[6]_hough_transform.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4316,
     "status": "ok",
     "timestamp": 1611337902709,
     "user": {
      "displayName": "Ly Nguyễn Thị Trúc",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTO4u7BVTP2x4w-IK207_mgn_G3S8sAQUR3sI=s64",
      "userId": "16613213413249005883"
     },
     "user_tz": -420
    },
    "id": "22VccXC5OiaH",
    "outputId": "f98688bb-3ad6-4c99-cbc1-813ad3059768"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1uSOMmTyb4DM9GKZ44PV5HK3qT5_szB9n\n",
      "To: /content/square.jpg\n",
      "100% 2.10k/2.10k [00:00<00:00, 753kB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=190g_R6_Aa2tD6sirNd4yWNTVtgrEJFht\n",
      "To: /content/bongban.jpg\n",
      "100% 26.5k/26.5k [00:00<00:00, 28.7MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1BBY0N1MzlvRWtaJCe_aodiimzpsKgEsO\n",
      "To: /content/circle.jpg\n",
      "100% 28.0k/28.0k [00:00<00:00, 38.3MB/s]\n"
     ]
    }
   ],
   "source": [
    "!gdown --id 1uSOMmTyb4DM9GKZ44PV5HK3qT5_szB9n\n",
    "!gdown --id 190g_R6_Aa2tD6sirNd4yWNTVtgrEJFht\n",
    "!gdown --id 1BBY0N1MzlvRWtaJCe_aodiimzpsKgEsO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 4308,
     "status": "ok",
     "timestamp": 1611337902713,
     "user": {
      "displayName": "Ly Nguyễn Thị Trúc",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTO4u7BVTP2x4w-IK207_mgn_G3S8sAQUR3sI=s64",
      "userId": "16613213413249005883"
     },
     "user_tz": -420
    },
    "id": "aqaUwYtMLStE"
   },
   "outputs": [],
   "source": [
    "import time\n",
    "from functools import wraps\n",
    "\n",
    "def calc_time(f):\n",
    "  @wraps(f)\n",
    "  def wrapper(*args, **kwds):\n",
    "    start = time.time()\n",
    "    result = f(*args, **kwds)\n",
    "    elapsed = time.time() - start\n",
    "    print(\"%s took %s\" % (f.__name__, elapsed))\n",
    "    return result\n",
    "  return wrapper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ejn2IXUd4-84"
   },
   "source": [
    "# **Hough Line Transform**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 4305,
     "status": "ok",
     "timestamp": 1611337902716,
     "user": {
      "displayName": "Ly Nguyễn Thị Trúc",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTO4u7BVTP2x4w-IK207_mgn_G3S8sAQUR3sI=s64",
      "userId": "16613213413249005883"
     },
     "user_tz": -420
    },
    "id": "cA1kCIi5BLv2"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "class HoughLineDetection:\n",
    "    def __init__(self, rho=1, theta=np.pi/180, threshold=120):\n",
    "        self.rho = rho\n",
    "        self.theta = theta\n",
    "        self.threshold = threshold\n",
    "    \n",
    "    @calc_time\n",
    "    def detect_line(self, image, angle_step=1):\n",
    "        \"\"\"\n",
    "        Hough transform for lines\n",
    "        Input:\n",
    "        image - 2D binary image with nonzeros representing edges\n",
    "        angle_step - Spacing between angles to use every n-th angle\n",
    "                    between -90 and 90 degrees. Default step is 1.\n",
    "        Returns:\n",
    "        accumulator - 2D array of the hough transform accumulator\n",
    "        theta - array of angles used in computation, in radians.\n",
    "        rhos - array of rho values. Max size is 2 times the diagonal\n",
    "            distance of the input image.\n",
    "        \"\"\"\n",
    "        # Rho and Theta ranges\n",
    "        thetas = np.deg2rad(np.arange(0., 180.0, angle_step))\n",
    "        width, height = image.shape\n",
    "        diagonal = int(round((width * width + height * height) ** 0.5))\n",
    "        rhos = np.linspace(- diagonal, diagonal, diagonal * 2)\n",
    "        num_rhos = diagonal // self.rho\n",
    "\n",
    "        # Cache some resuable values\n",
    "        cos_thetas = np.cos(thetas)\n",
    "        sin_thetas = np.sin(thetas)\n",
    "        num_thetas = len(thetas)\n",
    "\n",
    "        # Hough accumulator array of theta vs rho\n",
    "        edge_matrix = np.zeros((2 * diagonal, num_thetas), dtype=np.uint8)\n",
    "        # (row, col) indexes to edges\n",
    "        y_idxs, x_idxs = np.nonzero(image)\n",
    "\n",
    "        # Vote in the hough accumulator\n",
    "        for i in range(len(x_idxs)):\n",
    "            x = x_idxs[i]\n",
    "            y = y_idxs[i]\n",
    "\n",
    "            for t_idx in range(num_thetas):\n",
    "                # Calculate rho. diag_len is added for a positive index\n",
