APO_Lab3_OpSasiedztwa.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JXAve7azp2Qy"
   },
   "source": [
    "# APO Lab 3 Operacje sąsiedztwa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "c9v3DebgjLgI"
   },
   "outputs": [],
   "source": [
    "#from google.colab.patches import cv2_imshow\n",
    "import cv2\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E7AthW3AVYw4"
   },
   "source": [
    "!WAŻNE! Aby poniższy plik działał na Google Colab, należy wgrać plik 'lena_gray.bmp' oraz 'testing.bmp'. Z panelu po lewej stronie wybieramy ikonę folderu ('Files') a następnie 'Upload' i wybieramy zdjęcie (wcześniej ściągnięte na dysk twardy z UBI)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bhiwTnFcLXMl"
   },
   "source": [
    "# Zadanie 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 529
    },
    "id": "OfYoPimSjPaX",
    "outputId": "ac968285-4965-4233-f6c3-1efe56162dbe"
   },
   "outputs": [],
   "source": [
    "# Wczytanie obrazu pierwotnego\n",
    "img = cv2.imread('lena_gray.bmp', cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "#cv2_imshow(img)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MyLze_e9LPM5"
   },
   "source": [
    "## 1. a) blur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 529
    },
    "id": "TanGLU-RmSvG",
    "outputId": "c7fcdfe9-b4e4-40fb-bccf-cf99eda1da62"
   },
   "outputs": [],
   "source": [
    "# ustawienie rozmiaru maski - okna przekształcenia (kernel) \n",
    "kernel_size = (5,5)\n",
    "# standardowe wygładzanie liniowe funkcją blur z biblioteki OpenCV przy użyciu powyższego rozmiaru maski\n",
    "blured_img = cv2.blur(img,kernel_size)\n",
    "\n",
    "#cv2_imshow(blured_img)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(cv2.cvtColor(blured_img, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5KZ1H6iAIeix"
   },
   "source": [
    "Funkcja blur używa maski przekształcenia w postaci:\n",
    "\n",
    "![alt text](https://github.com/knave88/APO/raw/main/opencv_blur.png)\n",
    "\n",
    "czyli tzw. normalized box filter.\n",
    "Dla przypomnienia im większe okno tym większe rozmycie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d2Crt1jqOPGD"
   },
   "outputs": [],
   "source": [
    "# funkcja ta może przyjąć jako dodatkowy parametr określenie metody ekstrapolacji wartości brzegowych obrazu\n",
    "blured_img = cv2.blur(img,(5,5), borderType = cv2.BORDER_REPLICATE)\n",
    "\n",
    "# pozostałe opcje do zaimplementowania:\n",
    "# 1.\tpozostawienie wartości pikseli brzegowych bez zmian (cv2.BORDER_ISOLATED), \n",
    "# 2.\tpowielenie wartości pikseli brzegowych przez odbicie lustrzane (cv2.BORDER_REFLECT), \n",
    "# 3.\tpowielenie wartości pikseli brzegowych przez powielenie skrajnego piksela (cv2.BORDER_REPLICATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CbKaaK_aLjDY"
   },
   "source": [
    "## 1. a) gaussianBlur"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sfUYyyLqMEZR"
   },
   "source": [
    "Funkcja gaussianBlur, zgodnie z nazwą, używa filtra gaussowskiego do wygładzenia obrazu.\n",
    "Parametry wejściowe które należy ustawić:\n",
    "\n",
    "* rozmiar filtra\n",
    "* odchylenie standardowe w kierunku X\n",
    "* odchylenie standardowe w kierunku Y (jeśli nie podamy wartości przyjmowana jest wartość z kierunku X)\n",
    "* metoda ekstrapolacji pikseli brzegowych\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 529
    },
    "id": "sKmwB_xrGi7R",
    "outputId": "a9256036-6950-4b1b-ea04-737a6bb55ab2"
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   "source": [
    "kernel_size = (5,5) # rozmiar filtra\n",
    "sigmaX = 0 # odch. std. w kiernku X; !Uwaga jeśli podamy wartość 0, parametr jest liczony automatycznie na podstawie rozmiaru kernela\n",
    "borderType = cv2.BORDER_REPLICATE # metoda ekstrapolacji wartości brzegowych\n",
    "                                  # alternatywne opcje: BORDER_REPLICATE BORDER_REFLECT BORDER_ISOLATED\n",
    "gaussblured_img = cv2.GaussianBlur(img, kernel_size, sigmaX, borderType) \n",
    "\n",
