PMCE_Algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "186fa43c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c1a0cf39",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "output_type": "stream",
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   ],
   "source": [
    "def sharpen(img):\n",
    "    kernel = np.array([[-1, -1, -1], [-1, 9.3, -1], [-1, -1, -1]])\n",
    "    img = cv2.filter2D(img, -1, kernel)\n",
    "    thresh = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    lut_in = np.array([0, 93, 140, 255])\n",
    "    lut_out = np.array([150, 50, 40, 40])\n",
    "    lut_8u = np.interp(np.arange(0, 256), lut_in, lut_out).astype(np.uint8)\n",
    "\n",
    "    thresh = cv2.LUT(thresh, lut_8u)\n",
    "    ret, thresh = cv2.threshold(thresh, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
    "    thresh = cv2.bitwise_not(thresh)\n",
    "    return thresh\n",
    "\n",
    "def enhance_image(image):\n",
    "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))\n",
    "    enhanced = clahe.apply(gray)\n",
    "    enhanced_image = cv2.cvtColor(enhanced, cv2.COLOR_GRAY2BGR)\n",
    "    return enhanced_image\n",
    "\n",
    "def find_blobs(image_path, contour_array, debug=False, blobs_are_black=False):\n",
    "    image = cv2.imread(image_path)\n",
    "    if blobs_are_black == False:\n",
    "        image = cv2.bitwise_not(image)\n",
    "    enhanced_image = enhance_image(image)    \n",
    "    kernel = np.ones((2, 2), np.uint8)\n",
    "    ed = cv2.erode(enhanced_image, kernel, iterations=1)\n",
    "    thresh = sharpen(ed)\n",
    "    \n",
    "    cnts, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1)\n",
    "    cnts = [cnts[i] for i in range(len(cnts)) if hierarchy[0][i][2] == -1]\n",
    "    for c in cnts:\n",
    "        area = cv2.contourArea(c)\n",
    "        cv2.drawContours(enhanced_image, [c], -1, (0,0,255), cv2.FILLED)\n",
    "        contour_array.append(c)\n",
    "        \n",
    "    if debug:\n",
    "        plt.imshow(enhanced_image, cmap=\"binary\")\n",
    "        plt.title(f'Detected Contours with Cell Count: {len(contour_array)}')\n",
    "        plt.show()\n",
    "\n",
    "frame_directory = r\"C:\\Users\\Jerry Zhou\\Video_frames\"\n",
    "frame_files = sorted(os.listdir(frame_directory))\n",
    "total = 0\n",
    "all_contours = []\n",
    "frame_height = 0\n",
    "frame_width = 0\n",
    "\n",
    "for index, frame_file in enumerate(frame_files):\n",
    "    frame_path = os.path.join(frame_directory, frame_file)\n",
    "    frame = cv2.imread(frame_path)\n",
    "    \n",
    "    # Extract the dimensions of the frame (only if it hasn't been done before)\n",
    "    if frame_height == 0 and frame_width == 0:\n",
    "        frame_height, frame_width, _ = frame.shape\n",
    "    \n",
    "    find_blobs(frame_path, all_contours, debug=False, blobs_are_black=False)\n",
    "    total += len(all_contours)\n",
    "    progress_percent = (index + 1) / len(frame_files) * 100\n",
    "    print(f\"Processing frames: {progress_percent:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c1b7f17",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blank_frame = np.full((frame_height, frame_width, 3), 255, dtype=np.uint8)\n",
    "for contour in all_contours:\n",
    "    cv2.drawContours(blank_frame, [contour], -1, (0, 0, 255), cv2.FILLED)\n",
    "\n",
    "output_directory = r\"C:\\Users\\Jerry Zhou\\Output_Image\"\n",
    "os.makedirs(output_directory, exist_ok=True)\n",
    "\n",
    "output_path = os.path.join(output_directory, \"blank_frame.png\")\n",
    "cv2.imwrite(output_path, blank_frame)\n",
    "\n",
    "plt.imshow(blank_frame)\n",
    "plt.title(\"Particulate Mapping\")\n",
    "plt.axis(\"on\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "543645c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def detect_contours(image_path, approx_method=cv2.CHAIN_APPROX_TC89_L1):\n",
    "    image = cv2.imread(image_path)\n",
    "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "    _, thresh = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY)\n",
    "    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, approx_method)\n",
    "\n",
    "    contour_frame = np.zeros_like(image)\n",
    "    cv2.drawContours(contour_frame, contours, -1, (0, 0, 255), 2)\n",
    "    return contour_frame, contours\n",
    "\n",
    "image_path = r\"C:\\Users\\Jerry Zhou\\Output_Image\\blank_frame.png\"\n",
    "result_frame, contour_list = detect_contours(image_path)\n",
    "result_frame_rgb = cv2.cvtColor(result_frame, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "second_image_path = r\"C:\\Users\\Jerry Zhou\\Video_frames\\frame_1.jpg\"\n",
    "second_image = cv2.imread(second_image_path)\n",
    "second_image_rgb = cv2.cvtColor(second_image, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "combined_image = cv2.addWeighted(second_image_rgb, 1, result_frame_rgb, 1, 0)\n",
    "\n",
    "plt.imshow(combined_image)\n",
    "plt.title(\"Boundary Detection Overlay\")\n",
    "plt.axis(\"on\")  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "53961bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_duplicate_points(contours):\n",
    "    cleaned_contours = []\n",
    "\n",
    "    for contour in contours:\n",
    "        unique_points_set = set()\n",
    "        cleaned_contour = []\n",
    "\n",
    "        for point in contour:\n",
    "            point_tuple = tuple(point[0])  # Convert point array to a tuple for set comparison\n",
    "            if point_tuple not in unique_points_set:\n",
    "                unique_points_set.add(point_tuple)\n",
    "                cleaned_contour.append(point)\n",
    "\n",
    "        cleaned_contours.append(cleaned_contour)\n",
    "\n",
    "    return cleaned_contours\n",
    "def extract_points_from_contours(contour_list):\n",
    "    points_list = []\n",
    "    for contour in contour_list:\n",
    "        for point in contour:\n",
    "            x, y = point[0]\n",
    "            points_list.append((x, y))\n",
    "    return points_list\n",
    "new_points = remove_duplicate_points(contour_list)\n",
    "points_to_use = extract_points_from_contours(new_points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6981d77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y-Value: 0, Points: [(0, 0), (716, 0), (719, 0), (731, 0), (809, 0), (825, 0), (828, 0), (830, 0), (832, 0), (1279, 0)]\n",
      "Y-Value: 2, Points: [(716, 2), (825, 2)]\n",
      "Y-Value: 3, Points: [(731, 3)]\n",
      "Y-Value: 4, Points: [(828, 4)]\n",
      "Y-Value: 5, Points: [(731, 5)]\n",
      "Y-Value: 6, Points: [(733, 6), (826, 6)]\n",
      "Y-Value: 7, Points: [(831, 7)]\n",
      "Y-Value: 8, Points: [(730, 8), (811, 8)]\n",
      "Y-Value: 9, Points: [(732, 9)]\n",
      "Y-Value: 11, Points: [(731, 11), (829, 11)]\n",
      "Y-Value: 15, Points: [(732, 15)]\n",
      "Y-Value: 16, Points: [(730, 16), (811, 16)]\n",
      "Y-Value: 17, Points: [(732, 17)]\n",
      "Y-Value: 20, Points: [(811, 20)]\n",
      "Y-Value: 23, Points: [(732, 23), (811, 23), (828, 23)]\n",
      "Y-Value: 24, Points: [(730, 24), (829, 24)]\n",
      "Y-Value: 26, Points: [(730, 26)]\n",
      "Y-Value: 27, Points: [(732, 27), (829, 27)]\n",
      "Y-Value: 29, Points: [(811, 29)]\n",
      "Y-Value: 30, Points: [(733, 30)]\n",
      "Y-Value: 33, Points: [(731, 33)]\n",
      "Y-Value: 35, Points: [(811, 35), (828, 35)]\n",
      "Y-Value: 36, Points: [(731, 36), (826, 36), (829, 36)]\n",
      "Y-Value: 39, Points: [(732, 39)]\n",
      "Y-Value: 40, Points: [(811, 40)]\n",
      "Y-Value: 41, Points: [(809, 41)]\n",
      "Y-Value: 42, Points: [(829, 42)]\n",
      "Y-Value: 43, Points: [(811, 43)]\n",
      "Y-Value: 45, Points: [(732, 45), (811, 45)]\n",
      "Y-Value: 47, Points: [(811, 47)]\n",
      "Y-Value: 49, Points: [(732, 49)]\n",
      "Y-Value: 51, Points: [(810, 51)]\n",
      "Y-Value: 54, Points: [(732, 54)]\n",
      "Y-Value: 56, Points: [(730, 56)]\n",
