master_folder/algorithm_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
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     "end_time": "2024-08-11T08:36:51.482089Z",
     "start_time": "2024-08-11T08:36:45.725481Z"
    }
   },
   "source": [
    "!pip install opencv-python\n",
    "!pip install matplotlib\n",
    "!pip install numpy\n",
    "!pip install scipy\n",
    "!pip install pandas\n",
    "!pip install scikit-image"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencv-python in /Users/shubhamgupta/Desktop/Adobe-Gensolve/venv/lib/python3.12/site-packages (4.5.5.62)\r\n",
      "Requirement already satisfied: numpy>=1.21.2 in /Users/shubhamgupta/Desktop/Adobe-Gensolve/venv/lib/python3.12/site-packages (from opencv-python) (1.26.4)\r\n",
      "\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m24.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.2\u001B[0m\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
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      "\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m24.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.2\u001B[0m\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
      "Requirement already satisfied: numpy in /Users/shubhamgupta/Desktop/Adobe-Gensolve/venv/lib/python3.12/site-packages (1.26.4)\r\n",
      "\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m24.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.2\u001B[0m\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
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      "\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m24.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.2\u001B[0m\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
      "Requirement already satisfied: pandas in /Users/shubhamgupta/Desktop/Adobe-Gensolve/venv/lib/python3.12/site-packages (2.2.2)\r\n",
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      "\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m24.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.2\u001B[0m\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
      "Requirement already satisfied: scikit-image in /Users/shubhamgupta/Desktop/Adobe-Gensolve/venv/lib/python3.12/site-packages (0.24.0)\r\n",
      "Requirement already satisfied: numpy>=1.23 in /Users/shubhamgupta/Desktop/Adobe-Gensolve/venv/lib/python3.12/site-packages (from scikit-image) (1.26.4)\r\n",
      "Requirement already satisfied: scipy>=1.9 in /Users/shubhamgupta/Desktop/Adobe-Gensolve/venv/lib/python3.12/site-packages (from scikit-image) (1.14.0)\r\n",
      "Requirement already satisfied: networkx>=2.8 in /Users/shubhamgupta/Desktop/Adobe-Gensolve/venv/lib/python3.12/site-packages (from scikit-image) (3.3)\r\n",
      "Requirement already satisfied: pillow>=9.1 in /Users/shubhamgupta/Desktop/Adobe-Gensolve/venv/lib/python3.12/site-packages (from scikit-image) (9.5.0)\r\n",
      "Requirement already satisfied: imageio>=2.33 in /Users/shubhamgupta/Desktop/Adobe-Gensolve/venv/lib/python3.12/site-packages (from scikit-image) (2.34.2)\r\n",
      "Requirement already satisfied: tifffile>=2022.8.12 in /Users/shubhamgupta/Desktop/Adobe-Gensolve/venv/lib/python3.12/site-packages (from scikit-image) (2024.7.24)\r\n",
      "Requirement already satisfied: packaging>=21 in /Users/shubhamgupta/Desktop/Adobe-Gensolve/venv/lib/python3.12/site-packages (from scikit-image) (23.2)\r\n",
      "Requirement already satisfied: lazy-loader>=0.4 in /Users/shubhamgupta/Desktop/Adobe-Gensolve/venv/lib/python3.12/site-packages (from scikit-image) (0.4)\r\n",
      "\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m24.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.2\u001B[0m\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n"
     ]
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {
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     "end_time": "2024-08-11T08:36:51.485918Z",
     "start_time": "2024-08-11T08:36:51.483453Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict\n",
    "from skimage.feature import canny\n",
    "from scipy.ndimage import sobel"
   ],
   "id": "a823a3e4147a1c5a",
   "outputs": [],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.488378Z",
     "start_time": "2024-08-11T08:36:51.486581Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Constants for edge detection\n",
    "MIN_CANNY_THRESHOLD = 1\n",
    "MAX_CANNY_THRESHOLD = 400\n",
    "threshold = 0.5  # Example threshold"
   ],
   "id": "be297fad549f26bc",
   "outputs": [],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.491757Z",
