vjmXfV-Au",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "89b7b483-0983-44e7-acfd-a27133e96f65"
      },
      "source": [
        "print(h)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.14461473e+00 2.54446646e-04 1.01173461e-02]\n",
            " [7.21420714e-02 1.00051436e+00 7.22811135e-03]\n",
            " [2.82604671e-04 2.06788161e-06 1.00000000e+00]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skhWZg4pkgRY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "8ee32b6c-02f2-4b9f-a72b-7218a8708753"
      },
      "source": [
        "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize= (15, 5))\n",
        "f.tight_layout()\n",
        "ax1.imshow(imgPersp, cmap= 'gray')\n",
        "ax1.set_title('imgPesp', fontsize=50)\n",
        "ax2.imshow(imgDest, cmap= 'gray')\n",
        "ax2.set_title('imgDist', fontsize=50)\n",
        "ax3.imshow(imgTrans, cmap= 'gray')\n",
        "ax3.set_title('imgTrans', fontsize=50)\n",
        "plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2Gm-Xn-k0uu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " imgPersp.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zcGg65DSxPSd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}