image_augmentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: efficientnet_pytorch in c:\\users\\happy\\anaconda3\\lib\\site-packages (0.7.1)\n",
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      "Note: you may need to restart the kernel to use updated packages.\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install efficientnet_pytorch\n",
    "%pip install pandas\n",
    "%pip install tqdm\n",
    "%pip install sklearn\n",
    "%pip install torchvision\n",
    "%pip install seaborn\n",
    "%pip install opencv-python\n",
    "%pip install mediapipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['desktop.ini', 'testing_set', 'training_set']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check data directory\n",
    "import os\n",
    "os.listdir(\"../Face-Shape-EfficientNetB5/FaceShape Dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from PIL import Image\n",
    "from tqdm.auto import tqdm\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import numpy as np\n",
    "import cv2\n",
    "import torchvision.transforms as transforms\n",
    "from torch.autograd import Variable\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from efficientnet_pytorch import EfficientNet\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_gray_train_val(data_dir):\n",
    "    train, test = [], []\n",
    "    for dset in os.listdir(data_dir):\n",
    "        # check is directory\n",
    "        subdir = os.path.join(data_dir, dset)\n",
    "        if os.path.isdir(subdir):\n",
    "            for label in os.listdir(subdir):\n",
    "                imgdir = os.path.join(subdir, label)\n",
    "                if os.path.isdir(imgdir):\n",
    "                    for image_path in os.listdir(imgdir):\n",
    "                        if image_path.endswith(\".jpg\"):\n",
    "                            sample = {\n",
    "                                \"path\": os.path.join(subdir, label, image_path),\n",
    "                                \"label\": label\n",
    "                            }\n",
    "                            if dset == \"training_set\":\n",
    "                                train.append(sample)\n",
    "                            elif dset == \"testing_set\":\n",
    "                                test.append(sample)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing testing\n",
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      "successfully saved image!!heart (649).jpg\n",
      "[[ 87 136 240 240]]\n",
      "successfully saved image!!heart (650).jpg\n",
      "[[ 89  94 213 213]]\n",
      "successfully saved image!!heart (651).jpg\n",
      "[[ 34  41 246 246]]\n",
      "successfully saved image!!heart (657).jpg\n",
      "[[ 54  37 183 183]]\n",
      "successfully saved image!!heart (658).jpg\n",
      "[[121  65 213 213]]\n",
      "successfully saved image!!heart (659).jpg\n",
      "[[ 52  67 200 200]]\n",
      "successfully saved image!!heart (660).jpg\n",
      "[[186 185 205 205]]\n",
      "successfully saved image!!heart (661).jpg\n",
      "[[264 431 596 596]]\n",
      "successfully saved image!!heart (665).jpg\n",
      "[[100 116 315 315]]\n",
      "successfully saved image!!heart (673).jpg\n",
      "[[104 120 278 278]]\n",
      "successfully saved image!!heart (674).jpg\n",
      "[[104 111 214 214]]\n",
      "successfully saved image!!heart (675).jpg\n",
      "[[ 75  58 271 271]]\n",
      "successfully saved image!!heart (676).jpg\n",
      "[[ 75 114 230 230]]\n",
      "successfully saved image!!heart (683).jpg\n",
      "[[377 266 368 368]]\n",
      "successfully saved image!!heart (685).jpg\n",
      "[[126 104 288 288]]\n",
      "successfully saved image!!heart (686).jpg\n",
      "[[101 172 330 330]]\n",
      "successfully saved image!!heart (691).jpg\n",
      "[[177 133 276 276]\n",
      " [237 347 186 186]]\n",
      "successfully saved image!!heart (7).jpg\n",
      "[[ 68  80 209 209]]\n",
      "successfully saved image!!heart (702).jpg\n",
      "[[169 122 380 380]]\n",
      "successfully saved image!!heart (703).jpg\n",
      "[[102  79 171 171]]\n",
      "successfully saved image!!heart (712).jpg\n",
      "[[102 108 156 156]]\n",
      "successfully saved image!!heart (722).jpg\n",
      "[[ 69  26 170 170]]\n",
      "successfully saved image!!heart (724).jpg\n",
      "[[ 72 130 193 193]]\n",
      "successfully saved image!!heart (726).jpg\n",
      "[[ 64  58 127 127]]\n",
      "successfully saved image!!heart (743).jpg\n",
      "[[ 91  74 244 244]]\n",
      "successfully saved image!!heart (757).jpg\n",
      "[[132 202 402 402]]\n",
      "successfully saved image!!heart (766).jpg\n",
      "[[ 90  78 261 261]]\n",
      "successfully saved image!!heart (767).jpg\n",
      "[[ 77  88 217 217]]\n",
      "successfully saved image!!heart (768).jpg\n",
      "[[ 87 148 497 497]]\n",
      "successfully saved image!!heart (769).jpg\n",
      "[[ 55  74 206 206]]\n",
      "successfully saved image!!heart (770).jpg\n",
      "[[190 214 243 243]]\n",
      "successfully saved image!!heart (776).jpg\n",
      "[[ 53  64 144 144]]\n",
      "successfully saved image!!heart (777).jpg\n",
      "[[114 132 266 266]]\n",
      "successfully saved image!!heart (778).jpg\n",
      "[[ 48  66 176 176]]\n",
      "successfully saved image!!heart (779).jpg\n",
      "[[ 82  62 380 380]]\n",
      "successfully saved image!!heart (780).jpg\n",
      "[[ 147  450 1392 1392]]\n",
      "successfully saved image!!heart (786).jpg\n",
      "[[ 92  99 255 255]]\n",
      "successfully saved image!!heart (787).jpg\n",
      "[[343 367 783 783]]\n",
      "successfully saved image!!heart (788).jpg\n",
      "[[172 232 385 385]]\n",
      "successfully saved image!!heart (789).jpg\n",
      "[[ 31 107 258 258]]\n",
      "successfully saved image!!heart (790).jpg\n",
      "[[125  51 279 279]]\n",
      "successfully saved image!!heart (803).jpg\n",
      "[[ 91 133 422 422]]\n",
      "successfully saved image!!heart (843).jpg\n",
      "[[130 114 167 167]]\n",
      "successfully saved image!!heart (851).jpg\n",
      "[[ 13 105 200 200]]\n",
      "successfully saved image!!heart (854).jpg\n",
      "[[130 163 258 258]]\n",
      "successfully saved image!!heart (856).jpg\n",
      "[[ 65  96 164 164]]\n",
      "successfully saved image!!heart (864).jpg\n",
      "[[125 132 193 193]]\n",
      "successfully saved image!!heart (872).jpg\n",
      "[[123 165 233 233]]\n",
      "successfully saved image!!heart (873).jpg\n",
      "[[ 69 175 435 435]]\n",
      "successfully saved image!!heart (892).jpg\n",
      "[[ 58  80 198 198]]\n",
      "successfully saved image!!heart (901).jpg\n",
      "[[ 96 125 402 402]]\n",
      "successfully saved image!!heart (91).jpg\n",
      "[[ 97 135 281 281]]\n",
      "successfully saved image!!heart (910).jpg\n",
      "[[143 200 332 332]]\n",
      "successfully saved image!!heart (912).jpg\n",
      "[[139 170 282 282]]\n",
      "successfully saved image!!heart (913).jpg\n",
      "[[ 81  78 245 245]]\n",
      "successfully saved image!!heart (918).jpg\n",
      "[[ 24  53 179 179]]\n",
      "successfully saved image!!heart (919).jpg\n",
      "[[ 49  44 135 135]]\n",
      "successfully saved image!!heart (92).jpg\n",
      "[[129 103 154 154]]\n",
      "successfully saved image!!heart (920).jpg\n",
      "[[ 335  301  868  868]\n",
      " [1049 1842  128  128]]\n",
      "successfully saved image!!heart (923).jpg\n",
      "[[219 183 468 468]]\n",
      "successfully saved image!!heart (928).jpg\n",
      "[[130 219 332 332]]\n",
      "successfully saved image!!heart (929).jpg\n",
      "[[292 561 924 924]]\n",
      "successfully saved image!!heart (930).jpg\n",
      "[[122  62 172 172]]\n",
      "successfully saved image!!heart (94).jpg\n",
      "[[180 182 435 435]]\n",
      "successfully saved image!!heart (942).jpg\n",
      "[[149 239 814 814]]\n",
      "successfully saved image!!heart (971).jpg\n",
      "[[197 205 452 452]]\n",
      "successfully saved image!!heart (973).jpg\n",
      "[[117  91 199 199]]\n",
      "successfully saved image!!heart (975).jpg\n",
      "[[154 130 236 236]]\n",
      "successfully saved image!!heart (986).jpg\n",
      "[[ 49  47 158 158]]\n",
      "successfully saved image!!heart (995).jpg\n",
      "Done Heart\n",
      "Processing Oblong\n",
      "[[218 160 296 296]]\n",
      "successfully saved image!!oblong (10).jpg\n",
      "[[434 300 668 668]]\n",
      "successfully saved image!!oblong (106).jpg\n",
      "[[286 119 626 626]]\n",
      "successfully saved image!!oblong (107).jpg\n",
      "[[149 163 314 314]]\n",
      "successfully saved image!!oblong (11).jpg\n",
      "[[113 318 533 533]]\n",
      "successfully saved image!!oblong (13).jpg\n",
      "[[136 120 163 163]]\n",
      "successfully saved image!!oblong (139).jpg\n",
      "[[124 102 226 226]]\n",
      "successfully saved image!!oblong (14).jpg\n",
      "[[119 181 372 372]]\n",
      "successfully saved image!!oblong (143).jpg\n",
      "[[180 142 293 293]]\n",
      "successfully saved image!!oblong (144).jpg\n",
      "[[224  98 186 186]]\n",
      "successfully saved image!!oblong (146).jpg\n",
      "[[ 98  86 140 140]]\n",
      "successfully saved image!!oblong (147).jpg\n",
      "[[108 108 309 309]]\n",
      "successfully saved image!!oblong (148).jpg\n",
      "[[118 270 444 444]]\n",
      "successfully saved image!!oblong (149).jpg\n",
      "[[48 47 83 83]]\n",
      "successfully saved image!!oblong (15).jpg\n",
      "[[204 155 462 462]]\n",
      "successfully saved image!!oblong (150).jpg\n",
      "[[131 116 227 227]]\n",
      "successfully saved image!!oblong (151).jpg\n",
      "[[206 395 987 987]]\n",
      "successfully saved image!!oblong (152).jpg\n",
      "[[152 127 278 278]]\n",
      "successfully saved image!!oblong (154).jpg\n",
      "[[ 42 149 253 253]]\n",
      "successfully saved image!!oblong (155).jpg\n",
      "[[129 162 339 339]]\n",
      "successfully saved image!!oblong (156).jpg\n",
      "[[ 88  70 347 347]\n",
      " [ 50 495  78  78]]\n",
      "successfully saved image!!oblong (157).jpg\n",
      "[[103  85 267 267]]\n",
      "successfully saved image!!oblong (158).jpg\n",
      "SAVE ERROR!!oblong (179).jpg\n",
      "[[ 25  47 118 118]]\n",
      "successfully saved image!!oblong (181).jpg\n",
      "[[ 26 100 175 175]]\n",
      "successfully saved image!!oblong (191).jpg\n",
      "[[212 135 192 192]]\n",
      "successfully saved image!!oblong (192).jpg\n",
      "[[43 31 69 69]]\n",
      "successfully saved image!!oblong (193).jpg\n",
      "[[104 185 306 306]]\n",
      "successfully saved image!!oblong (197).jpg\n",
      "[[166 112 269 269]]\n",
      "successfully saved image!!oblong (205).jpg\n",
      "[[123 143 330 330]]\n",
      "successfully saved image!!oblong (21).jpg\n",
      "[[230 262 997 997]]\n",
      "successfully saved image!!oblong (216).jpg\n",
      "[[102 144 170 170]]\n",
      "successfully saved image!!oblong (217).jpg\n",
      "[[ 53  41 101 101]]\n",
      "successfully saved image!!oblong (22).jpg\n",
      "[[315 281 601 601]]\n",
      "successfully saved image!!oblong (242).jpg\n",
      "[[ 99 203 232 232]]\n",
      "successfully saved image!!oblong (256).jpg\n",
      "[[264 201 470 470]]\n",
      "successfully saved image!!oblong (26).jpg\n",
      "[[ 70  81 350 350]]\n",
      "successfully saved image!!oblong (272).jpg\n",
      "[[111 175 252 252]]\n",
      "successfully saved image!!oblong (278).jpg\n",
      "[[ 66 121 191 191]]\n",
      "successfully saved image!!oblong (280).jpg\n",
      "[[132 227 441 441]]\n",
      "successfully saved image!!oblong (307).jpg\n",
      "[[122 137 278 278]]\n",
      "successfully saved image!!oblong (313).jpg\n",
      "[[145 141 234 234]]\n",
      "successfully saved image!!oblong (314).jpg\n",
      "[[ 54  88 225 225]]\n",
      "successfully saved image!!oblong (315).jpg\n",
      "[[ 64 138 317 317]]\n",
      "successfully saved image!!oblong (316).jpg\n",
      "[[1681  414  777  777]]\n",
      "successfully saved image!!oblong (317).jpg\n",
      "[[146 190 646 646]]\n",
      "successfully saved image!!oblong (318).jpg\n",
      "[[ 85  89 164 164]]\n",
      "successfully saved image!!oblong (319).jpg\n",
      "[[177 217 522 522]]\n",
      "successfully saved image!!oblong (320).jpg\n",
      "[[149 121 321 321]]\n",
      "successfully saved image!!oblong (321).jpg\n",
      "[[212 167 350 350]]\n",
      "successfully saved image!!oblong (322).jpg\n",
      "[[ 66  89 171 171]]\n",
      "successfully saved image!!oblong (323).jpg\n",
      "[[159  83 350 350]]\n",
      "successfully saved image!!oblong (324).jpg\n",
      "[[231 252 427 427]]\n",
      "successfully saved image!!oblong (325).jpg\n",
      "[[126 112 349 349]]\n",
      "successfully saved image!!oblong (326).jpg\n",
      "[[ 25  52 114 114]]\n",
      "successfully saved image!!oblong (327).jpg\n",
      "[[ 91 105 179 179]]\n",
      "successfully saved image!!oblong (329).jpg\n",
      "[[ 59  98 176 176]]\n",
      "successfully saved image!!oblong (335).jpg\n",
      "[[260  73 174 174]]\n",
      "successfully saved image!!oblong (337).jpg\n",
      "[[ 73  95 174 174]]\n",
      "successfully saved image!!oblong (339).jpg\n",
      "[[171 147 281 281]]\n",
      "successfully saved image!!oblong (34).jpg\n",
      "[[ 26  51 226 226]]\n",
      "successfully saved image!!oblong (356).jpg\n",
      "[[184 517 924 924]]\n",
      "successfully saved image!!oblong (365).jpg\n",
      "[[105 100 295 295]]\n",
      "successfully saved image!!oblong (381).jpg\n",
      "[[ 86  48 125 125]]\n",
      "successfully saved image!!oblong (383).jpg\n",
      "[[ 75 134 306 306]]\n",
      "successfully saved image!!oblong (408).jpg\n",
      "[[574 420 984 984]]\n",
      "successfully saved image!!oblong (439).jpg\n",
      "[[ 38 122 348 348]]\n",
      "successfully saved image!!oblong (447).jpg\n",
      "[[134  89 191 191]]\n",
      "successfully saved image!!oblong (461).jpg\n",
      "[[136 154 279 279]]\n",
      "successfully saved image!!oblong (471).jpg\n",
      "[[116 152 176 176]]\n",
      "successfully saved image!!oblong (477).jpg\n",
      "[[131 104 175 175]]\n",
      "successfully saved image!!oblong (494).jpg\n",
      "[[195 162 330 330]]\n",
      "successfully saved image!!oblong (495).jpg\n",
      "[[104 137 196 196]]\n",
      "successfully saved image!!oblong (496).jpg\n",
      "[[148 132 157 157]]\n",
      "successfully saved image!!oblong (503).jpg\n",
      "[[ 41  82 185 185]]\n",
      "successfully saved image!!oblong (508).jpg\n",
      "[[105  58 113 113]]\n",
      "successfully saved image!!oblong (509).jpg\n",
      "[[121  92 168 168]]\n",
      "successfully saved image!!oblong (516).jpg\n",
      "[[ 80  86 186 186]]\n",
      "successfully saved image!!oblong (521).jpg\n",
      "[[ 92  79 208 208]]\n",
      "successfully saved image!!oblong (530).jpg\n",
      "[[30 20 47 47]]\n",
      "successfully saved image!!oblong (547).jpg\n",
      "[[116 128 185 185]]\n",