      "Y-Value: 57, Points: [(810, 57)]\n",
      "Y-Value: 58, Points: [(732, 58)]\n",
      "Y-Value: 60, Points: [(731, 60), (810, 60)]\n",
      "Y-Value: 63, Points: [(732, 63), (810, 63)]\n",
      "Y-Value: 64, Points: [(730, 64), (812, 64)]\n",
      "Y-Value: 65, Points: [(810, 65)]\n",
      "Y-Value: 67, Points: [(811, 67)]\n",
      "Y-Value: 69, Points: [(811, 69)]\n",
      "Y-Value: 72, Points: [(811, 72)]\n",
      "Y-Value: 73, Points: [(809, 73)]\n",
      "Y-Value: 75, Points: [(829, 75)]\n",
      "Y-Value: 79, Points: [(733, 79), (828, 79)]\n",
      "Y-Value: 82, Points: [(809, 82)]\n",
      "Y-Value: 85, Points: [(730, 85)]\n",
      "Y-Value: 87, Points: [(732, 87)]\n",
      "Y-Value: 92, Points: [(730, 92)]\n",
      "Y-Value: 94, Points: [(731, 94)]\n",
      "Y-Value: 95, Points: [(733, 95)]\n",
      "Y-Value: 96, Points: [(731, 96)]\n",
      "Y-Value: 100, Points: [(731, 100)]\n",
      "Y-Value: 101, Points: [(811, 101)]\n",
      "Y-Value: 103, Points: [(731, 103), (826, 103)]\n",
      "Y-Value: 109, Points: [(732, 109), (825, 109)]\n",
      "Y-Value: 115, Points: [(730, 115), (809, 115)]\n",
      "Y-Value: 118, Points: [(730, 118), (825, 118)]\n",
      "Y-Value: 119, Points: [(732, 119), (810, 119)]\n",
      "Y-Value: 120, Points: [(812, 120), (826, 120)]\n",
      "Y-Value: 121, Points: [(732, 121), (809, 121)]\n",
      "Y-Value: 123, Points: [(732, 123)]\n",
      "Y-Value: 124, Points: [(825, 124)]\n",
      "Y-Value: 127, Points: [(731, 127), (733, 127)]\n",
      "Y-Value: 129, Points: [(731, 129)]\n",
      "Y-Value: 131, Points: [(731, 131)]\n",
      "Y-Value: 134, Points: [(731, 134)]\n",
      "Y-Value: 135, Points: [(811, 135)]\n",
      "Y-Value: 136, Points: [(731, 136)]\n",
      "Y-Value: 137, Points: [(731, 137), (733, 137), (809, 137)]\n",
      "Y-Value: 141, Points: [(730, 141)]\n",
      "Y-Value: 144, Points: [(811, 144)]\n",
      "Y-Value: 145, Points: [(732, 145)]\n",
      "Y-Value: 147, Points: [(730, 147)]\n",
      "Y-Value: 148, Points: [(733, 148)]\n",
      "Y-Value: 152, Points: [(731, 152)]\n",
      "Y-Value: 154, Points: [(733, 154)]\n",
      "Y-Value: 155, Points: [(811, 155)]\n",
      "Y-Value: 157, Points: [(731, 157), (811, 157)]\n",
      "Y-Value: 159, Points: [(809, 159)]\n",
      "Y-Value: 160, Points: [(731, 160), (811, 160)]\n",
      "Y-Value: 161, Points: [(733, 161)]\n",
      "Y-Value: 165, Points: [(731, 165)]\n",
      "Y-Value: 167, Points: [(731, 167), (812, 167)]\n",
      "Y-Value: 169, Points: [(733, 169), (810, 169)]\n",
      "Y-Value: 171, Points: [(810, 171)]\n",
      "Y-Value: 172, Points: [(733, 172)]\n",
      "Y-Value: 179, Points: [(731, 179)]\n",
      "Y-Value: 181, Points: [(810, 181)]\n",
      "Y-Value: 183, Points: [(810, 183)]\n",
      "Y-Value: 184, Points: [(812, 184)]\n",
      "Y-Value: 185, Points: [(810, 185)]\n",
      "Y-Value: 186, Points: [(732, 186)]\n",
      "Y-Value: 187, Points: [(734, 187)]\n",
      "Y-Value: 189, Points: [(732, 189)]\n",
      "Y-Value: 190, Points: [(811, 190)]\n",
      "Y-Value: 192, Points: [(731, 192)]\n",
      "Y-Value: 193, Points: [(811, 193)]\n",
      "Y-Value: 194, Points: [(731, 194)]\n",
      "Y-Value: 196, Points: [(732, 196)]\n",
      "Y-Value: 198, Points: [(731, 198), (733, 198), (811, 198)]\n",
      "Y-Value: 204, Points: [(731, 204)]\n",
      "Y-Value: 205, Points: [(733, 205)]\n",
      "Y-Value: 206, Points: [(810, 206)]\n",
      "Y-Value: 207, Points: [(730, 207)]\n",
      "Y-Value: 208, Points: [(733, 208)]\n",
      "Y-Value: 211, Points: [(733, 211)]\n",
      "Y-Value: 212, Points: [(731, 212)]\n",
      "Y-Value: 215, Points: [(732, 215), (734, 215), (810, 215)]\n",
      "Y-Value: 216, Points: [(730, 216)]\n",
      "Y-Value: 218, Points: [(733, 218)]\n",
      "Y-Value: 220, Points: [(733, 220)]\n",
      "Y-Value: 224, Points: [(731, 224), (811, 224)]\n",
      "Y-Value: 225, Points: [(732, 225)]\n",
      "Y-Value: 226, Points: [(810, 226)]\n",
      "Y-Value: 227, Points: [(733, 227)]\n",
      "Y-Value: 232, Points: [(731, 232), (733, 232)]\n",
      "Y-Value: 233, Points: [(810, 233)]\n",
      "Y-Value: 235, Points: [(733, 235)]\n",
      "Y-Value: 236, Points: [(731, 236)]\n",
      "Y-Value: 237, Points: [(733, 237)]\n",
      "Y-Value: 238, Points: [(731, 238), (811, 238)]\n",
      "Y-Value: 239, Points: [(733, 239)]\n",
      "Y-Value: 240, Points: [(811, 240)]\n",
      "Y-Value: 241, Points: [(733, 241)]\n",
      "Y-Value: 242, Points: [(719, 242)]\n",
      "Y-Value: 243, Points: [(811, 243)]\n",
      "Y-Value: 247, Points: [(810, 247)]\n",
      "Y-Value: 249, Points: [(731, 249)]\n",
      "Y-Value: 250, Points: [(733, 250)]\n",
      "Y-Value: 251, Points: [(811, 251)]\n",
      "Y-Value: 252, Points: [(813, 252)]\n",
      "Y-Value: 253, Points: [(810, 253)]\n",
      "Y-Value: 257, Points: [(731, 257), (810, 257)]\n",
      "Y-Value: 260, Points: [(812, 260)]\n",
      "Y-Value: 263, Points: [(813, 263)]\n",
      "Y-Value: 265, Points: [(810, 265)]\n",
      "Y-Value: 266, Points: [(733, 266)]\n",
      "Y-Value: 267, Points: [(811, 267)]\n",
      "Y-Value: 268, Points: [(731, 268)]\n",
      "Y-Value: 269, Points: [(733, 269)]\n",
      "Y-Value: 270, Points: [(810, 270)]\n",
      "Y-Value: 271, Points: [(733, 271)]\n",
      "Y-Value: 272, Points: [(731, 272), (812, 272)]\n",
      "Y-Value: 274, Points: [(809, 274)]\n",
      "Y-Value: 275, Points: [(811, 275)]\n",
      "Y-Value: 276, Points: [(731, 276)]\n",
      "Y-Value: 277, Points: [(733, 277)]\n",
      "Y-Value: 279, Points: [(734, 279)]\n",
      "Y-Value: 280, Points: [(731, 280), (812, 280)]\n",
      "Y-Value: 281, Points: [(810, 281)]\n",
      "Y-Value: 285, Points: [(811, 285)]\n",
      "Y-Value: 287, Points: [(733, 287), (811, 287)]\n",
      "Y-Value: 288, Points: [(715, 288), (731, 288)]\n",
      "Y-Value: 289, Points: [(733, 289)]\n",
      "Y-Value: 290, Points: [(731, 290)]\n",
      "Y-Value: 294, Points: [(809, 294), (811, 294)]\n",
      "Y-Value: 296, Points: [(732, 296)]\n",
      "Y-Value: 297, Points: [(734, 297)]\n",
      "Y-Value: 299, Points: [(734, 299), (810, 299)]\n",
      "Y-Value: 300, Points: [(732, 300)]\n",
      "Y-Value: 302, Points: [(732, 302)]\n",
      "Y-Value: 303, Points: [(734, 303)]\n",
      "Y-Value: 304, Points: [(732, 304)]\n",
      "Y-Value: 305, Points: [(811, 305)]\n",
      "Y-Value: 308, Points: [(733, 308), (812, 308)]\n",
      "Y-Value: 309, Points: [(732, 309)]\n",
      "Y-Value: 310, Points: [(734, 310), (810, 310)]\n",
      "Y-Value: 312, Points: [(731, 312), (811, 312)]\n",
      "Y-Value: 315, Points: [(731, 315), (733, 315)]\n",
      "Y-Value: 318, Points: [(732, 318)]\n",
      "Y-Value: 319, Points: [(810, 319)]\n",
      "Y-Value: 320, Points: [(733, 320)]\n",
      "Y-Value: 321, Points: [(731, 321), (810, 321)]\n",
      "Y-Value: 322, Points: [(733, 322), (812, 322)]\n",
      "Y-Value: 324, Points: [(734, 324), (813, 324)]\n",
      "Y-Value: 325, Points: [(811, 325)]\n",
      "Y-Value: 326, Points: [(731, 326), (733, 326)]\n",
      "Y-Value: 327, Points: [(811, 327)]\n",
      "Y-Value: 328, Points: [(813, 328)]\n",
      "Y-Value: 331, Points: [(731, 331)]\n",
      "Y-Value: 332, Points: [(813, 332)]\n",
      "Y-Value: 333, Points: [(734, 333), (811, 333)]\n",
      "Y-Value: 334, Points: [(732, 334)]\n",
      "Y-Value: 335, Points: [(828, 335)]\n",
      "Y-Value: 336, Points: [(731, 336)]\n",
      "Y-Value: 338, Points: [(810, 338)]\n",
      "Y-Value: 339, Points: [(813, 339)]\n",
      "Y-Value: 340, Points: [(811, 340)]\n",
      "Y-Value: 341, Points: [(732, 341), (813, 341)]\n",
      "Y-Value: 343, Points: [(732, 343), (734, 343), (810, 343)]\n",
      "Y-Value: 344, Points: [(813, 344)]\n",
      "Y-Value: 345, Points: [(732, 345), (734, 345), (810, 345)]\n",
      "Y-Value: 346, Points: [(812, 346)]\n",
      "Y-Value: 348, Points: [(810, 348)]\n",
      "Y-Value: 349, Points: [(732, 349)]\n",
      "Y-Value: 350, Points: [(811, 350)]\n",