     "start_time": "2024-08-11T08:36:51.489803Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def generate_shifts(max_shift):\n",
    "    shifts = []\n",
    "    for shift_y in range(-max_shift, max_shift + 1):\n",
    "        for shift_x in range(-max_shift, max_shift + 1):\n",
    "            shifts.append((shift_y, shift_x))\n",
    "    return shifts"
   ],
   "id": "c662da989cbac2a3",
   "outputs": [],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.494940Z",
     "start_time": "2024-08-11T08:36:51.492551Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def read_csv_(csv_path):\n",
    "    np_path_XYs = np.genfromtxt(csv_path, delimiter=',')\n",
    "    path_XYs = []\n",
    "    for i in np.unique(np_path_XYs[:, 0]):\n",
    "        npXYs = np_path_XYs[np_path_XYs[:, 0] == i][:, 1:]\n",
    "        XYs = []\n",
    "        for j in np.unique(npXYs[:, 0]):\n",
    "            XY = npXYs[npXYs[:, 0] == j][:, 1:]\n",
    "            XYs.append(XY)\n",
    "        path_XYs.append(XYs)\n",
    "    return path_XYs"
   ],
   "id": "d9ca87030d52c4b",
   "outputs": [],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.497415Z",
     "start_time": "2024-08-11T08:36:51.495638Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def gradient_orientation(image):\n",
    "    dx = sobel(image, axis=0, mode='constant')\n",
    "    dy = sobel(image, axis=1, mode='constant')\n",
    "    gradient = np.arctan2(dy, dx) * 180 / np.pi\n",
    "    return gradient"
   ],
   "id": "6c03ed5e0460da4b",
   "outputs": [],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.499723Z",
     "start_time": "2024-08-11T08:36:51.497945Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def build_r_table(image, origin):\n",
    "    edges = canny(image, low_threshold=MIN_CANNY_THRESHOLD,\n",
    "                  high_threshold=MAX_CANNY_THRESHOLD)\n",
    "    gradient = gradient_orientation(edges)\n",
    "\n",
    "    r_table = defaultdict(list)\n",
    "    for (i, j), value in np.ndenumerate(edges):\n",
    "        if value:\n",
    "            r_table[gradient[i, j]].append((origin[0] - i, origin[1] - j))\n",
    "    return r_table"
   ],
   "id": "534c99b3c8d15f25",
   "outputs": [],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.502594Z",
     "start_time": "2024-08-11T08:36:51.500356Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def accumulate_gradients(r_table, grayImage):\n",
    "    edges = canny(grayImage, low_threshold=MIN_CANNY_THRESHOLD,\n",
    "                  high_threshold=MAX_CANNY_THRESHOLD)\n",
    "    gradient = gradient_orientation(edges)\n",
    "\n",
    "    accumulator = np.zeros(grayImage.shape)\n",
    "    for (i, j), value in np.ndenumerate(edges):\n",
    "        if value:\n",
    "            for r in r_table[gradient[i, j]]:\n",
    "                accum_i, accum_j = i + r[0], j + r[1]\n",
    "                if 0 <= accum_i < accumulator.shape[0] and 0 <= accum_j < accumulator.shape[1]:\n",
    "                    accumulator[accum_i, accum_j] += 1\n",
    "    return accumulator"
   ],
   "id": "f1b5e0cd4e5ba230",
   "outputs": [],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.504880Z",
     "start_time": "2024-08-11T08:36:51.503200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def general_hough_closure(reference_image):\n",
    "    referencePoint = (\n",
    "        reference_image.shape[0] // 2, reference_image.shape[1] // 2)\n",
    "    r_table = build_r_table(reference_image, referencePoint)\n",
    "\n",
    "    def f(query_image):\n",
    "        return accumulate_gradients(r_table, query_image)\n",
    "\n",
    "    return f"
   ],
   "id": "18fd428c8d492df3",
   "outputs": [],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.508367Z",
     "start_time": "2024-08-11T08:36:51.506672Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def n_max(a, n):\n",
    "    indices = a.ravel().argsort()[-n:]\n",
    "    indices = (np.unravel_index(i, a.shape) for i in indices)\n",
    "    return [(a[i], i) for i in indices]"
   ],
   "id": "6284b3ca485bcdae",
   "outputs": [],
   "execution_count": 39
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.511715Z",
     "start_time": "2024-08-11T08:36:51.508868Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def overlay_reference_image(query_image, reference_image, position):\n",
    "    '''\n",
    "    Overlay the reference image on the query image at the specified position.\n",
    "    '''\n",
