Executable_ROS_decode.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Decoding photographs of the IBM 5100's Executable ROS\n",
    "\n",
    "![A picture of an IBM 5100 computer on a desk. The top cover is removed and wires sprout from the system backplane, some going to a logic analyser to the right of the computer, others going to a solderless breadboard resting on top of a nearby jar of pickled jalapeño peppers. The breadboard has a row of DIP switches, a resistor pack, and wiring. To the left, a digital SLR camera rests on a tripod, pointed at the computer's tiny screen, which is showing 16 rows of hexadecimal digits. The lap and right hand of a person wearing tan trousers can be seen at bottom left; a grounding strap extends from the person's hand to a metal part of the 5100's frame. Regarding other images in this file: I'm unaware of how to add alt text to these images, but these are a few cursory visualisations of screen images and digits at various processing stages and are not at all necessary for understanding the procedure described herein.](dumpros_small.jpg)\n",
    "\n",
    "_TL/DR? Just want to see the code? Well, go on and scroll then..._ ↓↓↓\n",
    "\n",
    "## Objective\n",
    "\n",
    "Use elementary image processing and machine learning techniques to decode images of a computer screen showing hexadecimal digits. The data in these images are ROM contents from an interesting old computer.\n",
    "\n",
    "## Background\n",
    "\n",
    "The [IBM 5100](https://en.wikipedia.org/wiki/IBM_5100) is an early personal computer (ostensibly portable at 24 kg). Depending on customer-selected options, a 5100 could have interactive programming environments for [APL](https://en.wikipedia.org/wiki/APL_programming_language) and [BASIC](https://en.wikipedia.org/wiki/BASIC) built into its ROM. Or, if you prefer, its ROS (\"read-only storage\"), which seems to have been the IBM-favoured term.\n",
    "\n",
    "The 5100's design took advantage of existing hardware and software components at IBM. In particular, the APL and BASIC interpreters were not native 5100 programs---instead, they were modified versions of the interpreters IBM sold for their [System/360](https://en.wikipedia.org/wiki/IBM_System/360) mainframe and [System/3](https://en.wikipedia.org/wiki/IBM_System/3) midrange computers respectively. When a 5100 presents the APL workspace, it's actually running a (limited) System/360 emulator software that hosts the interpreter. For BASIC, an analogous situation obtains where the 5100 emulates a System/3.\n",
    "\n",
    "The 5100's real processor is a 16-bit device called [PALM](https://en.wikipedia.org/wiki/IBM_PALM_processor), and of course this is the architecture that the emulators themselves are written for. (Trivia: PALM isn't a microprocessor but a collection of ICs and gate arrays on a few separate circuit board modules.)\n",
    "\n",
    "The youngest 5100s are a bit over 40 at time of writing, and some accounts online suggest that the ROS devices are no longer dependable. This notebook is part of an effort to back up the entire IBM 5100 ROS to modern media. Specifically, this notebook contains code that analyses screenshots (that is, photographs taken with a camera) containing 512-byte portions of the \"Executable ROS\"---the ROS containing the native PALM code. \n",
    "\n",
    "## Data and strategy\n",
    "\n",
    "The IBM 5100's processor cannot address its Executable ROS code---in this sense, the 5100 is a [Harvard architecture](https://en.wikipedia.org/wiki/Harvard_architecture) machine. It's thus not possible for the 5100 to load this data into RAM (that's RWS in IBM-speak) or send it to the I/O port. This said, a moderatly-invasive recovery is possible, thanks to several propitious IBM 5100 features:\n",
    "\n",
    "1. The 5100 has a hardware switch that causes the display to show the PALM's register contents in hexadecimal; however, on the very first instruction cycle, this display actually shows the first 512 bytes of the Executable ROS instead.\n",
    "\n",
    "2. Inside the case, the 5100 has two special switches intended for IBM customer engineers. These switches freeze the PALM clock and allow manual stepping through code. Rebooting the 5100 with the PALM frozen causesd  the Executable ROS data to remain in the register display indefinitely.\n",
    "\n",
    "3. The 5100 backplane feaures thousands of [wire wrap](https://en.wikipedia.org/wiki/Wire_wrap) pins, one for each module socket and ribbon cable connector line, making it easy to attach wires temporarily.\n",
    "\n",
    "4. The address bus for the Executable ROS is active-low, so an all-zero address can be manipulated into other addresses by pulling address lines to ground.\n",
    "\n",
    "Together, these features enable a recovery the Executable ROS data as follows. **WARNING: I provide no guarantee that the following instructions will not damage your IBM 5100; do the following at your own risk!**\n",
    "\n",
    "With the top cover removed, place the run switch in the single-step position and start the 5100. Set the DISPLAY REGISTERS switch on the front panel to on. The screen will display the first 512 bytes of the Executable ROS. By carefully and selectively clipping 100Ω resistors between ground and the seven highest-order address bus lines, other 512-byte blocks of ROS data may be viewed.\n",
    "\n",
    "After photographing all 128-possible blocks (ideally with a good camera on a tripod, perhaps with a shutter delay to minimise vibration), it is necessary to hand-transcribe a small subset of the \"interesting\" images (i.e. not all `0` or `F` digits) into accompanying text files. These will be used to help train the digit classifier.\n",
    "\n",
    "When finished with that, you can apply image processing and machine learning techniques like those in this notebook to \"read\" the digits in the images. Finally, after reading is complete, the textual \"screen dumps\" can be converted into binary data with a script like the accompanying `5100_display_undump.py`.\n",
    "\n",
    "**If you'd prefer not to undertake this procedure with your own 5100, you can download [here](https://drive.google.com/open?id=1q5YYjurnq5yGZfUq_Jt4KSqdqZ9yo5cY) the data used by (and results saved from) this notebook.** Be aware that the archive file at this link is 1.7GB (over 27,000 times larger than the Executable ROS). Or, to obtain the end product directly, along with a disassembly of the ROS code, visit [here](ftp://ftp.informatik.uni-stuttgart.de/pub/cm/ibm/ibm5100/).\n",
    "\n",
    "## Usage note\n",
    "\n",
    "This notebook assumes a particular file layout that is fairly apparent after some inspection of the code.\n",
    "\n",
    "If using the data archive linked above, untar the files in the working directory of this notebook. A subdirectory called `executable` will be created with all of the necessary data files inside.\n",
    "\n",
    "## Acknowledgements\n",
    "\n",
