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Get Outstanding Harris Corner Detection Technique Homework Help

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Finding Data Points of an Image Using Harris Corner Detection Technique

Use Harris corner detection technique to find the data points (key points) of the image. Write a program to perform K-means clustering technique using the strongest 100 points of part 1. You need to create your own function/code for this part (K-mean algorithm).
• Compare the outcomes of multiple runs with different values for K and choose the best one based on a predefined criterion.
• Display the original image with the best detected clusters on it (use different colors).
• Draw a bounding box for each cluster of the data points of part 2.
You may use the built-in function/library (cv2.rectangle()) or any other one for this part. Display the original image with the bounding boxes on it (use different colors).

Cornerstone Detection code 1

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "importnumpy as np\n",
    "import cv2 as cv\n",
    "importmatplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q1. Detect corner using Harris detector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'D:/freelancer/corner/image.jpg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = cv.imread(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gray = cv.cvtColor(im, cv.COLOR_BGR2GRAY)\n",
    "gray = np.float32(gray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dst = cv.cornerHarris(gray, 2, 3, 0.04)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "im_new = im.copy()\n",
    "im_new[dst>0.01*dst.max()]=[255,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
Cornerstone Code Solution 2
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "importnumpy as np\n",
    "import cv2 as cv\n",
    "importmatplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detect corner using Harris detector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'D:/freelancer/corner/image.jpg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = cv.imread(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "gray = cv.cvtColor(im, cv.COLOR_BGR2GRAY)\n",
    "gray = np.float32(gray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "dst = cv.cornerHarris(gray, 2, 3, 0.04)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "im_new = im.copy()\n",
    "im_new[dst>0.01*dst.max()]=[255,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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