      "Y-Value: 352, Points: [(732, 352), (811, 352)]\n",
      "Y-Value: 354, Points: [(810, 354)]\n",
      "Y-Value: 355, Points: [(734, 355), (812, 355)]\n",
      "Y-Value: 356, Points: [(732, 356)]\n",
      "Y-Value: 357, Points: [(734, 357), (810, 357)]\n",
      "Y-Value: 358, Points: [(732, 358)]\n",
      "Y-Value: 359, Points: [(733, 359), (810, 359)]\n",
      "Y-Value: 361, Points: [(813, 361)]\n",
      "Y-Value: 362, Points: [(810, 362)]\n",
      "Y-Value: 364, Points: [(812, 364)]\n",
      "Y-Value: 365, Points: [(734, 365)]\n",
      "Y-Value: 367, Points: [(810, 367)]\n",
      "Y-Value: 368, Points: [(733, 368)]\n",
      "Y-Value: 369, Points: [(810, 369)]\n",
      "Y-Value: 370, Points: [(733, 370), (813, 370)]\n",
      "Y-Value: 373, Points: [(811, 373)]\n",
      "Y-Value: 375, Points: [(734, 375)]\n",
      "Y-Value: 376, Points: [(732, 376), (811, 376), (829, 376)]\n",
      "Y-Value: 380, Points: [(810, 380)]\n",
      "Y-Value: 384, Points: [(812, 384)]\n",
      "Y-Value: 387, Points: [(732, 387)]\n",
      "Y-Value: 389, Points: [(810, 389)]\n",
      "Y-Value: 390, Points: [(829, 390)]\n",
      "Y-Value: 391, Points: [(734, 391), (811, 391), (813, 391)]\n",
      "Y-Value: 393, Points: [(732, 393), (734, 393), (810, 393)]\n",
      "Y-Value: 394, Points: [(812, 394)]\n",
      "Y-Value: 395, Points: [(732, 395), (812, 395)]\n",
      "Y-Value: 397, Points: [(733, 397), (812, 397)]\n",
      "Y-Value: 399, Points: [(810, 399)]\n",
      "Y-Value: 400, Points: [(812, 400)]\n",
      "Y-Value: 401, Points: [(810, 401)]\n",
      "Y-Value: 402, Points: [(732, 402), (827, 402)]\n",
      "Y-Value: 406, Points: [(811, 406)]\n",
      "Y-Value: 408, Points: [(732, 408), (812, 408)]\n",
      "Y-Value: 409, Points: [(734, 409)]\n",
      "Y-Value: 410, Points: [(810, 410)]\n",
      "Y-Value: 413, Points: [(733, 413), (734, 413), (810, 413)]\n",
      "Y-Value: 414, Points: [(732, 414)]\n",
      "Y-Value: 417, Points: [(810, 417)]\n",
      "Y-Value: 419, Points: [(812, 419)]\n",
      "Y-Value: 420, Points: [(732, 420)]\n",
      "Y-Value: 423, Points: [(732, 423)]\n",
      "Y-Value: 426, Points: [(734, 426), (810, 426)]\n",
      "Y-Value: 427, Points: [(732, 427), (812, 427)]\n",
      "Y-Value: 429, Points: [(733, 429), (810, 429)]\n",
      "Y-Value: 431, Points: [(734, 431), (810, 431)]\n",
      "Y-Value: 432, Points: [(732, 432)]\n",
      "Y-Value: 434, Points: [(810, 434)]\n",
      "Y-Value: 436, Points: [(733, 436)]\n",
      "Y-Value: 438, Points: [(810, 438)]\n",
      "Y-Value: 439, Points: [(733, 439)]\n",
      "Y-Value: 440, Points: [(731, 440), (813, 440)]\n",
      "Y-Value: 441, Points: [(733, 441), (811, 441)]\n",
      "Y-Value: 443, Points: [(810, 443)]\n",
      "Y-Value: 444, Points: [(717, 444)]\n",
      "Y-Value: 445, Points: [(732, 445)]\n",
      "Y-Value: 447, Points: [(810, 447)]\n",
      "Y-Value: 448, Points: [(813, 448)]\n",
      "Y-Value: 449, Points: [(810, 449)]\n",
      "Y-Value: 451, Points: [(732, 451), (734, 451)]\n",
      "Y-Value: 453, Points: [(734, 453)]\n",
      "Y-Value: 455, Points: [(731, 455)]\n",
      "Y-Value: 457, Points: [(734, 457)]\n",
      "Y-Value: 459, Points: [(733, 459)]\n",
      "Y-Value: 461, Points: [(734, 461)]\n",
      "Y-Value: 463, Points: [(732, 463)]\n",
      "Y-Value: 465, Points: [(733, 465), (812, 465)]\n",
      "Y-Value: 468, Points: [(812, 468)]\n",
      "Y-Value: 470, Points: [(732, 470)]\n",
      "Y-Value: 473, Points: [(732, 473), (734, 473)]\n",
      "Y-Value: 474, Points: [(812, 474)]\n",
      "Y-Value: 479, Points: [(732, 479), (734, 479), (812, 479)]\n",
      "Y-Value: 481, Points: [(734, 481), (810, 481)]\n",
      "Y-Value: 482, Points: [(732, 482), (812, 482)]\n",
      "Y-Value: 483, Points: [(734, 483)]\n",
      "Y-Value: 485, Points: [(733, 485)]\n",
      "Y-Value: 487, Points: [(734, 487), (813, 487)]\n",
      "Y-Value: 488, Points: [(716, 488), (731, 488)]\n",
      "Y-Value: 489, Points: [(811, 489)]\n",
      "Y-Value: 490, Points: [(732, 490), (734, 490)]\n",
      "Y-Value: 491, Points: [(810, 491)]\n",
      "Y-Value: 492, Points: [(812, 492)]\n",
      "Y-Value: 495, Points: [(734, 495), (813, 495)]\n",
      "Y-Value: 497, Points: [(717, 497), (811, 497)]\n",
      "Y-Value: 498, Points: [(732, 498)]\n",
      "Y-Value: 499, Points: [(811, 499)]\n",
      "Y-Value: 500, Points: [(734, 500)]\n",
      "Y-Value: 501, Points: [(812, 501)]\n",
      "Y-Value: 503, Points: [(735, 503), (811, 503)]\n",
      "Y-Value: 504, Points: [(716, 504)]\n",
      "Y-Value: 505, Points: [(813, 505)]\n",
      "Y-Value: 506, Points: [(810, 506)]\n",
      "Y-Value: 508, Points: [(812, 508)]\n",
      "Y-Value: 510, Points: [(813, 510)]\n",
      "Y-Value: 511, Points: [(720, 511), (734, 511)]\n",
      "Y-Value: 512, Points: [(813, 512)]\n",
      "Y-Value: 513, Points: [(811, 513)]\n",
      "Y-Value: 514, Points: [(735, 514)]\n",
      "Y-Value: 515, Points: [(733, 515)]\n",
      "Y-Value: 517, Points: [(811, 517)]\n",
      "Y-Value: 518, Points: [(732, 518)]\n",
      "Y-Value: 519, Points: [(735, 519), (813, 519)]\n",
      "Y-Value: 521, Points: [(734, 521)]\n",
      "Y-Value: 522, Points: [(813, 522)]\n",
      "Y-Value: 523, Points: [(734, 523)]\n",
      "Y-Value: 524, Points: [(813, 524)]\n",
      "Y-Value: 526, Points: [(732, 526)]\n",
      "Y-Value: 527, Points: [(811, 527)]\n",
      "Y-Value: 528, Points: [(732, 528), (813, 528)]\n",
      "Y-Value: 532, Points: [(813, 532)]\n",
      "Y-Value: 535, Points: [(716, 535), (811, 535)]\n",
      "Y-Value: 536, Points: [(734, 536), (813, 536)]\n",
      "Y-Value: 537, Points: [(811, 537)]\n",
      "Y-Value: 538, Points: [(734, 538)]\n",
      "Y-Value: 539, Points: [(810, 539)]\n",
      "Y-Value: 541, Points: [(734, 541), (810, 541)]\n",
      "Y-Value: 543, Points: [(733, 543), (735, 543)]\n",
      "Y-Value: 544, Points: [(812, 544)]\n",
      "Y-Value: 545, Points: [(717, 545), (734, 545), (812, 545)]\n",
      "Y-Value: 546, Points: [(810, 546)]\n",
      "Y-Value: 548, Points: [(733, 548)]\n",
      "Y-Value: 550, Points: [(810, 550)]\n",
      "Y-Value: 551, Points: [(717, 551), (812, 551)]\n",
      "Y-Value: 552, Points: [(733, 552)]\n",
      "Y-Value: 553, Points: [(735, 553), (810, 553)]\n",
      "Y-Value: 554, Points: [(812, 554)]\n",
      "Y-Value: 555, Points: [(810, 555)]\n",
      "Y-Value: 557, Points: [(811, 557)]\n",
      "Y-Value: 558, Points: [(733, 558)]\n",
      "Y-Value: 561, Points: [(811, 561)]\n",
      "Y-Value: 562, Points: [(734, 562)]\n",
      "Y-Value: 565, Points: [(735, 565)]\n",
      "Y-Value: 566, Points: [(717, 566), (811, 566)]\n",
      "Y-Value: 568, Points: [(716, 568), (732, 568), (814, 568)]\n",
      "Y-Value: 570, Points: [(732, 570), (810, 570), (812, 570)]\n",
      "Y-Value: 572, Points: [(733, 572), (813, 572)]\n",
      "Y-Value: 573, Points: [(735, 573)]\n",
      "Y-Value: 575, Points: [(735, 575)]\n",
      "Y-Value: 576, Points: [(732, 576), (814, 576)]\n",
      "Y-Value: 577, Points: [(735, 577), (812, 577)]\n",
      "Y-Value: 579, Points: [(812, 579)]\n",
      "Y-Value: 580, Points: [(733, 580)]\n",
      "Y-Value: 582, Points: [(733, 582)]\n",
      "Y-Value: 583, Points: [(734, 583)]\n",
      "Y-Value: 585, Points: [(812, 585)]\n",
      "Y-Value: 587, Points: [(735, 587)]\n",
      "Y-Value: 588, Points: [(813, 588), (828, 588)]\n",
      "Y-Value: 591, Points: [(735, 591), (812, 591), (827, 591)]\n",
      "Y-Value: 593, Points: [(733, 593)]\n",
      "Y-Value: 594, Points: [(827, 594)]\n",
      "Y-Value: 596, Points: [(733, 596), (813, 596)]\n",
      "Y-Value: 601, Points: [(734, 601)]\n",
      "Y-Value: 602, Points: [(812, 602)]\n",
      "Y-Value: 603, Points: [(813, 603)]\n",
      "Y-Value: 606, Points: [(812, 606)]\n",
      "Y-Value: 608, Points: [(733, 608), (813, 608)]\n",
      "Y-Value: 611, Points: [(811, 611)]\n",
      "Y-Value: 612, Points: [(734, 612), (813, 612)]\n",
      "Y-Value: 614, Points: [(733, 614), (735, 614)]\n",