    "The IBM 5100 Executable ROS data recovery documented here would not have been possible without:\n",
    "\n",
    "* Christian Corti, who made me aware of the address bus manipulation strategy described above, and whose [IBM 5110 technical information website](http://computermuseum.informatik.uni-stuttgart.de/dev_en/ibm_5110/technik/en/5110.html) provided valuable background information.\n",
    "* [bitsavers.org](http://www.bitsavers.org/), for its archived [IBM 5100](http://bitsavers.informatik.uni-stuttgart.de/pdf/ibm/5100/) and [IBM 5110](http://bitsavers.informatik.uni-stuttgart.de/pdf/ibm/5110/) technical documentation.\n",
    "\n",
    "## Licensing\n",
    "\n",
    "This notebook and any supporting programs, software libraries, and documentation distributed alongside it are released into the public domain without any warranty. See the LICENSE file for details.\n",
    "\n",
    "## Who, where, when\n",
    "\n",
    "Tom Stepleton, London, 2019-01-24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import h5py\n",
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import random\n",
    "import scipy as sp\n",
    "import skimage\n",
    "import skimage.io\n",
    "import skimage.morphology\n",
    "import sklearn.neighbors\n",
    "import sklearn.svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image loading and cropping\n",
    "\n",
    "Note: in this early part of the notebook, I'm mainly examining the data. Many superfluous cells from this investigation have since been removed. In this section, I've determined the correct image to crop so that my screen images will contain almost entirely digits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2698, 3722)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = skimage.io.imread('executable/run1/IMG_2545.JPG')\n",
    "img = skimage.color.rgb2gray(img)\n",
    "img = img[550:-400, 1020:-730]\n",
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff7d21ccd30>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff81033fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Masking, dilation, connected components\n",
    "\n",
    "In this section I establish a procedure for turning a 256-level greyscale image into a binary image and then identifying individual digits contained therein. Applying a [morphological operator](http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.binary_dilation) that basically smears the image vertically helped rejoin digits that appeared fragmented.\n",
    "\n",
    "The [SciPy `label` operation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.label.html) detects contiguous `True` regions of the image, returning a new image where the pixels in each region are replaced by a region-specific unique ID number. It's intended that each region corresponds to a single digit, and in nearly all cases, they do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff7cb4cabe0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7d21e1278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = img > (180/255)\n",
    "mask = skimage.morphology.binary_dilation(mask, [[1]] * 11)\n",
    "plt.imshow(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "labeled, num = sp.ndimage.label(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1030"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slicing connected components\n",
    "\n",
    "Here I'm using [`find_objects`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.find_objects.html) to identify rectangles that fit snugly around each unique region in the image. I can use these to extract single-digit image patches like the one below. Additionally, I create a histogram of the heights and widths of these rectangles---the smaller, tiny outliers correspond to isolated specks in the image and can be discarded. For this image, it appears that the correct number of rectangles remains afterwards: 1024, for 1024 hex digits, for 512 bytes of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "slices = sp.ndimage.find_objects(labeled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff7c75b1c18>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7c9c61550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img[slices[77]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_rows = collections.defaultdict(lambda: 0)\n",
    "hist_cols = collections.defaultdict(lambda: 0)\n",
    "for rs, cs in slices:\n",
    "    hist_rows[rs.stop - rs.start] += 1\n",
    "    hist_cols[cs.stop - cs.start] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(<function __main__.<lambda>>,\n",
       "            {11: 2,\n",
       "             12: 1,\n",
       "             14: 2,\n",
       "             22: 1,\n",
       "             98: 7,\n",
       "             99: 40,\n",
       "             100: 98,\n",
       "             101: 194,\n",
       "             102: 301,\n",
       "             103: 258,\n",
       "             104: 118,\n",
       "             105: 7,\n",
       "             108: 1})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(<function __main__.<lambda>>,\n",
       "            {1: 1,\n",
       "             2: 2,\n",
       "             3: 1,\n",
       "             4: 1,\n",
       "             16: 1,\n",
       "             33: 9,\n",
       "             34: 57,\n",
       "             35: 85,\n",
       "             36: 38,\n",
       "             37: 32,\n",
       "             38: 28,\n",
       "             39: 19,\n",
       "             40: 14,\n",
       "             41: 4,\n",
       "             43: 6,\n",
       "             44: 65,\n",
       "             45: 173,\n",
       "             46: 146,\n",
       "             47: 95,\n",
       "             48: 61,\n",
       "             49: 40,\n",
       "             50: 45,\n",
       "             51: 56,\n",
       "             52: 38,\n",
       "             53: 13})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1024"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(85 < (rs.stop-rs.start) < 110 and 28 < (cs.stop-cs.start) < 57 for rs, cs in slices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions for automated digit recovery\n",
    "\n",
    "Now that we've worked out the basics of isolating digit images from the screen photos, it's time to automate the process. These functions handle image loading, binarisation, and the collection of bounding rectangles. Besides the steps shown in earlier cells, the functions include an additional heuristic for splitting up adjacent digits that have been joined by accident, plus some manual rectangle-drawing for connecting some stubborn digit fragments.\n",
    "\n",
    "It takes time to make image processing robust. Sometimes hacks are the fastest option.\n",
    "\n",
    "The digit images taken from the original photos are larger than our digit classifier will need. At the bottom of this section, some experimentation to determine a practical size: 27x18, cutting the per-image pixel count by around 80%-90%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtain_slices(mask):\n",
