png2hdf5.ipynb

{
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     "text": [
      "/c/Users/Clive/anaconda3/envs/ml/lib/python3.7/site-packages/PIL/TiffImagePlugin.py:754: UserWarning: Possibly corrupt EXIF data.  Expecting to read 7864320 bytes but only got 0. Skipping tag 0\n",
      "  \" Skipping tag %s\" % (size, len(data), tag))\n"
     ]
    },
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      "1617000\n",
      "defaultdict(<class 'int'>, {(18, 24, 4): 17, (27, 21, 4): 5, (27, 22, 4): 4, (27, 20, 4): 6, (27, 18, 4): 4, (27, 19, 4): 4, (30, 30, 4): 237, (28, 25, 4): 14, (27, 39, 4): 1, (32, 24, 4): 6, (27, 29, 4): 1, (27, 23, 4): 2, (32, 25, 4): 6, (28, 21, 4): 34, (30, 36, 4): 7, (27, 17, 4): 2, (24, 24, 4): 62, (27, 27, 4): 7, (32, 32, 4): 25, (28, 28, 4): 1504517, (30, 40, 4): 7, (30, 19, 4): 2, (28, 19, 4): 12, (28, 41, 4): 1, (30, 28, 4): 11, (30, 29, 4): 8, (30, 21, 4): 9, (30, 24, 4): 21, (29, 22, 4): 1, (22, 37, 4): 2, (18, 41, 4): 1, (20, 37, 4): 1, (30, 70, 4): 2, (25, 70, 4): 1, (22, 52, 4): 1, (21, 70, 4): 3, (30, 23, 4): 28, (28, 22, 4): 23, (37, 28, 4): 1, (36, 28, 4): 2, (27, 36, 4): 2, (32, 22, 4): 4, (25, 32, 4): 7, (36, 29, 4): 1, (32, 83, 4): 1, (26, 30, 4): 2, (26, 25, 4): 4, (23, 74, 4): 1, (26, 74, 4): 1, (22, 62, 4): 1, (24, 46, 4): 1, (26, 26, 4): 21, (19, 19, 4): 2, (19, 66, 4): 1, (25, 60, 4): 2, (32, 41, 4): 1, (27, 31, 4): 1, (32, 28, 4): 5, (26, 23, 4): 2, (24, 74, 4): 1, (9, 44, 4): 3, (18, 44, 4): 2, (16, 88, 4): 1, (12, 88, 4): 1, (18, 18, 4): 35, (29, 35, 3): 2, (29, 35, 4): 2, (28, 28): 4712, (28, 27, 4): 33, (24, 30, 4): 5, (21, 30, 4): 2, (30, 20, 4): 15, (30, 27, 4): 8, (30, 22, 4): 10, (22, 22, 4): 1, (20, 60, 4): 2, (33, 30, 4): 1, (37, 30, 4): 2, (24, 80, 4): 1, (17, 65, 4): 1, (20, 70, 4): 1, (22, 67, 4): 1, (37, 25, 4): 3, (29, 25, 4): 1, (30, 34, 4): 5, (18, 60, 4): 5, (35, 34, 4): 1, (25, 120, 4): 1, (25, 93, 4): 3, (30, 111, 3): 1, (30, 30, 3): 6, (32, 23, 4): 4, (14, 68, 4): 1, (24, 67, 4): 1, (18, 97, 4): 1, (24, 95, 4): 1, (25, 30, 4): 64, (15, 90, 4): 1, (30, 120, 4): 5, (26, 22, 4): 1, (20, 15, 4): 1, (20, 17, 4): 2, (20, 30, 4): 2, (28, 28, 3): 71858, (30, 98, 4): 2, (15, 94, 4): 1, (30, 110, 4): 1, (30, 125, 4): 1, (30, 100, 3): 1, (32, 29, 4): 8, (27, 25, 4): 2, (30, 107, 4): 1, (27, 34, 4): 1, (29, 32, 4): 3, (32, 80, 4): 1, (18, 21, 4): 14, (30, 25, 4): 14, (28, 71, 4): 1, (63, 30, 4): 1, (30, 63, 4): 2, (16, 59, 4): 1, (16, 44, 4): 5, (16, 42, 4): 1, (16, 46, 4): 2, (30, 60, 4): 28, (18, 20, 4): 14, (22, 20, 4): 1, (18, 27, 4): 1, (42, 27, 4): 1, (42, 20, 4): 1, (24, 29, 4): 1, (30, 54, 4): 1, (24, 18, 4): 2, (24, 20, 4): 4, (22, 33, 4): 2, (33, 34, 4): 1, (22, 103, 4): 8, (23, 109, 4): 2, (60, 45, 4): 1, (33, 25, 4): 1, (14, 11, 4): 1, (24, 35, 4): 1, (23, 24, 4): 1, (31, 21, 4): 4, (32, 17, 4): 3, (32, 31, 4): 7, (31, 32, 4): 8, (26, 32, 4): 61, (29, 31, 4): 4, (31, 30, 4): 4, (32, 27, 4): 5, (27, 32, 4): 9, (28, 32, 4): 5, (32, 20, 4): 2, (32, 21, 4): 3, (30, 49, 4): 2, (22, 91, 4): 1, (23, 71, 4): 2, (30, 32, 4): 2, (35, 30, 4): 1, (28, 30, 4): 2, (30, 43, 4): 7, (30, 32): 1, (30, 50, 4): 2, (30, 30): 2, (30, 39, 4): 1, (25, 25, 4): 11, (20, 81, 4): 1, (20, 33, 4): 1, (24, 60, 4): 2, (30, 114, 4): 