    "    ref_h, ref_w = reference_image.shape\n",
    "    q_h, q_w = query_image.shape\n",
    "\n",
    "    ref_h_half = ref_h // 2\n",
    "    ref_w_half = ref_w // 2\n",
    "\n",
    "    # Position where the top-left corner of the reference image should be placed\n",
    "    pos_y, pos_x = position\n",
    "    start_y = max(0, pos_y - ref_h_half)\n",
    "    start_x = max(0, pos_x - ref_w_half)\n",
    "    end_y = min(q_h, pos_y + ref_h_half)\n",
    "    end_x = min(q_w, pos_x + ref_w_half)\n",
    "\n",
    "    # Overlay reference image\n",
    "    ref_start_y = ref_h_half - (pos_y - start_y)\n",
    "    ref_start_x = ref_w_half - (pos_x - start_x)\n",
    "    ref_end_y = ref_start_y + (end_y - start_y)\n",
    "    ref_end_x = ref_start_x + (end_x - start_x)\n",
    "\n",
    "    query_image[start_y:end_y, start_x:end_x] = np.maximum(\n",
    "        query_image[start_y:end_y, start_x:end_x],\n",
    "        reference_image[ref_start_y:ref_end_y, ref_start_x:ref_end_x]\n",
    "    )\n",
    "\n",
    "    return query_image"
   ],
   "id": "27b3194949ff8fb5",
   "outputs": [],
   "execution_count": 40
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.515136Z",
     "start_time": "2024-08-11T08:36:51.512418Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def multi_scale_and_shift_detection(reference_images, query_image, scales, shifts):\n",
    "    best_accumulator = None\n",
    "    best_position = None\n",
    "    best_scale = 1\n",
    "    best_shift = (0, 0)\n",
    "    best_reference_image = None\n",
    "    max_accumulator_value = 0\n",
    "\n",
    "    for reference_image in reference_images:\n",
    "        for scale in scales:\n",
    "            # Resize the reference image\n",
    "            scaled_reference_image = cv2.resize(\n",
    "                reference_image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)\n",
    "\n",
    "            # Apply Hough transform\n",
    "            detect_s = general_hough_closure(scaled_reference_image)\n",
    "            accumulator = detect_s(query_image)\n",
    "\n",
    "            # Find the best shift for this scale\n",
    "            for shift_y, shift_x in shifts:\n",
    "                # Apply the shift\n",
    "                shifted_accumulator = np.roll(accumulator, shift=(shift_y, shift_x), axis=(0, 1))\n",
    "                max_value = shifted_accumulator.max()\n",
    "                print(shift_x, shift_y, scale, max_value)\n",
    "                \n",
    "                if max_value >= max_accumulator_value:\n",
    "                    max_accumulator_value = max_value\n",
    "                    best_accumulator = shifted_accumulator\n",
    "                    best_position = np.unravel_index(shifted_accumulator.argmax(), shifted_accumulator.shape)\n",
    "                    best_scale = scale\n",
    "                    best_shift = (shift_y, shift_x)\n",
    "                    best_reference_image = scaled_reference_image\n",
    "\n",
    "    return best_accumulator, best_position, best_scale, best_shift, best_reference_image\n"
   ],
   "id": "4981bd762570de62",
   "outputs": [],
   "execution_count": 41
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.518390Z",
     "start_time": "2024-08-11T08:36:51.515671Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def test_general_hough(reference_images, query_image):\n",
    "    scales = [1.0]  # Example scales\n",
    "    shifts = [(0, 0)]  # Example shifts\n",
    "    \n",
    "    best_accumulator, best_position, best_scale, best_shift, best_reference_image = multi_scale_and_shift_detection(\n",
    "        reference_images, query_image, scales, shifts)\n",
    "\n",
    "    plt.clf()\n",
    "    plt.gray()\n",
    "\n",
    "    fig = plt.figure()\n",
    "    fig.add_subplot(2, 2, 1)\n",
    "    plt.title('Best Reference Image')\n",
    "    plt.imshow(best_reference_image, cmap='gray')\n",
    "\n",
    "    fig.add_subplot(2, 2, 2)\n",
    "    plt.title('Query Image with Red Points')\n",
    "\n",
    "    # Convert query image to RGB\n",
    "    query_image_colored = cv2.cvtColor(query_image, cv2.COLOR_GRAY2BGR)\n",
    "\n",
    "    # Draw the detected position in red\n",
    "    if best_position:\n",
    "        i, j = best_position\n",
    "        # Red circle with radius 5\n",
    "        cv2.circle(query_image_colored, (j, i), 5, (0, 0, 255), -1)\n",
    "\n",
    "    plt.imshow(query_image_colored)\n",
    "\n",
    "    fig.add_subplot(2, 2, 3)\n",
    "    plt.title('Accumulator')\n",