    "#cv2_imshow(gaussblured_img)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(cv2.cvtColor(gaussblured_img, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I6W6QgZenwVp"
   },
   "source": [
    "## 1. b) Detekcja krawędzi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IRbPeTfOpO3P"
   },
   "source": [
    "Detekcja krawędz może się odbywać na przykład przy użyciu odpowiediego filtra (maski), np:\n",
    "* Laplacian - oblicza Laplacian obrazu\n",
    "\n",
    "* Sobel - oparator ten obliczany jest w bibliotece OpenCV w jednym kierunku dlatego należy go poiczyć zarówno dla kierunku X jak i Y\n",
    "\n",
    "* Canny - algorytm znajdywania krawędzi opracowany przez J. Canny w 1986 roku\n",
    "\n",
    "Dla tych metod należy podać jako argument wejściowy ddepth/dtype (*Desired depth of the destination image*) czyli format obrazu wyjściowego np. uint8 lub float. Warto skorzystać tu z natywnych formatów OpenCV jak np. *cv2.CV_64F*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
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   "outputs": [],
   "source": [
    "# Laplacian\n",
    "ddepth = cv2.CV_64F # format obrazu wyjściowego\n",
    "ksize = 3 # rozmiar filtra\n",
    "img_laplacian = cv2.Laplacian(img,ddepth, ksize, borderType = cv2.BORDER_REPLICATE)\n",
    "\n",
    "#cv2_imshow(img_laplacian)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(img_laplacian, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 529
    },
    "id": "UX-6w4busEIB",
    "outputId": "bfa3ccfe-0f78-4dbe-e9eb-46021954dfdf"
   },
   "outputs": [],
   "source": [
    "# Sobel\n",
    "sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)\n",
    "sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5) # parametr trzeci i czwarty określają kierunkowość, najpierw X, potem Y\n",
    "\n",
    "frame_sobel = cv2.hconcat((sobelx, sobely))\n",
    "\n",
    "#cv2_imshow(frame_sobel)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(frame_sobel, cmap='gray')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 529
    },
    "id": "EzHxle50sx4w",
    "outputId": "fca4aa3c-b262-4849-8689-2529a2bbbf9b"
   },
   "outputs": [],
   "source": [
    "# Canny\n",
    "# prócz obrazu wejściowego jako argumenty wejściowe podajemy dwa progi, gdzie wartości między tymi progami służą do wyznaczenia pikseli połaczonych następnie w krawędzie\n",
    "threshold1 = 100\n",
    "threshold2 = 200\n",
    "img_canny = cv2.Canny(img,threshold1,threshold2)\n",
    "\n",
    "#cv2_imshow(img_canny)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(cv2.cvtColor(img_canny, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FQzvrcYJtmve"
   },
   "source": [
    "**!Uwaga!**\n",
    "Ustawenia parametru ddepth/dtype ma znaczenie.\n",
    "\n",
    "![alt text](https://github.com/knave88/APO/raw/main/double_edge.jpg)\n",
    "\n",
    "W przypadku algorytmu Sobel'a i ostrych przejść czarne-białe i białe-czarne jeśli ustawimy dtype = cv2.CV_8U czyli uint8 to wykrywamy tylko jedną krawędz. Powód jest prosty. Wykryta kawędź ma wartości dodatnie natomiast pomijana ma wartości ujemne które nie mogą wystąpić w formacie uint8. Dlatego lepiej jest stosować \"większy\" format a nastepnie stosować wartość absolutną aby sprowadzić np. do uint8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pWdzj77hn8Br"
   },
   "source": [
    "## 1. c) Wyostrzanie liniowe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xGdjl5iHxxDQ"
   },
   "source": [
    "Wyostrzanie obrazu podobnie jak wygładzanie może być zrealizowane przez zastosowanie filtracji i odpowiedniej maski. Użycie konwolucji w znacznym stopniu ułatwia zadanie. \n",
    "W bibliotece OpenCV filtrację dowolną maską można wykonać przy użyciu funkcji *filter2D*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 614
    },
    "id": "jMuA16Ywxe4T",
    "outputId": "13246c8d-cca5-4951-fb47-9181d62eb990"
   },
   "outputs": [],
   "source": [
    "# wyostrzanie\n",
    "mask_sharp1 = np.array([[ 0,-1, 0],[-1, 5,-1],[ 0,-1, 0]])\n",
    "mask_sharp2 = np.array([[-1,-1,-1],[-1, 9,-1],[-1,-1,-1]])\n",
    "mask_sharp3 = np.array([[ 1,-2, 1],[-2, 5,-2],[ 1,-2, 1]])\n",
    "\n",
    "mask_sharp = mask_sharp2\n",
    "print('My mask:')\n",