    "    \"\"\"Get array slices corresponding to hex digits in the mask image.\n",
    "    \n",
    "    This processing is all bespoke to our dataset of executable ROS photos.\n",
    "\n",
    "    Args:\n",
    "      mask: mask image\n",
    "\n",
    "    Returns:\n",
    "      A list of 16 lists of 64 pairs of array slices for the character image\n",
    "      at the corresponding [row, col] location.\n",
    "    \"\"\"\n",
    "    # Dimensional constraints for character image slices.\n",
    "    MIN_HEIGHT = 85\n",
    "    MAX_HEIGHT = 125\n",
    "    MIN_WIDTH = 25\n",
    "    MAX_WIDTH = 60\n",
    "\n",
    "    slen = lambda s: s.stop - s.start  # slen: \"slice len\", size of slice.\n",
    "\n",
    "    # 1. Identify all connected components in the mask image and collect slices.\n",
    "    labels, num = sp.ndimage.label(mask)\n",
    "    slices = sp.ndimage.find_objects(labels)\n",
    "    print('Initial connected component count:', num)\n",
    "\n",
    "    # 2. Filter out slices that are too small.\n",
    "    slices = [(sr, sc) for sr, sc in slices\n",
    "              if slen(sr) > MIN_HEIGHT and slen(sc) > MIN_WIDTH]\n",
    "    print('After filtering out smaller components:', len(slices))\n",
    "\n",
    "    # 3. Separate out slices that are too big.\n",
    "    big_slices = [(sr, sc) for sr, sc in slices\n",
    "                  if slen(sr) > MAX_HEIGHT or slen(sc) > MAX_WIDTH]\n",
    "    slices = [(sr, sc) for sr, sc in slices\n",
    "              if slen(sr) < MAX_HEIGHT and slen(sc) < MAX_WIDTH]\n",
    "    print('Number of oversize components:', len(big_slices))\n",
    "\n",
    "    # 4. Try to break up oversize slices.\n",
    "    while big_slices:\n",
    "        sr, sc = big_slices.pop()\n",
    "        # Oversized vertically? No idea how that would happen. Give up.\n",
    "        if slen(sr) > MAX_HEIGHT: raise RuntimeError(\n",
    "            'Overly tall slice! Giving up. Slice:', sr, sc)\n",
    "        # See if we can split this slice horizontally. In all likelihood,\n",
    "        # an over-wide slice will be off on the right-hand side of the\n",
    "        # screen where all of the letters are pressed together, so a simple\n",
    "        # split down the middle will yield two halves of legal size.\n",
    "        elif slen(sc) > MAX_WIDTH:\n",
    "            for div in [2, 3]:\n",
    "                new_width = slen(sc) // div\n",
    "                if MIN_WIDTH < new_width < MAX_WIDTH:\n",
    "                    slices.append(\n",
    "                        (sr, slice(sc.start, sc.start + new_width)))\n",
    "                    slices.append(\n",
    "                        (sr, slice(sc.stop - new_width, sc.stop)))\n",
    "                    break\n",
    "            else:\n",
    "                raise RuntimeError(\n",
    "                    'Devastatingly wide slice! Giving up. Slice:', sr, sc)\n",
    "        # This cut-down slice is ready to be added to the rest.\n",
    "        else:\n",
    "            slices.append((sr, sc))\n",
    "    print('Final component count:', len(slices))\n",
    "    if len(slices) != 1024: raise RuntimeError(\n",
    "        '{} is not 1024. Giving up.'.format(len(slices)))\n",
    "\n",
    "    # 5. Assemble slices into 16 rows of 64.\n",
    "    # First sort by starting column. The first sixteen slices should be\n",
    "    # the leftmost elements in each row. Note sort by top bounds so that\n",
    "    # rows are in the proper order.\n",
    "    slices.sort(key=lambda ss: ss[1].start)\n",
    "    rows = [[s] for s in sorted(slices[:16], key=lambda ss: ss[0].start)]\n",
    "    slices = slices[16:]\n",
    "\n",
    "    # For all the remaining slices, take each one by one and attach it to\n",
    "    # the row whose last element's top bound is closest to the new slice's\n",
    "    # top bound.\n",
    "    for sr, sc in slices:\n",
    "        row = min(rows, key=lambda r: abs(r[-1][0].start - sr.start))\n",
    "        row.append((sr, sc))\n",
    "\n",
    "    return rows\n",
    "\n",
    "\n",
    "def prepare_image_and_mask(filename):\n",
    "    \"\"\"Load and prepare a mask image from our dataset.\n",
    "    \n",
    "    The mask image shouldn't be used for recognition (although it might\n",
    "    work out OK), but just for segmentation.\n",
    "    \n",
    "    This function recognises certain filenames and adds regions to the\n",
    "    mask manually to join non-contiguous regions (*shame*).\n",
    "    \n",
    "    Args:\n",
    "      filename: Image to load and convert to a mask image.\n",
    "      \n",
    "    Returns:\n",
    "      A 2-D boolean array mask image.\n",
    "    \"\"\"\n",
    "    # 1. Load and crop.\n",
    "    img = skimage.io.imread(filename)\n",
    "    img = skimage.color.rgb2gray(img)\n",
    "    img = img[550:-400, 1020:-730]\n",
    "    \n",
    "    # 2. Threshold and dilate vertically to obtain the mask.\n",
    "    mask = img > (180/255)\n",
    "    mask = skimage.morphology.binary_dilation(mask, [[1]] * 11)\n",
    "    mask = skimage.morphology.binary_closing(mask)\n",
    "\n",
    "    # 3. Add custom patches for the images that fail to segment correctly.\n",
    "    # Super sophisticated machine learning!\n",
    "    if filename.endswith('_2548.JPG'):\n",
    "        mask[1730:1740, 3540:3560] = True\n",
    "    elif (filename.endswith('_2552.JPG') or\n",
    "          filename.endswith('_2617.JPG') or\n",
    "          filename.endswith('_2738.JPG')):\n",
    "        mask[2210:2230, 440:455] = True\n",
    "    elif (filename.endswith('_2676.JPG')):\n",
    "        mask[2560:2570, 1220:1240] = True\n",
    "        mask[2580:2590, 1205:1225] = True\n",
    "    elif (filename.endswith('_2690.JPG') or\n",
    "          filename.endswith('_2720.JPG') or\n",
    "          filename.endswith('_2730.JPG') or\n",
    "          filename.endswith('_2743.JPG') or\n",
    "          filename.endswith('_2753.JPG')):\n",
    "        mask[2550:2590, 1265:1275] = True\n",
    "    elif (filename.endswith('_2713.JPG') or\n",
    "          filename.endswith('_2729.JPG')):\n",
    "        mask[2535:2545, 1210:1220] = True\n",
    "    elif filename.endswith('_2734.JPG'):\n",
    "        mask[2570:2580, 1225:1245] = True\n",
    "\n",
    "    return img, mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Slices num rows: 16\n",
      "Slices row lengths: {64}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff7c4e96390>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7c9c61d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img, mask = prepare_image_and_mask('executable/run2/IMG_2753.JPG')\n",
    "slices = obtain_slices(mask)\n",
    "print('Slices num rows:', len(slices))\n",
    "print('Slices row lengths:', {len(r) for r in slices})\n",
