osrs_ocr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os, glob, sys\n",
    "import argparse\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import imageio\n",
    "import string\n",
    "\n",
    "import PIL.Image, PIL.ImageDraw, PIL.ImageFont\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensornetwork as tn\n",
    "\n",
    "import random \n",
    "if hasattr(random,'choices'):\n",
    "    choices = random.choices\n",
    "else:\n",
    "    def choices(seq,weights=None,k=1):\n",
    "        if weights is None:\n",
    "            return [random.choice(seq) for i in range(k)]\n",
    "        else:\n",
    "            weights = np.asarray(weights)\n",
    "            cdf = np.cumsum(weights)\n",
    "            np.insert(cdf,0,0)\n",
    "            rnd = np.random.randint(cdf[-1],size=k)\n",
    "            idx = np.digitize(rnd,cdf)\n",
    "            return list(np.asarray(seq)[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_font = PIL.ImageFont.truetype('/usr/share/fonts/noto/NotoSans-Regular.ttf',12)\n",
    "def render_text(txt,origin=(2,-1),size=(256,16),render_scale=1,font=default_font,bg=(0,0,0),fg=(255,255,255)):\n",
    "    img = PIL.Image.new('RGB', (size[0]*render_scale,size[1]*render_scale), bg)\n",
    "    draw = PIL.ImageDraw.Draw(img)\n",
    "    draw.text((origin[0]*render_scale,origin[1]*render_scale), txt, fg, font=font)\n",
    "    img = img.resize(size, PIL.Image.ANTIALIAS)\n",
    "    return np.asarray(img.getdata()).reshape((size[1],size[0],3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def char_boundaries(txt,font):\n",
    "    return np.asarray([(s:= font.font.getsize(txt[:i]))[0][0]+s[1][0] for i in range(len(txt))])\n",
    "\n",
    "def chunks(width,chunking=24,stride=5):\n",
    "    return [(i,i+chunking) for i in range(0,width-chunking,stride)]\n",
    "\n",
    "def chunk(img,slices):\n",
    "    return [img[:,l:r,:] for l,r in slices] \n",
    "\n",
    "def chunk_and_tag(txt,font=default_font,width=512,height=16,chunking=24,stride=5):\n",
    "    txt = txt.strip()+' '#conditioning\n",
    "    offset = 2+np.random.randint(-1,2)\n",
    "    img = render_text(txt,origin=(offset,-1),size=(width,height),font=font)\n",
    "    boundaries = char_boundaries(txt,font)+font.font.getsize(txt[0])[1][0]+offset\n",
    "    slices = chunks(width,chunking=chunking,stride=stride)\n",
    "    text_masks = [np.logical_and(left+1 <= boundaries, boundaries <= right-1) for left,right in slices]\n",
    "    txt = np.asarray(list(txt))\n",
    "    tags = [txt[(np.where(mask)[0][:-1],)] for mask in text_masks]\n",
    "    bounds = [[j for j in boundaries-i if j >= 1 and j < chunking-1] for i in range(0,img.shape[1]-chunking,stride)]\n",
    "    bounds = [b for b in bounds if len(b) > 1]\n",
    "    return tags,bounds,chunk(img,slices)\n",
    "\n",
    "def gen_tagged_data(txt,**kwargs):\n",
    "    tags,bounds,imgs = chunk_and_tag(txt,**kwargs)\n",
    "    yield from ((tag,bound,img) for tag,bound,img in zip(tags,bounds,imgs) if len(tag)>0)\n",
    "    \n",
    "def gen_char_batch(tag,bound,img,pad_width=16):\n",
    "    left = lambda j: bound[j]+np.random.randint(-1,2)-1\n",
    "    right = lambda j,l: (bound[j+1]+np.random.randint(-1,1)+1 if np.random.random()<0.95 else l+pad_width)\n",
    "    glyph = [img[:,(l:=left(j)):right(j,l),:] for j in range(len(bound)-1)]\n",
    "    glyph = [np.pad(g,pad_width=((0,0),(0,pad_width-g.shape[1]),(0,0)),mode='constant', constant_values=0) for g in glyph]\n",
    "    yield from zip(tag,glyph)\n",
    "    \n",
    "def gen_data(txt,**kwargs):\n",
    "    gen_it = (gen_char_batch(*chunk) for chunk in gen_tagged_data(txt,**kwargs))\n",
    "    for gen in gen_it:\n",
    "        yield from gen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm',\n",
       "       'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z',\n",