      "Y-Value: 615, Points: [(813, 615)]\n",
      "Y-Value: 616, Points: [(733, 616)]\n",
      "Y-Value: 617, Points: [(735, 617)]\n",
      "Y-Value: 618, Points: [(814, 618)]\n",
      "Y-Value: 619, Points: [(735, 619)]\n",
      "Y-Value: 622, Points: [(812, 622)]\n",
      "Y-Value: 623, Points: [(735, 623)]\n",
      "Y-Value: 624, Points: [(813, 624)]\n",
      "Y-Value: 627, Points: [(735, 627), (812, 627)]\n",
      "Y-Value: 628, Points: [(814, 628)]\n",
      "Y-Value: 629, Points: [(812, 629)]\n",
      "Y-Value: 632, Points: [(733, 632), (813, 632)]\n",
      "Y-Value: 633, Points: [(735, 633)]\n",
      "Y-Value: 635, Points: [(811, 635)]\n",
      "Y-Value: 636, Points: [(813, 636)]\n",
      "Y-Value: 640, Points: [(733, 640), (814, 640)]\n",
      "Y-Value: 641, Points: [(732, 641)]\n",
      "Y-Value: 643, Points: [(734, 643)]\n",
      "Y-Value: 644, Points: [(731, 644), (812, 644)]\n",
      "Y-Value: 645, Points: [(734, 645)]\n",
      "Y-Value: 646, Points: [(812, 646)]\n",
      "Y-Value: 648, Points: [(733, 648), (814, 648)]\n",
      "Y-Value: 649, Points: [(735, 649)]\n",
      "Y-Value: 651, Points: [(735, 651)]\n",
      "Y-Value: 654, Points: [(735, 654)]\n",
      "Y-Value: 655, Points: [(812, 655)]\n",
      "Y-Value: 656, Points: [(734, 656)]\n",
      "Y-Value: 658, Points: [(812, 658)]\n",
      "Y-Value: 659, Points: [(734, 659)]\n",
      "Y-Value: 662, Points: [(814, 662)]\n",
      "Y-Value: 664, Points: [(734, 664)]\n",
      "Y-Value: 665, Points: [(735, 665)]\n",
      "Y-Value: 666, Points: [(812, 666)]\n",
      "Y-Value: 669, Points: [(735, 669), (812, 669)]\n",
      "Y-Value: 671, Points: [(814, 671)]\n",
      "Y-Value: 672, Points: [(734, 672)]\n",
      "Y-Value: 674, Points: [(811, 674), (813, 674)]\n",
      "Y-Value: 677, Points: [(813, 677)]\n",
      "Y-Value: 678, Points: [(734, 678)]\n",
      "Y-Value: 679, Points: [(812, 679)]\n",
      "Y-Value: 681, Points: [(735, 681), (814, 681)]\n",
      "Y-Value: 683, Points: [(734, 683)]\n",
      "Y-Value: 685, Points: [(813, 685)]\n",
      "Y-Value: 687, Points: [(811, 687)]\n",
      "Y-Value: 688, Points: [(813, 688)]\n",
      "Y-Value: 689, Points: [(735, 689)]\n",
      "Y-Value: 692, Points: [(734, 692)]\n",
      "Y-Value: 695, Points: [(735, 695), (812, 695)]\n",
      "Y-Value: 697, Points: [(717, 697), (812, 697)]\n",
      "Y-Value: 698, Points: [(734, 698), (830, 698)]\n",
      "Y-Value: 699, Points: [(735, 699), (815, 699)]\n",
      "Y-Value: 700, Points: [(813, 700)]\n",
      "Y-Value: 701, Points: [(815, 701)]\n",
      "Y-Value: 702, Points: [(812, 702)]\n",
      "Y-Value: 704, Points: [(813, 704), (830, 704)]\n",
      "Y-Value: 705, Points: [(735, 705)]\n",
      "Y-Value: 707, Points: [(812, 707)]\n",
      "Y-Value: 709, Points: [(814, 709)]\n",
      "Y-Value: 710, Points: [(735, 710), (811, 710)]\n",
      "Y-Value: 711, Points: [(814, 711)]\n",
      "Y-Value: 712, Points: [(733, 712)]\n",
      "Y-Value: 713, Points: [(735, 713)]\n",
      "Y-Value: 716, Points: [(814, 716)]\n",
      "Y-Value: 717, Points: [(811, 717)]\n",
      "Y-Value: 718, Points: [(1016, 718)]\n",
      "Y-Value: 719, Points: [(0, 719), (719, 719), (721, 719), (735, 719), (813, 719), (827, 719), (829, 719), (1279, 719)]\n"
     ]
    }
   ],
   "source": [
    "unique_y_values = set()\n",
    "for x, y in points_to_use:\n",
    "    unique_y_values.add(y)\n",
    "\n",
    "unique_y_values_list = list(unique_y_values)\n",
    "y_value_points = {}\n",
    "\n",
    "for y_value in unique_y_values_list:\n",
    "    points_with_y_value = [point for point in points_to_use if point[1] == y_value]\n",
    "    sorted_points = sorted(points_with_y_value, key=lambda p: p[0])  # Sort based on x-coordinate\n",
    "    y_value_points[y_value] = sorted_points\n",
    "\n",
    "for y_value, points in y_value_points.items():\n",
    "    print(f\"Y-Value: {y_value}, Points: {points}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ca8cb82d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y-Value: 0, Points: ((0, 0), (716, 0))\n",
      "Y-Value: 2, Points: ((716, 2), (825, 2))\n",
      "Y-Value: 3, Points: None\n",
      "Y-Value: 4, Points: None\n",
      "Y-Value: 5, Points: None\n",
      "Y-Value: 6, Points: ((733, 6), (826, 6))\n",
      "Y-Value: 7, Points: None\n",
      "Y-Value: 8, Points: ((730, 8), (811, 8))\n",
      "Y-Value: 9, Points: None\n",
      "Y-Value: 11, Points: ((731, 11), (829, 11))\n",
      "Y-Value: 15, Points: None\n",
      "Y-Value: 16, Points: ((730, 16), (811, 16))\n",
      "Y-Value: 17, Points: None\n",
      "Y-Value: 20, Points: None\n",
      "Y-Value: 23, Points: ((732, 23), (811, 23))\n",
      "Y-Value: 24, Points: ((730, 24), (829, 24))\n",
      "Y-Value: 26, Points: None\n",
      "Y-Value: 27, Points: ((732, 27), (829, 27))\n",
      "Y-Value: 29, Points: None\n",
      "Y-Value: 30, Points: None\n",
      "Y-Value: 33, Points: None\n",
      "Y-Value: 35, Points: ((811, 35), (828, 35))\n",
      "Y-Value: 36, Points: ((731, 36), (826, 36))\n",
      "Y-Value: 39, Points: None\n",
      "Y-Value: 40, Points: None\n",
      "Y-Value: 41, Points: None\n",
      "Y-Value: 42, Points: None\n",
      "Y-Value: 43, Points: None\n",
      "Y-Value: 45, Points: ((732, 45), (811, 45))\n",
      "Y-Value: 47, Points: None\n",
      "Y-Value: 49, Points: None\n",
      "Y-Value: 51, Points: None\n",
      "Y-Value: 54, Points: None\n",
      "Y-Value: 56, Points: None\n",
      "Y-Value: 57, Points: None\n",
      "Y-Value: 58, Points: None\n",
      "Y-Value: 60, Points: ((731, 60), (810, 60))\n",
      "Y-Value: 63, Points: ((732, 63), (810, 63))\n",
      "Y-Value: 64, Points: ((730, 64), (812, 64))\n",
      "Y-Value: 65, Points: None\n",
      "Y-Value: 67, Points: None\n",
      "Y-Value: 69, Points: None\n",
      "Y-Value: 72, Points: None\n",
      "Y-Value: 73, Points: None\n",
      "Y-Value: 75, Points: None\n",
      "Y-Value: 79, Points: ((733, 79), (828, 79))\n",
      "Y-Value: 82, Points: None\n",
      "Y-Value: 85, Points: None\n",
      "Y-Value: 87, Points: None\n",
      "Y-Value: 92, Points: None\n",
      "Y-Value: 94, Points: None\n",
      "Y-Value: 95, Points: None\n",
      "Y-Value: 96, Points: None\n",
      "Y-Value: 100, Points: None\n",
      "Y-Value: 101, Points: None\n",
      "Y-Value: 103, Points: ((731, 103), (826, 103))\n",
      "Y-Value: 109, Points: ((732, 109), (825, 109))\n",
      "Y-Value: 115, Points: ((730, 115), (809, 115))\n",
      "Y-Value: 118, Points: ((730, 118), (825, 118))\n",
      "Y-Value: 119, Points: ((732, 119), (810, 119))\n",
      "Y-Value: 120, Points: ((812, 120), (826, 120))\n",
      "Y-Value: 121, Points: ((732, 121), (809, 121))\n",
      "Y-Value: 123, Points: None\n",
      "Y-Value: 124, Points: None\n",
      "Y-Value: 127, Points: ((731, 127), (733, 127))\n",
      "Y-Value: 129, Points: None\n",
      "Y-Value: 131, Points: None\n",
      "Y-Value: 134, Points: None\n",
      "Y-Value: 135, Points: None\n",
      "Y-Value: 136, Points: None\n",
      "Y-Value: 137, Points: ((733, 137), (809, 137))\n",
      "Y-Value: 141, Points: None\n",
      "Y-Value: 144, Points: None\n",
      "Y-Value: 145, Points: None\n",
      "Y-Value: 147, Points: None\n",
      "Y-Value: 148, Points: None\n",
      "Y-Value: 152, Points: None\n",
      "Y-Value: 154, Points: None\n",
      "Y-Value: 155, Points: None\n",
      "Y-Value: 157, Points: ((731, 157), (811, 157))\n",
      "Y-Value: 159, Points: None\n",
      "Y-Value: 160, Points: ((731, 160), (811, 160))\n",
      "Y-Value: 161, Points: None\n",
      "Y-Value: 165, Points: None\n",
      "Y-Value: 167, Points: ((731, 167), (812, 167))\n",
      "Y-Value: 169, Points: ((733, 169), (810, 169))\n",
      "Y-Value: 171, Points: None\n",
      "Y-Value: 172, Points: None\n",
      "Y-Value: 179, Points: None\n",
      "Y-Value: 181, Points: None\n",
      "Y-Value: 183, Points: None\n",
      "Y-Value: 184, Points: None\n",
      "Y-Value: 185, Points: None\n",
      "Y-Value: 186, Points: None\n",