    "                rho = diagonal + int(round(x * cos_thetas[t_idx] + y * sin_thetas[t_idx]))\n",
    "                edge_matrix[rho, t_idx] += 1\n",
    "        \n",
    "        rho_values, theta_values = np.where(edge_matrix > self.threshold)    \n",
    "        rho_values = [r - num_rhos for r in rho_values]\n",
    "        theta_values = [t / 180.0 * np.pi for t in theta_values]\n",
    "        line_indices = [(r, theta) for r, theta in zip(rho_values, theta_values)]   \n",
    "\n",
    "        return line_indices, edge_matrix, thetas, rhos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8582,
     "status": "ok",
     "timestamp": 1611337907002,
     "user": {
      "displayName": "Ly Nguyễn Thị Trúc",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTO4u7BVTP2x4w-IK207_mgn_G3S8sAQUR3sI=s64",
      "userId": "16613213413249005883"
     },
     "user_tz": -420
    },
    "id": "-jPdtwcAvzKy",
    "outputId": "9b050ab8-071d-4eff-c510-30ae47c33e0c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "detect_line took 3.9898266792297363\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "\n",
    "image = cv2.imread('square.jpg', 0)\n",
    "image_detected = image.copy()\n",
    "opencv_out = image.copy()\n",
    "\n",
    "edges = cv2.Canny(image, 50, 150)\n",
    "\n",
    "hough = HoughLineDetection()\n",
    "line_indices = hough.detect_line(edges)[0]\n",
    "\n",
    "for line in line_indices:\n",
    "    rho, theta = line\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_detected, (x1,y1), (x2,y2), (0,0,255), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8578,
     "status": "ok",
     "timestamp": 1611337907003,
     "user": {
      "displayName": "Ly Nguyễn Thị Trúc",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTO4u7BVTP2x4w-IK207_mgn_G3S8sAQUR3sI=s64",
      "userId": "16613213413249005883"
     },
     "user_tz": -420
    },
    "id": "ouv9AJ8iOAuh",
    "outputId": "514b20aa-4d2c-4a63-f8df-ab64bcd4b504"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "openCV took 0.003339052200317383s\n"
     ]
    }
   ],
   "source": [
    "# OpenCV\n",
    "import time\n",
    "\n",
    "t1 = time.time()\n",
    "lines = cv2.HoughLines(edges, rho=1, theta=np.pi/180, threshold=100)\n",
    "t2 = time.time()\n",
    "\n",
    "print(f'openCV took {t2 - t1}s')\n",
    "\n",
    "for line in lines:\n",
    "    rho, theta = line[0]\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(opencv_out, (x1,y1), (x2,y2), (0,0,255), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "executionInfo": {
     "elapsed": 1146,
     "status": "ok",
     "timestamp": 1611337915037,
     "user": {
      "displayName": "Ly Nguyễn Thị Trúc",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTO4u7BVTP2x4w-IK207_mgn_G3S8sAQUR3sI=s64",
      "userId": "16613213413249005883"
     },
     "user_tz": -420
    },
    "id": "tdrpGZjbOGAF",
    "outputId": "daff58d0-8bdc-4c73-8cfa-f90a14f7b608"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
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      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 3, figsize=(16, 10))\n",
    "\n",
    "ax[0].imshow(image, cmap=\"gray\")\n",
    "ax[0].set_title('Input image')\n",
    "ax[0].axis('off')\n",
    "\n",
    "ax[1].imshow(image_detected, cmap=\"gray\")\n",
    "ax[1].set_title('Implement')\n",
    "ax[1].axis('off')\n",
    "\n",
    "ax[2].imshow(opencv_out, cmap=\"gray\")\n",
    "ax[2].set_title('openCV')\n",
    "ax[2].axis('off')\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZZXt_rNxPRrl"
   },
   "source": [
    "# Detection of Circle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 1047,
     "status": "ok",
     "timestamp": 1611337934715,
     "user": {
      "displayName": "Ly Nguyễn Thị Trúc",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTO4u7BVTP2x4w-IK207_mgn_G3S8sAQUR3sI=s64",
      "userId": "16613213413249005883"
     },
     "user_tz": -420
    },
    "id": "scENKnF1PXWy"
   },
   "outputs": [],
   "source": [
    "class HoughCircleDetection:\n",
    "    def __init__(self, rmin=10, rmax=20, step=180, threshold=0.4):\n",
    "        self.rmin = rmin\n",
    "        self.rmax = rmax\n",
    "        self.step = step\n",
    "        self.threshold = threshold\n",
    "        \n",
    "    def detect(self, image):\n",
    "        points = []\n",
    "        angle = 2 * np.pi / self.step\n",
    "        for r in range(self.rmin, self.rmax + 1):\n",
    "            for t in range(self.step):\n",