    "    plt.imshow(best_accumulator, cmap='gray')\n",
    "\n",
    "    fig.add_subplot(2, 2, 4)\n",
    "    plt.title('Detection')\n",
    "\n",
    "    # Overlay the reference image at the detected location\n",
    "    scaled_reference_image = cv2.resize(\n",
    "        best_reference_image, None, fx=best_scale, fy=best_scale, interpolation=cv2.INTER_LINEAR)\n",
    "    \n",
    "    # Adjust position for the best shift\n",
    "    shifted_position = (best_position[0] + best_shift[0], best_position[1] + best_shift[1])\n",
    "    \n",
    "    overlayed_image = overlay_reference_image(\n",
    "        query_image.copy(), scaled_reference_image, shifted_position)\n",
    "    plt.imshow(overlayed_image, cmap='gray')\n",
    "\n",
    "    plt.show()\n",
    "    return"
   ],
   "id": "de4d9eacf6aa0c4b",
   "outputs": [],
   "execution_count": 42
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.521030Z",
     "start_time": "2024-08-11T08:36:51.519014Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def rotate_image(image, angle):\n",
    "    # Get the dimensions of the image\n",
    "    (h, w) = image.shape[:2]\n",
    "\n",
    "    # Calculate the center of the image\n",
    "    center = (w / 2, h / 2)\n",
    "\n",
    "    # Get the rotation matrix\n",
    "    M = cv2.getRotationMatrix2D(center, angle, 1.0)\n",
    "\n",
    "    # Perform the rotation\n",
    "    rotated_image = cv2.warpAffine(image, M, (w, h))\n",
    "\n",
    "    return rotated_image"
   ],
   "id": "aab4001e2c7366a5",
   "outputs": [],
   "execution_count": 43
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.522984Z",
     "start_time": "2024-08-11T08:36:51.521476Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def shapes_to_image(shapes, image_shape=(250, 250)):\n",
    "    image = np.zeros(image_shape, dtype=np.uint8)\n",
    "    for shape in shapes:\n",
    "        for points in shape:\n",
    "            for x, y in points:\n",
    "                # Set pixel value to 255 (white) for shape points\n",
    "                image[int(y), int(x)] = 255\n",
    "    return image"
   ],
   "id": "eda89f2a1b2bf7f0",
   "outputs": [],
   "execution_count": 44
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:51.525276Z",
     "start_time": "2024-08-11T08:36:51.523545Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def test1():\n",
    "    reference_shapes_list = [\n",
    "        read_csv_(\"utils/images/single_ellipse.csv\"),\n",
    "        read_csv_(\"utils/images/double_ellipse.csv\")\n",
    "    ]\n",
    "    query_shapes = read_csv_(\"utils/tc/occlusion2.csv\")\n",
    "\n",
    "    # Convert shapes to images\n",
    "    reference_images = [shapes_to_image(shapes) for shapes in reference_shapes_list]\n",
    "    query_image = shapes_to_image(query_shapes)\n",
    "\n",
    "    test_general_hough(reference_images, query_image)"
   ],
   "id": "da085af6b95aff07",
   "outputs": [],
   "execution_count": 45
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:37:33.528339Z",
     "start_time": "2024-08-11T08:37:33.525415Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def test2():\n",
    "    reference_shapes_list = [\n",
    "        read_csv_(\"utils/images/single_ellipse.csv\"),\n",
    "        read_csv_(\"utils/images/double_ellipse.csv\")\n",
    "    ]\n",
    "    query_shapes = read_csv_(\"utils/images/occlusion1.csv\")\n",
    "\n",
    "    # Convert shapes to images\n",
    "    reference_images = [shapes_to_image(shapes) for shapes in reference_shapes_list]\n",
    "    query_image = shapes_to_image(query_shapes)\n",
    "\n",
    "    test_general_hough(reference_images, query_image)"
   ],
   "id": "cd9368436920ab68",
   "outputs": [],
   "execution_count": 48
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:37:36.199697Z",
     "start_time": "2024-08-11T08:37:34.882104Z"
    }
   },
   "cell_type": "code",
   "source": [
    "if __name__ == '__main__':\n",
    "    test1()\n",
    "    test2()"
   ],
   "id": "208cc318f06d3109",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 1.0 98.0\n",
      "0 0 1.0 520.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 1.0 238.0\n",
      "0 0 1.0 131.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 49
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-11T08:36:52.604659Z",
     "start_time": "2024-08-11T08:36:52.602959Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "240d9b89d506252b",
   "outputs": [],
   "execution_count": 47
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}