      "Y-Value: 187, Points: None\n",
      "Y-Value: 189, Points: None\n",
      "Y-Value: 190, Points: None\n",
      "Y-Value: 192, Points: None\n",
      "Y-Value: 193, Points: None\n",
      "Y-Value: 194, Points: None\n",
      "Y-Value: 196, Points: None\n",
      "Y-Value: 198, Points: ((733, 198), (811, 198))\n",
      "Y-Value: 204, Points: None\n",
      "Y-Value: 205, Points: None\n",
      "Y-Value: 206, Points: None\n",
      "Y-Value: 207, Points: None\n",
      "Y-Value: 208, Points: None\n",
      "Y-Value: 211, Points: None\n",
      "Y-Value: 212, Points: None\n",
      "Y-Value: 215, Points: ((734, 215), (810, 215))\n",
      "Y-Value: 216, Points: None\n",
      "Y-Value: 218, Points: None\n",
      "Y-Value: 220, Points: None\n",
      "Y-Value: 224, Points: ((731, 224), (811, 224))\n",
      "Y-Value: 225, Points: None\n",
      "Y-Value: 226, Points: None\n",
      "Y-Value: 227, Points: None\n",
      "Y-Value: 232, Points: ((731, 232), (733, 232))\n",
      "Y-Value: 233, Points: None\n",
      "Y-Value: 235, Points: None\n",
      "Y-Value: 236, Points: None\n",
      "Y-Value: 237, Points: None\n",
      "Y-Value: 238, Points: ((731, 238), (811, 238))\n",
      "Y-Value: 239, Points: None\n",
      "Y-Value: 240, Points: None\n",
      "Y-Value: 241, Points: None\n",
      "Y-Value: 242, Points: None\n",
      "Y-Value: 243, Points: None\n",
      "Y-Value: 247, Points: None\n",
      "Y-Value: 249, Points: None\n",
      "Y-Value: 250, Points: None\n",
      "Y-Value: 251, Points: None\n",
      "Y-Value: 252, Points: None\n",
      "Y-Value: 253, Points: None\n",
      "Y-Value: 257, Points: ((731, 257), (810, 257))\n",
      "Y-Value: 260, Points: None\n",
      "Y-Value: 263, Points: None\n",
      "Y-Value: 265, Points: None\n",
      "Y-Value: 266, Points: None\n",
      "Y-Value: 267, Points: None\n",
      "Y-Value: 268, Points: None\n",
      "Y-Value: 269, Points: None\n",
      "Y-Value: 270, Points: None\n",
      "Y-Value: 271, Points: None\n",
      "Y-Value: 272, Points: ((731, 272), (812, 272))\n",
      "Y-Value: 274, Points: None\n",
      "Y-Value: 275, Points: None\n",
      "Y-Value: 276, Points: None\n",
      "Y-Value: 277, Points: None\n",
      "Y-Value: 279, Points: None\n",
      "Y-Value: 280, Points: ((731, 280), (812, 280))\n",
      "Y-Value: 281, Points: None\n",
      "Y-Value: 285, Points: None\n",
      "Y-Value: 287, Points: ((733, 287), (811, 287))\n",
      "Y-Value: 288, Points: ((715, 288), (731, 288))\n",
      "Y-Value: 289, Points: None\n",
      "Y-Value: 290, Points: None\n",
      "Y-Value: 294, Points: ((809, 294), (811, 294))\n",
      "Y-Value: 296, Points: None\n",
      "Y-Value: 297, Points: None\n",
      "Y-Value: 299, Points: ((734, 299), (810, 299))\n",
      "Y-Value: 300, Points: None\n",
      "Y-Value: 302, Points: None\n",
      "Y-Value: 303, Points: None\n",
      "Y-Value: 304, Points: None\n",
      "Y-Value: 305, Points: None\n",
      "Y-Value: 308, Points: ((733, 308), (812, 308))\n",
      "Y-Value: 309, Points: None\n",
      "Y-Value: 310, Points: ((734, 310), (810, 310))\n",
      "Y-Value: 312, Points: ((731, 312), (811, 312))\n",
      "Y-Value: 315, Points: ((731, 315), (733, 315))\n",
      "Y-Value: 318, Points: None\n",
      "Y-Value: 319, Points: None\n",
      "Y-Value: 320, Points: None\n",
      "Y-Value: 321, Points: ((731, 321), (810, 321))\n",
      "Y-Value: 322, Points: ((733, 322), (812, 322))\n",
      "Y-Value: 324, Points: ((734, 324), (813, 324))\n",
      "Y-Value: 325, Points: None\n",
      "Y-Value: 326, Points: ((731, 326), (733, 326))\n",
      "Y-Value: 327, Points: None\n",
      "Y-Value: 328, Points: None\n",
      "Y-Value: 331, Points: None\n",
      "Y-Value: 332, Points: None\n",
      "Y-Value: 333, Points: ((734, 333), (811, 333))\n",
      "Y-Value: 334, Points: None\n",
      "Y-Value: 335, Points: None\n",
      "Y-Value: 336, Points: None\n",
      "Y-Value: 338, Points: None\n",
      "Y-Value: 339, Points: None\n",
      "Y-Value: 340, Points: None\n",
      "Y-Value: 341, Points: ((732, 341), (813, 341))\n",
      "Y-Value: 343, Points: ((734, 343), (810, 343))\n",
      "Y-Value: 344, Points: None\n",
      "Y-Value: 345, Points: ((734, 345), (810, 345))\n",
      "Y-Value: 346, Points: None\n",
      "Y-Value: 348, Points: None\n",
      "Y-Value: 349, Points: None\n",
      "Y-Value: 350, Points: None\n",
      "Y-Value: 352, Points: ((732, 352), (811, 352))\n",
      "Y-Value: 354, Points: None\n",
      "Y-Value: 355, Points: ((734, 355), (812, 355))\n",
      "Y-Value: 356, Points: None\n",
      "Y-Value: 357, Points: ((734, 357), (810, 357))\n",
      "Y-Value: 358, Points: None\n",
      "Y-Value: 359, Points: ((733, 359), (810, 359))\n",
      "Y-Value: 361, Points: None\n",
      "Y-Value: 362, Points: None\n",
      "Y-Value: 364, Points: None\n",
      "Y-Value: 365, Points: None\n",
      "Y-Value: 367, Points: None\n",
      "Y-Value: 368, Points: None\n",
      "Y-Value: 369, Points: None\n",
      "Y-Value: 370, Points: ((733, 370), (813, 370))\n",
      "Y-Value: 373, Points: None\n",
      "Y-Value: 375, Points: None\n",
      "Y-Value: 376, Points: ((732, 376), (811, 376))\n",
      "Y-Value: 380, Points: None\n",
      "Y-Value: 384, Points: None\n",
      "Y-Value: 387, Points: None\n",
      "Y-Value: 389, Points: None\n",
      "Y-Value: 390, Points: None\n",
      "Y-Value: 391, Points: ((734, 391), (811, 391))\n",
      "Y-Value: 393, Points: ((734, 393), (810, 393))\n",
      "Y-Value: 394, Points: None\n",
      "Y-Value: 395, Points: ((732, 395), (812, 395))\n",
      "Y-Value: 397, Points: ((733, 397), (812, 397))\n",
      "Y-Value: 399, Points: None\n",
      "Y-Value: 400, Points: None\n",
      "Y-Value: 401, Points: None\n",
      "Y-Value: 402, Points: ((732, 402), (827, 402))\n",
      "Y-Value: 406, Points: None\n",
      "Y-Value: 408, Points: ((732, 408), (812, 408))\n",
      "Y-Value: 409, Points: None\n",
      "Y-Value: 410, Points: None\n",
      "Y-Value: 413, Points: ((734, 413), (810, 413))\n",
      "Y-Value: 414, Points: None\n",
      "Y-Value: 417, Points: None\n",
      "Y-Value: 419, Points: None\n",
      "Y-Value: 420, Points: None\n",
      "Y-Value: 423, Points: None\n",
      "Y-Value: 426, Points: ((734, 426), (810, 426))\n",
      "Y-Value: 427, Points: ((732, 427), (812, 427))\n",
      "Y-Value: 429, Points: ((733, 429), (810, 429))\n",
      "Y-Value: 431, Points: ((734, 431), (810, 431))\n",
      "Y-Value: 432, Points: None\n",
      "Y-Value: 434, Points: None\n",
      "Y-Value: 436, Points: None\n",
      "Y-Value: 438, Points: None\n",
      "Y-Value: 439, Points: None\n",
      "Y-Value: 440, Points: ((731, 440), (813, 440))\n",
      "Y-Value: 441, Points: ((733, 441), (811, 441))\n",
      "Y-Value: 443, Points: None\n",
      "Y-Value: 444, Points: None\n",
      "Y-Value: 445, Points: None\n",
      "Y-Value: 447, Points: None\n",
      "Y-Value: 448, Points: None\n",
      "Y-Value: 449, Points: None\n",
      "Y-Value: 451, Points: ((732, 451), (734, 451))\n",
      "Y-Value: 453, Points: None\n",
      "Y-Value: 455, Points: None\n",
      "Y-Value: 457, Points: None\n",
      "Y-Value: 459, Points: None\n",
      "Y-Value: 461, Points: None\n",
      "Y-Value: 463, Points: None\n",
      "Y-Value: 465, Points: ((733, 465), (812, 465))\n",
      "Y-Value: 468, Points: None\n",
      "Y-Value: 470, Points: None\n",
      "Y-Value: 473, Points: ((732, 473), (734, 473))\n",
      "Y-Value: 474, Points: None\n",
      "Y-Value: 479, Points: ((734, 479), (812, 479))\n",
      "Y-Value: 481, Points: ((734, 481), (810, 481))\n",
      "Y-Value: 482, Points: ((732, 482), (812, 482))\n",
      "Y-Value: 483, Points: None\n",
      "Y-Value: 485, Points: None\n",
      "Y-Value: 487, Points: ((734, 487), (813, 487))\n",
      "Y-Value: 488, Points: ((716, 488), (731, 488))\n",
      "Y-Value: 489, Points: None\n",
      "Y-Value: 490, Points: ((732, 490), (734, 490))\n",
      "Y-Value: 491, Points: None\n",