1, (30, 184, 4): 1, (30, 132, 4): 1, (30, 79, 4): 1, (30, 172, 4): 2, (30, 140, 4): 1, (30, 180, 4): 2, (27, 35, 4): 1, (17, 45, 4): 6, (30, 56, 4): 1, (30, 48, 4): 1, (30, 31, 4): 2, (30, 33, 4): 3, (30, 45, 4): 1, (30, 42, 4): 1, (30, 38, 4): 1, (30, 26, 4): 6, (18, 11): 1, (29, 34, 4): 1, (32, 30, 4): 1, (21, 60, 4): 3, (28, 20, 4): 18, (28, 23, 4): 23, (28, 56, 4): 37, (28, 31, 4): 2, (25, 27, 4): 1, (28, 29, 4): 13, (26, 28, 4): 8, (28, 24, 4): 17, (25, 28, 4): 8, (17, 18, 4): 1, (22, 28, 4): 7, (20, 28, 4): 4, (28, 36, 4): 117, (18, 40, 4): 2, (44, 40, 4): 1, (29, 70, 4): 1, (32, 48, 4): 1, (28, 16, 4): 1, (27, 28, 4): 15, (23, 28, 4): 11, (28, 26, 4): 13, (18, 28, 4): 5, (18, 17, 4): 1, (28, 18, 4): 7, (24, 39, 4): 1, (24, 62, 4): 1, (24, 32, 4): 1, (24, 92, 4): 1, (28, 39, 4): 1, (28, 42, 4): 1, (21, 28, 4): 4, (17, 28, 4): 2, (24, 28, 4): 8, (23, 60, 4): 1, (8, 60, 4): 1, (11, 60, 4): 2, (19, 60, 4): 2, (26, 60, 4): 2, (10, 60, 4): 1, (13, 60, 4): 10, (16, 60, 4): 1, (17, 60, 4): 2, (15, 60, 4): 4, (12, 60, 4): 1, (19, 30, 4): 2, (28, 15, 4): 2, (24, 27, 4): 1, (34, 30, 4): 1, (20, 20): 1, (29, 27, 4): 1, (28, 17, 4): 3, (28, 50, 4): 47, (16, 28, 4): 5, (27, 44, 4): 4, (25, 35, 4): 7, (24, 49, 4): 8, (28, 38, 4): 6, (28, 47, 4): 17, (29, 47, 4): 1, (20, 50, 4): 6, (28, 35, 4): 1, (27, 57, 4): 7, (37, 37, 4): 8, (101, 112, 4): 2, (56, 56, 4): 313, (29, 28, 4): 12, (112, 112, 4): 1032, (46, 28, 4): 1, (208, 251, 4): 1, (19, 28, 4): 1, (32, 32, 3): 2, (36, 36, 4): 7, (12, 12, 4): 2, (73, 72, 3): 1, (73, 72, 4): 1, (27, 24, 4): 2, (34, 27, 4): 1, (34, 29, 4): 1, (34, 26, 4): 1, (29, 29, 4): 8, (960, 960, 4): 1, (22, 29, 4): 2, (112, 112, 3): 107, (13, 28, 4): 1, (27, 28, 3): 1, (31, 31, 4): 5, (1229, 1229, 4): 9, (930, 525, 4): 1, (25, 21, 4): 1, (39, 39, 4): 1, (27, 26, 4): 1, (126, 117, 4): 1, (128, 128, 4): 3, (18, 18, 3): 2, (512, 512, 4): 2, (66, 66, 4): 2, (633, 633, 4): 1, (28, 24, 3): 1, (50, 50, 4): 1, (39, 25, 4): 1, (2165, 2019, 4): 1, (72, 72, 4): 2, (38, 28, 4): 2, (28, 40, 4): 2, (56, 56, 3): 37, (43, 43, 4): 1, (49, 36, 4): 1, (48, 32, 4): 1, (51, 37, 4): 2, (43, 37, 4): 1, (49, 31, 4): 1, (43, 40, 4): 1, (55, 32, 4): 1, (45, 23, 4): 1, (45, 31, 4): 1, (43, 26, 4): 1, (44, 34, 4): 1, (12, 28, 4): 1, (23, 27, 4): 1, (12, 27, 4): 1, (33, 28, 4): 1, (2048, 2048, 4): 1, (72, 72, 3): 1, (100, 100, 4): 1, (28, 25): 1, (178, 178, 4): 1, (4434, 4042, 4): 2, (724, 513, 4): 1, (767, 710, 4): 2, (333, 421, 4): 1, (609, 640, 4): 1, (894, 894, 4): 1, (881, 900, 4): 1, (112, 113, 4): 1, (300, 300, 4): 5, (57, 56, 4): 1, (16, 16, 4): 3, (56, 63, 4): 1, (58, 58, 4): 2, (78, 78, 4): 1, (111, 112, 4): 1, (28, 27, 3): 2, (202, 108, 4): 1, (760, 1348, 4): 1, (35, 35, 4): 1, (583, 432, 4): 1, (400, 400, 4): 1, (108, 108, 4): 1, (233, 233, 4): 1, (293, 293, 4): 1, (108, 97, 4): 1, (56, 71, 4): 1, (244, 375, 3): 1, (750, 750, 4): 1, (112, 105, 4): 2, (54, 54, 4): 2, (395, 345, 4): 1, (262, 315, 4): 2, (1252, 1252, 4): 1, (360, 480, 4): 1, (474, 456, 4): 2, (28, 67, 4): 1, (152, 152, 4): 1, (375, 249, 4): 3, (178, 167, 4): 4, (490, 406, 4): 4, (89, 29, 4): 1, (28, 22, 3): 1, (154, 150, 4): 1, (79, 79, 4): 1, (111, 112, 3): 1, (311, 309, 4): 1, (18, 12, 4): 1, (14, 14, 4): 2, (28, 37, 4): 1, (122, 112, 4): 2, (112, 112): 2, (570, 570, 4): 1, (63, 63, 4): 1, (1000, 1000, 4): 1, (380, 266, 3): 1, (171, 294, 4): 1, (61, 73, 4): 1, (170, 170, 4): 1, (59, 60, 4): 1, (320, 320, 4): 1, (94, 89, 4): 1, (31, 34, 4): 1, (56, 57, 4): 1, (46, 56, 4): 1, (302, 555, 4): 1, (29, 29, 3): 1, (26, 26, 3): 1, (394, 387, 3): 1})\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import imageio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "\n",
