CG Lab python scripts for image processing

CGlab_exps5-9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e9ea178c",
   "metadata": {},
   "source": [
    "# Exp 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "da0a9bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7666f5fe",
   "metadata": {},
   "source": [
    "callback to trackbars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7161aea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_change(value):\n",
    "    img = cv2.rectangle(image, (0,0), (550,550), (0,0,0), -1)\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78dc4dfe",
   "metadata": {},
   "source": [
    "create a window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "43374279",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.namedWindow(\"Shape Properties\")\n",
    "image = np.zeros((550,550,3), np.uint8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a913b776",
   "metadata": {},
   "source": [
    "initial values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8b70b6a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_width = 200\n",
    "initial_height = 100\n",
    "initial_color = (0,255,0) #given"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19ab15b8",
   "metadata": {},
   "source": [
    "create trackbars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "aadad5fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.createTrackbar(\"Width\", \"Shape Properties\", initial_width, 500, on_change)\n",
    "cv2.createTrackbar(\"Height\", \"Shape Properties\", initial_height, 500, on_change)\n",
    "cv2.createTrackbar(\"Red\", \"Shape Properties\", initial_color[0], 255, on_change)\n",
    "cv2.createTrackbar(\"Green\", \"Shape Properties\", initial_color[1], 255, on_change)\n",
    "cv2.createTrackbar(\"Blue\", \"Shape Properties\", initial_color[2], 255, on_change)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a6e844",
   "metadata": {},
   "source": [
    "get current values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e6393858",
   "metadata": {},
   "outputs": [],
   "source": [
    "while True:\n",
    "    width = cv2.getTrackbarPos(\"Width\",\"Shape Properties\")\n",
    "    height = cv2.getTrackbarPos(\"Height\",\"Shape Properties\")\n",
    "    red = cv2.getTrackbarPos(\"Red\",\"Shape Properties\")\n",
    "    green = cv2.getTrackbarPos(\"Green\",\"Shape Properties\")\n",
    "    blue = cv2.getTrackbarPos(\"Blue\",\"Shape Properties\")\n",
    "    \n",
    "    # draw rectangle with dynamically adjusted properties\n",
    "    shape = (width, height)\n",
    "    color = (blue,green,red) #openCV uses BGR format\n",
    "    \n",
    "    #display the image\n",
    "    img = cv2.rectangle(image,(10,10),shape,color,-1)\n",
    "    cv2.imshow(\"Show\", img)\n",
    "    \n",
    "    #check for key press\n",
    "    key = cv2.waitKey(1) & 0xFF\n",
    "    if key == ord('q'):\n",
    "        break\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc1dd562",
   "metadata": {},
   "source": [
    "# Exp 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "95e16bda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "img =cv2.imread('/home/sahyadri/Desktop/images.jpeg')\n",
    "\n",
    "# cv2.imread() takes an image as the input\n",
    "h, w, channels = img.shape\n",
    "\n",
    "half1, half2 = w//2, h//2\n",
    "\n",
    "up_left = img[:half2, :half1]\n",
    "up_right = img[:half2, half1:]\n",
    "down_left = img[half2:, :half1]\n",
    "down_right = img[half2:, half1:]\n",
    "\n",
    "cv2.imshow('Original image', img)\n",
    "cv2.imshow('Top-left part', up_left)\n",
    "cv2.imshow('Top-right part', up_right)\n",
    "cv2.imshow('Bottom-left part', down_left)\n",
    "cv2.imshow('Bottom-right part', down_right)\n",
    "\n",
    "# saving all images using cv2.imwrite()\n",
    "cv2.imwrite('up_left.jpg', up_left)\n",
    "cv2.imwrite('up_right.jpg', up_right)\n",
    "cv2.imwrite('down_left.jpg', down_left)\n",
    "cv2.imwrite('down_right.jpg', down_right)\n",
    "\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8772ac20",
   "metadata": {},
   "source": [
    "# Exp 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e441b21d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# load the image\n",
    "image_path = \"/home/sahyadri/Desktop/images.jpeg\"\n",
    "img = cv2.imread(image_path)\n",
    "\n",