      "successfully saved image!!oblong (548).jpg\n",
      "[[124 107 233 233]]\n",
      "successfully saved image!!oblong (549).jpg\n",
      "[[104 166 323 323]]\n",
      "successfully saved image!!oblong (55).jpg\n",
      "[[ 69  79 166 166]]\n",
      "successfully saved image!!oblong (550).jpg\n",
      "[[ 48 105 169 169]]\n",
      "successfully saved image!!oblong (551).jpg\n",
      "[[ 23  94 194 194]]\n",
      "successfully saved image!!oblong (552).jpg\n",
      "[[ 93 141 188 188]]\n",
      "successfully saved image!!oblong (553).jpg\n",
      "[[195 112 374 374]]\n",
      "successfully saved image!!oblong (554).jpg\n",
      "[[113  68 184 184]]\n",
      "successfully saved image!!oblong (555).jpg\n",
      "[[ 51  71 130 130]]\n",
      "successfully saved image!!oblong (556).jpg\n",
      "[[ 77  57 211 211]]\n",
      "successfully saved image!!oblong (557).jpg\n",
      "[[144 126 196 196]]\n",
      "successfully saved image!!oblong (558).jpg\n",
      "[[27 27 60 60]]\n",
      "successfully saved image!!oblong (559).jpg\n",
      "[[ 69 127 293 293]]\n",
      "successfully saved image!!oblong (560).jpg\n",
      "[[109  79 195 195]\n",
      " [296 159  89  89]]\n",
      "successfully saved image!!oblong (563).jpg\n",
      "SAVE ERROR!!oblong (576).jpg\n",
      "[[283  48  86  86]\n",
      " [ 98 111 203 203]]\n",
      "successfully saved image!!oblong (583).jpg\n",
      "SAVE ERROR!!oblong (6).jpg\n",
      "[[ 91 114 202 202]]\n",
      "successfully saved image!!oblong (604).jpg\n",
      "[[179 195 347 347]]\n",
      "successfully saved image!!oblong (62).jpg\n",
      "[[ 517  561 1076 1076]]\n",
      "successfully saved image!!oblong (622).jpg\n",
      "[[283 145 426 426]]\n",
      "successfully saved image!!oblong (640).jpg\n",
      "[[237  81 390 390]]\n",
      "successfully saved image!!oblong (662).jpg\n",
      "[[102  97 161 161]]\n",
      "successfully saved image!!oblong (664).jpg\n",
      "[[ 86 100 178 178]]\n",
      "successfully saved image!!oblong (675).jpg\n",
      "[[ 68  45 151 151]]\n",
      "successfully saved image!!oblong (676).jpg\n",
      "[[ 82  70 128 128]]\n",
      "successfully saved image!!oblong (677).jpg\n",
      "[[362 371 768 768]]\n",
      "successfully saved image!!oblong (678).jpg\n",
      "[[123  97 238 238]]\n",
      "successfully saved image!!oblong (679).jpg\n",
      "[[ 71 107 249 249]]\n",
      "successfully saved image!!oblong (680).jpg\n",
      "[[ 40  47 109 109]]\n",
      "successfully saved image!!oblong (681).jpg\n",
      "[[ 78  51 276 276]]\n",
      "successfully saved image!!oblong (682).jpg\n",
      "[[160  80 209 209]]\n",
      "successfully saved image!!oblong (683).jpg\n",
      "[[ 69  82 243 243]]\n",
      "successfully saved image!!oblong (684).jpg\n",
      "[[ 244  395 1107 1107]]\n",
      "successfully saved image!!oblong (685).jpg\n",
      "[[393 345 759 759]]\n",
      "successfully saved image!!oblong (686).jpg\n",
      "[[433 424 804 804]]\n",
      "successfully saved image!!oblong (687).jpg\n",
      "[[ 69  65 152 152]]\n",
      "successfully saved image!!oblong (688).jpg\n",
      "[[ 92  58 166 166]]\n",
      "successfully saved image!!oblong (691).jpg\n",
      "[[ 67  90 264 264]]\n",
      "successfully saved image!!oblong (692).jpg\n",
      "[[ 93  49 145 145]]\n",
      "successfully saved image!!oblong (694).jpg\n",
      "[[155 280 225 225]]\n",
      "successfully saved image!!oblong (695).jpg\n",
      "[[ 74 183 275 275]]\n",
      "successfully saved image!!oblong (697).jpg\n",
      "[[296 341 821 821]]\n",
      "successfully saved image!!oblong (698).jpg\n",
      "[[ 67  64 220 220]]\n",
      "successfully saved image!!oblong (699).jpg\n",
      "[[183 185 339 339]]\n",
      "successfully saved image!!oblong (7).jpg\n",
      "SAVE ERROR!!oblong (700).jpg\n",
      "[[ 63  53 132 132]]\n",
      "successfully saved image!!oblong (701).jpg\n",
      "[[ 69 181 280 280]]\n",
      "successfully saved image!!oblong (702).jpg\n",
      "SAVE ERROR!!oblong (703).jpg\n",
      "[[164 102 161 161]]\n",
      "successfully saved image!!oblong (704).jpg\n",
      "[[ 42  75 128 128]]\n",
      "successfully saved image!!oblong (705).jpg\n",
      "[[130 198 337 337]]\n",
      "successfully saved image!!oblong (706).jpg\n",
      "[[336 393 738 738]]\n",
      "successfully saved image!!oblong (707).jpg\n",
      "[[ 13  40  58  58]\n",
      " [102 117 291 291]]\n",
      "successfully saved image!!oblong (708).jpg\n",
      "[[ 85  63 123 123]]\n",
      "successfully saved image!!oblong (710).jpg\n",
      "[[139 197 378 378]]\n",
      "successfully saved image!!oblong (734).jpg\n",
      "[[ 50  35  88  88]\n",
      " [ 57 146  76  76]]\n",
      "successfully saved image!!oblong (8).jpg\n",
      "[[268 311 521 521]]\n",
      "successfully saved image!!oblong (802).jpg\n",
      "[[245 152 354 354]]\n",
      "successfully saved image!!oblong (81).jpg\n",
      "[[227 167 358 358]]\n",
      "successfully saved image!!oblong (83).jpg\n",
      "[[245 172 359 359]]\n",
      "successfully saved image!!oblong (834).jpg\n",
      "[[134  82 315 315]]\n",
      "successfully saved image!!oblong (857).jpg\n",
      "[[202 134 445 445]]\n",
      "successfully saved image!!oblong (873).jpg\n",
      "[[117 140 342 342]]\n",
      "successfully saved image!!oblong (875).jpg\n",
      "[[120 197 383 383]]\n",
      "successfully saved image!!oblong (877).jpg\n",
      "[[165 179 343 343]]\n",
      "successfully saved image!!oblong (878).jpg\n",
      "[[118 158 428 428]]\n",
      "successfully saved image!!oblong (879).jpg\n",
      "[[166 427  89  89]\n",
      " [ 77 126 282 282]]\n",
      "successfully saved image!!oblong (881).jpg\n",
      "[[127 147 385 385]]\n",
      "successfully saved image!!oblong (882).jpg\n",
      "[[160 182 643 643]]\n",
      "successfully saved image!!oblong (884).jpg\n",
      "[[199 221 420 420]]\n",
      "successfully saved image!!oblong (885).jpg\n",
      "[[155 283 381 381]]\n",
      "successfully saved image!!oblong (886).jpg\n",
      "[[232 204 279 279]]\n",
      "successfully saved image!!oblong (887).jpg\n",
      "[[221 162 257 257]]\n",
      "successfully saved image!!oblong (888).jpg\n",
      "[[100 177 355 355]]\n",
      "successfully saved image!!oblong (889).jpg\n",
      "[[208 156 388 388]]\n",
      "successfully saved image!!oblong (890).jpg\n",
      "[[180 142 360 360]]\n",
      "successfully saved image!!oblong (891).jpg\n",
      "[[123 186 172 172]]\n",
      "successfully saved image!!oblong (892).jpg\n",
      "[[130 122 390 390]]\n",
      "successfully saved image!!oblong (893).jpg\n",
      "[[121 396 559 559]]\n",
      "successfully saved image!!oblong (894).jpg\n",
      "[[191 124 463 463]]\n",
      "successfully saved image!!oblong (895).jpg\n",
      "[[353 199 415 415]]\n",
      "successfully saved image!!oblong (896).jpg\n",
      "[[188 174 362 362]]\n",
      "successfully saved image!!oblong (897).jpg\n",
      "[[116 100 340 340]]\n",
      "successfully saved image!!oblong (898).jpg\n",
      "[[ 49 137 379 379]]\n",
      "successfully saved image!!oblong (899).jpg\n",
      "[[ 67  57 183 183]]\n",
      "successfully saved image!!oblong (9).jpg\n",
      "[[ 91 156 395 395]]\n",
      "successfully saved image!!oblong (900).jpg\n",
      "[[210 200 271 271]]\n",
      "successfully saved image!!oblong (901).jpg\n",
      "[[201 220 302 302]]\n",
      "successfully saved image!!oblong (905).jpg\n",
      "[[ 69 148 662 662]]\n",
      "successfully saved image!!oblong (906).jpg\n",
      "[[227 295 472 472]]\n",
      "successfully saved image!!oblong (907).jpg\n",
      "[[156 127 364 364]]\n",
      "successfully saved image!!oblong (908).jpg\n",
      "SAVE ERROR!!oblong (909).jpg\n",
      "[[188 215 346 346]]\n",
      "successfully saved image!!oblong (910).jpg\n",
      "[[376 298 415 415]]\n",
      "successfully saved image!!oblong (911).jpg\n",
      "[[124 152 371 371]]\n",
      "successfully saved image!!oblong (912).jpg\n",
      "[[378 226 502 502]\n",
      " [974 775  98  98]]\n",
      "successfully saved image!!oblong (913).jpg\n",
      "[[196 120 215 215]\n",
      " [159 376 252 252]]\n",
      "successfully saved image!!oblong (914).jpg\n",
      "[[130 149 363 363]]\n",
      "successfully saved image!!oblong (915).jpg\n",
      "[[183 239 441 441]]\n",
      "successfully saved image!!oblong (916).jpg\n",
      "[[168 294 498 498]]\n",
      "successfully saved image!!oblong (917).jpg\n",
      "[[120 171 428 428]]\n",
      "successfully saved image!!oblong (918).jpg\n",
      "[[184 234 393 393]]\n",
      "successfully saved image!!oblong (919).jpg\n",
      "[[104 126 383 383]]\n",
      "successfully saved image!!oblong (920).jpg\n",
      "[[100 176 505 505]]\n",
      "successfully saved image!!oblong (921).jpg\n",
      "[[166 247 519 519]]\n",
      "successfully saved image!!oblong (922).jpg\n",
      "[[168 225 338 338]]\n",
      "successfully saved image!!oblong (923).jpg\n",
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      "[[ 55 138 281 281]]\n",
      "successfully saved image!!oblong (997).jpg\n",
      "Done Oblong\n",
      "Processing Oval\n",
      "[[ 75  89 176 176]]\n",
      "successfully saved image!!oval (106).jpg\n",
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      "[[ 238  582  922  922]\n",
      " [1299 2044   55   55]\n",
      " [ 840 1980   60   60]\n",
      " [ 588 1887   75   75]]\n",
      "successfully saved image!!oval (196).jpg\n",
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      " [ 12   6  82  82]]\n",
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      " [753 156 166 166]]\n",
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      "SAVE ERROR!!oval (350).jpg\n",
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      "[[ 441  711 2020 2020]]\n",
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      " [1862  878  119  119]]\n",
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      "[[141 102 217 217]]\n",
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      "[[ 47  39 110 110]]\n",
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      "[[42 35 86 86]]\n",
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      "[[219 289 687 687]]\n",
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      "[[ 71  57 180 180]]\n",
      "successfully saved image!!oval (523).jpg\n",
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      "[[ 96 103 263 263]]\n",
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      "[[412 207 465 465]]\n",
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      "[[ 91 108 297 297]]\n",
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      "[[535 305 652 652]]\n",
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      "[[163 171 286 286]]\n",
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      "[[ 42  69 197 197]]\n",
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      "[[ 96  53 168 168]]\n",
      "successfully saved image!!oval (554).jpg\n",
      "[[128 137 330 330]]\n",
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      "[[187  90 310 310]]\n",
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      "[[141  73 191 191]]\n",
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      "[[ 75  87 212 212]]\n",
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      "[[ 70 100 322 322]]\n",
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      "[[106 104 221 221]]\n",
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      "[[142 128 225 225]]\n",
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      "[[185 159 375 375]]\n",
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      "[[105  24 411 411]]\n",
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      "[[226 218 387 387]]\n",
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      "[[179 176 484 484]]\n",
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      "SAVE ERROR!!oval (662).jpg\n",
      "[[ 99 125 312 312]]\n",
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      "[[ 62  55 168 168]]\n",
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      "SAVE ERROR!!oval (673).jpg\n",
      "[[ 32  68 220 220]]\n",
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      "[[107 156 285 285]]\n",
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      "[[171 153 264 264]]\n",
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      "[[520 520 897 897]]\n",
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      "SAVE ERROR!!oval (679).jpg\n",
      "[[ 87  74 157 157]]\n",
      "successfully saved image!!oval (680).jpg\n",
      "[[ 46  40 120 120]]\n",
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      "[[ 71  70 144 144]]\n",
      "successfully saved image!!oval (682).jpg\n",
      "[[44 60 89 89]]\n",
      "successfully saved image!!oval (683).jpg\n",
      "[[110 244 501 501]]\n",
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      "[[ 51  53 142 142]]\n",
      "successfully saved image!!oval (686).jpg\n",
      "[[ 56 183 202 202]]\n",
      "successfully saved image!!oval (687).jpg\n",
      "[[107  61 214 214]]\n",
      "successfully saved image!!oval (688).jpg\n",
      "[[ 61  95 295 295]\n",
      " [ 97 323 196 196]]\n",
      "successfully saved image!!oval (689).jpg\n",
      "SAVE ERROR!!oval (690).jpg\n",
      "[[ 94 165 234 234]]\n",
      "successfully saved image!!oval (691).jpg\n",
      "[[ 93  92 220 220]]\n",
      "successfully saved image!!oval (692).jpg\n",
      "[[65 61 93 93]]\n",
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      "[[ 77  72 226 226]]\n",
      "successfully saved image!!oval (694).jpg\n",
      "[[ 22  66 165 165]]\n",
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      "[[165  99 284 284]]\n",
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      "[[ 74  71 194 194]]\n",
      "successfully saved image!!oval (697).jpg\n",
      "SAVE ERROR!!oval (698).jpg\n",
      "[[ 62 116 216 216]]\n",
      "successfully saved image!!oval (699).jpg\n",
      "[[ 67 213 455 455]]\n",
      "successfully saved image!!oval (703).jpg\n",
      "[[ 71 101 177 177]]\n",
      "successfully saved image!!oval (704).jpg\n",
      "[[ 89  71 131 131]]\n",
      "successfully saved image!!oval (73).jpg\n",
      "[[ 55  30 107 107]]\n",
      "successfully saved image!!oval (77).jpg\n",
      "[[1580 2872   58   58]\n",
      " [ 684  847 1198 1198]]\n",
      "successfully saved image!!oval (770).jpg\n",
      "[[ 365  356 1101 1101]]\n",
      "successfully saved image!!oval (772).jpg\n",
      "[[ 502  490 1087 1087]]\n",
      "successfully saved image!!oval (793).jpg\n",
      "[[507 346 995 995]]\n",
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      "[[ 400  494 1109 1109]]\n",
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      "[[ 76  48 174 174]]\n",
      "successfully saved image!!oval (8).jpg\n",
      "[[ 69  76 230 230]]\n",
      "successfully saved image!!oval (804).jpg\n",
      "[[ 81  90 228 228]]\n",