      "Y-Value: 492, Points: None\n",
      "Y-Value: 495, Points: ((734, 495), (813, 495))\n",
      "Y-Value: 497, Points: ((717, 497), (811, 497))\n",
      "Y-Value: 498, Points: None\n",
      "Y-Value: 499, Points: None\n",
      "Y-Value: 500, Points: None\n",
      "Y-Value: 501, Points: None\n",
      "Y-Value: 503, Points: ((735, 503), (811, 503))\n",
      "Y-Value: 504, Points: None\n",
      "Y-Value: 505, Points: None\n",
      "Y-Value: 506, Points: None\n",
      "Y-Value: 508, Points: None\n",
      "Y-Value: 510, Points: None\n",
      "Y-Value: 511, Points: ((720, 511), (734, 511))\n",
      "Y-Value: 512, Points: None\n",
      "Y-Value: 513, Points: None\n",
      "Y-Value: 514, Points: None\n",
      "Y-Value: 515, Points: None\n",
      "Y-Value: 517, Points: None\n",
      "Y-Value: 518, Points: None\n",
      "Y-Value: 519, Points: ((735, 519), (813, 519))\n",
      "Y-Value: 521, Points: None\n",
      "Y-Value: 522, Points: None\n",
      "Y-Value: 523, Points: None\n",
      "Y-Value: 524, Points: None\n",
      "Y-Value: 526, Points: None\n",
      "Y-Value: 527, Points: None\n",
      "Y-Value: 528, Points: ((732, 528), (813, 528))\n",
      "Y-Value: 532, Points: None\n",
      "Y-Value: 535, Points: ((716, 535), (811, 535))\n",
      "Y-Value: 536, Points: ((734, 536), (813, 536))\n",
      "Y-Value: 537, Points: None\n",
      "Y-Value: 538, Points: None\n",
      "Y-Value: 539, Points: None\n",
      "Y-Value: 541, Points: ((734, 541), (810, 541))\n",
      "Y-Value: 543, Points: ((733, 543), (735, 543))\n",
      "Y-Value: 544, Points: None\n",
      "Y-Value: 545, Points: ((734, 545), (812, 545))\n",
      "Y-Value: 546, Points: None\n",
      "Y-Value: 548, Points: None\n",
      "Y-Value: 550, Points: None\n",
      "Y-Value: 551, Points: ((717, 551), (812, 551))\n",
      "Y-Value: 552, Points: None\n",
      "Y-Value: 553, Points: ((735, 553), (810, 553))\n",
      "Y-Value: 554, Points: None\n",
      "Y-Value: 555, Points: None\n",
      "Y-Value: 557, Points: None\n",
      "Y-Value: 558, Points: None\n",
      "Y-Value: 561, Points: None\n",
      "Y-Value: 562, Points: None\n",
      "Y-Value: 565, Points: None\n",
      "Y-Value: 566, Points: ((717, 566), (811, 566))\n",
      "Y-Value: 568, Points: ((732, 568), (814, 568))\n",
      "Y-Value: 570, Points: ((732, 570), (810, 570))\n",
      "Y-Value: 572, Points: ((733, 572), (813, 572))\n",
      "Y-Value: 573, Points: None\n",
      "Y-Value: 575, Points: None\n",
      "Y-Value: 576, Points: ((732, 576), (814, 576))\n",
      "Y-Value: 577, Points: ((735, 577), (812, 577))\n",
      "Y-Value: 579, Points: None\n",
      "Y-Value: 580, Points: None\n",
      "Y-Value: 582, Points: None\n",
      "Y-Value: 583, Points: None\n",
      "Y-Value: 585, Points: None\n",
      "Y-Value: 587, Points: None\n",
      "Y-Value: 588, Points: ((813, 588), (828, 588))\n",
      "Y-Value: 591, Points: ((735, 591), (812, 591))\n",
      "Y-Value: 593, Points: None\n",
      "Y-Value: 594, Points: None\n",
      "Y-Value: 596, Points: ((733, 596), (813, 596))\n",
      "Y-Value: 601, Points: None\n",
      "Y-Value: 602, Points: None\n",
      "Y-Value: 603, Points: None\n",
      "Y-Value: 606, Points: None\n",
      "Y-Value: 608, Points: ((733, 608), (813, 608))\n",
      "Y-Value: 611, Points: None\n",
      "Y-Value: 612, Points: ((734, 612), (813, 612))\n",
      "Y-Value: 614, Points: ((733, 614), (735, 614))\n",
      "Y-Value: 615, Points: None\n",
      "Y-Value: 616, Points: None\n",
      "Y-Value: 617, Points: None\n",
      "Y-Value: 618, Points: None\n",
      "Y-Value: 619, Points: None\n",
      "Y-Value: 622, Points: None\n",
      "Y-Value: 623, Points: None\n",
      "Y-Value: 624, Points: None\n",
      "Y-Value: 627, Points: ((735, 627), (812, 627))\n",
      "Y-Value: 628, Points: None\n",
      "Y-Value: 629, Points: None\n",
      "Y-Value: 632, Points: ((733, 632), (813, 632))\n",
      "Y-Value: 633, Points: None\n",
      "Y-Value: 635, Points: None\n",
      "Y-Value: 636, Points: None\n",
      "Y-Value: 640, Points: ((733, 640), (814, 640))\n",
      "Y-Value: 641, Points: None\n",
      "Y-Value: 643, Points: None\n",
      "Y-Value: 644, Points: ((731, 644), (812, 644))\n",
      "Y-Value: 645, Points: None\n",
      "Y-Value: 646, Points: None\n",
      "Y-Value: 648, Points: ((733, 648), (814, 648))\n",
      "Y-Value: 649, Points: None\n",
      "Y-Value: 651, Points: None\n",
      "Y-Value: 654, Points: None\n",
      "Y-Value: 655, Points: None\n",
      "Y-Value: 656, Points: None\n",
      "Y-Value: 658, Points: None\n",
      "Y-Value: 659, Points: None\n",
      "Y-Value: 662, Points: None\n",
      "Y-Value: 664, Points: None\n",
      "Y-Value: 665, Points: None\n",
      "Y-Value: 666, Points: None\n",
      "Y-Value: 669, Points: ((735, 669), (812, 669))\n",
      "Y-Value: 671, Points: None\n",
      "Y-Value: 672, Points: None\n",
      "Y-Value: 674, Points: ((811, 674), (813, 674))\n",
      "Y-Value: 677, Points: None\n",
      "Y-Value: 678, Points: None\n",
      "Y-Value: 679, Points: None\n",
      "Y-Value: 681, Points: ((735, 681), (814, 681))\n",
      "Y-Value: 683, Points: None\n",
      "Y-Value: 685, Points: None\n",
      "Y-Value: 687, Points: None\n",
      "Y-Value: 688, Points: None\n",
      "Y-Value: 689, Points: None\n",
      "Y-Value: 692, Points: None\n",
      "Y-Value: 695, Points: ((735, 695), (812, 695))\n",
      "Y-Value: 697, Points: ((717, 697), (812, 697))\n",
      "Y-Value: 698, Points: ((734, 698), (830, 698))\n",
      "Y-Value: 699, Points: ((735, 699), (815, 699))\n",
      "Y-Value: 700, Points: None\n",
      "Y-Value: 701, Points: None\n",
      "Y-Value: 702, Points: None\n",
      "Y-Value: 704, Points: ((813, 704), (830, 704))\n",
      "Y-Value: 705, Points: None\n",
      "Y-Value: 707, Points: None\n",
      "Y-Value: 709, Points: None\n",
      "Y-Value: 710, Points: ((735, 710), (811, 710))\n",
      "Y-Value: 711, Points: None\n",
      "Y-Value: 712, Points: None\n",
      "Y-Value: 713, Points: None\n",
      "Y-Value: 716, Points: None\n",
      "Y-Value: 717, Points: None\n",
      "Y-Value: 718, Points: None\n",
      "Y-Value: 719, Points: ((0, 719), (719, 719))\n"
     ]
    }
   ],
   "source": [
    "y_value_points = {}\n",
    "for y_value in unique_y_values_list:\n",
    "    points_with_y_value = [point for point in points_to_use if point[1] == y_value]\n",
    "    sorted_points = sorted(points_with_y_value, key=lambda p: p[0])  # Sort based on x-coordinate\n",
    "    y_value_points[y_value] = sorted_points\n",
    "\n",
    "largest_difference_points_list = []\n",
    "\n",
    "for y_value, points in y_value_points.items():\n",
    "    largest_difference_points = None\n",
    "    largest_difference = 0\n",
    "\n",
    "    for i in range(len(points) - 1):\n",
    "        x1, y1 = points[i]\n",
    "        x2, y2 = points[i + 1]\n",
    "        difference = abs(x2 - x1)\n",
    "\n",
    "        if difference > largest_difference:\n",
    "            largest_difference = difference\n",
    "            largest_difference_points = (x1, y1), (x2, y2)\n",
    "\n",
    "    if largest_difference_points:\n",
    "        largest_difference_points_list.append(largest_difference_points)\n",
    "    print(f\"Y-Value: {y_value}, Points: {largest_difference_points}\")\n",
    "\n",
    "largest_difference_points_array = np.array(largest_difference_points_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77981363",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [[733,   6],\n",
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       "\n",
       "       [[730,   8],\n",
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       "\n",
       "       [[731,  11],\n",
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       "       [[811,  35],\n",
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       "       [[734, 324],\n",
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       "       [[731, 326],\n",