       "       'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',\n",
       "       'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z',\n",
       "       '!', '\"', '#', '$', '%', '&', \"'\", '(', ')', '*', '+', ',', '-',\n",
       "       '.', '/', ':', ';', '<', '=', '>', '?', '@', '[', '\\\\', ']', '^',\n",
       "       '_', '`', '{', '|', '}', '~', '0', '1', '2', '3', '4', '5', '6',\n",
       "       '7', '8', '9', ' '], dtype='<U1')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approx_freq = {' ':1000,'!':200,',':200,'.':200,'?':200,'A':29,'B':3,'C':16,'D':18,'E':37,'F':3,'G':3,'H':3,\n",
    "               'I':42,'J':14,'K':13,'L':21,'M':17,'N':24,'O':29,'P':11,'Q':5,'R':22,'S':18,'T':32,'U':28,\n",
    "               'V':3,'W':3,'X':3,'Y':3,'Z':2,'a':29,'b':3,'c':16,'d':18,'e':37,'f':3,'g':3,'h':3,'i':42,\n",
    "               'j':14,'k':13,'l':21,'m':17,'n':24,'o':29,'p':11,'q':5,'r':22,'s':18,'t':32,'u':28,'v':3,\n",
    "               'w':3,'x':3,'y':3,'z':2}\n",
    "letters = np.asarray(list(string.ascii_letters+string.punctuation+string.digits+' '))\n",
    "for letter in letters:\n",
    "    if letter not in approx_freq:\n",
    "        approx_freq[letter] = 10\n",
    "for letter in string.ascii_lowercase:\n",
    "    approx_freq[letter] *=  10\n",
    "\n",
    "def speach_like(i,j):\n",
    "    vals = np.asarray(list(approx_freq.keys()))\n",
    "    w = np.asarray(list(approx_freq.values()))\n",
    "    txt_block = np.asarray(choices(vals,weights=w,k=i*j)).reshape((i,j))\n",
    "    return txt_block\n",
    "\n",
    "letters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' ?js i!pp,6  .q |!i n !!n j u dl.en o nupuj dRtud ',\n",
       " 'eui ?,n csiiek,!yuqegi,o e Iiat rk?llI?ridOald lle',\n",
       " 'jitlinv_?DDe!aldini.pog`u  l!psnik!s  ekcntk. }iu,',\n",
       " '5 m.e !d m. igl p  liIiee??t !e.u kdem,iR,yi ezrie',\n",
       " 'e,in!mityj3e!uvraq.e !ntnt!2jlskzibiaatac`da!Is t?']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generated = list(map(lambda x: ''.join(x), speach_like(5,50)))\n",
    "generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x2880 with 45 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "txt = generated[0]\n",
    "tags,bounds,imgs = chunk_and_tag(txt,width=320)\n",
    "\n",
    "fig = plt.figure(figsize=[15,40])\n",
    "for i,(tag,bound,img) in enumerate(zip(tags,bounds,imgs)):\n",
    "    ax = fig.add_subplot((len(imgs)+3)//4,4,1+i)\n",
    "    ax.set_title(''.join(tag))\n",
    "    plt.imshow(imgs[i])\n",
    "    plt.scatter([[b,b] for b in bound],[0,14]*len(bound))\n",
    "plt.close(fig)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 50 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[12,8])\n",
    "maxnum = 50\n",
    "for i,(tag,glyph) in enumerate(gen_data(txt)):\n",
    "    if i >= maxnum:\n",
    "        break\n",
    "    ax = fig.add_subplot(maxnum//10+1,10,i+1)\n",
    "    ax.set_title(tag)\n",
    "    plt.imshow(glyph)\n",
    "    plt.axis('off')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_split_model(width=24,height=16,channels=3,features=[((3,3),2),((2,14),8)],lstm_depth=0,lstm_space=10,dense_depth=1,dense_space=1000):\n",
    "    img_input = tn.Input(shape=(width,height,channels))()\n",
    "    prev_layer = img_input\n",
    "    \n",
    "    conv_layers = []\n",
    "    for i,(shape,num) in enumerate(features):\n",
    "        prev_layer = tn.Conv(shape, kernel_stride=(1,1), out_shape=(num,), pad=False, activation=tn.ReLU())(prev_layer)\n",
    "        conv_layers.append(prev_layer)\n",
    "        \n",
    "    lstm_layers = []\n",
    "    for i in range(lstm_depth):\n",
    "        prev_layer = tn.LSTM(lstm_space, go_backwards=(i%2!=0))(prev_layer)\n",
    "        lstm_layers.append(prev_layer)\n",
    "        \n",