      "successfully saved image!!oval (833).jpg\n",
      "[[ 61 112 421 421]]\n",
      "successfully saved image!!oval (836).jpg\n",
      "[[241 254 472 472]]\n",
      "successfully saved image!!oval (852).jpg\n",
      "[[ 21  43 182 182]]\n",
      "successfully saved image!!oval (882).jpg\n",
      "[[ 53  52 128 128]]\n",
      "successfully saved image!!oval (885).jpg\n",
      "[[ 19  28 166 166]]\n",
      "successfully saved image!!oval (886).jpg\n",
      "[[ 426  351 1115 1115]]\n",
      "successfully saved image!!oval (888).jpg\n",
      "SAVE ERROR!!oval (896).jpg\n",
      "[[ 722 1095   60   60]\n",
      " [ 320  184  332  332]]\n",
      "successfully saved image!!oval (904).jpg\n",
      "[[ 52  71 188 188]]\n",
      "successfully saved image!!oval (906).jpg\n",
      "[[128  98 326 326]]\n",
      "successfully saved image!!oval (912).jpg\n",
      "[[ 66  51 189 189]]\n",
      "successfully saved image!!oval (988).jpg\n",
      "[[177 235 443 443]]\n",
      "successfully saved image!!oval (994).jpg\n",
      "Done Oval\n",
      "Processing Round\n",
      "[[106 115 235 235]]\n",
      "successfully saved image!!round (1).jpg\n",
      "[[ 32  33 162 162]]\n",
      "successfully saved image!!round (1000).jpg\n",
      "[[ 43  66 220 220]]\n",
      "successfully saved image!!round (11).jpg\n",
      "[[ 50  72 205 205]]\n",
      "successfully saved image!!round (12).jpg\n",
      "[[ 16  50 117 117]]\n",
      "successfully saved image!!round (13).jpg\n",
      "[[364 291 664 664]]\n",
      "successfully saved image!!round (135).jpg\n",
      "[[ 59  57 136 136]]\n",
      "successfully saved image!!round (14).jpg\n",
      "[[ 24  67 153 153]]\n",
      "successfully saved image!!round (144).jpg\n",
      "[[186 187 538 538]]\n",
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      "[[222 248 685 685]]\n",
      "successfully saved image!!round (15).jpg\n",
      "[[145 132 440 440]]\n",
      "successfully saved image!!round (152).jpg\n",
      "[[ 63  79 211 211]]\n",
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      "successfully saved image!!round (154).jpg\n",
      "[[305 278 609 609]]\n",
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      "[[110 154 336 336]]\n",
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      "[[ 77 115 236 236]]\n",
      "successfully saved image!!round (159).jpg\n",
      "[[110  70 289 289]]\n",
      "successfully saved image!!round (160).jpg\n",
      "SAVE ERROR!!round (161).jpg\n",
      "[[ 83  59 282 282]]\n",
      "successfully saved image!!round (162).jpg\n",
      "[[ 95 106 415 415]]\n",
      "successfully saved image!!round (17).jpg\n",
      "[[ 58  66 230 230]]\n",
      "successfully saved image!!round (18).jpg\n",
      "[[ 79  54 261 261]]\n",
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      "[[160  95 237 237]]\n",
      "successfully saved image!!round (19).jpg\n",
      "[[ 55  58 172 172]]\n",
      "successfully saved image!!round (2).jpg\n",
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      "[[ 67  79 168 168]]\n",
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      "[[114 111 431 431]]\n",
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      "[[131 116 376 376]]\n",
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      "[[174 242 428 428]]\n",
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      "[[ 62  70 196 196]]\n",
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      "[[ 30  78 196 196]]\n",
      "successfully saved image!!round (271).jpg\n",
      "[[ 52  97 195 195]]\n",
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      "[[ 68  97 151 151]]\n",
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      "[[ 48  40 144 144]]\n",
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      "[[ 54  97 192 192]]\n",
      "successfully saved image!!round (275).jpg\n",
      "[[ 51  93 181 181]]\n",
      "successfully saved image!!round (276).jpg\n",
      "[[ 47  90 196 196]]\n",
      "successfully saved image!!round (279).jpg\n",
      "[[120 190 383 383]]\n",
      "successfully saved image!!round (281).jpg\n",
      "[[149 170 517 517]]\n",
      "successfully saved image!!round (295).jpg\n",
      "[[ 65   9 255 255]]\n",
      "successfully saved image!!round (299).jpg\n",
      "[[ 77  86 180 180]]\n",
      "successfully saved image!!round (3).jpg\n",
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      "[[ 86 159 364 364]]\n",
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      "[[197 171 383 383]]\n",
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      "successfully saved image!!round (4).jpg\n",
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      "successfully saved image!!round (422).jpg\n",
      "[[121 196 635 635]]\n",
      "successfully saved image!!round (423).jpg\n",
      "SAVE ERROR!!round (424).jpg\n",
      "[[ 52  62 212 212]]\n",
      "successfully saved image!!round (425).jpg\n",
      "[[137 138 274 274]]\n",
      "successfully saved image!!round (426).jpg\n",
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      "successfully saved image!!round (438).jpg\n",
      "[[ 35  44 196 196]]\n",
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      "SAVE ERROR!!round (464).jpg\n",
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      "successfully saved image!!round (467).jpg\n",
      "[[36 43 70 70]]\n",
      "successfully saved image!!round (475).jpg\n",
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      "[[137 143 348 348]]\n",
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      "[[151 141 305 305]]\n",
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      "SAVE ERROR!!round (563).jpg\n",
      "[[ 80  63 128 128]]\n",
      "successfully saved image!!round (567).jpg\n",
      "[[128 238 381 381]]\n",
      "successfully saved image!!round (569).jpg\n",
      "[[228  64 263 263]]\n",
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      "SAVE ERROR!!round (597).jpg\n",
      "[[96 50 96 96]]\n",
      "successfully saved image!!round (598).jpg\n",
      "[[218 224 446 446]]\n",
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      "[[ 27  49 200 200]]\n",
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      "SAVE ERROR!!round (600).jpg\n",
      "[[111  58 148 148]]\n",
      "successfully saved image!!round (602).jpg\n",
      "[[ 96  65 186 186]]\n",
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      "[[ 41  53 117 117]]\n",
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      "SAVE ERROR!!round (605).jpg\n",
      "[[ 38  52 141 141]]\n",
      "successfully saved image!!round (606).jpg\n",
      "[[ 97 119 397 397]]\n",
      "successfully saved image!!round (608).jpg\n",
      "SAVE ERROR!!round (609).jpg\n",
      "[[ 320  871 1402 1402]]\n",
      "successfully saved image!!round (61).jpg\n",
      "SAVE ERROR!!round (610).jpg\n",
      "SAVE ERROR!!round (611).jpg\n",
      "[[184 203 383 383]]\n",
      "successfully saved image!!round (612).jpg\n",
      "SAVE ERROR!!round (675).jpg\n",
      "[[583 324 900 900]]\n",
      "successfully saved image!!round (68).jpg\n",
      "[[230 331 981 981]]\n",
      "successfully saved image!!round (7).jpg\n",
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      "successfully saved image!!round (90).jpg\n",
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      "[[ 38  59 117 117]]\n",
      "successfully saved image!!round (94).jpg\n",
      "[[110 129 343 343]]\n",
      "successfully saved image!!round (944).jpg\n",
      "[[ 56  50 176 176]]\n",
      "successfully saved image!!round (945).jpg\n",
      "[[ 91 252 395 395]]\n",
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      "[[ 43 165 372 372]]\n",
      "successfully saved image!!round (948).jpg\n",
      "[[ 270  318  792  792]\n",
      " [1084 1480   53   53]]\n",
      "successfully saved image!!round (95).jpg\n",
      "[[ 51  64 197 197]]\n",
      "successfully saved image!!round (950).jpg\n",
      "[[188 126 909 909]]\n",
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      "[[175 186 453 453]]\n",
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      "[[123 189 472 472]]\n",
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      "[[ 380  498 1047 1047]]\n",
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      "[[ 91 180 275 275]]\n",
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      "[[121 283 397 397]]\n",
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      "[[ 85  83 186 186]]\n",
      "successfully saved image!!round (979).jpg\n",
      "[[ 271  286 1084 1084]]\n",
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      "[[130 177 308 308]]\n",
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      "[[ 316  355 1093 1093]]\n",
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      "[[132  89 167 167]]\n",
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      "[[120 143 361 361]]\n",
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      "[[ 80  27 167 167]]\n",
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      "successfully saved image!!round (987).jpg\n",
      "[[123  88 378 378]]\n",
      "successfully saved image!!round (988).jpg\n",
      "[[ 79  64 134 134]]\n",
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      "[[ 82 137 310 310]]\n",
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      "[[125 166 262 262]]\n",
      "successfully saved image!!round (991).jpg\n",
      "[[ 99  96 214 214]]\n",
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      "[[122 100 298 298]]\n",
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      "[[ 97 143 361 361]]\n",
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      "[[ 47  40 102 102]]\n",
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      "[[154 306 496 496]]\n",
      "successfully saved image!!round (996).jpg\n",
      "[[ 71  65 145 145]]\n",
      "successfully saved image!!round (999).jpg\n",
      "Done Round\n",
      "Processing Square\n",
      "[[ 45  46 169 169]]\n",
      "successfully saved image!!square (1).jpg\n",
      "[[ 83 133 387 387]]\n",
      "successfully saved image!!square (12).jpg\n",
      "[[ 80  23 236 236]]\n",
      "successfully saved image!!square (13).jpg\n",
      "[[176 124 271 271]]\n",
      "successfully saved image!!square (146).jpg\n",
      "SAVE ERROR!!square (147).jpg\n",
      "[[ 69  99 201 201]]\n",
      "successfully saved image!!square (148).jpg\n",
      "[[ 31  44 126 126]]\n",
      "successfully saved image!!square (149).jpg\n",
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      "[[151 242 548 548]]\n",
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      "[[201 281 786 786]]\n",
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      "[[ 523  384 1181 1181]]\n",
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      "successfully saved image!!square (160).jpg\n",
      "[[158 293 731 731]]\n",
      "successfully saved image!!square (161).jpg\n",
      "SAVE ERROR!!square (162).jpg\n",
      "SAVE ERROR!!square (17).jpg\n",
      "[[ 95  83 222 222]]\n",
      "successfully saved image!!square (2).jpg\n",
      "[[187 218 635 635]]\n",
      "successfully saved image!!square (201).jpg\n",
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      "[[ 92 124 298 298]]\n",
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      "[[156 170 308 308]]\n",
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      "[[304 286 672 672]]\n",
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      "[[365 422 776 776]]\n",
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      "[[ 30  94 180 180]]\n",
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      "[[507 320 782 782]]\n",
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      "[[ 343  506 1096 1096]]\n",
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      "[[ 50  65 213 213]]\n",
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      "[[ 65 134 386 386]]\n",
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      "[[ 90 127 275 275]]\n",
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      "[[ 28  40 117 117]]\n",
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      "[[155 256 780 780]]\n",
      "successfully saved image!!square (58).jpg\n",
      "[[ 69  97 216 216]]\n",
      "successfully saved image!!square (59).jpg\n",
      "[[243 355 709 709]]\n",
      "successfully saved image!!square (6).jpg\n",
      "[[114 101 302 302]]\n",
      "successfully saved image!!square (60).jpg\n",
      "[[347  96 473 473]]\n",
      "successfully saved image!!square (600).jpg\n",
      "[[ 330  392 1135 1135]]\n",
      "successfully saved image!!square (61).jpg\n",
      "[[ 87  70 174 174]]\n",
      "successfully saved image!!square (619).jpg\n",
      "[[ 253  445 1141 1141]]\n",
      "successfully saved image!!square (62).jpg\n",
      "[[133  65 220 220]]\n",
      "successfully saved image!!square (621).jpg\n",
      "[[ 23  38 115 115]]\n",
      "successfully saved image!!square (622).jpg\n",
      "[[119 188 379 379]]\n",
      "successfully saved image!!square (623).jpg\n",
      "[[128 104 286 286]]\n",
      "successfully saved image!!square (624).jpg\n",
      "[[ 76  88 233 233]]\n",
      "successfully saved image!!square (625).jpg\n",
      "[[ 35  55 134 134]]\n",
      "successfully saved image!!square (626).jpg\n",
      "[[ 45  43 100 100]]\n",
      "successfully saved image!!square (627).jpg\n",
      "[[203 116 331 331]]\n",
      "successfully saved image!!square (629).jpg\n",
      "[[ 88 132 292 292]]\n",
      "successfully saved image!!square (63).jpg\n",
      "[[ 96 113 285 285]]\n",
      "successfully saved image!!square (630).jpg\n",
      "[[125 138 284 284]]\n",
      "successfully saved image!!square (631).jpg\n",
      "[[ 31  49 126 126]]\n",
      "successfully saved image!!square (633).jpg\n",
      "[[124 112 183 183]]\n",
      "successfully saved image!!square (634).jpg\n",
      "[[153 106 301 301]]\n",
      "successfully saved image!!square (635).jpg\n",
      "[[116 144 278 278]]\n",
      "successfully saved image!!square (636).jpg\n",
      "[[128 168 379 379]]\n",
      "successfully saved image!!square (65).jpg\n",
      "[[ 98 121 356 356]]\n",
      "successfully saved image!!square (66).jpg\n",
      "[[ 68  61 221 221]]\n",
      "successfully saved image!!square (67).jpg\n",
      "[[128 112 245 245]]\n",
      "successfully saved image!!square (673).jpg\n",
      "[[ 70  88 169 169]]\n",
      "successfully saved image!!square (674).jpg\n",
      "[[114 134 244 244]]\n",
      "successfully saved image!!square (675).jpg\n",
      "[[ 46  14 117 117]]\n",
      "successfully saved image!!square (676).jpg\n",
      "[[ 48  54 109 109]]\n",
      "successfully saved image!!square (677).jpg\n",
      "[[ 80 103 247 247]]\n",
      "successfully saved image!!square (678).jpg\n",
      "[[174  86 233 233]]\n",
      "successfully saved image!!square (679).jpg\n",
      "[[152  79 241 241]]\n",
      "successfully saved image!!square (68).jpg\n",