    "imgshapes = defaultdict(int)\n",
    "imgerrors = defaultdict(int)\n",
    "\n",
    "for i in range(1617254):\n",
    "    if i % 1000 == 0:\n",
    "        print(i)\n",
    "    try:\n",
    "        img = imageio.imread(\"dataset/\" + str(i+1) + \".png\")\n",
    "    except Exception as e:\n",
    "        imgerrors[str(e)] += 1\n",
    "    else:\n",
    "        imgshapes[img.shape] += 1\n",
    "\n",
    "print(imgshapes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[((28, 28, 4), 1504517), ((28, 28, 3), 71858), ((28, 28), 4712), ((112, 112, 4), 1032), ((56, 56, 4), 313), ((30, 30, 4), 237), ((28, 36, 4), 117), ((112, 112, 3), 107), ((25, 30, 4), 64), ((24, 24, 4), 62), ((26, 32, 4), 61), ((28, 50, 4), 47), ((28, 56, 4), 37), ((56, 56, 3), 37), ((18, 18, 4), 35), ((28, 21, 4), 34), ((28, 27, 4), 33), ((30, 23, 4), 28), ((30, 60, 4), 28), ((32, 32, 4), 25), ((28, 22, 4), 23), ((28, 23, 4), 23), ((30, 24, 4), 21), ((26, 26, 4), 21), ((28, 20, 4), 18), ((18, 24, 4), 17), ((28, 24, 4), 17), ((28, 47, 4), 17), ((30, 20, 4), 15), ((27, 28, 4), 15), ((28, 25, 4), 14), ((18, 21, 4), 14), ((30, 25, 4), 14), ((18, 20, 4), 14), ((28, 29, 4), 13), ((28, 26, 4), 13), ((28, 19, 4), 12), ((29, 28, 4), 12), ((30, 28, 4), 11), ((25, 25, 4), 11), ((23, 28, 4), 11), ((30, 22, 4), 10), ((13, 60, 4), 10), ((30, 21, 4), 9), ((27, 32, 4), 9), ((1229, 1229, 4), 9), ((30, 29, 4), 8), ((30, 27, 4), 8), ((32, 29, 4), 8), ((22, 103, 4), 8), ((31, 32, 4), 8), ((26, 28, 4), 8), ((25, 28, 4), 8), ((24, 28, 4), 8), ((24, 49, 4), 8), ((37, 37, 4), 8), ((29, 29, 4), 8), ((30, 36, 4), 7), ((27, 27, 4), 7), ((30, 40, 4), 7), ((25, 32, 4), 7), ((32, 31, 4), 7), ((30, 43, 4), 7), ((22, 28, 4), 7), ((28, 18, 4), 7), ((25, 35, 4), 7), ((27, 57, 4), 7), ((36, 36, 4), 7), ((27, 20, 4), 6), ((32, 24, 4), 6), ((32, 25, 4), 6), ((30, 30, 3), 6), ((17, 45, 4), 6), ((30, 26, 4), 6), ((28, 38, 4), 6), ((20, 50, 4), 6), ((27, 21, 4), 5), ((32, 28, 4), 5), ((24, 30, 4), 5), ((30, 34, 4), 5), ((18, 60, 4), 5), ((30, 120, 4), 5), ((16, 44, 4), 5), ((32, 27, 4), 5), ((28, 32, 4), 5), ((18, 28, 4), 5), ((16, 28, 4), 5), ((31, 31, 4), 5), ((300, 300, 4), 5), ((27, 22, 4), 4), ((27, 18, 4), 4), ((27, 19, 4), 4), ((32, 22, 4), 4), ((26, 25, 4), 4), ((32, 23, 4), 4), ((24, 20, 4), 4), ((31, 21, 4), 4), ((29, 31, 4), 4), ((31, 30, 4), 4), ((20, 28, 4), 4), ((21, 28, 4), 4), ((15, 60, 4), 4), ((27, 44, 4), 4), ((178, 167, 4), 4), ((490, 406, 4), 4), ((21, 70, 4), 3), ((9, 44, 4), 3), ((37, 25, 4), 3), ((25, 93, 4), 3), ((29, 32, 4), 3), ((32, 17, 4), 3), ((32, 21, 4), 3), ((30, 33, 4), 3), ((21, 60, 4), 3), ((28, 17, 4), 3), ((128, 128, 4), 3), ((16, 16, 4), 3), ((375, 249, 4), 3), ((27, 23, 4), 2), ((27, 17, 4), 2), ((30, 19, 4), 2), ((22, 37, 4), 2), ((30, 70, 4), 2), ((36, 28, 