filtering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "de7a5ca0-419f-47ca-b1bb-2944a530a548",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6986765a-f570-4264-88a9-19c1e1bcb7c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "width = 720, height = 404, fps = 30\n"
     ]
    }
   ],
   "source": [
    "dip = cv2.VideoCapture('C:\\\\LastbackUp\\\\디지털영상처리\\\\assignment1\\\\out.avi',0)\n",
    "\n",
    "w = round(dip.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "h = round(dip.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "fps = dip.get(cv2.CAP_PROP_FPS)\n",
    "\n",
    "print(\"width = %d, height = %d, fps = %d\" %(w, h, fps))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "911189d4-a4e2-47b0-82c2-cc157fab0587",
   "metadata": {},
   "outputs": [],
   "source": [
    "fourcc = cv2.VideoWriter_fourcc(*'DIVX')\n",
    "out = cv2.VideoWriter('C:\\\\LastbackUp\\\\디지털영상처리\\\\assignment1\\\\filtered.avi', fourcc, 30.0, (w, h))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21772bbc-c77b-45b4-a42e-66a87f3a1ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# border 의 기본값이 미러패딩이기 때문에, 따로 지정하지는 않는다\n",
    "class Filtering :\n",
    "    # 이동평균 필터\n",
    "    def avg(self, k, img) : \n",
    "        f = f / (k*k)\n",
    "        result = cv2.filter2D(img, -1, f)\n",
    "        return result\n",
    "    # 중간값 필터\n",
    "    def med(self, k, img) :\n",
    "        return cv2.medianBlur(img, k)\n",
    "    # 라플라시안 필터\n",
    "    def lap(self, k, img) :\n",
    "        if k == 3 :\n",
    "            f = np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])\n",
    "        elif k == 5:\n",
    "            f = np.array([[4, 1, 0, 1, 4],\n",
    "                          [1, -2, -3, -2, 1],\n",
    "                          [0, -3, -4, -3, 0],\n",
    "                          [1, -2, -3, -2, 1],\n",
    "                          [4, 1, 0, 1, 4]])\n",
    "        elif k == 8:\n",
    "            f = np.array([[10, 5, 2, 1, 2, 5, 10],\n",
    "                          [5, 0, -3, -4, -3, 0, 5],\n",
    "                          [2, -3, -6, -7, -6, -3, 2],\n",
    "                          [1, -4, -7, -8, -7, -4, 1],\n",
    "                          [2, -3, -6, -7, -6, -3, 2],\n",
    "                          [5, 0, -3, -4, -3, 0, 5],\n",
    "                          [10, 5, 2, 1, 2, 5, 10]])\n",
    "        else :\n",
    "            print(\"허용 범위를 넘은 필터 사이즈입니다. 다시 시도해 주세요.\")\n",
    "            f = np.zeros((k, k), np.float32)\n",
    "        result = cv2.filter2D(img, -1, f)\n",
    "        return result\n",
    "    # sobel 필터\n",
    "    def sob(self, k, img) :\n",
    "        fx = cv2.getDerivKernels(1, 0, k)\n",
    "        fy = cv2.getDerivKernels(0, 1, k)\n",
    "        hor = cv2.filter2D(img, -1, np.outer(fx[0], fx[1]))\n",
    "        ver = cv2.filter2D(img, -1, np.outer(fy[0], fx[1]))\n",
    "        return hor + ver\n",
    "    #Gaussian 필터 적용\n",
    "    def gas(self, k, img) :\n",
    "        s1 = 1 / (2 * math.pi)\n",
    "        f = np.zeros((k, k))\n",
    "        K = int((k-1)/2)\n",
    "        for i in range(-K, K+1) :\n",
    "            for j in range(-K, K+1) :\n",
    "                s2 = (i**2 + j**2) / 2\n",
    "                s3 = math.exp(-s2)\n",
    "                f[i+K, j+K] = s1 * s3\n",
    "        result = cv2.filter2D(img, -1, f)\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd430651-dc34-4bf9-b7a6-5e255321b0bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Histogram :\n",
    "    three = ('b', 'g', 'r')\n",
    "    # 각 밴드의 히스토그램\n",
    "    def hist1_(self, t) :\n",
    "        plt.subplot(1, 3, 1)\n",
    "        for i, rgb in enumerate(self.three) :\n",
    "            hist1 = cv2.calcHist([t], [i], None, [256], [0, 256])\n",