      "[[234 121 196 196]]\n",
      "successfully saved image!!square (680).jpg\n",
      "[[ 48  66 116 116]]\n",
      "successfully saved image!!square (681).jpg\n",
      "[[ 49  34 126 126]]\n",
      "successfully saved image!!square (682).jpg\n",
      "SAVE ERROR!!square (683).jpg\n",
      "[[ 92 178 478 478]]\n",
      "successfully saved image!!square (684).jpg\n",
      "[[ 48 137 476 476]]\n",
      "successfully saved image!!square (685).jpg\n",
      "[[128 227 377 377]]\n",
      "successfully saved image!!square (686).jpg\n",
      "[[46 31 70 70]]\n",
      "successfully saved image!!square (687).jpg\n",
      "[[ 58 112 267 267]]\n",
      "successfully saved image!!square (688).jpg\n",
      "[[114 104 168 168]]\n",
      "successfully saved image!!square (689).jpg\n",
      "[[ 622  546  990  990]\n",
      " [1429 2340  111  111]]\n",
      "successfully saved image!!square (69).jpg\n",
      "[[108  94 206 206]]\n",
      "successfully saved image!!square (690).jpg\n",
      "[[ 313  507 1197 1197]]\n",
      "successfully saved image!!square (7).jpg\n",
      "[[394 143 376 376]]\n",
      "successfully saved image!!square (71).jpg\n",
      "[[ 68  70 215 215]]\n",
      "successfully saved image!!square (738).jpg\n",
      "[[235  65 213 213]]\n",
      "successfully saved image!!square (757).jpg\n",
      "[[ 404  586 1521 1521]]\n",
      "successfully saved image!!square (763).jpg\n",
      "[[141 185 352 352]]\n",
      "successfully saved image!!square (764).jpg\n",
      "[[126 202 533 533]]\n",
      "successfully saved image!!square (765).jpg\n",
      "[[228 305 558 558]]\n",
      "successfully saved image!!square (766).jpg\n",
      "[[105 125 263 263]]\n",
      "successfully saved image!!square (767).jpg\n",
      "[[160 215 395 395]]\n",
      "successfully saved image!!square (768).jpg\n",
      "[[ 69 103 281 281]]\n",
      "successfully saved image!!square (769).jpg\n",
      "[[ 39  66 181 181]]\n",
      "successfully saved image!!square (770).jpg\n",
      "[[ 77 170 289 289]]\n",
      "successfully saved image!!square (771).jpg\n",
      "[[ 62  76 195 195]]\n",
      "successfully saved image!!square (772).jpg\n",
      "[[ 65 154 328 328]]\n",
      "successfully saved image!!square (773).jpg\n",
      "[[ 40 672  61  61]\n",
      " [209  47 820 820]]\n",
      "successfully saved image!!square (774).jpg\n",
      "[[ 65 160 259 259]]\n",
      "successfully saved image!!square (788).jpg\n",
      "[[ 35  48 169 169]]\n",
      "successfully saved image!!square (8).jpg\n",
      "[[43 38 79 79]]\n",
      "successfully saved image!!square (824).jpg\n",
      "[[209 183 605 605]]\n",
      "successfully saved image!!square (825).jpg\n",
      "[[ 84  85 270 270]]\n",
      "successfully saved image!!square (826).jpg\n",
      "[[ 79 112 281 281]]\n",
      "successfully saved image!!square (827).jpg\n",
      "[[146 290 611 611]]\n",
      "successfully saved image!!square (828).jpg\n",
      "[[136  73 239 239]]\n",
      "successfully saved image!!square (875).jpg\n",
      "[[124  74 260 260]]\n",
      "successfully saved image!!square (899).jpg\n",
      "[[ 84 121 228 228]]\n",
      "successfully saved image!!square (9).jpg\n",
      "[[ 445  479 1062 1062]]\n",
      "successfully saved image!!square (901).jpg\n",
      "[[ 66  95 275 275]]\n",
      "successfully saved image!!square (902).jpg\n",
      "[[ 35  60 131 131]]\n",
      "successfully saved image!!square (921).jpg\n",
      "SAVE ERROR!!square (922).jpg\n",
      "[[108 145 374 374]]\n",
      "successfully saved image!!square (925).jpg\n",
      "[[294 341 611 611]]\n",
      "successfully saved image!!square (934).jpg\n",
      "[[256 296 945 945]]\n",
      "successfully saved image!!square (936).jpg\n",
      "[[150 258 726 726]]\n",
      "successfully saved image!!square (940).jpg\n",
      "[[ 248  278 1597 1597]]\n",
      "successfully saved image!!square (974).jpg\n",
      "[[ 29  76 293 293]]\n",
      "successfully saved image!!square (977).jpg\n",
      "Done Square\n",
      "Done testing\n",
      "Processing training\n",
      "Processing Heart\n",
      "[[ 49  55 165 165]]\n",
      "successfully saved image!!heart (1).jpg\n",
      "[[171 165 373 373]]\n",
      "successfully saved image!!heart (10).jpg\n",
      "[[ 47  89 240 240]]\n",
      "successfully saved image!!heart (100).jpg\n",
      "[[159 264 644 644]]\n",
      "successfully saved image!!heart (1000).jpg\n",
      "[[159 169 441 441]]\n",
      "successfully saved image!!heart (103).jpg\n",
      "[[ 72  76 221 221]]\n",
      "successfully saved image!!heart (104).jpg\n",
      "[[ 66  90 214 214]]\n",
      "successfully saved image!!heart (105).jpg\n",
      "[[ 61 134 336 336]]\n",
      "successfully saved image!!heart (106).jpg\n",
      "[[ 96  98 266 266]]\n",
      "successfully saved image!!heart (107).jpg\n",
      "[[ 72 108 235 235]]\n",
      "successfully saved image!!heart (108).jpg\n",
      "[[206 157 345 345]]\n",
      "successfully saved image!!heart (109).jpg\n",
      "[[ 51  72 163 163]\n",
      " [ 71 199 110 110]]\n",
      "successfully saved image!!heart (11).jpg\n",
      "[[281 468 662 662]]\n",
      "successfully saved image!!heart (110).jpg\n",
      "[[178 411 986 986]]\n",
      "successfully saved image!!heart (111).jpg\n",
      "[[ 43  56 141 141]]\n",
      "successfully saved image!!heart (112).jpg\n",
      "[[120 177 296 296]]\n",
      "successfully saved image!!heart (113).jpg\n",
      "[[ 86  79 304 304]]\n",
      "successfully saved image!!heart (114).jpg\n",
      "[[ 50  70 203 203]]\n",
      "successfully saved image!!heart (115).jpg\n",
      "[[492 386 853 853]]\n",
      "successfully saved image!!heart (116).jpg\n",
      "[[466 610 992 992]]\n",
      "successfully saved image!!heart (117).jpg\n",
      "[[ 75 244 341 341]\n",
      " [447 343  53  53]]\n",
      "successfully saved image!!heart (118).jpg\n",
      "[[328 244 612 612]]\n",
      "successfully saved image!!heart (119).jpg\n",
      "[[ 95  99 175 175]]\n",
      "successfully saved image!!heart (12).jpg\n",
      "SAVE ERROR!!heart (120).jpg\n",
      "SAVE ERROR!!heart (121).jpg\n",
      "[[124  92 318 318]]\n",
      "successfully saved image!!heart (122).jpg\n",
      "SAVE ERROR!!heart (124).jpg\n",
      "[[111 102 198 198]]\n",
      "successfully saved image!!heart (125).jpg\n",
      "SAVE ERROR!!heart (126).jpg\n",
      "[[105 144 290 290]]\n",
      "successfully saved image!!heart (127).jpg\n",
      "[[147 161 438 438]\n",
      " [242 516 255 255]]\n",
      "successfully saved image!!heart (128).jpg\n",
      "[[ 68  41 545 545]]\n",
      "successfully saved image!!heart (129).jpg\n",
      "[[ 61  39 127 127]]\n",
      "successfully saved image!!heart (130).jpg\n",
      "[[ 78  95 265 265]]\n",
      "successfully saved image!!heart (131).jpg\n",
      "[[ 49  36 266 266]]\n",
      "successfully saved image!!heart (132).jpg\n",
      "[[ 80  59 150 150]]\n",
      "successfully saved image!!heart (133).jpg\n",
      "[[ 225  327 1052 1052]]\n",
      "successfully saved image!!heart (134).jpg\n",
      "[[ 62  72 150 150]]\n",
      "successfully saved image!!heart (14).jpg\n",
      "[[ 74  49 168 168]]\n",
      "successfully saved image!!heart (142).jpg\n",
      "[[ 81  56 196 196]]\n",
      "successfully saved image!!heart (143).jpg\n",
      "[[134  79 237 237]]\n",
      "successfully saved image!!heart (144).jpg\n",
      "[[ 97  94 255 255]]\n",
      "successfully saved image!!heart (145).jpg\n",
      "[[100  96 219 219]]\n",
      "successfully saved image!!heart (146).jpg\n",
      "[[320 152 480 480]]\n",
      "successfully saved image!!heart (147).jpg\n",
      "[[107 124 319 319]]\n",
      "successfully saved image!!heart (148).jpg\n",
      "[[ 75  53 153 153]]\n",
      "successfully saved image!!heart (149).jpg\n",
      "[[101  91 148 148]]\n",
      "successfully saved image!!heart (15).jpg\n",
      "[[ 97  70 244 244]]\n",
      "successfully saved image!!heart (150).jpg\n",
      "[[ 91  43 165 165]]\n",
      "successfully saved image!!heart (151).jpg\n",
      "[[116  97 265 265]]\n",
      "successfully saved image!!heart (152).jpg\n",
      "[[165 186 442 442]]\n",
      "successfully saved image!!heart (153).jpg\n",
      "[[ 52  81 262 262]]\n",
      "successfully saved image!!heart (154).jpg\n",
      "[[133 119 429 429]]\n",
      "successfully saved image!!heart (155).jpg\n",
      "[[123 134 465 465]]\n",
      "successfully saved image!!heart (157).jpg\n",
      "[[155 165 333 333]]\n",
      "successfully saved image!!heart (158).jpg\n",
      "[[ 58  52 177 177]]\n",
      "successfully saved image!!heart (159).jpg\n",
      "[[127 231 552 552]]\n",
      "successfully saved image!!heart (16).jpg\n",
      "[[106 158 193 193]]\n",
      "successfully saved image!!heart (160).jpg\n",
      "[[152 301 378 378]]\n",
      "successfully saved image!!heart (162).jpg\n",
      "[[ 78  85 158 158]]\n",
      "successfully saved image!!heart (164).jpg\n",
      "[[ 55  50 136 136]]\n",
      "successfully saved image!!heart (165).jpg\n",
      "[[510 541 984 984]]\n",
      "successfully saved image!!heart (166).jpg\n",
      "[[ 27  60 128 128]]\n",
      "successfully saved image!!heart (167).jpg\n",
      "[[102 149 334 334]]\n",
      "successfully saved image!!heart (168).jpg\n",
      "[[ 179  494 1126 1126]]\n",
      "successfully saved image!!heart (169).jpg\n",
      "[[258 200 291 291]]\n",
      "successfully saved image!!heart (17).jpg\n",
      "[[ 73  59 159 159]]\n",
      "successfully saved image!!heart (170).jpg\n",
      "[[ 63  72 247 247]]\n",
      "successfully saved image!!heart (171).jpg\n",
      "[[160 272 592 592]]\n",
      "successfully saved image!!heart (172).jpg\n",
      "[[130 108 223 223]]\n",
      "successfully saved image!!heart (173).jpg\n",
      "[[ 90  75 198 198]]\n",
      "successfully saved image!!heart (174).jpg\n",
      "[[354 379 679 679]]\n",
      "successfully saved image!!heart (175).jpg\n",
      "[[314 511  68  68]\n",
      " [118  87 143 143]]\n",
      "successfully saved image!!heart (176).jpg\n",
      "[[ 59 101 172 172]]\n",
      "successfully saved image!!heart (177).jpg\n",
      "[[117 191 491 491]]\n",
      "successfully saved image!!heart (178).jpg\n",
      "[[110 117 268 268]]\n",
      "successfully saved image!!heart (179).jpg\n",
      "[[179 244 483 483]]\n",
      "successfully saved image!!heart (18).jpg\n",
      "[[ 93 100 615 615]]\n",
      "successfully saved image!!heart (180).jpg\n",
      "[[ 81  82 184 184]]\n",
      "successfully saved image!!heart (181).jpg\n",
      "[[ 78  72 167 167]]\n",
      "successfully saved image!!heart (182).jpg\n",
      "[[138 239 464 464]]\n",
      "successfully saved image!!heart (183).jpg\n",
      "[[169 188 317 317]]\n",
      "successfully saved image!!heart (184).jpg\n",
      "[[ 71  70 158 158]]\n",
      "successfully saved image!!heart (185).jpg\n",
      "[[145 145 285 285]]\n",
      "successfully saved image!!heart (186).jpg\n",
      "[[131 129 150 150]]\n",
      "successfully saved image!!heart (187).jpg\n",
      "[[112 160 335 335]]\n",
      "successfully saved image!!heart (188).jpg\n",
      "[[103 101 232 232]]\n",
      "successfully saved image!!heart (19).jpg\n",
      "[[102 170 353 353]]\n",
      "successfully saved image!!heart (190).jpg\n",
      "[[102 178 585 585]]\n",
      "successfully saved image!!heart (191).jpg\n",
      "[[ 85 122 163 163]]\n",
      "successfully saved image!!heart (193).jpg\n",
      "[[253 376 502 502]]\n",
      "successfully saved image!!heart (195).jpg\n",
      "[[ 67  35 124 124]]\n",
      "successfully saved image!!heart (198).jpg\n",
      "[[ 41  53 286 286]]\n",
      "successfully saved image!!heart (199).jpg\n",
      "[[ 45 115 206 206]]\n",
      "successfully saved image!!heart (2).jpg\n",
      "[[140 136 563 563]]\n",
      "successfully saved image!!heart (20).jpg\n",
      "[[143 193 379 379]]\n",
      "successfully saved image!!heart (201).jpg\n",
      "[[1025  494   69   69]]\n",
      "successfully saved image!!heart (202).jpg\n",
      "[[391 409 836 836]]\n",
      "successfully saved image!!heart (205).jpg\n",
      "[[157 142 263 263]]\n",
      "successfully saved image!!heart (206).jpg\n",
      "[[123 118 281 281]]\n",
      "successfully saved image!!heart (207).jpg\n",
      "[[171 171 428 428]]\n",
      "successfully saved image!!heart (208).jpg\n",
      "[[120 136 293 293]]\n",
      "successfully saved image!!heart (209).jpg\n",
      "[[ 82  95 163 163]]\n",
      "successfully saved image!!heart (21).jpg\n",
      "[[142 164 514 514]]\n",
      "successfully saved image!!heart (210).jpg\n",
      "[[220 279 641 641]]\n",
      "successfully saved image!!heart (211).jpg\n",
      "[[247 168 476 476]]\n",
      "successfully saved image!!heart (212).jpg\n",
      "[[ 53  67 113 113]]\n",
      "successfully saved image!!heart (213).jpg\n",
      "[[216 325 659 659]]\n",
      "successfully saved image!!heart (214).jpg\n",
      "[[ 62 106 257 257]]\n",
      "successfully saved image!!heart (215).jpg\n",
      "[[ 40  62 171 171]]\n",
      "successfully saved image!!heart (216).jpg\n",
      "[[ 91 136 230 230]]\n",
      "successfully saved image!!heart (217).jpg\n",
      "[[108 142 234 234]]\n",
      "successfully saved image!!heart (218).jpg\n",
      "[[107 107 200 200]]\n",
      "successfully saved image!!heart (219).jpg\n",
      "[[ 64  88 168 168]]\n",
      "successfully saved image!!heart (22).jpg\n",
      "[[121 151 230 230]]\n",
      "successfully saved image!!heart (220).jpg\n",
      "[[106  73 168 168]]\n",
      "successfully saved image!!heart (221).jpg\n",
      "[[364 202 287 287]]\n",
      "successfully saved image!!heart (222).jpg\n",
      "[[ 97  79 145 145]]\n",
      "successfully saved image!!heart (23).jpg\n",
      "[[136  64 404 404]]\n",
      "successfully saved image!!heart (231).jpg\n",
      "[[157 228 245 245]]\n",
      "successfully saved image!!heart (232).jpg\n",
      "[[ 98  88 245 245]]\n",
      "successfully saved image!!heart (24).jpg\n",
      "[[ 98 166 626 626]\n",
      " [600 978  69  69]]\n",
      "successfully saved image!!heart (241).jpg\n",
      "[[134 178 254 254]]\n",
      "successfully saved image!!heart (242).jpg\n",
      "[[123  98 237 237]]\n",
      "successfully saved image!!heart (243).jpg\n",
      "[[ 72 140 245 245]]\n",
      "successfully saved image!!heart (244).jpg\n",
      "[[148 108 220 220]]\n",
      "successfully saved image!!heart (245).jpg\n",
      "[[196 108 254 254]]\n",
      "successfully saved image!!heart (246).jpg\n",
      "[[ 87  77 169 169]]\n",
      "successfully saved image!!heart (247).jpg\n",
      "[[139 201 413 413]]\n",
      "successfully saved image!!heart (248).jpg\n",
      "[[105 131 272 272]]\n",
      "successfully saved image!!heart (25).jpg\n",
      "[[ 55  49 228 228]]\n",
      "successfully saved image!!heart (250).jpg\n",
      "[[ 74  58 157 157]]\n",
      "successfully saved image!!heart (251).jpg\n",
      "[[258 306 536 536]]\n",