4), 2), ((27, 36, 4), 2), ((26, 30, 4), 2), ((19, 19, 4), 2), ((25, 60, 4), 2), ((26, 23, 4), 2), ((18, 44, 4), 2), ((29, 35, 3), 2), ((29, 35, 4), 2), ((21, 30, 4), 2), ((20, 60, 4), 2), ((37, 30, 4), 2), ((20, 17, 4), 2), ((20, 30, 4), 2), ((30, 98, 4), 2), ((27, 25, 4), 2), ((30, 63, 4), 2), ((16, 46, 4), 2), ((24, 18, 4), 2), ((22, 33, 4), 2), ((23, 109, 4), 2), ((32, 20, 4), 2), ((30, 49, 4), 2), ((23, 71, 4), 2), ((30, 32, 4), 2), ((28, 30, 4), 2), ((30, 50, 4), 2), ((30, 30), 2), ((24, 60, 4), 2), ((30, 172, 4), 2), ((30, 180, 4), 2), ((30, 31, 4), 2), ((28, 31, 4), 2), ((18, 40, 4), 2), ((17, 28, 4), 2), ((11, 60, 4), 2), ((19, 60, 4), 2), ((26, 60, 4), 2), ((17, 60, 4), 2), ((19, 30, 4), 2), ((28, 15, 4), 2), ((101, 112, 4), 2), ((32, 32, 3), 2), ((12, 12, 4), 2), ((27, 24, 4), 2), ((22, 29, 4), 2), ((18, 18, 3), 2), ((512, 512, 4), 2), ((66, 66, 4), 2), ((72, 72, 4), 2), ((38, 28, 4), 2), ((28, 40, 4), 2), ((51, 37, 4), 2), ((4434, 4042, 4), 2), ((767, 710, 4), 2), ((58, 58, 4), 2), ((28, 27, 3), 2), ((112, 105, 4), 2), ((54, 54, 4), 2), ((262, 315, 4), 2), ((474, 456, 4), 2), ((14, 14, 4), 2), ((122, 112, 4), 2), ((112, 112), 2), ((27, 39, 4), 1), ((27, 29, 4), 1), ((28, 41, 4), 1), ((29, 22, 4), 1), ((18, 41, 4), 1), ((20, 37, 4), 1), ((25, 70, 4), 1), ((22, 52, 4), 1), ((37, 28, 4), 1), ((36, 29, 4), 1), ((32, 83, 4), 1), ((23, 74, 4), 1), ((26, 74, 4), 1), ((22, 62, 4), 1), ((24, 46, 4), 1), ((19, 66, 4), 1), ((32, 41, 4), 1), ((27, 31, 4), 1), ((24, 74, 4), 1), ((16, 88, 4), 1), ((12, 88, 4), 1), ((22, 22, 4), 1), ((33, 30, 4), 1), ((24, 80, 4), 1), ((17, 65, 4), 1), ((20, 70, 4), 1), ((22, 67, 4), 1), ((29, 25, 4), 1), ((35, 34, 4), 1), ((25, 120, 4), 1), ((30, 111, 3), 1), ((14, 68, 4), 1), ((24, 67, 4), 1), ((18, 97, 4), 1), ((24, 95, 4), 1), ((15, 90, 4), 1), ((26, 22, 4), 1), ((20, 15, 4), 1), ((15, 94, 4), 1), ((30, 110, 4), 1), ((30, 125, 4), 1), ((30, 100, 3), 1), ((30, 107, 4), 1), ((27, 34, 4), 1), ((32, 80, 4), 1), ((28, 71, 4), 1), ((63, 30, 4), 1), ((16, 59, 4), 1), ((16, 42, 4), 1), ((22, 20, 4), 1), ((18, 27, 4), 1), ((42, 27, 4), 1), ((42, 20, 4), 1), ((24, 29, 4), 1), ((30, 54, 4), 1), ((33, 34, 4), 1), ((60, 45, 4), 1), ((33, 25, 4), 1), ((14, 11, 4), 1), ((24, 35, 4), 1), ((23, 24, 4), 1), ((22, 91, 4), 1), ((35, 30, 4), 1), ((30, 32), 1), ((30, 39, 4), 1), ((20, 81, 4), 1), ((20, 33, 4), 1), ((30, 114, 4), 1), ((30, 184, 4), 1), ((30, 132, 4), 1), ((30, 79, 4), 1), ((30, 140, 4), 1), ((27, 35, 4), 1), ((30, 56, 4), 1), ((30, 48, 4), 1), ((30, 45, 4), 1), ((30, 42, 4), 1), ((30, 38, 4), 1), ((18, 11), 1), ((29, 34, 4), 1), ((32, 30, 4), 1), ((25, 27, 4), 1), ((17, 18, 4), 1), ((44, 40, 4), 1), ((29, 70, 4), 1), ((32, 48, 4), 1), ((28, 16, 4), 1), ((18, 17, 4), 1), ((24, 39, 4), 1), ((24, 62, 4), 1), ((24, 32, 4), 1), ((24, 92, 4), 1), ((28, 39, 4), 1), ((28, 42, 4), 1), ((23, 60, 4), 1), ((8, 60, 4), 1), ((10, 60, 4), 1), ((16, 60, 4), 1), ((12, 60, 4), 1), ((24, 27, 4), 1), ((34, 30, 4), 1), ((20, 20), 1), ((29, 27, 4), 1), ((29, 47, 4), 1), ((28, 35, 4), 1), ((46, 28, 4), 1), ((208, 251, 4), 1), ((19, 28, 4), 1), ((73, 72, 3), 1), ((73, 72, 4), 1), ((34, 27, 4), 1), ((34, 29, 4), 1), ((34, 26, 4), 1), ((960, 960, 4), 1), ((13, 28, 4), 1), ((27, 