    "plt.imshow(img[slices[2][5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff7c75e9198>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7c7555240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Picking a canonical size for the images.\n",
    "plt.imshow(\n",
    "    skimage.transform.resize(\n",
    "        img[slices[4][42]],\n",
    "        output_shape=(27, 18),\n",
    "        order=1,\n",
    "        mode='reflect',\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting digit images from all files\n",
    "\n",
    "Now we load all of the digit images from all of the image files. This can take a while..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Directories with full sets of images to decode.\n",
    "DIR_RUN1 = 'executable/run1'\n",
    "DIR_RUN2 = 'executable/run2'\n",
    "\n",
    "# Which subranges of the files in those directories are useful?\n",
    "# In other words, these files aren't just full of $FF bytes.\n",
    "GOOD_RANGES = [slice(0, 24), slice(40, 88)]\n",
    "\n",
    "def obtain_all_digits(path):\n",
    "    \"\"\"Load digit images from images in a specified path.\n",
    "    \n",
    "    Args:\n",
    "        path: Directory containing digit images.\n",
    "        \n",
    "    Returns:\n",
    "        An ordered dict mapping filenames (sans path) to lists of 16\n",
    "        lists of 64 27x18 digit images. This could use a fair amount\n",
    "        of memory.\n",
    "    \"\"\"\n",
    "    digits = collections.OrderedDict()\n",
    "    \n",
    "    # List JPGs, select files in \"good ranges\".\n",
    "    filenames = [fn for fn in sorted(os.listdir(path))\n",
    "                 if fn.endswith('.JPG')]\n",
    "    filenames = list(itertools.chain(*[filenames[r] for r in GOOD_RANGES]))\n",
    "\n",
    "    for filename in filenames:\n",
    "        # 1. Load image, compute mask, and segment.\n",
    "        print('Processing', filename)\n",
    "        img, mask = prepare_image_and_mask(os.path.join(path, filename))\n",
    "        slices = obtain_slices(mask)\n",
    "        \n",
    "        # 2. Double-check segmentation slices.\n",
    "        assert len(slices) == 16\n",
    "        for row in slices: assert len(row) == 64\n",
    "\n",
    "        # 3. Collect scaled digit images.\n",
    "        def get_digit(img, s):\n",
    "            return skimage.transform.resize(\n",
    "                img[s], output_shape=(27, 18), order=1, mode='reflect',\n",
    "            ).astype(np.float32)\n",
    "        digits[filename] = [\n",
    "            [get_digit(img, s) for s in row] for row in slices]\n",
    "\n",
    "    return digits\n",
    "\n",
    "\n",
    "def save_digits(digits, filename):\n",
    "    \"\"\"Save digit images to an hdf5 file.\n",
    "    \n",
    "    Args:\n",
    "        digits: digit image collection, as gathered by `obtain_all_images`.\n",
    "        filename: name of file receiving data.\n",
    "    \"\"\"\n",
    "    with h5py.File(filename, 'w') as f:\n",
    "        for img_fn, digit_grid in digits.items():\n",
    "\n",
    "            for i, row in enumerate(digit_grid):\n",
    "                for j, digit in enumerate(row):\n",
    "\n",
    "                    dset = f.create_dataset(\n",
    "                        '{}/{}/{}'.format(img_fn, i, j),\n",
    "                        data=digit,\n",
    "                        compression=\"gzip\",\n",
    "                        compression_opts=9,\n",
    "                    )\n",
    "\n",
    "\n",
    "def load_digits(filename):\n",
    "    \"\"\"Load digit images from an hdf5 file.\n",
    "    \n",
    "    Args:\n",
    "        filename: file to load.\n",
    "        \n",
    "    Returns:\n",
    "        A digit image collection, as gathered by `obtain_all_images`.\n",
    "    \"\"\"\n",
    "    digits = collections.OrderedDict()\n",
    "\n",
    "    with h5py.File(filename) as f:\n",
    "        for img_fn, digit_grid in f.items():\n",
    "            digits[img_fn] = [[list() for _b in range(64)]\n",
    "                              for _a in range(16)]\n",
    "            for i, row in digit_grid.items():\n",
    "                for j, digit in row.items():\n",
    "                    digits[img_fn][int(i)][int(j)] = np.array(digit)\n",
    "    return digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing IMG_2541.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2542.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2543.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1023\n",
      "Number of oversize components: 1\n",
      "Final component count: 1024\n",
      "Processing IMG_2544.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2545.JPG\n",
      "Initial connected component count: 1024\n",
      "After filtering out smaller components: 1023\n",
      "Number of oversize components: 1\n",
      "Final component count: 1024\n",
      "Processing IMG_2546.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2547.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2548.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2549.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2550.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2551.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2552.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2553.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2554.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2555.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2556.JPG\n",
      "Initial connected component count: 1028\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2557.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2558.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2559.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2560.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2561.JPG\n",
      "Initial connected component count: 1028\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2562.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2563.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2564.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2581.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2582.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2583.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2584.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2585.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2586.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2587.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2588.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2589.JPG\n",
      "Initial connected component count: 1028\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2590.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2591.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2592.JPG\n",