      "successfully saved image!!heart (252).jpg\n",
      "[[ 66  81 171 171]]\n",
      "successfully saved image!!heart (253).jpg\n",
      "[[166 234 769 769]]\n",
      "successfully saved image!!heart (254).jpg\n",
      "[[164 191 380 380]]\n",
      "successfully saved image!!heart (255).jpg\n",
      "[[148 131 330 330]]\n",
      "successfully saved image!!heart (256).jpg\n",
      "[[168 218 480 480]]\n",
      "successfully saved image!!heart (257).jpg\n",
      "[[211 252 486 486]]\n",
      "successfully saved image!!heart (258).jpg\n",
      "[[186 253 852 852]]\n",
      "successfully saved image!!heart (259).jpg\n",
      "[[ 59  64 209 209]]\n",
      "successfully saved image!!heart (26).jpg\n",
      "[[172 120 290 290]]\n",
      "successfully saved image!!heart (261).jpg\n",
      "[[137 307 618 618]]\n",
      "successfully saved image!!heart (262).jpg\n",
      "[[161 220 351 351]]\n",
      "successfully saved image!!heart (263).jpg\n",
      "[[248 203 533 533]]\n",
      "successfully saved image!!heart (264).jpg\n",
      "[[ 57  75 188 188]]\n",
      "successfully saved image!!heart (265).jpg\n",
      "[[141 147 307 307]]\n",
      "successfully saved image!!heart (266).jpg\n",
      "[[ 102  272  589  589]\n",
      " [  53 1090   61   61]]\n",
      "successfully saved image!!heart (267).jpg\n",
      "[[173  82 328 328]]\n",
      "successfully saved image!!heart (268).jpg\n",
      "[[ 69  64 134 134]]\n",
      "successfully saved image!!heart (269).jpg\n",
      "[[215 294 455 455]]\n",
      "successfully saved image!!heart (27).jpg\n",
      "[[ 42  81 171 171]]\n",
      "successfully saved image!!heart (270).jpg\n",
      "[[ 39  57 146 146]]\n",
      "successfully saved image!!heart (272).jpg\n",
      "[[ 40 116 255 255]]\n",
      "successfully saved image!!heart (273).jpg\n",
      "[[ 78  84 151 151]]\n",
      "successfully saved image!!heart (274).jpg\n",
      "[[ 90 107 154 154]]\n",
      "successfully saved image!!heart (275).jpg\n",
      "[[ 70  22 308 308]]\n",
      "successfully saved image!!heart (276).jpg\n",
      "[[ 39  55 226 226]]\n",
      "successfully saved image!!heart (277).jpg\n",
      "[[176 200 511 511]]\n",
      "successfully saved image!!heart (278).jpg\n",
      "[[ 67  54 147 147]]\n",
      "successfully saved image!!heart (279).jpg\n",
      "[[173 165 345 345]]\n",
      "successfully saved image!!heart (28).jpg\n",
      "[[ 69  85 187 187]]\n",
      "successfully saved image!!heart (280).jpg\n",
      "[[ 60  68 132 132]]\n",
      "successfully saved image!!heart (281).jpg\n",
      "[[337 198 230 230]]\n",
      "successfully saved image!!heart (284).jpg\n",
      "SAVE ERROR!!heart (285).jpg\n",
      "[[613 483 828 828]]\n",
      "successfully saved image!!heart (286).jpg\n",
      "[[186 158 472 472]]\n",
      "successfully saved image!!heart (287).jpg\n",
      "[[ 62  97 225 225]]\n",
      "successfully saved image!!heart (288).jpg\n",
      "[[226 120 499 499]]\n",
      "successfully saved image!!heart (289).jpg\n",
      "[[123 139 228 228]]\n",
      "successfully saved image!!heart (29).jpg\n",
      "[[210 194 325 325]]\n",
      "successfully saved image!!heart (290).jpg\n",
      "[[ 36 120 347 347]]\n",
      "successfully saved image!!heart (292).jpg\n",
      "[[254 300 578 578]]\n",
      "successfully saved image!!heart (293).jpg\n",
      "[[123  91 183 183]]\n",
      "successfully saved image!!heart (295).jpg\n",
      "[[ 83  88 164 164]\n",
      " [108 309  68  68]]\n",
      "successfully saved image!!heart (296).jpg\n",
      "[[106 336 516 516]\n",
      " [ 30  95 177 177]]\n",
      "successfully saved image!!heart (297).jpg\n",
      "[[ 80  78 193 193]]\n",
      "successfully saved image!!heart (298).jpg\n",
      "[[190  98 271 271]]\n",
      "successfully saved image!!heart (299).jpg\n",
      "[[ 81  84 121 121]]\n",
      "successfully saved image!!heart (30).jpg\n",
      "[[130 124 269 269]]\n",
      "successfully saved image!!heart (300).jpg\n",
      "[[ 87  52 277 277]\n",
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      "[[ 70  68 169 169]]\n",
      "successfully saved image!!heart (899).jpg\n",
      "[[ 57 129 227 227]]\n",
      "successfully saved image!!heart (9).jpg\n",
      "[[216  89 205 205]]\n",
      "successfully saved image!!heart (90).jpg\n",
      "[[ 68  81 165 165]]\n",
      "successfully saved image!!heart (900).jpg\n",
      "[[ 45  80 206 206]]\n",
      "successfully saved image!!heart (902).jpg\n",
      "[[ 70  95 162 162]]\n",
      "successfully saved image!!heart (903).jpg\n",
      "SAVE ERROR!!heart (904).jpg\n",
      "[[ 60 114 195 195]]\n",
      "successfully saved image!!heart (905).jpg\n",
      "[[ 57 114 152 152]]\n",
      "successfully saved image!!heart (906).jpg\n",
      "[[ 94  73 317 317]]\n",
      "successfully saved image!!heart (907).jpg\n",
      "[[188 141 288 288]]\n",
      "successfully saved image!!heart (908).jpg\n",
      "[[139 114 230 230]]\n",
      "successfully saved image!!heart (909).jpg\n",
      "[[110 145 284 284]]\n",
      "successfully saved image!!heart (911).jpg\n",
      "[[113 140 355 355]]\n",
      "successfully saved image!!heart (914).jpg\n",
      "[[ 52  45 157 157]]\n",
      "successfully saved image!!heart (915).jpg\n",
      "[[107 115 458 458]]\n",
      "successfully saved image!!heart (916).jpg\n",
      "[[ 807  550 1237 1237]]\n",
      "successfully saved image!!heart (917).jpg\n",
      "[[108  84 266 266]]\n",
      "successfully saved image!!heart (921).jpg\n",
      "[[190 118 198 198]]\n",
      "successfully saved image!!heart (922).jpg\n",
      "[[ 63  47 106 106]]\n",
      "successfully saved image!!heart (924).jpg\n",
      "[[ 36  33 255 255]]\n",
      "successfully saved image!!heart (925).jpg\n",
      "[[ 51  53 143 143]]\n",
      "successfully saved image!!heart (926).jpg\n",
      "[[ 92 132  72  72]\n",
      " [224 327 473 473]]\n",
      "successfully saved image!!heart (927).jpg\n",
      "[[127 103 237 237]]\n",
      "successfully saved image!!heart (93).jpg\n",
      "[[124 122 174 174]]\n",
      "successfully saved image!!heart (931).jpg\n",
      "[[ 83  92 157 157]]\n",
      "successfully saved image!!heart (932).jpg\n",
      "[[158 197 474 474]]\n",
      "successfully saved image!!heart (933).jpg\n",
      "[[ 72 142 464 464]]\n",
      "successfully saved image!!heart (934).jpg\n",
      "[[ 25  76 212 212]]\n",
      "successfully saved image!!heart (935).jpg\n",
      "[[ 61  94 251 251]]\n",
      "successfully saved image!!heart (936).jpg\n",
      "[[ 65  59 199 199]]\n",
      "successfully saved image!!heart (937).jpg\n",
      "[[ 88  81 179 179]]\n",
      "successfully saved image!!heart (938).jpg\n",
      "[[ 69   6 430 430]]\n",
      "successfully saved image!!heart (939).jpg\n",
      "[[ 79  94 207 207]]\n",
      "successfully saved image!!heart (940).jpg\n",
      "[[ 50  45 171 171]]\n",
      "successfully saved image!!heart (941).jpg\n",
      "[[ 75 115 311 311]]\n",
      "successfully saved image!!heart (943).jpg\n",
      "[[106 281 417 417]]\n",
      "successfully saved image!!heart (944).jpg\n",
      "[[ 45  72 189 189]]\n",
      "successfully saved image!!heart (945).jpg\n",
      "[[153 187 435 435]]\n",
      "successfully saved image!!heart (946).jpg\n",
      "[[111 208 556 556]]\n",
      "successfully saved image!!heart (947).jpg\n",
      "[[ 56  89 162 162]]\n",
      "successfully saved image!!heart (948).jpg\n",
      "[[ 77  54 195 195]]\n",
      "successfully saved image!!heart (949).jpg\n",
      "[[ 401  390 1026 1026]]\n",
      "successfully saved image!!heart (95).jpg\n",
      "[[ 91 178 214 214]]\n",
      "successfully saved image!!heart (950).jpg\n",
      "[[123 137 410 410]]\n",
      "successfully saved image!!heart (951).jpg\n",
      "[[153 194 377 377]]\n",
      "successfully saved image!!heart (952).jpg\n",
      "[[116  50 346 346]]\n",
      "successfully saved image!!heart (953).jpg\n",
      "[[ 70  48 166 166]]\n",
      "successfully saved image!!heart (954).jpg\n",
      "[[ 78  84 224 224]]\n",
      "successfully saved image!!heart (955).jpg\n",
      "[[ 86  95 304 304]]\n",
      "successfully saved image!!heart (956).jpg\n",
      "[[ 82 329 622 622]]\n",
      "successfully saved image!!heart (957).jpg\n",
      "[[ 74  73 176 176]]\n",
      "successfully saved image!!heart (958).jpg\n",
      "[[ 71  70 200 200]]\n",
      "successfully saved image!!heart (959).jpg\n",
      "[[ 49 141 259 259]]\n",
      "successfully saved image!!heart (96).jpg\n",
      "[[408  87 553 553]]\n",
      "successfully saved image!!heart (960).jpg\n",
      "[[586 420 833 833]]\n",
      "successfully saved image!!heart (961).jpg\n",
      "[[ 38  75 212 212]]\n",
      "successfully saved image!!heart (962).jpg\n",
      "[[131  86 165 165]]\n",
      "successfully saved image!!heart (963).jpg\n",
      "[[244 155 434 434]]\n",
      "successfully saved image!!heart (964).jpg\n",
      "[[152 103 205 205]]\n",
      "successfully saved image!!heart (965).jpg\n",
      "[[130 148 321 321]]\n",
      "successfully saved image!!heart (966).jpg\n",
      "[[ 67  55 188 188]]\n",
      "successfully saved image!!heart (967).jpg\n",
      "[[184 256 529 529]]\n",
      "successfully saved image!!heart (968).jpg\n",
      "[[ 51  78 196 196]]\n",
      "successfully saved image!!heart (969).jpg\n",
      "[[ 69  45 127 127]]\n",
      "successfully saved image!!heart (97).jpg\n",
      "[[ 50  50 143 143]]\n",
      "successfully saved image!!heart (970).jpg\n",
      "[[ 84  66 176 176]]\n",
      "successfully saved image!!heart (972).jpg\n",
      "[[ 51 130 206 206]]\n",
      "successfully saved image!!heart (974).jpg\n",
      "[[ 53  57 255 255]]\n",
      "successfully saved image!!heart (976).jpg\n",
      "[[ 41 154 271 271]]\n",
      "successfully saved image!!heart (977).jpg\n",
      "[[ 62  87 280 280]]\n",
      "successfully saved image!!heart (978).jpg\n",
      "[[141  74 202 202]]\n",
      "successfully saved image!!heart (979).jpg\n",
      "[[ 74 116 166 166]]\n",
      "successfully saved image!!heart (98).jpg\n",
      "[[ 58  82 171 171]]\n",
      "successfully saved image!!heart (980).jpg\n",
      "[[ 52  43 210 210]]\n",
      "successfully saved image!!heart (981).jpg\n",
      "[[146 111 360 360]]\n",
      "successfully saved image!!heart (982).jpg\n",
      "[[ 54 106 221 221]]\n",
      "successfully saved image!!heart (983).jpg\n",
      "[[ 58 124 284 284]]\n",
      "successfully saved image!!heart (984).jpg\n",
      "[[137 115 174 174]]\n",
      "successfully saved image!!heart (985).jpg\n",
      "[[ 83  66 173 173]]\n",
      "successfully saved image!!heart (987).jpg\n",
      "[[ 71  78 171 171]]\n",
      "successfully saved image!!heart (988).jpg\n",
      "[[123 144 422 422]]\n",
      "successfully saved image!!heart (989).jpg\n",
      "[[ 64 150 269 269]]\n",
      "successfully saved image!!heart (99).jpg\n",
      "[[ 47  51 209 209]]\n",
      "successfully saved image!!heart (990).jpg\n",
      "[[ 27  95 217 217]]\n",
      "successfully saved image!!heart (991).jpg\n",
      "[[258 120 397 397]]\n",
      "successfully saved image!!heart (992).jpg\n",
      "[[137 279 491 491]]\n",
      "successfully saved image!!heart (993).jpg\n",
      "SAVE ERROR!!heart (994).jpg\n",
      "[[170 176 305 305]\n",
      " [317 589  69  69]]\n",
      "successfully saved image!!heart (996).jpg\n",
      "[[104 142 317 317]]\n",
      "successfully saved image!!heart (997).jpg\n",
      "[[ 308  337  717  717]\n",
      " [ 472 1081  430  430]]\n",
      "successfully saved image!!heart (998).jpg\n",
      "[[112  79 371 371]]\n",
      "successfully saved image!!heart (999).jpg\n",
      "Done Heart\n",
      "Processing Oblong\n",
      "[[146 127 426 426]]\n",
      "successfully saved image!!oblong (1).jpg\n",
      "[[135 114 303 303]]\n",
      "successfully saved image!!oblong (100).jpg\n",
      "[[170 133 371 371]]\n",
      "successfully saved image!!oblong (1000).jpg\n",
      "[[114  94 254 254]]\n",
      "successfully saved image!!oblong (101).jpg\n",
      "[[240 201 641 641]]\n",
      "successfully saved image!!oblong (102).jpg\n",
      "[[310 210 309 309]]\n",
      "successfully saved image!!oblong (103).jpg\n",
      "[[388 433 474 474]]\n",
      "successfully saved image!!oblong (104).jpg\n",
      "[[140 183 242 242]]\n",
      "successfully saved image!!oblong (105).jpg\n",
      "[[ 85 199 371 371]]\n",
      "successfully saved image!!oblong (108).jpg\n",
      "[[251 196 286 286]]\n",
      "successfully saved image!!oblong (109).jpg\n",
      "[[229 135 603 603]]\n",
      "successfully saved image!!oblong (110).jpg\n",
      "[[136 153 242 242]]\n",
      "successfully saved image!!oblong (111).jpg\n",
      "[[179 241 445 445]]\n",
      "successfully saved image!!oblong (112).jpg\n",
      "[[ 33  65 142 142]]\n",
      "successfully saved image!!oblong (113).jpg\n",
      "[[ 89  75 147 147]]\n",
      "successfully saved image!!oblong (114).jpg\n",
      "[[298 327 585 585]]\n",
      "successfully saved image!!oblong (115).jpg\n",
      "[[107 156 366 366]]\n",
      "successfully saved image!!oblong (116).jpg\n",
      "[[ 63  66 131 131]]\n",
      "successfully saved image!!oblong (117).jpg\n",
      "[[ 63  67 151 151]]\n",
      "successfully saved image!!oblong (118).jpg\n",
      "[[116 116 281 281]]\n",
      "successfully saved image!!oblong (119).jpg\n",
      "[[186 185 293 293]]\n",
      "successfully saved image!!oblong (12).jpg\n",
      "[[106  92 175 175]]\n",
      "successfully saved image!!oblong (120).jpg\n",
      "[[ 77  67 168 168]]\n",
      "successfully saved image!!oblong (121).jpg\n",
      "[[165 173 295 295]]\n",
      "successfully saved image!!oblong (122).jpg\n",
      "[[ 49  44 108 108]]\n",
      "successfully saved image!!oblong (123).jpg\n",
      "[[235 295 486 486]]\n",
      "successfully saved image!!oblong (124).jpg\n",
      "[[ 80 277 348 348]]\n",
      "successfully saved image!!oblong (125).jpg\n",
      "[[110 118 243 243]]\n",
      "successfully saved image!!oblong (126).jpg\n",
      "[[101  77 183 183]]\n",
      "successfully saved image!!oblong (127).jpg\n",
      "[[179 136 441 441]]\n",
      "successfully saved image!!oblong (128).jpg\n",
      "[[ 66  70 233 233]]\n",
      "successfully saved image!!oblong (129).jpg\n",
      "[[ 86 105 268 268]]\n",
      "successfully saved image!!oblong (130).jpg\n",
      "[[212 112 220 220]]\n",
      "successfully saved image!!oblong (131).jpg\n",
      "[[ 93 109 299 299]]\n",
      "successfully saved image!!oblong (132).jpg\n",
      "[[191 260 521 521]]\n",
      "successfully saved image!!oblong (133).jpg\n",
      "[[ 70 115 341 341]]\n",
      "successfully saved image!!oblong (134).jpg\n",
      "[[154 117 231 231]]\n",
      "successfully saved image!!oblong (135).jpg\n",
      "[[ 80  75 164 164]]\n",
      "successfully saved image!!oblong (136).jpg\n",
      "[[ 89 128 367 367]]\n",
      "successfully saved image!!oblong (137).jpg\n",
