reconstruction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install opencv-python numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Image Loading**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "image = cv2.imread(\"image.jpg\")\n",
    "\n",
    "cv2.imshow(\"Original Image\", image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gray Image Conversion**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "cv2.imshow(\"Gray Image\", gray_image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Binary Thresholding**\n",
    "* Pixels with intensity values above the threshold (128) in the grayscale image become white (255), and pixels below the threshold become black (0) in the binary image. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, thresholded_image = cv2.threshold(gray_image, 128, 255, cv2.THRESH_BINARY)\n",
    "cv2.imshow(\"Thresholded Image\", thresholded_image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Edge Detection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges = cv2.Canny(gray_image, 50, 150)\n",
    "cv2.imshow(\"Edges\", edges)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Contour Detection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "contour_image = image.copy()\n",
    "cv2.drawContours(contour_image, contours, -1, (0, 255, 0), 1)\n",
    "cv2.imshow(\"Contours\", contour_image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Color Inversion**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inverted_image = cv2.bitwise_not(image)\n",
    "cv2.imshow(\"Inverted Image\", inverted_image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Histogram Equalization**\n",
    "* Enhance the contrast of an image by redistributing pixel intensities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "equalized_image = cv2.equalizeHist(gray_image)\n",
    "cv2.imshow(\"Gray Image\", gray_image)\n",
    "cv2.imshow(\"Equalized Image\", equalized_image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Morphological Operations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "kernel = np.ones((6, 6), np.uint8)\n",
    "eroded_image = cv2.erode(image, kernel, iterations=1)\n",
    "dilated_image = cv2.dilate(image, kernel, iterations=1)\n",
    "cv2.imshow(\"Eroded Image\", eroded_image)\n",
    "cv2.imshow(\"Dilated Image\", dilated_image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Blending Images**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image1 = cv2.imread(\"sample.jpg\")\n",
    "image2 = cv2.imread(\"image.jpg\")\n",
    "\n",
    "image1_resized = cv2.resize(image1, (image2.shape[1], image2.shape[0]))\n",
    "\n",
    "blended_image = cv2.addWeighted(image2, 0.5, image1_resized, 0.5, 0)\n",
    "\n",
    "cv2.imshow(\"Blended Image\", blended_image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Non-Local Means Denoising**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "denoised_image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)\n",
    "cv2.imshow(\"Filtered Image\", denoised_image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gaussian Blurring**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)\n",
    "cv2.imshow(\"Blurred Image\", blurred_image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  }
 ],
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