      "Initial connected component count: 1025\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2593.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2594.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2595.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2596.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2597.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2598.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2599.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2600.JPG\n",
      "Initial connected component count: 1025\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2601.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2602.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2603.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2604.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2605.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing IMG_2606.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2607.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2608.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2609.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2610.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2611.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2612.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2613.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2614.JPG\n",
      "Initial connected component count: 1027\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2615.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2616.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2617.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2618.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2619.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2620.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2621.JPG\n",
      "Initial connected component count: 1025\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2622.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2623.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2624.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2625.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2626.JPG\n",
      "Initial connected component count: 1030\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2627.JPG\n",
      "Initial connected component count: 1028\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2628.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2669.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2670.JPG\n",
      "Initial connected component count: 1025\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2671.JPG\n",
      "Initial connected component count: 1025\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2672.JPG\n",
      "Initial connected component count: 1025\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2673.JPG\n",
      "Initial connected component count: 1026\n",
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      "Final component count: 1024\n",
      "Processing IMG_2674.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2675.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2676.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2677.JPG\n",
      "Initial connected component count: 1025\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2678.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2679.JPG\n",
      "Initial connected component count: 1025\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2680.JPG\n",
      "Initial connected component count: 1025\n",
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      "Final component count: 1024\n",
      "Processing IMG_2681.JPG\n",
      "Initial connected component count: 1027\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2682.JPG\n",
      "Initial connected component count: 1025\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2683.JPG\n",
      "Initial connected component count: 1025\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2684.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2685.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2686.JPG\n",
      "Initial connected component count: 1025\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2687.JPG\n",
      "Initial connected component count: 1026\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2688.JPG\n",
      "Initial connected component count: 1026\n",
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      "Final component count: 1024\n",
      "Processing IMG_2689.JPG\n",
      "Initial connected component count: 1025\n",
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      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2690.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2691.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2692.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2709.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2710.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing IMG_2711.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2712.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2713.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2714.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2715.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2716.JPG\n",
      "Initial connected component count: 1028\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2717.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2718.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2719.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2720.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2721.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2722.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2723.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2724.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2725.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2726.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2727.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2728.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2729.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2730.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2731.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2732.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2733.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2734.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2735.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2736.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2737.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2738.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2739.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2740.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2741.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2742.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2743.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2744.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2745.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2746.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2747.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2748.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2749.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2750.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2751.JPG\n",
