Image Analysis using hough transform.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff2306a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import gaussian_kde\n",
    "\n",
    "# Step 1: Cropping Image\n",
    "def crop_image(image_path, x, y, w, h):\n",
    "    \"\"\"\n",
    "    Crop the input image to the specified region of interest.\n",
    "    Args:\n",
    "        image_path (str): Path to the input image.\n",
    "        x (int): x-coordinate of the top-left corner of the region.\n",
    "        y (int): y-coordinate of the top-left corner of the region.\n",
    "        w (int): Width of the region.\n",
    "        h (int): Height of the region.\n",
    "    Returns:\n",
    "        np.ndarray: Cropped image.\n",
    "    \"\"\"\n",
    "    image = cv2.imread(image_path)\n",
    "    cropped_image = image[y:y+h, x:x+w]\n",
    "    return cropped_image\n",
    "\n",
    "# ... (Steps 2-6 remain specific to your experiment)\n",
    "\n",
    "# Step 7: Determining Cylinder Orientation (updated with KDE for smoother distribution)\n",
    "def detect_cylinder_orientation(image_path):\n",
    "    \"\"\"\n",
    "    Detect cylinder orientations in an image and visualize the distribution.\n",
    "    Args:\n",
    "        image_path (str): Path to the input image.\n",
    "    \"\"\"\n",
    "    image = cv2.imread(image_path)\n",
    "    \n",
    "    # Convert image to HSV\n",
    "    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n",
    "    v_channel = hsv[:,:,2]\n",
    "    \n",
    "    # Thresholding to enhance edges\n",
    "    _, thresh = cv2.threshold(v_channel, 127, 255, cv2.THRESH_BINARY)\n",
    "    \n",
    "    # Remove small objects (potentially spherical particles)\n",
    "    cleaned_image = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, np.ones((5,5),np.uint8))\n",
    "    \n",
    "    # Perform Hough Line Transform\n",
    "    lines = cv2.HoughLines(cleaned_image, 1, np.pi / 180, 200)\n",
    "    \n",
    "    orientations = []\n",
    "    \n",
    "    if lines is not None:\n",
    "        for line in lines:\n",
    "            rho, theta = line[0]\n",
    "            orientations.append(theta)\n",
    "            a = np.cos(theta)\n",
    "            b = np.sin(theta)\n",
    "            x0 = a * rho\n",
    "            y0 = b * rho\n",
    "            x1 = int(x0 + 1000 * (-b))\n",
    "            y1 = int(y0 + 1000 * (a))\n",
    "            x2 = int(x0 - 1000 * (-b))\n",
    "            y2 = int(y0 - 1000 * (a))\n",
    "            cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2)\n",
    "    \n",
    "    # KDE for smoother distribution\n",
    "    kde = gaussian_kde(orientations)\n",
    "    x_vals = np.linspace(0, np.pi, 1000)\n",
    "    kde_vals = kde(x_vals)\n",
    "    \n",
    "    # Plot Histogram with KDE\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.hist(orientations, bins=30, range=(0, np.pi), density=True, color='skyblue', alpha=0.7, edgecolor='black')\n",
    "    plt.plot(x_vals, kde_vals, color='red', label='KDE')\n",
    "    plt.xlabel('Orientation (radians)')\n",
    "    plt.ylabel('Density')\n",
    "    plt.title('Distribution of Cylinder Orientations')\n",
    "    plt.grid(axis='y', alpha=0.75)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "    cv2.imshow('Detected Cylinders', image)\n",
    "    cv2.waitKey(0)\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "# Example Usage\n",
    "cropped_image = crop_image(\"C:/Users/HP/.jupyter/test_image.png\", 100, 100, 300, 300)\n",
    "detect_cylinder_orientation(\"C:/Users/HP/.jupyter/test_image.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9694004",
   "metadata": {},
   "source": [
    "Summary Report:\n",
    "\n",
    "Added descriptive comments for each function, explaining their purpose and usage.\n",
    "Implemented cropping function to extract a specific region of interest from the input image.\n",
    "Applied image processing techniques (thresholding, morphological operations) to enhance edge detection for cylinder orientation.\n",
    "Utilized Hough Line Transform to detect orientations of cylindrical particles.\n",
    "Employed a Kernel Density Estimation (KDE) for a smoother distribution of cylinder orientations.\n",
    "Created a histogram with overlaid KDE curve to visualize the distribution.\n",
    "Added comments for code readability and clarity."
   ]
  }
 ],
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