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       "       [[734, 333],\n",
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       "       [[732, 341],\n",
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       "       [[734, 343],\n",
       "        [810, 343]],\n",
       "\n",
       "       [[734, 345],\n",
       "        [810, 345]],\n",
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       "       [[734, 355],\n",
       "        [812, 355]],\n",
       "\n",
       "       [[734, 357],\n",
       "        [810, 357]],\n",
       "\n",
       "       [[733, 359],\n",
       "        [810, 359]],\n",
       "\n",
       "       [[733, 370],\n",
       "        [813, 370]],\n",
       "\n",
       "       [[732, 376],\n",
       "        [811, 376]],\n",
       "\n",
       "       [[734, 391],\n",
       "        [811, 391]],\n",
       "\n",
       "       [[734, 393],\n",
       "        [810, 393]],\n",
       "\n",
       "       [[732, 395],\n",
       "        [812, 395]],\n",
       "\n",
       "       [[733, 397],\n",
       "        [812, 397]],\n",
       "\n",
       "       [[732, 402],\n",
       "        [827, 402]],\n",
       "\n",
       "       [[732, 408],\n",
       "        [812, 408]],\n",
       "\n",
       "       [[734, 413],\n",
       "        [810, 413]],\n",
       "\n",
       "       [[734, 426],\n",
       "        [810, 426]],\n",
       "\n",
       "       [[732, 427],\n",
       "        [812, 427]],\n",
       "\n",
       "       [[733, 429],\n",
       "        [810, 429]],\n",
       "\n",
       "       [[734, 431],\n",
       "        [810, 431]],\n",
       "\n",
       "       [[731, 440],\n",
       "        [813, 440]],\n",
       "\n",
       "       [[733, 441],\n",
       "        [811, 441]],\n",
       "\n",
       "       [[732, 451],\n",
       "        [734, 451]],\n",
       "\n",
       "       [[733, 465],\n",
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       "\n",
       "       [[732, 473],\n",
       "        [734, 473]],\n",
       "\n",
       "       [[734, 479],\n",
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       "\n",
       "       [[734, 481],\n",
       "        [810, 481]],\n",
       "\n",
       "       [[732, 482],\n",
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       "\n",
       "       [[734, 487],\n",
       "        [813, 487]],\n",
       "\n",
       "       [[716, 488],\n",
       "        [731, 488]],\n",
       "\n",
       "       [[732, 490],\n",
       "        [734, 490]],\n",
       "\n",
       "       [[734, 495],\n",
       "        [813, 495]],\n",
       "\n",
       "       [[717, 497],\n",
       "        [811, 497]],\n",
       "\n",
       "       [[735, 503],\n",
       "        [811, 503]],\n",
       "\n",
       "       [[720, 511],\n",
       "        [734, 511]],\n",
       "\n",
       "       [[735, 519],\n",
       "        [813, 519]],\n",
       "\n",
       "       [[732, 528],\n",
       "        [813, 528]],\n",
       "\n",
       "       [[716, 535],\n",
       "        [811, 535]],\n",
       "\n",
       "       [[734, 536],\n",
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       "\n",
       "       [[734, 541],\n",
       "        [810, 541]],\n",
       "\n",
       "       [[733, 543],\n",
       "        [735, 543]],\n",
       "\n",
       "       [[734, 545],\n",
       "        [812, 545]],\n",
       "\n",
       "       [[717, 551],\n",
       "        [812, 551]],\n",
       "\n",
       "       [[735, 553],\n",
       "        [810, 553]],\n",
       "\n",
       "       [[717, 566],\n",
       "        [811, 566]],\n",
       "\n",
       "       [[732, 568],\n",
       "        [814, 568]],\n",
       "\n",
       "       [[732, 570],\n",
       "        [810, 570]],\n",
       "\n",
       "       [[733, 572],\n",
       "        [813, 572]],\n",
       "\n",
       "       [[732, 576],\n",
       "        [814, 576]],\n",
       "\n",
       "       [[735, 577],\n",
       "        [812, 577]],\n",
       "\n",
       "       [[813, 588],\n",
       "        [828, 588]],\n",
       "\n",
       "       [[735, 591],\n",
       "        [812, 591]],\n",
       "\n",
       "       [[733, 596],\n",
       "        [813, 596]],\n",
       "\n",
       "       [[733, 608],\n",
       "        [813, 608]],\n",
       "\n",
       "       [[734, 612],\n",
       "        [813, 612]],\n",
       "\n",
       "       [[733, 614],\n",
       "        [735, 614]],\n",
       "\n",
       "       [[735, 627],\n",
       "        [812, 627]],\n",
       "\n",
       "       [[733, 632],\n",
       "        [813, 632]],\n",
       "\n",
       "       [[733, 640],\n",
       "        [814, 640]],\n",
       "\n",
       "       [[731, 644],\n",
       "        [812, 644]],\n",
       "\n",
       "       [[733, 648],\n",
       "        [814, 648]],\n",
       "\n",
       "       [[735, 669],\n",
       "        [812, 669]],\n",
       "\n",
       "       [[811, 674],\n",
       "        [813, 674]],\n",
       "\n",
       "       [[735, 681],\n",
       "        [814, 681]],\n",
       "\n",
       "       [[735, 695],\n",
       "        [812, 695]],\n",
       "\n",
       "       [[717, 697],\n",
       "        [812, 697]],\n",
       "\n",
       "       [[734, 698],\n",
       "        [830, 698]],\n",
       "\n",
       "       [[735, 699],\n",
       "        [815, 699]],\n",
       "\n",
       "       [[813, 704],\n",
       "        [830, 704]],\n",
       "\n",
       "       [[735, 710],\n",
       "        [811, 710]],\n",
       "\n",
       "       [[  0, 719],\n",
       "        [719, 719]]], dtype=int32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flattened_list = largest_difference_points_array.flatten().tolist()\n",
    "largest_difference_points_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "18d36099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_values = flattened_list[::2]  # Get every second element starting from index 0\n",
    "y_values = flattened_list[1::2]  # Get every second element starting from index 1\n",
    "plt.scatter(x_values, y_values, marker='.', color='blue', s = 10)\n",
    "plt.xlabel(\"X-Coordinate\")\n",
    "plt.ylabel(\"Y-Coordinate\")\n",
    "plt.title(\"Scatter Plot of Tuples in flattened_list\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b6118bdf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data as (x-coordinate, its counts) tuples:\n",
      "[(734, 27), (733, 26), (812, 24), (811, 24), (732, 22), (810, 22), (731, 22), (813, 17), (735, 13), (730, 6), (814, 5), (717, 4), (826, 4), (716, 4), (809, 4), (825, 3), (829, 3), (828, 3), (830, 2), (0, 2), (720, 1), (719, 1), (815, 1), (827, 1), (715, 1)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_x, counts = np.unique(x_values, return_counts=True)\n",
    "sorted_indices = np.argsort(counts)[::-1]\n",
    "sorted_unique_x = unique_x[sorted_indices]\n",
    "sorted_counts = counts[sorted_indices]\n",
    "\n",
    "largest_difference_points_list = list(zip(sorted_unique_x, sorted_counts))\n",
    "print(\"Data as (x-coordinate, its counts) tuples:\")\n",
    "print(largest_difference_points_list)\n",
    "\n",
    "plt.bar(unique_x, counts)\n",
    "plt.xlabel('X-coordinate')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Number of Points vs X-coordinate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1614868a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorted tuples in ascending x-coordinate order:\n",
      "[(0, 2), (715, 1), (716, 4), (717, 4), (719, 1), (720, 1), (730, 6), (731, 22), (732, 22), (733, 26), (734, 27), (735, 13), (809, 4), (810, 22), (811, 24), (812, 24), (813, 17), (814, 5), (815, 1), (825, 3), (826, 4), (827, 1), (828, 3), (829, 3), (830, 2)]\n"
     ]
    }
   ],
   "source": [
    "sorted_largest_difference_points = sorted(largest_difference_points_list, key=lambda item: item[0])\n",
    "\n",
    "print(\"Sorted tuples in ascending x-coordinate order:\")\n",