      "Initial connected component count: 1028\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2752.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2753.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2754.JPG\n",
      "Initial connected component count: 1026\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2755.JPG\n",
      "Initial connected component count: 1027\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n",
      "Processing IMG_2756.JPG\n",
      "Initial connected component count: 1025\n",
      "After filtering out smaller components: 1024\n",
      "Number of oversize components: 0\n",
      "Final component count: 1024\n"
     ]
    }
   ],
   "source": [
    "digits_run1 = obtain_all_digits(DIR_RUN1)\n",
    "# save_digits(digits_run1, 'executable/digits_run1.h5')\n",
    "digits_run2 = obtain_all_digits(DIR_RUN2)\n",
    "# save_digits(digits_run2, 'executable/digits_run2.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification label I/O\n",
    "\n",
    "And here we load the text files containing manual transcriptions of the screen images. A few things to note:\n",
    "\n",
    "- The filenames of these files don't match the image filenames; label files and image files are paired by ordering.\n",
    "- There is a text file for every image, but many are unfinished placeholders (they end in `_todo.txt`) and are ignored.\n",
    "- Additionally, some images containing only `$FF` bytes are ignored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Directories with text-file sets of labels to read.\n",
    "DIR_TEXT1 = 'executable/text1'\n",
    "DIR_TEXT2 = 'executable/text2'  # Not used at this point.\n",
    "\n",
    "\n",
    "def obtain_all_labels(path):\n",
    "    \"\"\"Load textual digit labels from a specified path.\n",
    "    \n",
    "    Label files must be text files of 16 64-character lines composed\n",
    "    entirely of the characters [0123456789ABCDEF]. When listed in order,\n",
    "    files ending in .txt in the path are assumed to correspond to images\n",
    "    in the same ordinal position loaded from the image directories.\n",
    "\n",
    "    Files ending in _todo.txt are assumed to contain bogus labels. Label\n",
    "    grids \"loaded\" from these files will contain only None values.\n",
    "\n",
    "    Args:\n",
    "        path: Directory containing label text files.\n",
    "    \n",
    "    Returns:\n",
    "        An ordered dict mapping label filenames (sans path) to 16 lists\n",
    "        (rows) of 64 values (digits) that are either numbers in [0, 15]\n",
    "        or None (if the filename ended in '_todo.txt').\n",
    "    \"\"\"\n",
    "    labels = collections.OrderedDict()\n",
    "    \n",
    "    # List .txt files, select files in \"good ranges\".\n",
    "    filenames = [fn for fn in sorted(os.listdir(path))\n",
    "                 if fn.endswith('.txt')]\n",
    "    filenames = list(itertools.chain(*[filenames[r] for r in GOOD_RANGES]))\n",
    "    \n",
    "    for filename in filenames:\n",
    "        # 1. Don't bother loading \"todo\" files.\n",
    "        if filename.endswith('_todo.txt'):\n",
    "            labels[filename] = [[None] * 64 for _ in range(16)]\n",
    "            continue\n",
    "        \n",
    "        # 2. Load file, validate data, and translate to numeric labels.\n",
    "        with open(os.path.join(path, filename), 'r') as f:\n",
    "            data = [l.upper() for l in f.read().splitlines()]\n",
    "        assert len(data) == 16, (\n",
    "            'Screen transcription files must be 16 lines long, but {} has {} lines.'\n",
    "            ''.format(filename, len(data)))\n",
    "        assert all(len(d) == 64 for d in data), (\n",
    "            'All lines in screen transcription files must contain exactly 64 '\n",
    "            'characters, but {} deviates from this requirement.'.format(filename))\n",
    "        assert all(c in '0123456789ABCDEF' for c in ''.join(data)), (\n",
    "            'Screen transcription files may only contain the characters \"0123456789A'\n",
    "            'BCDEF\", but {} has other characters.'.format(filename))\n",
    "        labels[filename] = [['0123456789ABCDEF'.find(d) for d in row]\n",
    "                            for row in data]\n",
    "\n",
    "    return labels\n",
    "\n",
    "\n",
    "def save_all_labels(labels, path):\n",
    "    \"\"\"Save textual digit labels to a specified path.\n",
    "    \n",
    "    Args:\n",
    "        labels: Ordered dict containing the same kind of mapping returned by\n",
    "            `obtain_all_labels`.\n",
    "        path: Path where these files will be saved.\n",
    "    \"\"\"\n",
    "    for filename, label_grid in labels.items():\n",
    "        with open(os.path.join(path, filename), 'w') as f:\n",
    "            for row in label_grid:\n",
    "                f.write(''.join('0123456789ABCDEF'[l] for l in row) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_run1 = obtain_all_labels(DIR_TEXT1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Universal addressing\n",
    "\n",
    "For a quick way to refer to individual elements in digit image and label data structures. Indexing in these structures is mostly the same, but the initial key differs. We use ordinal numbers instead in \"universal keys\": 3-tuples of `(index, row, column)`, and use the following helpers to access items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def univ_lookup(struct, ukeys):\n",
    "    \"\"\"Lookup items in digit image and label data structures.\n",
    "    \n",
    "    This is a generator that iterates over keys and yields\n",
    "    corresponding elements in `struct`.\n",
    "    \n",
    "    Args:\n",
    "      struct: string (filename)->int (row)->int (col)->element mapping.\n",
    "      ukeys: iterable of `(index, row, column)` universal key tuples.\n",
    "    \"\"\"\n",
    "    struct_keys = list(struct.keys())\n",
    "    for index, row, col in ukeys:\n",
    "        yield struct[struct_keys[index]][row][col]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and test data preparation\n",
    "\n",
    "We pair labels from our more interesting text files with digit images to create datasets to train our classifier. Some data is also set aside to test the classifier after training, giving a glimpse of how it will handle digit images it hasn't seen before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 : 3601 examples.\n",
      "1 : 1090 examples.\n",
      "2 : 402 examples.\n",
      "3 : 525 examples.\n",
      "4 : 442 examples.\n",
      "5 : 591 examples.\n",
      "6 : 193 examples.\n",
      "7 : 378 examples.\n",
      "8 : 852 examples.\n",
      "9 : 168 examples.\n",
      "10 : 359 examples.\n",
      "11 : 168 examples.\n",