    "# convert the image to grayscale\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# edge detection\n",
    "edges = cv2.Canny(gray, 100,200)\n",
    "\n",
    "# Texture extraction\n",
    "kernel = np.ones((5,5), np.float32)/25 # define a 5x5 averaging kernel\n",
    "texture = cv2.filter2D(gray, -1, kernel) # apply the averaging filter\n",
    "\n",
    "# display the orginal image, edges, and texture\n",
    "cv2.imshow(\"Original Image\", img)\n",
    "cv2.imshow(\"Edges\", edges)\n",
    "cv2.imshow(\"Texture\", texture)\n",
    "\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96abcde5",
   "metadata": {},
   "source": [
    "# Exp 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c75bff8e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "# load the image\n",
    "image_path = \"/home/sahyadri/Desktop/images.jpeg\"\n",
    "image = cv2.imread(image_path)\n",
    "\n",
    "# average blur\n",
    "average_blur = cv2.blur(image, (5,5))\n",
    "\n",
    "# gaussian blur\n",
    "gaussian_blur = cv2.GaussianBlur(image,(5,5),0)\n",
    "\n",
    "# median blur\n",
    "median_blur = cv2.medianBlur(image, 5)\n",
    "\n",
    "# bilateral filter\n",
    "bilateral_filter = cv2.bilateralFilter(image, 9, 75, 75)\n",
    "\n",
    "# display the original and processed images\n",
    "cv2.imshow(\"Original Image\", image)\n",
    "cv2.imshow(\"Average Blur\", average_blur)\n",
    "cv2.imshow(\"Gaussian Blur\", gaussian_blur)\n",
    "cv2.imshow(\"Median Blur\", median_blur)\n",
    "cv2.imshow(\"Bilateral Filter\", bilateral_filter)\n",
    "\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4da64a3",
   "metadata": {},
   "source": [
    "# Exp 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "297eb248",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# read the image\n",
    "image = cv2.imread(\"/home/sahyadri/Desktop/images.jpeg\")\n",
    "\n",
    "#convert the image to grayscale\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "#apply Canny edge detection\n",
    "edges = cv2.Canny(gray,100,200)\n",
    "\n",
    "# find contours in the edged image\n",
    "contours,_ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "# create a mask with the same dimensions as the image\n",
    "mask = np.zeros_like(gray)\n",
    "\n",
    "# draw contours on the mask\n",
    "cv2.drawContours(mask,contours, -1, (255), thickness=cv2.FILLED)\n",
    "\n",
    "# apply the mask to the original image\n",
    "segmented_image = cv2.bitwise_and(image,image,mask=mask)\n",
    "\n",
    "# display the segemented image\n",
    "cv2.imshow(\"Original Image\", image)\n",
    "cv2.imshow(\"Segmented Image\", segmented_image)\n",
    "\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b729fba2",
   "metadata": {},
   "source": [
    "# Playing Around: Finding edges in a blurred image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "724206b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# load the image\n",
    "image_path = \"/home/sahyadri/Desktop/images.jpeg\"\n",
    "image = cv2.imread(image_path)\n",
    "\n",
    "cv2.imshow(\"Original Image\", image)\n",
    "\n",
    "# blur the image\n",
    "blurs = [(\"Average Blur\", cv2.blur(image, (7,7))),                  # Only one to blur edges\n",
    "        (\"Gaussian Blur\", cv2.GaussianBlur(image,(5,5),0)),         # Preserves edges well\n",
    "        (\"Median Blur\", cv2.medianBlur(image, 5)),                  # Only removes noise\n",
    "        (\"Bilateral Filter\", cv2.bilateralFilter(image, 9, 75, 75))]# Preserves edged and smooths only selective parts\n",
    "\n",
    "for title, blurred_image in blurs:\n",
    "    # convert the blur to grayscale\n",
    "    gray = cv2.cvtColor(blurred_image, cv2.COLOR_BGR2GRAY)\n",
    "    \n",
    "    # edge detection\n",
    "    edges = cv2.Canny(gray, 100,200)\n",
    "    \n",
    "    # Texture extraction\n",
    "    kernel = np.ones((5,5), np.float32)/25 # define a 5x5 averaging kernel\n",
    "    texture = cv2.filter2D(gray, -1, kernel) # apply the averaging filter\n",
    "    \n",
    "    # display the edges, and texture\n",
    "    cv2.imshow(title, blurred_image)\n",
    "    cv2.imshow(f\"{title}- Gray Image\",gray)\n",
    "    cv2.imshow(f\"{title}- Edges\", edges)\n",
    "    cv2.imshow(f\"{title}- Texture\", texture)\n",
    "\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  }
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