    "print(mask_sharp)\n",
    "print('Result of filtering:')\n",
    "img_sharp = cv2.filter2D(img,cv2.CV_64F, mask_sharp, borderType = cv2.BORDER_REPLICATE)\n",
    "\n",
    "#cv2_imshow(img_sharp)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(img_sharp, cmap='gray')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JU3kwf-yb2aO"
   },
   "source": [
    "## 1. d) Kierunkowa detekcja krawędzi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gkt9Gn0B0yLy"
   },
   "source": [
    "Operator ten umożliwia wyznaczenie dla każdego piksela obrazu estymaty pochodnej kierunkowej w jednym z 8 kierunków. Przykładowe maski kierunkowe:\n",
    "\n",
    "![alt text](https://github.com/knave88/APO/raw/main/Prewitt.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 614
    },
    "id": "9ea3-76z0ylb",
    "outputId": "43067fce-6cb4-4d6a-907f-aca9880e2766"
   },
   "outputs": [],
   "source": [
    "# Prewitt dla kiernku NE\n",
    "mask_prewittNE = np.array([[0,+1,+1],[-1,0,+1],[-1,-1,0]])\n",
    "print('My mask:')\n",
    "print(mask_prewittNE)\n",
    "print('Result of filtering:')\n",
    "img_prewitt = cv2.filter2D(img,cv2.CV_64F, mask_prewittNE, borderType = cv2.BORDER_REPLICATE)\n",
    "\n",
    "#cv2_imshow(img_prewitt)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(img_prewitt, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OtN7bFPvoc8m"
   },
   "source": [
    "## 1. e) Uniwersalna operacja liniowa sąsiedztwa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dWoAQ83g16nr"
   },
   "source": [
    "Uniwersalna operacja liniowa sąsiedztwa oparta na masce 3x3 o wartościach zadawanych w sposób interakcyjny przez użytkowanika. Należy zadbać o to aby program akceptował jadynie poprawny typ zmiennej oraz sprawdzał czy zadana wartość jest w określonym zakresie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
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    "id": "PltWeZSTuoOv",
    "outputId": "e80be865-88c6-408d-de95-2d2304c6a483"
   },
   "outputs": [],
   "source": [
    "from ipywidgets import GridspecLayout, BoundedIntText, Layout\n",
    "\n",
    "def create_expanded_input(value):\n",
    "    return BoundedIntText(description='', value=1, min=-100, layout=Layout(height='auto', width='auto'))\n",
    "\n",
    "grid = GridspecLayout(3, 3)\n",
    "\n",
    "for i in range(3):\n",
    "    for j in range(3):\n",
    "        grid[i, j] = create_expanded_input(0);\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 614
    },
    "id": "0hk_ymMG28Ze",
    "outputId": "d21b8a86-56bd-4550-9766-a124722c2cf5"
   },
   "outputs": [],
   "source": [
    "kernel = np.zeros((3,3))\n",
    "for i in range(3):\n",
    "    for j in range(3):\n",
    "        kernel[i, j] = int(grid[i, j].value)\n",
    "        \n",
    "print('My mask:')\n",
    "kernel=np.int64(kernel)/np.sum(kernel)\n",
    "print(kernel)\n",
    "print('Result of filtering:')\n",
    "img_filtered = cv2.filter2D(img,cv2.CV_64F, kernel, borderType = cv2.BORDER_REPLICATE)\n",
    "\n",
    "#cv2_imshow(img_filtered)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(img_filtered, cmap='gray')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a6nW3jPWb7S6"
   },
   "source": [
    "# Zadanie 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LiuAvOYkeItF"
   },
   "source": [
    "## Filtr medianowy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "re4u-kS4cDdu"
   },
   "source": [
    "Opracja filtracji medianowej również może być w prosty sposób zrealizowana przy użyciu biblioteki OpenCV.  Podobnie jak w przykładach powyżej, argumenty wejściowe, prócz obrazu wejściowego, to rozmiar filtra. \n",
    "**Uwaga!** Należy pamiętać, że rozmiar okna musi mieć nieprzystą wartość całkowitą większą od 1, np.: 3, 5, 7 itd.\n",
    "\n",
    "Ponadto, w obrazach wielokanałowych każdy kanał jest przetwarzany odrębnie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 529
    },
    "id": "GHtsXYnNbMfQ",
    "outputId": "7035cd94-384e-4c5d-fb90-1f7f51f42332"
   },
   "outputs": [],
   "source": [
    "medianBlured_img = cv2.medianBlur(img, 11) \n",
    "\n",