28, 3), 1), ((930, 525, 4), 1), ((25, 21, 4), 1), ((39, 39, 4), 1), ((27, 26, 4), 1), ((126, 117, 4), 1), ((633, 633, 4), 1), ((28, 24, 3), 1), ((50, 50, 4), 1), ((39, 25, 4), 1), ((2165, 2019, 4), 1), ((43, 43, 4), 1), ((49, 36, 4), 1), ((48, 32, 4), 1), ((43, 37, 4), 1), ((49, 31, 4), 1), ((43, 40, 4), 1), ((55, 32, 4), 1), ((45, 23, 4), 1), ((45, 31, 4), 1), ((43, 26, 4), 1), ((44, 34, 4), 1), ((12, 28, 4), 1), ((23, 27, 4), 1), ((12, 27, 4), 1), ((33, 28, 4), 1), ((2048, 2048, 4), 1), ((72, 72, 3), 1), ((100, 100, 4), 1), ((28, 25), 1), ((178, 178, 4), 1), ((724, 513, 4), 1), ((333, 421, 4), 1), ((609, 640, 4), 1), ((894, 894, 4), 1), ((881, 900, 4), 1), ((112, 113, 4), 1), ((57, 56, 4), 1), ((56, 63, 4), 1), ((78, 78, 4), 1), ((111, 112, 4), 1), ((202, 108, 4), 1), ((760, 1348, 4), 1), ((35, 35, 4), 1), ((583, 432, 4), 1), ((400, 400, 4), 1), ((108, 108, 4), 1), ((233, 233, 4), 1), ((293, 293, 4), 1), ((108, 97, 4), 1), ((56, 71, 4), 1), ((244, 375, 3), 1), ((750, 750, 4), 1), ((395, 345, 4), 1), ((1252, 1252, 4), 1), ((360, 480, 4), 1), ((28, 67, 4), 1), ((152, 152, 4), 1), ((89, 29, 4), 1), ((28, 22, 3), 1), ((154, 150, 4), 1), ((79, 79, 4), 1), ((111, 112, 3), 1), ((311, 309, 4), 1), ((18, 12, 4), 1), ((28, 37, 4), 1), ((570, 570, 4), 1), ((63, 63, 4), 1), ((1000, 1000, 4), 1), ((380, 266, 3), 1), ((171, 294, 4), 1), ((61, 73, 4), 1), ((170, 170, 4), 1), ((59, 60, 4), 1), ((320, 320, 4), 1), ((94, 89, 4), 1), ((31, 34, 4), 1), ((56, 57, 4), 1), ((46, 56, 4), 1), ((302, 555, 4), 1), ((29, 29, 3), 1), ((26, 26, 3), 1), ((394, 387, 3), 1)]\n"
     ]
    }
   ],
   "source": [
    "print(sorted(imgshapes.items(), reverse=True, key=lambda x: x[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'int'>, {\"Could not find a format to read the specified file in mode 'i'\": 32504, 'unpack_from requires a buffer of at least 4 bytes': 171, 'string index out of range': 55, \"broken PNG file (bad header checksum in b'iCCP')\": 1, 'not a PSD file': 2, '(3, 32)': 3, 'read length must be non-negative or -1': 9, 'Could not seek to index 0': 25, 'index out of range': 5, 'Image index 0 > 0': 2, \"broken PNG file (bad header checksum in b'iTXt')\": 1, 'unknown pixel mode': 1, 'Decompressed Data Too Large': 1})\n"
     ]
    }
   ],
   "source": [
    "print(imgerrors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28, 4)\n",
      "uint8\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "img = cv2.imread(\"dataset/5402.png\", cv2.IMREAD_UNCHANGED); # load 'as is', don't convert to bgr\n",
    "print(img.shape)\n",
    "print(img.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(310000, 28, 28, 4)\n",
      "(320000, 28, 28, 4)\n",
      "(330000, 28, 28, 4)\n",
      "(340000, 28, 28, 4)\n",
      "(350000, 28, 28, 4)\n",
      "(360000, 28, 28, 4)\n",
      "(370000, 28, 28, 4)\n",
      "(380000, 28, 28, 4)\n",
      "(390000, 28, 28, 4)\n",
      "(400000, 28, 28, 4)\n",
      "(410000, 28, 28, 4)\n",
      "(420000, 28, 28, 4)\n",
      "(430000, 28, 28, 4)\n",
      "(440000, 28, 28, 4)\n",
      "(450000, 28, 28, 4)\n",
      "(460000, 28, 28, 4)\n",
      "(470000, 28, 28, 4)\n",
      "(480000, 28, 28, 4)\n",
      "(490000, 28, 28, 4)\n",
      "(500000, 28, 28, 4)\n",