      "[[151 130 300 300]]\n",
      "successfully saved image!!oblong (138).jpg\n",
      "[[143 141 347 347]]\n",
      "successfully saved image!!oblong (140).jpg\n",
      "[[48 47 77 77]]\n",
      "successfully saved image!!oblong (141).jpg\n",
      "[[41 43 85 85]]\n",
      "successfully saved image!!oblong (142).jpg\n",
      "[[ 79 101 290 290]]\n",
      "successfully saved image!!oblong (145).jpg\n",
      "[[187 133 292 292]]\n",
      "successfully saved image!!oblong (153).jpg\n",
      "[[219 240 457 457]]\n",
      "successfully saved image!!oblong (159).jpg\n",
      "[[ 91 158 342 342]]\n",
      "successfully saved image!!oblong (16).jpg\n",
      "[[137 124 272 272]]\n",
      "successfully saved image!!oblong (160).jpg\n",
      "[[119 151 337 337]]\n",
      "successfully saved image!!oblong (161).jpg\n",
      "[[100  79 237 237]]\n",
      "successfully saved image!!oblong (162).jpg\n",
      "[[ 66  92 268 268]]\n",
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      "[[131  88 238 238]]\n",
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      "[[179  77 150 150]]\n",
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      "[[138  99 293 293]]\n",
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      "[[ 43  68 141 141]]\n",
      "successfully saved image!!oblong (167).jpg\n",
      "[[154 106 229 229]]\n",
      "successfully saved image!!oblong (168).jpg\n",
      "[[ 97  76 340 340]]\n",
      "successfully saved image!!oblong (169).jpg\n",
      "[[139  83 195 195]]\n",
      "successfully saved image!!oblong (17).jpg\n",
      "[[  8  49 187 187]]\n",
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      "[[104 171 228 228]]\n",
      "successfully saved image!!oblong (171).jpg\n",
      "[[413 220 592 592]\n",
      " [387 888 448 448]]\n",
      "successfully saved image!!oblong (172).jpg\n",
      "[[ 41  84 209 209]]\n",
      "successfully saved image!!oblong (173).jpg\n",
      "[[180 109 285 285]]\n",
      "successfully saved image!!oblong (174).jpg\n",
      "[[252 231 479 479]]\n",
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      "[[124 108 164 164]]\n",
      "successfully saved image!!oblong (176).jpg\n",
      "[[ 45  53 198 198]]\n",
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      "[[ 87 103 379 379]]\n",
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      "[[118 108 280 280]]\n",
      "successfully saved image!!oblong (18).jpg\n",
      "[[ 50  63 189 189]]\n",
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      "[[100 163 208 208]]\n",
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      "[[ 42  35 216 216]]\n",
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      "[[ 42  55 229 229]]\n",
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      "[[ 78  75 359 359]]\n",
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      "[[194 166 405 405]]\n",
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      "[[ 42 112 218 218]]\n",
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      "[[204  77 346 346]]\n",
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      "[[294 235 569 569]]\n",
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      "[[ 50  25 102 102]]\n",
      "successfully saved image!!oblong (19).jpg\n",
      "[[196 332 676 676]]\n",
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      "[[ 48  99 190 190]]\n",
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      "[[102 115 224 224]]\n",
      "successfully saved image!!oblong (195).jpg\n",
      "[[112 170 285 285]]\n",
      "successfully saved image!!oblong (196).jpg\n",
      "[[ 36  46 161 161]]\n",
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      "[[ 83  53 170 170]]\n",
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      "[[ 43 108 217 217]]\n",
      "successfully saved image!!oblong (2).jpg\n",
      "[[254 195 303 303]]\n",
      "successfully saved image!!oblong (20).jpg\n",
      "[[ 98 109 298 298]]\n",
      "successfully saved image!!oblong (200).jpg\n",
      "[[128 138 377 377]]\n",
      "successfully saved image!!oblong (201).jpg\n",
      "[[ 65  60 193 193]]\n",
      "successfully saved image!!oblong (202).jpg\n",
      "[[ 69  63 164 164]]\n",
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      "[[219 245 551 551]]\n",
      "successfully saved image!!oblong (204).jpg\n",
      "[[ 38  58 143 143]\n",
      " [ 89  56 156 156]]\n",
      "successfully saved image!!oblong (206).jpg\n",
      "[[ 74  91 190 190]]\n",
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      "[[ 90 118 275 275]]\n",
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      "[[ 81  45 157 157]]\n",
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      "[[ 35  43 172 172]]\n",
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      "[[122 170 357 357]]\n",
      "successfully saved image!!oblong (211).jpg\n",
      "[[369 278 601 601]]\n",
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      "[[124  93 284 284]]\n",
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      "[[111  90 220 220]]\n",
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      "[[167 210 599 599]]\n",
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      "[[105 125 384 384]]\n",
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      "[[ 86  52 129 129]]\n",
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      "[[ 87  70 219 219]]\n",
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      "[[ 76 166 492 492]]\n",
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      "[[250 115 259 259]]\n",
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      "[[ 45  39 218 218]]\n",
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      "[[106 131 233 233]]\n",
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      "[[184 188 379 379]]\n",
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      "[[ 81 158 265 265]]\n",
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      "[[208 208 321 321]]\n",
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      "[[193 112 231 231]]\n",
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      "[[ 81 202 445 445]]\n",
      "successfully saved image!!oblong (229).jpg\n",
      "[[43 47 75 75]]\n",
      "successfully saved image!!oblong (23).jpg\n",
      "[[44 50 84 84]]\n",
      "successfully saved image!!oblong (230).jpg\n",
      "[[117  83 379 379]]\n",
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      "[[ 50  54 125 125]]\n",
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      "[[ 34  39 380 380]]\n",
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      "[[120 136 213 213]]\n",
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      "[[170  91 218 218]]\n",
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      "[[ 42  44 102 102]]\n",
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      "[[116 109 294 294]]\n",
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      "[[ 55  49 116 116]]\n",
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      "[[310 438 801 801]]\n",
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      "[[116 260 361 361]]\n",
      "successfully saved image!!oblong (24).jpg\n",
      "[[ 66  68 102 102]]\n",
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      "[[274 318 642 642]]\n",
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      "[[ 75 200 425 425]]\n",
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      "[[ 25  43 105 105]]\n",
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      "[[206 235 273 273]]\n",
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      "[[ 53  52 129 129]]\n",
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      "[[144 100 136 136]]\n",
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      "[[ 89  95 182 182]]\n",
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      "[[176 136 365 365]]\n",
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      "[[ 75  64 134 134]]\n",
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      "[[ 78  60 218 218]]\n",
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      "[[175 129 272 272]]\n",
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      "[[161  99 170 170]]\n",
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      "[[ 99  43 110 110]]\n",
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      "[[116 123 281 281]]\n",
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      "[[315  73 177 177]]\n",
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      "[[273 394 842 842]]\n",
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      "[[297 415 842 842]]\n",
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      "Done Oblong\n",
      "Processing Oval\n",
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      "[[120 139 224 224]]\n",
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      "[[ 40  71 276 276]]\n",
      "successfully saved image!!oval (999).jpg\n",
      "Done Oval\n",
      "Processing Round\n",
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      "successfully saved image!!round (10).jpg\n",
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      "SAVE ERROR!!round (121).jpg\n",
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      "[[624 860  53  53]\n",
      " [258 254 417 417]]\n",
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      "SAVE ERROR!!round (188).jpg\n",
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      "successfully saved image!!round (998).jpg\n",
      "Done Round\n",
      "Processing Square\n",
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      "successfully saved image!!square (839).jpg\n",
      "[[134 216 364 364]]\n",
      "successfully saved image!!square (84).jpg\n",
      "[[351 311 696 696]]\n",
      "successfully saved image!!square (840).jpg\n",
      "[[190 221 399 399]]\n",
      "successfully saved image!!square (841).jpg\n",
      "[[156 232 528 528]]\n",
      "successfully saved image!!square (842).jpg\n",
      "[[109  89 171 171]]\n",
      "successfully saved image!!square (843).jpg\n",
      "[[ 89 118 264 264]]\n",
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      "[[ 42  52 153 153]]\n",
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      "[[ 89  76 322 322]]\n",
      "successfully saved image!!square (846).jpg\n",
      "[[396 404 716 716]]\n",
      "successfully saved image!!square (847).jpg\n",
      "[[153 289 484 484]]\n",
      "successfully saved image!!square (848).jpg\n",
      "[[157 267 699 699]]\n",
      "successfully saved image!!square (849).jpg\n",
      "SAVE ERROR!!square (85).jpg\n",
      "[[120 244 493 493]]\n",
      "successfully saved image!!square (850).jpg\n",
      "[[119 217 478 478]]\n",
      "successfully saved image!!square (851).jpg\n",
      "[[ 61 156 491 491]]\n",
      "successfully saved image!!square (852).jpg\n",
      "[[ 20  48 149 149]]\n",
      "successfully saved image!!square (853).jpg\n",
      "[[ 40  55 134 134]]\n",
      "successfully saved image!!square (854).jpg\n",
      "[[ 28  82 161 161]]\n",
      "successfully saved image!!square (855).jpg\n",
      "[[38 29 94 94]]\n",
      "successfully saved image!!square (856).jpg\n",
      "[[151 212 374 374]]\n",
      "successfully saved image!!square (857).jpg\n",
      "[[24 32 96 96]]\n",
      "successfully saved image!!square (858).jpg\n",
      "[[ 72 119 265 265]]\n",
      "successfully saved image!!square (859).jpg\n",
      "[[109 135 251 251]]\n",
      "successfully saved image!!square (86).jpg\n",
      "[[ 79 105 221 221]]\n",
      "successfully saved image!!square (860).jpg\n",
      "[[233 121 397 397]]\n",
      "successfully saved image!!square (861).jpg\n",
      "[[26 42 82 82]]\n",
      "successfully saved image!!square (862).jpg\n",
      "[[ 555  774 1388 1388]]\n",
      "successfully saved image!!square (863).jpg\n",
      "[[110 172 360 360]]\n",
      "successfully saved image!!square (864).jpg\n",
      "[[37 45 84 84]]\n",
      "successfully saved image!!square (865).jpg\n",
      "[[ 63 156 306 306]]\n",
      "successfully saved image!!square (866).jpg\n",
      "[[ 32  56 130 130]]\n",
      "successfully saved image!!square (867).jpg\n",
      "[[117  91 232 232]]\n",
      "successfully saved image!!square (868).jpg\n",
      "[[ 46  68 146 146]]\n",
      "successfully saved image!!square (869).jpg\n",
      "[[126  60 177 177]]\n",
      "successfully saved image!!square (87).jpg\n",
      "[[ 34  50 196 196]]\n",
      "successfully saved image!!square (870).jpg\n",
      "[[ 49  97 268 268]]\n",
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      "[[ 50  66 192 192]]\n",
      "successfully saved image!!square (872).jpg\n",
      "[[137 148 635 635]]\n",
      "successfully saved image!!square (873).jpg\n",
      "[[ 44  57 111 111]]\n",
      "successfully saved image!!square (874).jpg\n",
      "[[ 28  73 324 324]]\n",
      "successfully saved image!!square (876).jpg\n",
      "[[101 136 306 306]]\n",
      "successfully saved image!!square (877).jpg\n",
      "SAVE ERROR!!square (878).jpg\n",
      "[[ 38  24 226 226]]\n",
      "successfully saved image!!square (879).jpg\n",
      "[[ 59  93 359 359]]\n",
      "successfully saved image!!square (88).jpg\n",
      "[[316 281 677 677]]\n",
      "successfully saved image!!square (880).jpg\n",
      "[[139 184 465 465]]\n",
      "successfully saved image!!square (881).jpg\n",
      "[[ 88  64 213 213]]\n",
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      "[[ 18  88 251 251]]\n",
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      "[[ 49  67 217 217]]\n",
      "successfully saved image!!square (884).jpg\n",
      "[[157  92 112 112]]\n",
      "successfully saved image!!square (885).jpg\n",
      "[[ 91 122 274 274]]\n",
      "successfully saved image!!square (886).jpg\n",
      "[[158 247 492 492]]\n",
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      "[[ 46  86 216 216]]\n",
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      "[[153 273 558 558]]\n",
      "successfully saved image!!square (89).jpg\n",
      "[[ 58 137 394 394]]\n",
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      "[[244 302 557 557]]\n",
      "successfully saved image!!square (891).jpg\n",
      "[[22 35 91 91]]\n",
      "successfully saved image!!square (892).jpg\n",
      "[[450 286 533 533]]\n",
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      "[[126 182 592 592]]\n",
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      "[[116 120 331 331]]\n",
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      "[[141 109 266 266]]\n",
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      "[[ 91 120 292 292]]\n",
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      "[[ 40  54 158 158]]\n",
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      "[[210 311 639 639]]\n",
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      "[[123 206 519 519]]\n",
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      "[[ 92  84 365 365]]\n",