    "#cv2_imshow(medianBlured_img)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(cv2.cvtColor(medianBlured_img, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qg8FawxFb88I"
   },
   "source": [
    "# Zadanie 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jSLv-QH6ecvl"
   },
   "source": [
    "## Operacje jednopunktowe dwuargumentowe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eoz4P7igfiCs"
   },
   "source": [
    "**Uwaga!** przy operacjach jednopunktowych dwuargumentowych należy zadbać aby oba obrazy miały ten sam rozmiar. Jeśli występuje niezgodność można zastosować jedną z dwóch opcji:\n",
    "* resize = przeskalowanie\n",
    "* padding = dopełnienie (np. wartością zero)\n",
    "\n",
    "---\n",
    "\n",
    "Aby program zadziałał należy dodać plik 'testing.bmp' (dostępny na UBI) tak samo jak obraz Leny."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 214
    },
    "id": "Ly3L9MWHfC55",
    "outputId": "9e169465-9204-4638-e62f-e69f0da99f36"
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   "source": [
    "# wczytanie drugiego obrazu\n",
    "img2 = cv2.imread('testing.bmp', cv2.IMREAD_GRAYSCALE)\n",
    "# zmiana rozmiaru (dopasowanie) do obrazu Leny\n",
    "img2 = cv2.resize(img2, img.shape) \n",
    "\n",
    "#cv2_imshow(img2)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7ovA_lNyhd99"
   },
   "source": [
    "### 3.1 Dodawanie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KIzSl9_Qb94v"
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   "source": [
    "# dodawanie obrazów realizowane w bibliotece OpenCV\n",
    "img_add = cv2.add(img, img2)\n",
    "\n",
    "#cv2_imshow(img_add)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(cv2.cvtColor(img_add, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nBG8ISuiht49"
   },
   "source": [
    "### 3.2 Mieszanie (blending)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vSfyEKP2iUkS"
   },
   "source": [
    "\"Mieszanie\" pozwala na doddanie do siebie obrazów z zadaną wagą dla każdego z nich. Pozwala to na uzyskanie efektu prześwitywania lub tworzenia tzw. 'overlay'. Dodatkowo pozwala również na dodanie zadanej wartości do każdej sumy (parametr gamma). Relizowane jest zgodnie z poniższym wzorem:\n",
    "\n",
    "![alt text](https://github.com/knave88/APO/raw/main/addWeighted.png)\n",
    "\n",
    "\n",
    "\n",
    "gdzie:\n",
    "* alpha określa udział obrazu pierwszego\n",
    "* beta określa udział obrazu drugiego\n",
    "* gamma określa skalar dodany do każdego sumowanego piksela (wartość dodatnia lub ujemna)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9GPRoeLwiGqO"
   },
   "outputs": [],
   "source": [
    "# przykład zastosowania\n",
    "img_blend = cv2.addWeighted(img,0.7,img2,0.5,-100)\n",
    "\n",
    "#cv2_imshow(img_blend)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(cv2.cvtColor(img_blend, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "izlzKg4LhtuC"
   },
   "source": [
    "### 3.3 Operacje logiczne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PMMo688BfJQq"
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    "# negacja = bitwise NOT\n",
    "img2_inv = cv2.bitwise_not(img2)\n",
    "\n",
    "frame3 = cv2.hconcat((img2, img2_inv))\n",
    "#cv2_imshow(frame3)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(cv2.cvtColor(frame3, cv2.COLOR_BGR2RGB))"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "# AND\n",
    "img_and = cv2.bitwise_and(img, img2)\n",
    "#cv2_imshow(img_and)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(cv2.cvtColor(img_and, cv2.COLOR_BGR2RGB))"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    "# OR\n",
    "img_or = cv2.bitwise_or(img, img2)\n",
    "#cv2_imshow(img_or)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(cv2.cvtColor(img_or, cv2.COLOR_BGR2RGB))"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    "# XOR\n",
    "img_xor = cv2.bitwise_xor(img, img2)\n",
    "#cv2_imshow(img_xor)\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.imshow(cv2.cvtColor(img_xor, cv2.COLOR_BGR2RGB))"
   ]
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