SobleFilter+Histogram.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM0xK1j7GqZXkDgizqENIFj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lowolf25/Face_Recognition/blob/main/SobleFilter%2BHistogram.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "5KKIC7ai7oU-"
      },
      "outputs": [],
      "source": [
        "#import libraries\n",
        "\n",
        "import cv2\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_path = \"/content/th__1_-removebg-preview.png\""
      ],
      "metadata": {
        "id": "gM8K4uAC8iKd"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)"
      ],
      "metadata": {
        "id": "PatK6L_f8oKs"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# apply soble operator in x and y directions\n",
        "sobelx = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize= 3)\n",
        "sobely = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize= 3)"
      ],
      "metadata": {
        "id": "lgiLrnD-8zb8"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get the gradient magnitude\n",
        "gradient_magnitude = np.sqrt(sobelx**2, sobely**2)"
      ],
      "metadata": {
        "id": "mhI2mcSY-m7K"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(gradient_magnitude)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KsYcFea_a67",
        "outputId": "b012a889-3e6d-40e1-e679-073befb2ffa5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalize the image\n",
        "gradient_magnitude_normalize = cv2.normalize(gradient_magnitude, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)"
      ],
      "metadata": {
        "id": "6xqwUbeX_gnL"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv2.imwrite('sobel_filter_image.jpg', gradient_magnitude_normalize)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8kL0F-AXBxiJ",
        "outputId": "eff6669f-3b45-4ace-e921-3cb8cd4d1a13"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aP_9m2vMEbxh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Histogram Equalizer"
      ],
      "metadata": {
        "id": "SDSmhEzTFAtd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image_path = '/content/th__1_-removebg-preview.png'"
      ],
      "metadata": {
        "id": "3TUO5P5ZFJHR"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)"
      ],
      "metadata": {
        "id": "mV170GYQFPVC"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "equalized_image = cv2.equalizeHist(image)"
      ],
      "metadata": {
        "id": "dUMxaa8mFYxI"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv2.imwrite(\"equalized_Histogram_image.jpg\", equalized_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qNgp9Yr6FkZf",
        "outputId": "802eb9ff-47eb-4dd7-d731-eff60bca9af1"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kkyVeSDdGIYE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}