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      "[[ 39  29 230 230]]\n",
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      "[[ 28  50 167 167]]\n",
      "successfully saved image!!square (906).jpg\n",
      "[[ 54 478 101 101]\n",
      " [263 180 409 409]]\n",
      "successfully saved image!!square (907).jpg\n",
      "[[ 98 198 322 322]]\n",
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      "[[ 30  41 112 112]]\n",
      "successfully saved image!!square (909).jpg\n",
      "[[ 197  388 1200 1200]]\n",
      "successfully saved image!!square (91).jpg\n",
      "[[113 144 357 357]]\n",
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      "[[117  44 261 261]]\n",
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      "[[206 216 521 521]]\n",
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      "[[246  34 188 188]]\n",
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      "[[ 44  68 131 131]]\n",
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      "[[ 96 260 562 562]]\n",
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      "[[199 289 779 779]]\n",
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      "[[199 253 568 568]]\n",
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      "[[218  95 282 282]]\n",
      "successfully saved image!!square (918).jpg\n",
      "[[ 43  53 147 147]]\n",
      "successfully saved image!!square (919).jpg\n",
      "SAVE ERROR!!square (92).jpg\n",
      "[[ 99   4 430 430]]\n",
      "successfully saved image!!square (920).jpg\n",
      "[[ 90  63 129 129]]\n",
      "successfully saved image!!square (923).jpg\n",
      "[[115  38 409 409]]\n",
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      "[[ 41  70 231 231]]\n",
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      "[[125 122 277 277]]\n",
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      "[[ 70 104 187 187]]\n",
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      "[[159 252 664 664]]\n",
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      "[[ 190  376 1023 1023]]\n",
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      "[[ 42  75 154 154]]\n",
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      "[[ 362  732 1418 1418]]\n",
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      "[[ 49  71 197 197]]\n",
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      "[[ 51  77 203 203]]\n",
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      "[[108 134 430 430]]\n",
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      "[[ 50  60 471 471]]\n",
      "successfully saved image!!square (937).jpg\n",
      "[[ 82 104 225 225]]\n",
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      "[[180 225 634 634]]\n",
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      "[[ 43  68 228 228]]\n",
      "successfully saved image!!square (94).jpg\n",
      "[[197 253 726 726]]\n",
      "successfully saved image!!square (941).jpg\n",
      "[[ 92 108 312 312]]\n",
      "successfully saved image!!square (942).jpg\n",
      "[[196 304 441 441]]\n",
      "successfully saved image!!square (943).jpg\n",
      "[[334 379 690 690]]\n",
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      "[[ 71  13 222 222]]\n",
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      "[[ 71  82 257 257]]\n",
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      "[[ 56  75 240 240]]\n",
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      "[[ 48  36 246 246]]\n",
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      "[[ 30  30 102 102]]\n",
      "successfully saved image!!square (949).jpg\n",
      "[[ 546 1052  430  430]\n",
      " [ 465  378  619  619]]\n",
      "successfully saved image!!square (95).jpg\n",
      "[[ 92 118 388 388]]\n",
      "successfully saved image!!square (950).jpg\n",
      "[[ 70  97 205 205]]\n",
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      "[[121 195 495 495]]\n",
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      "[[152 135 377 377]]\n",
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      "[[156 187 313 313]]\n",
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      "[[131 151 290 290]]\n",
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      "[[245 288 491 491]]\n",
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      "[[ 48  99 353 353]]\n",
      "successfully saved image!!square (959).jpg\n",
      "[[170  40 234 234]]\n",
      "successfully saved image!!square (96).jpg\n",
      "[[ 99 149 364 364]]\n",
      "successfully saved image!!square (960).jpg\n",
      "[[ 97 165 348 348]]\n",
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      "[[122 253 701 701]]\n",
      "successfully saved image!!square (962).jpg\n",
      "[[ 44  57 153 153]]\n",
      "successfully saved image!!square (963).jpg\n",
      "[[ 71  79 170 170]]\n",
      "successfully saved image!!square (964).jpg\n",
      "[[ 98 135 354 354]]\n",
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      "[[114 130 310 310]]\n",
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      "[[ 26  36 153 153]]\n",
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      "[[172 304 759 759]]\n",
      "successfully saved image!!square (968).jpg\n",
      "[[513 349 548 548]]\n",
      "successfully saved image!!square (969).jpg\n",
      "[[333 169 571 571]]\n",
      "successfully saved image!!square (97).jpg\n",
      "[[135 127 393 393]]\n",
      "successfully saved image!!square (970).jpg\n",
      "[[110 184 359 359]]\n",
      "successfully saved image!!square (971).jpg\n",
      "[[ 58 116 275 275]]\n",
      "successfully saved image!!square (972).jpg\n",
      "[[ 44  56 151 151]]\n",
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      "[[131 178 439 439]]\n",
      "successfully saved image!!square (975).jpg\n",
      "[[ 24  53 121 121]]\n",
      "successfully saved image!!square (976).jpg\n",
      "[[378 402 813 813]]\n",
      "successfully saved image!!square (978).jpg\n",
      "SAVE ERROR!!square (979).jpg\n",
      "SAVE ERROR!!square (98).jpg\n",
      "[[ 43  39 280 280]]\n",
      "successfully saved image!!square (980).jpg\n",
      "[[34 42 94 94]]\n",
      "successfully saved image!!square (981).jpg\n",
      "[[355 182 665 665]]\n",
      "successfully saved image!!square (982).jpg\n",
      "[[122 174 519 519]]\n",
      "successfully saved image!!square (983).jpg\n",
      "[[103 143 417 417]]\n",
      "successfully saved image!!square (984).jpg\n",
      "[[137 170 410 410]]\n",
      "successfully saved image!!square (985).jpg\n",
      "[[269 431 916 916]]\n",
      "successfully saved image!!square (986).jpg\n",
      "[[ 85  98 226 226]]\n",
      "successfully saved image!!square (987).jpg\n",
      "[[ 53  78 217 217]]\n",
      "successfully saved image!!square (988).jpg\n",
      "[[168 278 479 479]]\n",
      "successfully saved image!!square (989).jpg\n",
      "[[ 71 101 292 292]]\n",
      "successfully saved image!!square (99).jpg\n",
      "[[ 44  52 119 119]]\n",
      "successfully saved image!!square (990).jpg\n",
      "[[290 446 650 650]]\n",
      "successfully saved image!!square (991).jpg\n",
      "[[ 94  99 446 446]]\n",
      "successfully saved image!!square (992).jpg\n",
      "[[ 48  83 263 263]]\n",
      "successfully saved image!!square (993).jpg\n",
      "[[ 89  50 129 129]]\n",
      "successfully saved image!!square (994).jpg\n",
      "[[122 152 356 356]]\n",
      "successfully saved image!!square (995).jpg\n",
      "[[ 48  79 158 158]]\n",
      "successfully saved image!!square (996).jpg\n",
      "[[ 96 128 293 293]]\n",
      "successfully saved image!!square (997).jpg\n",
      "[[ 34  57 174 174]]\n",
      "successfully saved image!!square (998).jpg\n",
      "[[ 81 128 311 311]]\n",
      "successfully saved image!!square (999).jpg\n",
      "Done Square\n",
      "Done training\n",
      "0 Read Error Image:  oblong (724).jpg\n",
      "0 None Face Error Image:  heart (362).jpg\n",
      "1 None Face Error Image:  oblong (179).jpg\n",
      "2 None Face Error Image:  oblong (576).jpg\n",
      "3 None Face Error Image:  oblong (6).jpg\n",
      "4 None Face Error Image:  oblong (700).jpg\n",
      "5 None Face Error Image:  oblong (703).jpg\n",
      "6 None Face Error Image:  oblong (909).jpg\n",
      "7 None Face Error Image:  oval (350).jpg\n",
      "8 None Face Error Image:  oval (662).jpg\n",
      "9 None Face Error Image:  oval (673).jpg\n",
      "10 None Face Error Image:  oval (679).jpg\n",
      "11 None Face Error Image:  oval (690).jpg\n",
      "12 None Face Error Image:  oval (698).jpg\n",
      "13 None Face Error Image:  oval (896).jpg\n",
      "14 None Face Error Image:  round (161).jpg\n",
      "15 None Face Error Image:  round (424).jpg\n",
      "16 None Face Error Image:  round (464).jpg\n",
      "17 None Face Error Image:  round (563).jpg\n",
      "18 None Face Error Image:  round (597).jpg\n",
      "19 None Face Error Image:  round (600).jpg\n",
      "20 None Face Error Image:  round (605).jpg\n",
      "21 None Face Error Image:  round (609).jpg\n",
      "22 None Face Error Image:  round (610).jpg\n",
      "23 None Face Error Image:  round (611).jpg\n",
      "24 None Face Error Image:  round (675).jpg\n",
      "25 None Face Error Image:  square (147).jpg\n",
      "26 None Face Error Image:  square (162).jpg\n",
      "27 None Face Error Image:  square (17).jpg\n",
      "28 None Face Error Image:  square (683).jpg\n",
      "29 None Face Error Image:  square (922).jpg\n",
      "30 None Face Error Image:  heart (120).jpg\n",
      "31 None Face Error Image:  heart (121).jpg\n",
      "32 None Face Error Image:  heart (124).jpg\n",
      "33 None Face Error Image:  heart (126).jpg\n",
      "34 None Face Error Image:  heart (285).jpg\n",
      "35 None Face Error Image:  heart (383).jpg\n",
      "36 None Face Error Image:  heart (394).jpg\n",
      "37 None Face Error Image:  heart (399).jpg\n",
      "38 None Face Error Image:  heart (440).jpg\n",
      "39 None Face Error Image:  heart (492).jpg\n",
      "40 None Face Error Image:  heart (498).jpg\n",
      "41 None Face Error Image:  heart (690).jpg\n",
      "42 None Face Error Image:  heart (72).jpg\n",
      "43 None Face Error Image:  heart (798).jpg\n",
      "44 None Face Error Image:  heart (802).jpg\n",
      "45 None Face Error Image:  heart (847).jpg\n",
      "46 None Face Error Image:  heart (852).jpg\n",
      "47 None Face Error Image:  heart (893).jpg\n",
      "48 None Face Error Image:  heart (904).jpg\n",
      "49 None Face Error Image:  heart (994).jpg\n",
      "50 None Face Error Image:  oblong (448).jpg\n",
      "51 None Face Error Image:  oblong (586).jpg\n",
      "52 None Face Error Image:  oblong (66).jpg\n",
      "53 None Face Error Image:  oblong (753).jpg\n",
      "54 None Face Error Image:  oblong (825).jpg\n",
      "55 None Face Error Image:  oval (169).jpg\n",
      "56 None Face Error Image:  oval (310).jpg\n",
      "57 None Face Error Image:  oval (463).jpg\n",
      "58 None Face Error Image:  oval (469).jpg\n",
      "59 None Face Error Image:  oval (569).jpg\n",
      "60 None Face Error Image:  oval (715).jpg\n",
      "61 None Face Error Image:  oval (724).jpg\n",
      "62 None Face Error Image:  oval (735).jpg\n",
      "63 None Face Error Image:  oval (746).jpg\n",
      "64 None Face Error Image:  oval (749).jpg\n",
      "65 None Face Error Image:  oval (750).jpg\n",
      "66 None Face Error Image:  oval (787).jpg\n",
      "67 None Face Error Image:  oval (916).jpg\n",
      "68 None Face Error Image:  round (121).jpg\n",
      "69 None Face Error Image:  round (188).jpg\n",
      "70 None Face Error Image:  round (194).jpg\n",
      "71 None Face Error Image:  round (387).jpg\n",
      "72 None Face Error Image:  round (404).jpg\n",
      "73 None Face Error Image:  round (57).jpg\n",
      "74 None Face Error Image:  round (585).jpg\n",
      "75 None Face Error Image:  round (601).jpg\n",
      "76 None Face Error Image:  round (615).jpg\n",
      "77 None Face Error Image:  round (616).jpg\n",
      "78 None Face Error Image:  round (633).jpg\n",
      "79 None Face Error Image:  round (641).jpg\n",
      "80 None Face Error Image:  round (645).jpg\n",
      "81 None Face Error Image:  round (650).jpg\n",
      "82 None Face Error Image:  round (651).jpg\n",
      "83 None Face Error Image:  round (652).jpg\n",
      "84 None Face Error Image:  round (661).jpg\n",
      "85 None Face Error Image:  round (662).jpg\n",
      "86 None Face Error Image:  round (666).jpg\n",
      "87 None Face Error Image:  round (671).jpg\n",
      "88 None Face Error Image:  round (676).jpg\n",
      "89 None Face Error Image:  round (677).jpg\n",
      "90 None Face Error Image:  round (678).jpg\n",
      "91 None Face Error Image:  round (679).jpg\n",
      "92 None Face Error Image:  round (680).jpg\n",
      "93 None Face Error Image:  round (684).jpg\n",
      "94 None Face Error Image:  round (756).jpg\n",
      "95 None Face Error Image:  square (349).jpg\n",
      "96 None Face Error Image:  square (355).jpg\n",
      "97 None Face Error Image:  square (422).jpg\n",
      "98 None Face Error Image:  square (440).jpg\n",
      "99 None Face Error Image:  square (575).jpg\n",
      "100 None Face Error Image:  square (599).jpg\n",
      "101 None Face Error Image:  square (644).jpg\n",
      "102 None Face Error Image:  square (692).jpg\n",
      "103 None Face Error Image:  square (85).jpg\n",
      "104 None Face Error Image:  square (878).jpg\n",
      "105 None Face Error Image:  square (92).jpg\n",
      "106 None Face Error Image:  square (979).jpg\n",
      "107 None Face Error Image:  square (98).jpg\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import os\n",
    "\n",
    "import cv2\n",
    "import mediapipe as mp\n",
    "import numpy as np\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_drawing_styles = mp.solutions.drawing_styles\n",
    "mp_face_mesh = mp.solutions.face_mesh\n",
    "\n",
    "## 함수\n",
    "# 디렉토리가 존재하지 않으면 생성\n",
    "def makedirs(path): \n",
    "   try: \n",
    "        os.makedirs(path) \n",
    "   except OSError: \n",
    "       if not os.path.isdir(path): \n",
    "           raise\n",
    "\n",
    "\n",
    "# 경로 설정 변수\n",
    "readErrorImageList = []\n",
    "saveErrorImageList = []\n",
    "noneFaceErrorImageList = []\n",
    "labels = [ 'Heart', 'Oblong', 'Oval', 'Round', 'Square' ]\n",
    "setTypes = [ 'testing', 'training' ]\n",
    "\n",
    "# 얼굴부분 crop\n",
    "# haarcascade 불러오기\n",
    "face_cascade = cv2.CascadeClassifier(\n",
    "    cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
    "\n",
    "# Main\n",
    "for setType in setTypes:\n",
    "    print(\"Processing \"+setType)\n",
    "    for label in labels:\n",
    "        print(\"Processing \"+label)\n",
    "        IMAGE_DIR = './imageSet/'+setType+'_set/'+label+'/'\n",
    "        colorAug_DIR ='./imageSet/colorAug_'+setType+'_image/'+label+'/'\n",
    "        makedirs(IMAGE_DIR)\n",