      "(510000, 28, 28, 4)\n",
      "(520000, 28, 28, 4)\n",
      "(530000, 28, 28, 4)\n",
      "(540000, 28, 28, 4)\n",
      "(550000, 28, 28, 4)\n",
      "(560000, 28, 28, 4)\n",
      "(570000, 28, 28, 4)\n",
      "(580000, 28, 28, 4)\n",
      "(590000, 28, 28, 4)\n",
      "(600000, 28, 28, 4)\n",
      "(610000, 28, 28, 4)\n",
      "(620000, 28, 28, 4)\n",
      "(630000, 28, 28, 4)\n",
      "(640000, 28, 28, 4)\n",
      "(650000, 28, 28, 4)\n",
      "(660000, 28, 28, 4)\n",
      "(670000, 28, 28, 4)\n",
      "(680000, 28, 28, 4)\n",
      "(690000, 28, 28, 4)\n",
      "(700000, 28, 28, 4)\n",
      "(710000, 28, 28, 4)\n",
      "(720000, 28, 28, 4)\n",
      "(730000, 28, 28, 4)\n",
      "(740000, 28, 28, 4)\n",
      "(750000, 28, 28, 4)\n",
      "(760000, 28, 28, 4)\n",
      "(770000, 28, 28, 4)\n",
      "(780000, 28, 28, 4)\n",
      "(790000, 28, 28, 4)\n",
      "(800000, 28, 28, 4)\n",
      "(810000, 28, 28, 4)\n",
      "(820000, 28, 28, 4)\n",
      "(830000, 28, 28, 4)\n",
      "(840000, 28, 28, 4)\n",
      "(850000, 28, 28, 4)\n",
      "(860000, 28, 28, 4)\n",
      "(870000, 28, 28, 4)\n",
      "(880000, 28, 28, 4)\n",
      "(890000, 28, 28, 4)\n",
      "(900000, 28, 28, 4)\n",
      "(910000, 28, 28, 4)\n",
      "(920000, 28, 28, 4)\n",
      "(930000, 28, 28, 4)\n",
      "(940000, 28, 28, 4)\n",
      "(950000, 28, 28, 4)\n",
      "(960000, 28, 28, 4)\n",
      "(970000, 28, 28, 4)\n",
      "(980000, 28, 28, 4)\n",
      "(990000, 28, 28, 4)\n",
      "(1000000, 28, 28, 4)\n",
      "(1010000, 28, 28, 4)\n",
      "(1020000, 28, 28, 4)\n",
      "(1030000, 28, 28, 4)\n",
      "(1040000, 28, 28, 4)\n",
      "(1050000, 28, 28, 4)\n",
      "(1060000, 28, 28, 4)\n",
      "(1070000, 28, 28, 4)\n",
      "(1080000, 28, 28, 4)\n",
      "(1090000, 28, 28, 4)\n",
      "(1100000, 28, 28, 4)\n",
      "(1110000, 28, 28, 4)\n",
      "(1120000, 28, 28, 4)\n",
      "(1130000, 28, 28, 4)\n",
      "(1140000, 28, 28, 4)\n",
      "(1150000, 28, 28, 4)\n",
      "(1160000, 28, 28, 4)\n",
      "(1170000, 28, 28, 4)\n",
      "(1180000, 28, 28, 4)\n",
      "(1190000, 28, 28, 4)\n",
      "(1200000, 28, 28, 4)\n",
      "(1210000, 28, 28, 4)\n",
      "(1220000, 28, 28, 4)\n",
      "(1230000, 28, 28, 4)\n",
      "(1240000, 28, 28, 4)\n",
      "(1250000, 28, 28, 4)\n",
      "(1260000, 28, 28, 4)\n",
      "(1270000, 28, 28, 4)\n",
      "(1280000, 28, 28, 4)\n",
      "(1290000, 28, 28, 4)\n",
      "(1300000, 28, 28, 4)\n",
      "(1310000, 28, 28, 4)\n",
      "(1320000, 28, 28, 4)\n",
      "(1330000, 28, 28, 4)\n",
      "(1340000, 28, 28, 4)\n",
      "(1350000, 28, 28, 4)\n",
      "(1360000, 28, 28, 4)\n",
      "(1370000, 28, 28, 4)\n",
      "(1380000, 28, 28, 4)\n",
      "(1390000, 28, 28, 4)\n",
      "(1400000, 28, 28, 4)\n",
      "(1410000, 28, 28, 4)\n",
      "(1420000, 28, 28, 4)\n",
      "(1430000, 28, 28, 4)\n",
      "(1440000, 28, 28, 4)\n",
      "(1450000, 28, 28, 4)\n",
      "(1460000, 28, 28, 4)\n",
      "(1470000, 28, 28, 4)\n",
      "(1480000, 28, 28, 4)\n",
      "(1490000, 28, 28, 4)\n",
      "(1500000, 28, 28, 4)\n",
      "(1510000, 28, 28, 4)\n",
      "(1512076, 28, 28, 4)\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import h5py\n",
