mask_len_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding the length of the Predicted Mask (in pixels) with Open-CV and Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method:\n",
    "* Find Contours in the Binary Mask.\n",
    "* Using the largest contour, draw the Bounding Box.\n",
    "* Draw Horizontal, Vertical and Diagonal Line to measure the length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "binary_mask = cv2.imread(\"./masks/result15.png\", cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "mask_plot = plt.imshow(binary_mask, cmap=\"gray\")\n",
    "plt.title(\"Original Binary Mask\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Empty image to draw Contour, BBox and Lines on\n",
    "vis = np.ones_like(binary_mask, dtype=np.uint8)\n",
    "\n",
    "# Finding Contours\n",
    "contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "# Getting the Largest Contour\n",
    "largest_contour = max(contours, key=cv2.contourArea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the co-ordinates for smalles possible Bounding Box\n",
    "x, y, w, h = cv2.boundingRect(largest_contour)\n",
    "\n",
    "# Calculating line lengths\n",
    "diagonal_length = np.sqrt(w**2 + h**2)\n",
    "horizontal_length = w\n",
    "vertical_length = h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, ..., 1, 1, 1],\n",
       "       [1, 1, 1, ..., 1, 1, 1],\n",
       "       [1, 1, 1, ..., 1, 1, 1],\n",
       "       ...,\n",
       "       [1, 1, 1, ..., 1, 1, 1],\n",
       "       [1, 1, 1, ..., 1, 1, 1],\n",
       "       [1, 1, 1, ..., 1, 1, 1]], dtype=uint8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drawing\n",
    "\n",
    "# Contour - Boundary\n",
    "cv2.drawContours(vis, contours, -1, (255, 255, 255), 2)\n",
    "\n",
    "# Bounding Box\n",
    "cv2.rectangle(vis, (x, y), (x + w, y + h), (255, 255, 255), 1)\n",
    "\n",
    "# Diagonal Line\n",
    "cv2.line(vis, (x, y), (x + w, y + h), (255, 255, 255), 1)\n",
    "\n",
    "# Horizontal Line\n",
    "cv2.line(vis, (x, y + h // 2), (x + w, y + h // 2), (255, 255, 255), 1)\n",
    "\n",
    "# Vertical Line\n",
    "cv2.line(vis, (x + w // 2, y), (x + w // 2, y + h), (255, 255, 255), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diagonal Length: 159.23 px\n",
      "Horizontal Length: 108.00 px\n",
      "Vertical Length: 117.00 px\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taking a weighted sum of Original Binary Mask and Masked Image\n",
    "blended_image = cv2.addWeighted(binary_mask, 0.7, vis, 0.8, 0)\n",
    "\n",
    "print(f\"Diagonal Length: {diagonal_length:.2f} px\")\n",
    "print(f\"Horizontal Length: {horizontal_length:.2f} px\")\n",
    "print(f\"Vertical Length: {vertical_length:.2f} px\")\n",
    "\n",
    "blended_plot = plt.imshow(blended_image, cmap=\"gray\")\n",
    "plt.title(\"Binary Mask with BBox and Lines\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}