    "        makedirs(colorAug_DIR)\n",
    "        IMAGE_FILES = glob.glob(IMAGE_DIR+'*.jpg')\n",
    "        \n",
    "        # 표현되는 랜드마크의 굵기와 반경\n",
    "        drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=0)\n",
    "        \n",
    "        with mp_face_mesh.FaceMesh(\n",
    "                static_image_mode=True,\n",
    "                max_num_faces=1,\n",
    "                refine_landmarks=True,\n",
    "                min_detection_confidence=0.5) as face_mesh:\n",
    "            for idx, file in enumerate(IMAGE_FILES):\n",
    "                currentFileName = os.path.basename(file)\n",
    "\n",
    "\n",
    "                # 이미지 불러오기\n",
    "                image = cv2.imread(file)\n",
    "                if image is None:\n",
    "                    print(\"READ ERROR!!\" + currentFileName)\n",
    "                    readErrorImageList.append(currentFileName)\n",
    "                    continue\n",
    "\n",
    "                # 얼굴 crop\n",
    "                faces = face_cascade.detectMultiScale(image, 1.3, 5)\n",
    "                if len(faces) > 0:\n",
    "                    print(faces)\n",
    "                    color = (0, 0, 255)\n",
    "                    for (x, y, w, h) in faces:\n",
    "                        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)\n",
    "                        cropped = image[y: y+h, x: x+w]\n",
    "                        \n",
    "                        img_yuv = cv2.cvtColor(cropped, cv2.COLOR_BGR2YUV)\n",
    "\n",
    "                        # equalize the histogram of the Y channel\n",
    "                        img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\n",
    "\n",
    "                        # convert the YUV image back to RGB format\n",
    "                        equalized = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\n",
    "\n",
    "                        #edge enhancement\n",
    "                        kernel = np.array([[0, -1, 0],\n",
    "                                    [-1, 5,-1],\n",
    "                                    [0, -1, 0]])\n",
    "                        image_sharp = cv2.filter2D(src=equalized, ddepth=-1, kernel=kernel)\n",
    "                        \n",
    "                    writeReturn = cv2.imwrite(colorAug_DIR+ currentFileName, image_sharp)\n",
    "                    if writeReturn == True:\n",
    "                        print(\"successfully saved image!!\" + currentFileName)\n",
    "                    else: \n",
    "                        print(\"SAVE ERROR!!\" + currentFileName)\n",
    "                        saveErrorImageList.append(currentFileName)\n",
    "                else:\n",
    "                    print(\"SAVE ERROR!!\" + currentFileName)\n",
    "                    noneFaceErrorImageList.append(currentFileName)\n",
    "                \n",
    "        print(\"Done \"+label)\n",
    "    print(\"Done \"+setType)\n",
    "    \n",
    "for idx, readErrorImage in enumerate(readErrorImageList):\n",
    "    print(idx, \"Read Error Image: \", readErrorImage)\n",
    "    \n",
    "for idx, saveErrorImage in enumerate(saveErrorImageList):\n",
    "    print(idx, \"Save Error Image: \", saveErrorImage)\n",
    "    \n",
    "for idx, noneFaceErrorImage in enumerate(noneFaceErrorImageList):\n",
    "    print(idx, \"None Face Error Image: \", noneFaceErrorImage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "\n",
    "import cv2\n",
    "import mediapipe as mp\n",
    "import numpy as np\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_drawing_styles = mp.solutions.drawing_styles\n",
    "mp_face_mesh = mp.solutions.face_mesh\n",
    "\n",
    "## 함수\n",
    "# 디렉토리가 존재하지 않으면 생성\n",
    "def makedirs(path): \n",
    "   try: \n",
    "        os.makedirs(path) \n",
    "   except OSError: \n",
    "       if not os.path.isdir(path): \n",
    "           raise\n",
    "\n",
    "\n",
    "# 경로 설정 변수\n",
    "readErrorImageList = []\n",
    "saveErrorImageList = []\n",
    "noneFaceErrorImageList = []\n",
    "labels = [ 'Heart', 'Oblong', 'Oval', 'Round', 'Square' ]\n",
    "setTypes = [ 'testing', 'training' ]\n",
    "\n",
    "# 얼굴부분 crop\n",
    "# haarcascade 불러오기\n",
    "face_cascade = cv2.CascadeClassifier(\n",
    "    cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
    "\n",
    "# Main\n",
    "for setType in setTypes:\n",
    "    print(\"Processing \"+setType)\n",
    "    for label in labels:\n",
    "        print(\"Processing \"+label)\n",
    "        IMAGE_DIR = './imageSet/'+setType+'_set/'+label+'/'\n",
    "        grayAug_DIR ='./imageSet/grayAug_'+setType+'_image/'+label+'/'\n",
    "        makedirs(IMAGE_DIR)\n",
    "        makedirs(grayAug_DIR)\n",
    "        IMAGE_FILES = glob.glob(IMAGE_DIR+'*.jpg')\n",
    "        \n",
    "        # 표현되는 랜드마크의 굵기와 반경\n",
    "        drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=0)\n",
    "        \n",
    "        with mp_face_mesh.FaceMesh(\n",
    "                static_image_mode=True,\n",
    "                max_num_faces=1,\n",
    "                refine_landmarks=True,\n",
    "                min_detection_confidence=0.5) as face_mesh:\n",
    "            for idx, file in enumerate(IMAGE_FILES):\n",
    "                currentFileName = os.path.basename(file)\n",
    "\n",
    "\n",
    "                # 이미지 불러오기\n",
    "                image = cv2.imread(file)\n",
    "                if image is None:\n",
    "                    print(\"READ ERROR!!\" + currentFileName)\n",
    "                    readErrorImageList.append(currentFileName)\n",
    "                    continue\n",
    "                gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "                # 얼굴 crop\n",
    "                faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n",
    "                if len(faces) > 0:\n",
    "                    print(faces)\n",
    "                    color = (0, 0, 255)\n",
    "                    for (x, y, w, h) in faces:\n",
    "                        cv2.rectangle(gray, (x, y), (x + w, y + h), (255, 0, 0), 2)\n",
    "                        cropped = gray[y: y+h, x: x+w]\n",
    "                        \n",
    "                        equalized = cv2.equalizeHist(cropped)\n",
    "\n",
    "                        #edge enhancement\n",
    "                        kernel = np.array([[0, -1, 0],\n",
    "                                    [-1, 5,-1],\n",
    "                                    [0, -1, 0]])\n",
    "                        image_sharp = cv2.filter2D(src=equalized, ddepth=-1, kernel=kernel)\n",
    "                        \n",
    "                    writeReturn = cv2.imwrite(grayAug_DIR+ currentFileName, image_sharp)\n",
    "                    if writeReturn == True:\n",
    "                        print(\"successfully saved image!!\" + currentFileName)\n",
    "                    else: \n",
    "                        print(\"SAVE ERROR!!\" + currentFileName)\n",
    "                        saveErrorImageList.append(currentFileName)\n",
    "                else:\n",
    "                    print(\"SAVE ERROR!!\" + currentFileName)\n",
    "                    noneFaceErrorImageList.append(currentFileName)\n",
    "                \n",
    "        print(\"Done \"+label)\n",
    "    print(\"Done \"+setType)\n",
    "    \n",
    "for idx, readErrorImage in enumerate(readErrorImageList):\n",
    "    print(idx, \"Read Error Image: \", readErrorImage)\n",
    "    \n",
    "for idx, saveErrorImage in enumerate(saveErrorImageList):\n",
    "    print(idx, \"Save Error Image: \", saveErrorImage)\n",
    "    \n",
    "for idx, noneFaceErrorImage in enumerate(noneFaceErrorImageList):\n",
    "    print(idx, \"None Face Error Image: \", noneFaceErrorImage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing testing Heart\n",
      "Done Heart\n",
      "Processing testing Oblong\n",
      "Done Oblong\n",
      "Processing testing Oval\n",
      "Done Oval\n",
      "Processing testing Round\n",
      "Done Round\n",
      "Processing testing Square\n",
      "Done Square\n",
      "Done testing\n",
      "Processing training Heart\n",
      "Done Heart\n",
      "Processing training Oblong\n",
      "Done Oblong\n",
      "Processing training Oval\n",
      "Done Oval\n",
      "Processing training Round\n",
      "Done Round\n",
      "Processing training Square\n",
      "Done Square\n",
      "Done training\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import os\n",
    "from PIL import Image, ImageOps\n",
    "\n",
    "import cv2\n",
    "import mediapipe as mp\n",
    "import numpy as np\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_drawing_styles = mp.solutions.drawing_styles\n",
    "mp_face_mesh = mp.solutions.face_mesh\n",
    "\n",
    "## 함수\n",
    "# 디렉토리가 존재하지 않으면 생성\n",
    "def makedirs(path): \n",
    "   try: \n",
    "        os.makedirs(path) \n",
    "   except OSError: \n",
    "       if not os.path.isdir(path): \n",
    "           raise\n",
    "\n",
    "\n",
    "# 경로 설정 변수\n",
    "readErrorImageList = []\n",
    "saveErrorImageList = []\n",
    "noneFaceErrorImageList = []\n",
    "labels = [ 'Heart', 'Oblong', 'Oval', 'Round', 'Square' ]\n",
    "setTypes = [ 'testing', 'training' ]\n",
    "\n",
    "# Main\n",
    "for setType in setTypes:\n",
    "    for label in labels:\n",
    "        print(\"Processing \"+setType+\" \"+label)\n",
    "        grayAug_DIR ='./imageSet/colorAug/colorAug_'+setType+'_image/'+label+'/'\n",
    "        color_poster_DIR ='./imageSet/color_poster_'+setType+'_image/'+label+'/'\n",
    "        makedirs(color_poster_DIR)\n",
    "        IMAGE_FILES = glob.glob(grayAug_DIR+'*.jpg')\n",
    "        \n",
    "        # 표현되는 랜드마크의 굵기와 반경\n",
    "        drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=0)\n",
    "        \n",
    "        for idx, file in enumerate(IMAGE_FILES):\n",
    "            currentFileName = os.path.basename(file)\n",
    "            \n",
    "            # 이미지 불러오기\n",
    "            image = Image.open(file)\n",
    "            if image is None:\n",
    "                print(\"READ ERROR!!\" + currentFileName)\n",
    "                readErrorImageList.append(currentFileName)\n",
    "                continue\n",
    "\n",
    "            image2 = ImageOps.posterize(image, 2)\n",
    "            image2.save(color_poster_DIR + currentFileName)\n",
    "            \n",
    "\n",
    "        print(\"Done \"+label)\n",
    "    print(\"Done \"+setType)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.6.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image, ImageOps\n",
    "\n",
    "print(cv2.__version__)\n",
    "\n",
    "# 얼굴부분 crop\n",
    "# haarcascade 불러오기\n",
    "face_cascade = cv2.CascadeClassifier(\n",
    "    cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
    "\n",
    "# 이미지 불러오기\n",
    "img = cv2.imread('../Face-Shape-EfficientNetB5/imageSet/training_set/Heart/heart (2).jpg')\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "gray = cv2.resize(gray, (200, 200))\n",
    "\n",
    "# 얼굴 찾기\n",
    "faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n",
    "for (x, y, w, h) in faces:\n",
    "    cv2.rectangle(gray, (x, y), (x + w, y + h), (255, 0, 0), 2)\n",
    "    cropped = gray[y: y+h, x: x+w]\n",
    "    \n",
    "    equalized = cv2.equalizeHist(cropped)\n",
    "\n",
    "    #edge enhancement\n",
    "    kernel = np.array([[0, -1, 0],\n",
    "                   [-1, 5,-1],\n",
    "                   [0, -1, 0]])\n",
    "    image_sharp = cv2.filter2D(src=equalized, ddepth=-1, kernel=kernel)\n",
    "    \n",
    "    # 이미지 저장하기\n",
    "    #cv2.imwrite(\"./imageSet/croptest.jpg\", image_sharp)\n",
    "    #cv2.imshow('image_sharp', image_sharp)\n",
    "    #cv2.waitKey()\n",
    "    #cv2.destroyWindow('image_sharp')\n",
    "\n",
    "    fig = plt.figure()\n",
    "\n",
    "    ax1 = fig.add_subplot(2, 2, 1)\n",
    "    ax1.imshow(gray) \n",
    "    ax1.set_title('gray') \n",
    "    ax1.axis(\"off\") \n",
    "    \n",
    "    ax2 = fig.add_subplot(2, 2, 2)\n",
    "    ax2.imshow(cropped)\n",
    "    ax2.set_title('cropped')\n",
    "    ax2.axis(\"off\")\n",
    "\n",
    "    ax1 = fig.add_subplot(2, 2, 3)\n",
    "    ax1.imshow(image_sharp) \n",
    "    ax1.set_title('image_sharp') \n",
    "    ax1.axis(\"off\") \n",
    "    \n",
    "    ax2 = fig.add_subplot(2, 2, 4)\n",
    "    ax2.imshow(equalized)\n",
    "    ax2.set_title('equalized')\n",
    "    ax2.axis(\"off\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image, ImageOps\n",
    "\n",
    "# 얼굴부분 crop\n",
    "# haarcascade 불러오기\n",
    "face_cascade = cv2.CascadeClassifier(\n",
    "    cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
    "\n",
    "# 이미지 불러오기\n",
    "img = cv2.imread('./imageSet/training_set/Heart/heart (2).jpg')\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# 얼굴 찾기\n",
    "faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n",
    "for (x, y, w, h) in faces:\n",
    "    cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)\n",
    "    cropped = img[y: y+h, x: x+w]\n",
    "    resize = cv2.resize(cropped, (200, 200))\n",
    "\n",
    "    cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)\n",
    "\n",
    "    \n",
    "    img_yuv = cv2.cvtColor(cropped, cv2.COLOR_BGR2YUV)\n",
    "\n",
    "    # equalize the histogram of the Y channel\n",
    "    img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\n",
    "\n",
    "    # convert the YUV image back to RGB format\n",
    "    img_output = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\n",
    "\n",
    "    #edge enhancement\n",
    "    kernel = np.array([[0, -1, 0],\n",
    "                   [-1, 5,-1],\n",
    "                   [0, -1, 0]])\n",
    "    image_sharp_c = cv2.filter2D(src=img_output, ddepth=-1, kernel=kernel)\n",
    "\n",
    "    # 이미지 저장하기\n",
    "    cv2.imwrite(\"./imageSet/croptest.jpg\", resize)\n",
    "\n",
    "    #cv2.imshow(\"crop&resize\", resize)\n",
    "    #cv2.waitKey(0)\n",
    "    #cv2.destroyAllWindows()\n",
    "\n",
    "    fig = plt.figure()\n",
    "\n",
    "    ax1 = fig.add_subplot(2, 2, 1)\n",
    "    ax1.imshow(img) \n",
    "    ax1.set_title('img') \n",
    "    ax1.axis(\"off\") \n",
    "    \n",
    "    ax2 = fig.add_subplot(2, 2, 2)\n",
    "    ax2.imshow(cropped)\n",
    "    ax2.set_title('cropped')\n",
    "    ax2.axis(\"off\")\n",
    "\n",
    "    ax1 = fig.add_subplot(2, 2, 3)\n",
    "    ax1.imshow(image_sharp_c) \n",
    "    ax1.set_title('image_sharp') \n",
    "    ax1.axis(\"off\")\n",
    "    \n",
    "    ax2 = fig.add_subplot(2, 2, 4)\n",
    "    ax2.imshow(img_output)\n",
    "    ax2.set_title('equalized')\n",
    "    ax2.axis(\"off\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "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": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "6cc7455720652bebfd4a10eed685e54721d3b8ad110f1c429458684b8192be96"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}