    "                points.append((r, int(r * np.cos(angle * t)), int(r * np.sin(angle * t))))\n",
    "        \n",
    "        acc = defaultdict(int)\n",
    "        edges = np.squeeze(cv2.findNonZero(image))\n",
    "        for x, y in edges:\n",
    "            for r, dx, dy in points:\n",
    "                a = x - dx\n",
    "                b = y - dy\n",
    "                acc[(a, b, r)] += 1\n",
    "            \n",
    "        circles = []\n",
    "        for k, v in sorted(acc.items(), key=lambda i: -i[1]):\n",
    "            x, y, r = k\n",
    "            if v / self.step >= self.threshold and all((x - xc) ** 2 + (y - yc) ** 2 > rc ** 2 for xc, yc, rc in circles):\n",
    "                circles.append((x, y, r))\n",
    "        \n",
    "        return circles\n",
    "    \n",
    "    @calc_time\n",
    "    def detect_circles(self, image):\n",
    "        angles = np.arange(0, 2 * math.pi, 2 * math.pi / self.step)\n",
    "        radius = np.arange(self.rmin, self.rmax, 1)\n",
    "\n",
    "        radius_arr = np.broadcast_to(np.expand_dims(radius, axis=1), (radius.size, angles.size))\n",
    "        angles_arr = np.broadcast_to(np.expand_dims(angles, axis=0), (radius.size, angles.size))\n",
    "        dx = radius_arr * np.cos(angles_arr)\n",
    "        dy = radius_arr * np.sin(angles_arr) \n",
    "        dxy = np.stack((dx, dy), axis=2) \n",
    "        \n",
    "\n",
    "        edges = np.squeeze(cv2.findNonZero(image)) \n",
    "        \n",
    "        dxy_arr = np.broadcast_to(np.expand_dims(dxy, axis=0), shape=(edges.shape[0], dxy.shape[0], dxy.shape[1], dxy.shape[2]))\n",
    "        edges = np.broadcast_to(np.expand_dims(edges, axis=(1, 2)), shape=(edges.shape[0], dxy.shape[0], dxy.shape[1], edges.shape[1]))\n",
    "            \n",
    "        circles = edges - dxy_arr "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "executionInfo": {
     "elapsed": 58461,
     "status": "ok",
     "timestamp": 1611337998680,
     "user": {
      "displayName": "Ly Nguyễn Thị Trúc",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTO4u7BVTP2x4w-IK207_mgn_G3S8sAQUR3sI=s64",
      "userId": "16613213413249005883"
     },
     "user_tz": -420
    },
    "id": "rCQ5ye9zmTyS",
    "outputId": "aa4c1b2a-a632-46d7-f53a-f28b4e351e3a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 279.5, 279.5, -0.5)"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "import time\n",
    "\n",
    "image = cv2.imread('bongban.jpg', 0)\n",
    "image_detected = image.copy()\n",
    "\n",
    "start_time = time.time()\n",
    "edge = cv2.Canny(image, 50, 150)\n",
    "hough = HoughCircleDetection(15, 30, 180, 0.5)\n",
    "\n",
    "circles = hough.detect(edge)\n",
    "\n",
    "for x, y, r in circles:\n",
    "    cv2.circle(image_detected, (x, y), r, (0, 255, 0), 2)\n",
    "my_time = time.time() - start_time\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "ax[0].imshow(image, cmap='gray')\n",
    "ax[0].set_title('Input image')\n",
    "ax[0].axis('off')\n",
    "\n",
    "ax[1].imshow(image_detected, cmap=\"gray\")\n",
    "ax[1].set_title('Implement: ' + str(my_time))\n",
    "ax[1].axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ccZkBp8bVN4X"
   },
   "source": [
    "Hough Circle Detection using openCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "executionInfo": {
     "elapsed": 2348,
     "status": "ok",
     "timestamp": 1611338001050,
     "user": {
      "displayName": "Ly Nguyễn Thị Trúc",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTO4u7BVTP2x4w-IK207_mgn_G3S8sAQUR3sI=s64",
      "userId": "16613213413249005883"
     },
     "user_tz": -420
    },
    "id": "TyAKnHhUVNbD",
    "outputId": "0ad1355c-aa45-41bb-f0ae-af5b09a02206"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd2ff692780>"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = cv2.imread('circle.jpg')\n",
    "image_detected = image.copy()\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1.2, 100)\n",
    "if circles is not None:\n",
    "\tcircles = np.round(circles[0, :]).astype(\"int\")\n",
    "\tfor (x, y, r) in circles:\n",
    "\t\tcv2.circle(image_detected, (x, y), r, (0, 255, 0), 4)\n",
    "\t\tcv2.rectangle(image_detected, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)\n",
    "\n",
    "plt.axis('off')\n",
    "plt.imshow(np.hstack([image, image_detected]))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyMgaQCm7vViqgyWYnQo0PHl",
   "collapsed_sections": [],
   "name": "[6]_hough_transform.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}