    "print(sorted_largest_difference_points)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46b3a0fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[730, 731, 732, 733, 734, 735], [809, 810, 811, 812, 813, 814, 815]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_clusters(points_list, max_distance=2):\n",
    "    sorted_points = sorted(points_list)\n",
    "    clusters = []\n",
    "    current_cluster = []\n",
    "\n",
    "    for i in range(len(sorted_points)):\n",
    "        if not current_cluster:\n",
    "            current_cluster.append(sorted_points[i])\n",
    "        else:\n",
    "            prev_point = current_cluster[-1]\n",
    "            distance = abs(sorted_points[i] - prev_point)\n",
    "\n",
    "            if distance <= max_distance:\n",
    "                current_cluster.append(sorted_points[i])\n",
    "            else:\n",
    "                if len(current_cluster) >= 1:\n",
    "                    clusters.append(current_cluster)\n",
    "                current_cluster = [sorted_points[i]]\n",
    "\n",
    "    if len(current_cluster) >= 1:\n",
    "        clusters.append(current_cluster)\n",
    "\n",
    "    return clusters\n",
    "\n",
    "def find_clusters_with_counts(points_list, clusters):\n",
    "    cluster_counts = []\n",
    "    for cluster in clusters:\n",
    "        count = sum(point[1] for point in points_list if point[0] in cluster)\n",
    "        cluster_counts.append(count)\n",
    "\n",
    "    sorted_clusters_with_counts = sorted(zip(clusters, [sum(point[1] for point in points_list if point[0] in cluster) for cluster in clusters]), key=lambda x: x[1], reverse=True)\n",
    "    top_two_clusters = sorted_clusters_with_counts[:2]\n",
    "\n",
    "    return [cluster for cluster, _ in top_two_clusters]\n",
    " \n",
    "x_values = [point[0] for point in largest_difference_points_list]\n",
    "clusters = find_clusters(x_values, max_distance=1)\n",
    "\n",
    "top_two_clusters = find_clusters_with_counts(largest_difference_points_list, clusters)\n",
    "top_two_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6c5cb92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 1 x-coordinates: [730, 731, 732, 733, 734, 735]\n",
      "Cluster 2 x-coordinates: [809, 810, 811, 812, 813, 814, 815]\n"
     ]
    }
   ],
   "source": [
    "cluster1_x = []\n",
    "cluster2_x = []\n",
    "\n",
    "for cluster in top_two_clusters:\n",
    "    if cluster == top_two_clusters[0]:  \n",
    "        cluster1_x.extend(cluster)\n",
    "    elif cluster == top_two_clusters[1]:  \n",
    "        cluster2_x.extend(cluster)\n",
    "\n",
    "print(\"Cluster 1 x-coordinates:\", cluster1_x)\n",
    "print(\"Cluster 2 x-coordinates:\", cluster2_x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3245fe65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X-coordinate with the largest count in cluster1_x: 734\n",
      "Largest count: 27\n",
      "Data as (x-coordinate, its counts) tuples for cluster1_x:\n",
      "[(730, 6), (731, 22), (732, 22), (733, 26), (734, 27), (735, 13)]\n"
     ]
    }
   ],
   "source": [
    "matching_indices = np.where(np.isin(unique_x, cluster1_x))\n",
    "unique_x_cluster1 = unique_x[matching_indices]\n",
    "counts_cluster1 = counts[matching_indices]\n",
    "\n",
    "largest_difference_points_list_cluster1 = list(zip(unique_x_cluster1, counts_cluster1))\n",
    "x_with_largest_count = unique_x_cluster1[np.argmax(counts_cluster1)]\n",
    "largest_count = np.max(counts_cluster1)\n",
    "\n",
    "print(\"X-coordinate with the largest count in cluster1_x:\", x_with_largest_count)\n",
    "print(\"Largest count:\", largest_count)\n",
    "print(\"Data as (x-coordinate, its counts) tuples for cluster1_x:\")\n",
    "print(largest_difference_points_list_cluster1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2084556c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X-coordinate with the largest count in cluster2_x: 811\n",
      "Largest count in cluster2_x: 24\n",
      "Data as (x-coordinate, its counts) tuples for cluster2_x:\n",
      "[(809, 4), (810, 22), (811, 24), (812, 24), (813, 17), (814, 5), (815, 1)]\n"
     ]
    }
   ],
   "source": [
    "matching_indices_cluster2 = np.where(np.isin(unique_x, cluster2_x))\n",
    "unique_x_cluster2 = unique_x[matching_indices_cluster2]\n",
    "counts_cluster2 = counts[matching_indices_cluster2]\n",
    "\n",
    "largest_difference_points_list_cluster2 = list(zip(unique_x_cluster2, counts_cluster2))\n",
    "x_with_largest_count_cluster2 = unique_x_cluster2[np.argmax(counts_cluster2)]\n",
    "largest_count_cluster2 = np.max(counts_cluster2)\n",
    "\n",
    "print(\"X-coordinate with the largest count in cluster2_x:\", x_with_largest_count_cluster2)\n",
    "print(\"Largest count in cluster2_x:\", largest_count_cluster2)\n",
    "print(\"Data as (x-coordinate, its counts) tuples for cluster2_x:\")\n",
    "print(largest_difference_points_list_cluster2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e91d3df7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39\n",
      "40\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "midpoint = int((cluster1_x[0] + cluster2_x[0]) / 2)\n",
    "frame = cv2.imread(frame_path)\n",
    "\n",
    "frame_height, frame_width, _ = frame.shape\n",
    "midpoint_y = int(frame_height / 2)\n",
    "\n",
    "line_color_cluster1 = (0, 0, 255) \n",
    "line_color_cluster2 = (0, 255, 0)  \n",
    "line_color_midpoint = (255, 25, 0) \n",
    "line_thickness = 2  \n",
    "cv2.line(frame, (cluster1_x[0], 0), (cluster1_x[0], frame_height - 1), line_color_cluster1, line_thickness)\n",
    "cv2.line(frame, (cluster2_x[0], 0), (cluster2_x[0], frame_height - 1), line_color_cluster2, line_thickness)\n",
    "cv2.line(frame, (midpoint, 0), (midpoint, frame_height - 1), line_color_midpoint, line_thickness)\n",
    "\n",
    "frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "plt.imshow(frame_rgb)\n",
    "plt.title(\"Frame with Vertical Lines and Midpoint Line\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "output_directory = r\"C:\\Users\\Jerry Zhou\\50x50_dextran_EVID\"\n",
    "output_filename = \"Video24.png\"\n",
    "output_path = os.path.join(output_directory, output_filename)\n",
    "cv2.imwrite(output_path, frame_rgb)\n",
    "print(abs(midpoint - cluster1_x[0]))\n",
    "print(abs(midpoint - cluster2_x[0]))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c4984ddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of the Diameter (pixels): 74\n",
      "Length of the Radius (pixels): 37.0\n"
     ]
    }
   ],
   "source": [
    "def draw_horizontal_line(image_path):\n",
    "    image = cv2.imread(image_path)\n",
    "    image_copy = image.copy()\n",
    "    start_point = None\n",
    "    end_point = None\n",
    "\n",
    "    def draw_line(event, x, y, flags, param):\n",
    "        nonlocal start_point, end_point\n",
    "        \n",
    "        if event == cv2.EVENT_LBUTTONDOWN:\n",
    "            start_point = (x, y)\n",
    "\n",
    "        if event == cv2.EVENT_LBUTTONUP:\n",
    "            end_point = (x, y)\n",
    "            end_point = (end_point[0], start_point[1])\n",
    "            cv2.line(image_copy, start_point, end_point, (0, 0, 255), 2)\n",
    "            cv2.imshow(\"Draw Line\", image_copy)\n",
    "\n",
    "            length_pixels = abs(end_point[0] - start_point[0])\n",
    "            print(\"Length of the Diameter (pixels):\", length_pixels)\n",
    "            print(\"Length of the Radius (pixels):\", length_pixels/2)\n",
    "\n",
    "    cv2.namedWindow(\"Draw Line\")\n",
    "    cv2.setMouseCallback(\"Draw Line\", draw_line)\n",
    "    cv2.imshow(\"Draw Line\", image)\n",
    "    cv2.waitKewy(0)\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "image_path = r\"C:\\Users\\Jerry Zhou\\Video_frames\\frame_1.jpg\"\n",
    "draw_horizontal_line(image_path)"
   ]
  }
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