      "12 : 368 examples.\n",
      "13 : 271 examples.\n",
      "14 : 369 examples.\n",
      "15 : 463 examples.\n"
     ]
    }
   ],
   "source": [
    "# Scan through all of the training labels and assemble 16 lists of \n",
    "# all of the universal keys corresponding to each label.\n",
    "label_to_ukeys = [list() for _ in range(16)]\n",
    "for major_index, label_grid in enumerate(labels_run1.values()):\n",
    "    for i, row in enumerate(label_grid):\n",
    "        for j, label in enumerate(row):\n",
    "            if label is not None:\n",
    "                label_to_ukeys[label].append((major_index, i, j))\n",
    "\n",
    "for i, ukeys in enumerate(label_to_ukeys):\n",
    "    print(i, ':', len(ukeys), 'examples.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_train_test_data(digits, label_to_ukeys,\n",
    "                         train_prop=0.85, flatten=True,\n",
    "                         weighted=False):\n",
    "    \"\"\"Assemble training and test set data.\n",
    "    \n",
    "    Builds two datasets each comprising images and labels, suitable for\n",
    "    supplying to `sklearn.svm.SVC.fit` and related functions.\n",
    "\n",
    "    If `weighted` is False, each dataset is a 2-tuple whose first member\n",
    "    is the input data and whose second member is the label. Both members\n",
    "    will have the same number of \"rows\" (i.e. major index positions).\n",
    "    The datasets will be balanced, which is to say that each label will\n",
    "    appear the same number of times. (Therefore the least popular label\n",
    "    in your dataset will determine the size of the dataset, and some of\n",
    "    the data will be thrown away.)\n",
    "\n",
    "    If `weighted` is True, each dataset is a 3-tuple, with the third tuple\n",
    "    a set of weights that scale data inversely proportionally to label\n",
    "    prevalence in the dataset. This is an alternative strategy to\n",
    "    balancing the datasets that doesn't require discarding data.\n",
    "\n",
    "    Args:\n",
    "        digits: digit image collection, as gathered by `obtain_all_images`.\n",
    "        label_to_ukeys: a 16-member list of lists of universal keys. Keys\n",
    "            in the n'th list are assumed to refer to a place in the data\n",
    "            where an instance of the n'th digit (in 0-9A-F) can be found.\n",
    "        train_prop: What proportion of assembled data should appear in the\n",
    "            training set. Remaining data will be placed in the test set.\n",
    "        flatten: Whether to flatten images into vectors.\n",
    "    \n",
    "    Returns:\n",
    "        A 4-tuple with the following elements:\n",
    "        [0]: Training data, a 2- or 3-tuple suitable for use as positional\n",
    "             arguments to `sklearn.svm.SVC.fit`.\n",
    "        [1]: Test data, a 2- or 3-tuple suitable for use as positional\n",
    "             arguments to `sklearn.svm.SVC.fit`.\n",
    "        [2]: Training data universal keys.\n",
    "        [3]: Test data universal keys.\n",
    "        Returned data will not be in any particular order.\n",
    "    \"\"\"\n",
    "    input_train = []\n",
    "    input_test = []\n",
    "    labels_train = []\n",
    "    labels_test = []\n",
    "    weights_train = []\n",
    "    weights_test = []\n",
    "    ukeys_train = []\n",
    "    ukeys_test = []\n",
    "\n",
    "    # What is the smallest number of samples for any label?\n",
    "    least_samples = min(len(ukeys) for ukeys in label_to_ukeys)\n",
    "\n",
    "    # How many sample)s are we going to take from each label?\n",
    "    # None means \"all of them\".\n",
    "    samples_per_label = None if weighted else least_samples\n",
    "    if samples_per_label:\n",
    "        print('Picking', samples_per_label, 'samples per label.')\n",
    "\n",
    "    # Gather training and test data, label by label.\n",
    "    for label, ukeys in enumerate(label_to_ukeys):\n",
    "        sample_ukeys = random.sample(\n",
    "            ukeys, samples_per_label or len(ukeys))\n",
    "        weight = least_samples / len(ukeys)\n",
    "        # Training data. sample_ukeys is partially depleted.\n",
    "        for _ in range(int(train_prop * len(sample_ukeys))):\n",
    "            ukey = sample_ukeys.pop()\n",
    "            digit = next(univ_lookup(digits, [ukey]))\n",
    "            input_train.append(np.ravel(digit) if flatten else digit)\n",
    "            labels_train.append(label)\n",
    "            weights_train.append(weight)\n",
    "            ukeys_train.append(ukey)\n",
    "        # Test data. Remaining sample_ukeys is scanned.\n",
    "        for ukey in sample_ukeys:\n",
    "            digit = next(univ_lookup(digits, [ukey]))\n",
    "            input_test.append(np.ravel(digit) if flatten else digit)\n",
    "            labels_test.append(label)\n",
    "            weights_test.append(weight)\n",
    "            ukeys_test.append(ukey)\n",
    "\n",
    "    # Package data for returning to the caller.\n",
    "    input_train = np.stack(input_train)\n",
    "    input_test = np.stack(input_test)\n",
    "    labels_train = np.array(labels_train)\n",
    "    labels_test = np.array(labels_test)\n",
    "    weights_train = np.array(weights_train)\n",
    "    weights_test = np.array(weights_test)\n",
    "    if weighted:\n",
    "        return ((input_train, labels_train, weights_train),\n",
    "                (input_test, labels_test, weights_test),\n",
    "                ukeys_train, ukeys_test)\n",
    "    else:\n",
    "        return ((input_train, labels_train),\n",
    "                (input_test, labels_test),\n",
    "                ukeys_train, ukeys_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data size: 8696\n",
      "Test data size: 1544\n"
     ]
    }
   ],
   "source": [
    "train_data, test_data, train_ukeys, test_ukeys = make_train_test_data(digits_run1, label_to_ukeys, weighted=True)\n",
    "print('Train data size:', train_data[0].shape[0])\n",
    "print('Test data size:', test_data[0].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff7c6701eb8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7c687e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = 1\n",
    "print(train_data[1][tmp])\n",
    "plt.imshow(np.copy(train_data[0][tmp]).reshape(27,18))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifier training\n",
    "\n",
    "At last we can train a classifier to recognise different digits in our images. There are many to choose from in the [scikit-learn](https://scikit-learn.org/stable/) package. A [support-vector machine](https://en.wikipedia.org/wiki/Support-vector_machine) (SVM) is a conventional, well-understood technique, and scikit-learn uses a dependable, popular implementation. It is a sensible first choice.\n",
    "\n",