    "\n",
    "BLOCKSIZE = 1000\n",
    "CHUNKSIZE = 10000 # should be a multiple of the BLOCKSIZE for efficiency\n",
    "IMGSHAPE = (28, 28, 4)\n",
    "TOTAL = 1617254\n",
    "\n",
    "try:\n",
    "    with h5py.File('dataset.hdf5', 'x') as f:\n",
    "        dset = f.create_dataset(\"twitch_emotes\", (0,) + IMGSHAPE, dtype='uint8', chunks=(BLOCKSIZE,) + IMGSHAPE, maxshape=(None,) + IMGSHAPE, compression=\"gzip\", shuffle=True)\n",
    "except OSError:\n",
    "    pass\n",
    "\n",
    "i = 300001\n",
    "while i <= TOTAL:\n",
    "    image_chunk = []\n",
    "    while len(image_chunk) < CHUNKSIZE and i <= TOTAL:\n",
    "        try:\n",
    "            img = cv2.imread(\"dataset/\" + str(i+1) + \".png\", cv2.IMREAD_UNCHANGED); # load 'as is', don't convert to bgr\n",
    "            if img.shape == IMGSHAPE:\n",
    "                image_chunk.append(img)\n",
    "        except Exception as e:\n",
    "            pass\n",
    "        i += 1\n",
    "    with h5py.File('dataset.hdf5', 'r+') as f:\n",
    "        dset = f[\"twitch_emotes\"]\n",
    "        dset.resize(dset.shape[0] + len(image_chunk), axis=0)\n",
    "        dset[-len(image_chunk):] = image_chunk\n",
    "        print(dset.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xb3yX5rXTptH8eKBnfpFIqfhFIqXiF4mUil8kUip+kUip+EUipeIXiVRin9/MmgHcCaABgANY6e63mFktgLsAtADYAuAqd0/o+o7N/v17gtmb23jPd9HSmTQ/6+xWmq9ed3cwO7hjAx07eXoLzefO4dcJzJ1OYyxaED4ifC89vxvI9PP1+i++zI/B3tIZvsYAAGom1QSzI9v4semp7ADNF7TU0vzsK/4xmJ3UxN/3X0IfP8lInvmzAL7g7qcAeB+Az5nZKQBuAPC4u88D8HjudRE5TiQWv7vvdPcXcy93A3gVwAwAlwM4uv3NHQCuGK9Jikj+vaff+c2sBcBSAH8E0ODuO3PRLgz9WiAix4kRF7+Z1QC4B8D17n5geObujqG/Bxxr3AozazOzts5Ofm6ciBTOiIrfzDIYKvyfuvuvcje3m1lTLm8C0HGsse6+0t1b3b21vr4+H3MWkTxILH4zMwC3AXjV3W8eFt0P4Orcy1cDuC//0xOR8WJDP7GTNzA7B8DTANYAGMzdfCOGfu+/G8AsAFsx1OoL9+IALF58mj/0uweC+eqXX6VzSVXVBbNZM3nbJ2GnZuzv5q2ff/tqeKvml9dspGPPuOByms868XSaTx/opvns5vDjUjszvKQWANKpIzTf0s7XIz+3mi/53bouvH129yHeGU4ldI6bZ86gecNJ4SPd9/fwxzTdt5Pm131sKc1PPY3n48XMVrk771vnJPb53f0ZAKGF0Re+l4mJSOnQFX4ikVLxi0RKxS8SKRW/SKRU/CKRUvGLRKqgW3d3dx/CE0/9IZjXTOJXAFaWhXvx3YcP8bGZaprXTuH5xZecF8zWv8p73V3tO2g+fRZf0nuwjF/DcKCzPJgNHORbkpdN5Mdkz5nDnx+2rg9vjw0Ah8nntGEaPza9e88gzfd08aPPFy4Lr4WegvAyaADoPNxI81sf4/d9/YQ3aX7yPH7seiHomV8kUip+kUip+EUipeIXiZSKXyRSKn6RSKn4RSJV0D5/WXk56k4I9zd7e3nfFx7u++7dx/vZ5bwdjSm1vN+9cMHsYLZo8cl07J6EI7qrEzYb2NuVcJ1AfbifXTXhBDp2Rxffz+Gxp8Lr8QFg285dNN+/9/FgNq0xvA8BAAx6eNtvAKibxR/3XR3hbeOaGvljPnc6f1wOZPl1AD96lh8vfl1leKv5mc3NdGy+6JlfJFIqfpFIqfhFIqXiF4mUil8kUip+kUip+EUiVdA+v5mhrDz8/abCKun4snIy3Sxvprd30CMFUD2R931rqsNr5luXnkLHPvTken7f5DEBgJpZ4WsMAGDV+leC2ZGXEs5CqORHLHZ18iO6+/vbaN7XF+6179h8IJgBwEmtV9F8Yk0Vzbc/9+/BrH36Ejp2wRnn03zKFH7fk6fx8xJ+0tYbzD47hV+zMmkiv/5hpPTMLxIpFb9IpFT8IpFS8YtESsUvEikVv0ikVPwikUrs85tZM4A7ATQAcAAr3f0WM7sJwLUAjjZyb3T3BxPeF8pZrz7NvxeVWXg9/yD4+utD4bYqAKBrN+8519aG93lfkrCe//W1G2j++wfupvkFV1xD8zmnnRPM3nqT7x+/deMammd719H8/Uv4uvbzz10ezJZ/kn9c+3vvoflHPv4pmt/6618EsxdeeJ6OveW/76V5evIsmp959rk0b2gKr9m/63H+mF97xVk0H6mRXOSTBfAFd3/RzCYCWGVmj+ay77r7t/MyExEpqMTid/edAHbmXu42s1cBzBjviYnI+HpPv/ObWQuApQD+mLvpOjN7xcxuN7OpgTErzKzNzNr27d07psmKSP6MuPjNrAbAPQCud/cDAL4PYC6AJRj6yeA7xxrn7ivdvdXdW6dMPeb3BxEpghEVv5llMFT4P3X3XwGAu7e7+4C7DwL4AYBl4zdNEcm3xOI3MwNwG4BX3f3mYbc3DXuzKwGszf/0RGS8jOSv/e8H8HcA1pjZ6txtNwJYbmZLMNT+2wLgM8nvygELt+QqK/j3orSHx+7v5WMrKitovqudb7VsqfB919VPpmOvvuajNH/gN0/S/N6V/0TzuhktwWzSVL5kN937Fs3POr2J5v3d4SW7ANC+I9xqvPnmf6FjT5o/n+bz5vMWK3PmmfwH1S9P5Ed4f2L5p2g+ffokmi+bGf6b+Zoj/Ej2zVvC236XZTJ8rfHwt016A3d/BsCxdr2nPX0RKW26wk8kUip+kUip+EUipeIXiZSKXyRSKn6RSBV06+6UGSaUh4/CTpNrAACgbyA8XUvzM7gzab61t6f5Q7G7Y18wmzGrno6dMYsve/3sZ/kW1VOr+NHl997zm2CWnr2Ujj2xmfeUF83mH1t7Bz/a/Oknnw1m3731P+nYTCZD8/G04OSFNP/4Ry6j+R9Xhz9uAGhZcGo4mzmTjv3Ns+Gt2msm1/PBw+iZXyRSKn6RSKn4RSKl4heJlIpfJFIqfpFIqfhFImVO1sjn/c7MOgFsHXZTHYCugk3gvSnVuZXqvADNbbTyObfZ7s4vzsgpaPG/687N2ty9tWgTIEp1bqU6L0BzG61izU0/9otESsUvEqliF//KIt8/U6pzK9V5AZrbaBVlbkX9nV9EiqfYz/wiUiRFKX4zu9jMXjezTWZ2QzHmEGJmW8xsjZmtNrO2Is/ldjPrMLO1w26rNbNHzWxj7v+iHIMUmNtNZrY999itNrNLizS3ZjP7vZmtN7N1Zvb53O1FfezIvIryuBX8x34zSwPYAOCDALYBeAHAcndfX9CJBJjZFgCt7l70nrCZnQfgIIA73X1R7rZvAtjj7l/PfeOc6u5fKpG53QTgYLFPbs4dKNM0/GRpAFcA+BSK+NiReV2FIjxuxXjmXwZgk7u/4e59AH4O4PIizKPkuftTAPa84+bLAdyRe/kODH3xFFxgbiXB3Xe6+4u5l7sBHD1ZuqiPHZlXURSj+GcAGH7kyDaU1pHfDuARM1tlZiuKPZljaMgdmw4AuwDwI3kKL/Hk5kJ6x8nSJfPYjebE63zTH/ze7Rx3Px3AJQA+l/vxtiT50O9spdSuGdHJzYVyjJOl/6SYj91oT7zOt2IU/3YAzcNen5m7rSS4+/bc/x0A7kXpnT7cfvSQ1Nz/HUWez5+U0snNxzpZGiXw2JXSidfFKP4XAMwzszlmVg7gkwDuL8I83sXMqnN/iIGZVQO4CKV3+vD9AK7OvXw1gPuKOJc/UyonN4dOlkaRH7uSO/Ha3Qv+D8ClGPqL/2YA/1yMOQTmdSKAl3P/1hV7bgB+hqEfA/sx9LeRawBMA/A4gI0AHgNQW0Jz+zGANQBewVChNRVpbudg6Ef6VwCszv27tNiPHZlXUR43XeEnEin9wU8kUip+kUip+EUipeIXiZSKXyRSKn6RSKn4RSKl4heJ1P8Brv1a5TiWNx4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "with h5py.File('dataset.hdf5', 'r') as f:\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][142857])\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][314159])\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][271828])\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][1111111])\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][1510151])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reduce to first 50k\n",
    "with h5py.File(\"dataset.hdf5\", 'r') as f:\n",
    "    with h5py.File(\"dataset-first-50k.hdf5\", 'x') as s:\n",
    "        dset = s.create_dataset(\"twitch_emotes\", (0, 28, 28, 4), dtype='uint8', chunks=(1000,28,28,4), maxshape=(None,28,28,4),compression='gzip',shuffle=True)\n",
    "        dset.resize(50000, axis=0)\n",
    "        dset[:]=f['twitch_emotes'][:50000]\n",
    "        print(dset.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "with h5py.File('dataset.hdf5', 'r') as f:\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][1])\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][43217])\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][8768])\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][5678])\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][39495])\n",
    "\n",
    "with h5py.File('dataset-first-50k.hdf5', 'r') as f:\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][1])\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][43217])\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][8768])\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][5678])\n",
    "    plt.figure()\n",
    "    plt.imshow(f[\"twitch_emotes\"][39495])\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}