    "            plt.plot(hist1, color = rgb)\n",
    "        plt.title('RGB Histogram')\n",
    "    #누적 히스토그램을 위한 리스트 누적 함수\n",
    "    def sumlist(self, lst) :\n",
    "        new = []\n",
    "        new.append(lst[0])\n",
    "        for i in range(1, 256) :\n",
    "            new.append(lst[i] + new[i-1])\n",
    "        return new\n",
    "    # 각 밴드의 누적 히스토그램\n",
    "    def hist2_(self, t):\n",
    "        plt.subplot(1, 3, 2)\n",
    "        for i, rgb in enumerate(self.three):\n",
    "            hist2 = cv2.calcHist([t], [i], None, [256], [0, 256])\n",
    "            hist3 = np.concatenate(hist2, axis = None)\n",
    "            plt.plot(self.sumlist(hist3), color = rgb)\n",
    "        plt.title('RGB Cumulative Histogram')\n",
    "    # 흑백 히스토그램\n",
    "    def hist3_(self, t) :\n",
    "        gray = cv2.cvtColor(t, cv2.COLOR_BGR2GRAY)\n",
    "        plt.subplot(1, 3, 3)\n",
    "        hist4 = cv2.calcHist([gray], [0], None, [256], [0, 256])\n",
    "        plt.plot(hist4, color = 'gray')\n",
    "        plt.title('Grayscale Histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "348ac58a-4d06-489e-901a-058975e158ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========================================================================================\n",
      "이동평균 필터 : 1, 중간값 필터 : 2, Laplacian 필터 : 3, Sobel 필터 : 4, Gaussian 필터 : 5\n",
      "=========================================================================================\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "필터 종류 :  3\n",
      "필터 크기 :  3\n"
     ]
    }
   ],
   "source": [
    "print(\"=========================================================================================\")\n",
    "print(\"이동평균 필터 : 1, 중간값 필터 : 2, Laplacian 필터 : 3, Sobel 필터 : 4, Gaussian 필터 : 5\")\n",
    "print(\"=========================================================================================\")\n",
    "n = int(input(\"필터 종류 : \"))\n",
    "k = int(input(\"필터 크기 : \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd963690-bbf6-4699-bf6e-fe993abb1ec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "flt = Filtering()\n",
    "\n",
    "while(dip.isOpened()) :\n",
    "    ret, frame = dip.read()\n",
    "    if ret :\n",
    "        if n == 1:\n",
    "            result = flt.avg(k, frame)\n",
    "        elif n == 2:\n",
    "            result = flt.med(k, frame)\n",
    "        elif n == 3:\n",
    "            result = flt.lap(k, frame)\n",
    "        elif n == 4:\n",
    "            result = flt.sob(k, frame)\n",
    "        elif n == 5:\n",
    "            result = flt.gas(k, frame)\n",
    "            \n",
    "        out.write(result)\n",
    "        if 0xFF == ord('q'):\n",
    "            break\n",
    "    else:\n",
    "        break\n",
    "dip.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f92fb98e-90b8-4f6d-9eb2-929abbf39ad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hst = Histogram()\n",
    "fltdip = cv2.VideoCapture('C:\\\\LastbackUp\\\\디지털영상처리\\\\assignment1\\\\filtered.avi', 0)\n",
    "plt.figure(figsize = (15, 3))\n",
    "\n",
    "while(fltdip.isOpened()):\n",
    "    ret, frame = fltdip.read()\n",
    "    if ret:\n",
    "        cv2.imshow('filtered_image', frame)\n",
    "        hst.hist1_(frame)\n",
    "        hst.hist2_(frame)\n",
    "        hst.hist3_(frame)\n",
    "        \n",
    "        if cv2.waitKey(20) & 0xFF == ord('q') :\n",
    "            break\n",
    "    else:\n",
    "        break\n",
    "plt.show()\n",
    "fltdip.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56538e97-e2b4-49b5-84f1-d316b3eb36d8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "GIS",
   "language": "python",
   "name": "gis"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}