    "The maths behind SVM are complex, but in this case we can get good performance just by treating it as a black box. I've varied the `kernel` parameter to see what it would do, but all of the kernels produce the same outcome: a perfect 1.0 score on the test set data. This is encouraging. \n",
    "\n",
    "(Roughly, kernels are mathematical functions that can distort and/or expand the geometry of the data in hopes of making the digits easier to distinguish.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_misclassifications(classifer, test_data, ukeys_test, labels):\n",
    "    \"\"\"Display misclassified data elements.\n",
    "    \n",
    "    Args:\n",
    "      classifier: Classifier what misclassifies.\n",
    "      test_data: Data that could be misclassified.\n",
    "      ukeys_test: Data universal keys.\n",
    "      labels: Labels datastructure.\n",
    "    \"\"\"\n",
    "    labels_keys = list(labels.keys())\n",
    "    for i in range(test_data[0].shape[0]):\n",
    "        pred = classifier.predict(test_data[0][i:i+1,:])\n",
    "        if pred != test_data[1][i]:\n",
    "            ukey = ukeys_test[i]\n",
    "            print('Classifier said', pred,\n",
    "                  'but should have been {}.'.format(test_data[1][i]),\n",
    "                  'At', labels_keys[ukey[0]], ukey[1]+1, ukey[2]+1)\n",
    "            plt.imshow(np.copy(test_data[0][i:i+1,:]).reshape(27, 18))\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaults: 1.0\n",
      "linear kernel: 1.0\n",
      "rbf kernel: 1.0\n",
      "poly kernel: 1.0\n",
      "sigmoid kernel: 1.0\n"
     ]
    }
   ],
   "source": [
    "# Let's try an SVM first.\n",
    "\n",
    "classifier = sklearn.svm.SVC()  # Default params\n",
    "classifier.fit(*train_data)\n",
    "print('Defaults:', classifier.score(*test_data))\n",
    "\n",
    "for kernel in ['linear', 'rbf', 'poly', 'sigmoid']:\n",
    "    classifier = sklearn.svm.SVC(kernel=kernel)\n",
    "    classifier.fit(*train_data)\n",
    "    print(kernel, 'kernel:', classifier.score(*test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's try k-nearest neighbours.\n",
    "# Actually, it works pretty well too, but we'll stick with the SVM.\n",
    "\n",
    "# classifier = sklearn.neighbors.KNeighborsClassifier()  # Default params\n",
    "# classifier.fit(*train_data[:2])\n",
    "# print('Defaults:', classifier.score(*test_data[:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualise any cases where the classifier misclassifies the training set\n",
    "# or test set data.\n",
    "show_misclassifications(classifier, train_data, train_ukeys, labels_run1)\n",
    "show_misclassifications(classifier, test_data, test_ukeys, labels_run1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification\n",
    "\n",
    "**At last!** Now that we have our classifier, we can put it to work and have it read every single digit in every single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify_all(classifier, digits, flatten=True):\n",
    "    \"\"\"Classify all digits in a digits data structure.\n",
    "\n",
    "    Args:\n",
    "        classifier: sklearn classifier.\n",
    "        digits: Digits data structure, as created by `obtain_all_digits`.\n",
    "        flatten: Whether to flatten images into vectors.\n",
    "\n",
    "    Returns:\n",
    "        A labels data structure, as created by `obtain_all_labels`.\n",
    "    \"\"\"\n",
    "    labels = collections.OrderedDict()\n",
    "    \n",
    "    # Just label each collection of digits row-by-row.\n",
    "    for img_fn, digit_grid in digits.items():\n",
    "        print('Processing', img_fn)\n",
    "        img_fn += '.txt'\n",
    "        labels[img_fn] = []\n",
    "        for row in digit_grid:\n",
    "            if flatten: row = [np.ravel(d) for d in row]\n",
    "            inputs = np.stack(row)\n",
    "            labels[img_fn].append(list(classifier.predict(inputs)))\n",
    "\n",
    "    return labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Processing IMG_2622.JPG\n",
      "Processing IMG_2623.JPG\n",
      "Processing IMG_2624.JPG\n",
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      "Processing IMG_2626.JPG\n",
      "Processing IMG_2627.JPG\n",
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      "Processing IMG_2669.JPG\n",
      "Processing IMG_2670.JPG\n",
      "Processing IMG_2671.JPG\n",
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      "Processing IMG_2676.JPG\n",
      "Processing IMG_2677.JPG\n",
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      "Processing IMG_2716.JPG\n",
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      "Processing IMG_2746.JPG\n",
      "Processing IMG_2747.JPG\n",
      "Processing IMG_2748.JPG\n",
      "Processing IMG_2749.JPG\n",
      "Processing IMG_2750.JPG\n",
      "Processing IMG_2751.JPG\n",
      "Processing IMG_2752.JPG\n",
      "Processing IMG_2753.JPG\n",
      "Processing IMG_2754.JPG\n",
      "Processing IMG_2755.JPG\n",
      "Processing IMG_2756.JPG\n"
     ]
    }
   ],
   "source": [
    "DIR_OUT1 = 'executable/classif1'\n",
    "DIR_OUT2 = 'executable/classif2'\n",
    "\n",
    "classified_run1 = classify_all(classifier, digits_run1)\n",
    "classified_run2 = classify_all(classifier, digits_run2)\n",
    "\n",
    "save_all_labels(classified_run1, DIR_OUT1)\n",
    "save_all_labels(classified_run2, DIR_OUT2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Where to from here?\n",
    "\n",
    "The procedure has left two (hopefully identical!) copies of the image file transcriptions in the directories `executable/classif1` and `executable/classif2`. Each transcription file has the name of the original file with the additional suffix `.txt`, e.g. `IMG_2554.JPG.txt`.\n",
    "\n",
    "To recreate the entire contents of the original executable ROS, you'll need to add in transcription files for all the images that this procedure skipped---images that contain only `$FF` bytes. Compare your original image directory to the transcription directory, and for all image files that don't have a corresponding `.txt` file, create one with these contents:\n",
    "\n",
    "```\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF\n",
    "```\n",
    "\n",
    "The final step is to \"undump\" the text files into a single 64-kilobyte binary file. To do this, run the cell below, which invokes the external python program `5100_display_undump.py` on all of the transcription files in your `executable/classif1` directory, storing the output in the file `execros.bin`. It's assumed that your shell will expand the fileglob in the commandline so that the program sees the transcription files listed in the correct order.\n",
    "\n",
    "(See the commentary in the program file to learn about why this procedure isn't a direct conversion from lines of hex digits to binary data.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!./5100_display_undump.py executable/classif1/*.txt > execros.bin"
   ]
  }
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