    "    dense_layers = []\n",
    "    for i in range(dense_depth):\n",
    "        prev_layer = tn.Dense(dense_space, activation=tn.Tanh())(prev_layer)\n",
    "        dense_layers.append(prev_layer)\n",
    "        \n",
    "    output = tn.Dense(width, activation=tn.Sigmoid())(prev_layer)\n",
    "    return tn.System(inputs=[img_input], outputs=[output])\n",
    "    \n",
    "def get_char_model(width=16,height=16,channels=3,features=[((3,3),2)],lstm_depth=0,lstm_space=10,dense_depth=1,dense_space=5000,categories=len(letters)):\n",
    "    img_input = tn.Input(shape=(width,height,channels))()\n",
    "    prev_layer = img_input\n",
    "    \n",
    "    conv_layers = []\n",
    "    for i,(shape,num) in enumerate(features):\n",
    "        prev_layer = tn.Conv(shape, kernel_stride=(1,1), out_shape=(num,), pad=False, activation=tn.ReLU())(prev_layer)\n",
    "        conv_layers.append(prev_layer)\n",
    "                \n",
    "    lstm_layers = []\n",
    "    for i in range(lstm_depth):\n",
    "        prev_layer = tn.LSTM(lstm_space, go_backwards=(i%2!=0))(prev_layer)\n",
    "        lstm_layers.append(prev_layer)\n",
    "        \n",
    "    dense_layers = []\n",
    "    for i in range(dense_depth):\n",
    "        prev_layer = tn.Dense(dense_space, activation=tn.Tanh())(prev_layer)\n",
    "        dense_layers.append(prev_layer)\n",
    "    \n",
    "    output = tn.Dense(categories, activation=tn.Sigmoid())(prev_layer) #softmax\n",
    "    return tn.System(inputs=[img_input], outputs=[output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "smodel = get_split_model()\n",
    "smodel_name = 'nono_split6.h5'\n",
    "if os.path.exists(smodel_name):\n",
    "    smodel.load_weights(smodel_name)\n",
    "\n",
    "cmodel = get_char_model()\n",
    "cmodel_name = 'noto_char6.h5'\n",
    "if os.path.exists(cmodel_name):\n",
    "    cmodel.load_weights(cmodel_name)\n",
    "    \n",
    "def checkpoint():\n",
    "    smodel.save_weights(smodel_name)\n",
    "    cmodel.save_weights(cmodel_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#checkpoint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_char_model(cycles=100):\n",
    "    success = 0\n",
    "    trials = 0\n",
    "    random_arrays = speach_like(1000,75)\n",
    "    for txt in (''.join(x) for x in random_arrays):\n",
    "        for tag,glyph in gen_data(txt):\n",
    "            input = np.swapaxes(glyph,0,1)/255\n",
    "            true_out = np.asarray(letters == tag,dtype='float64')\n",
    "            trials += 1\n",
    "            guess_out,state = cmodel.guess([input],return_state=True)\n",
    "            if np.argmax(guess_out[0]) == np.argmax(true_out):\n",
    "                success += 1\n",
    "            cmodel.learn(state,[true_out],loss='ce',method='adam',scale=0.0001)\n",
    "            if trials >= cycles:\n",
    "                break\n",
    "        else:\n",
    "            continue\n",
    "        break\n",
    "    print(success,'/',trials)\n",
    "    return success,trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_split_model(cycles=1000):\n",
    "    success = 0\n",
    "    trials = 0\n",
    "    false_positives = 0\n",
    "    false_negatives = 0\n",
    "    true_total = 0\n",
    "    random_arrays = speach_like(cycles,50)\n",
    "    for txt in (''.join(x) for x in random_arrays):\n",
    "        for tag,bound,img in gen_tagged_data(txt,width=400,chunking=24,stride=5):\n",
    "            input = np.swapaxes(img,0,1)/255\n",
    "            true_out = np.zeros((24,))\n",
    "            true_out[bound] = 1\n",
    "            trials += 1\n",
    "            guess_out,state = smodel.guess([input],return_state=True)\n",
    "            delta = np.asarray(guess_out[0]>0.7,dtype=np.int32) - np.asarray(true_out>0.7,dtype=np.int32)\n",
    "            false_positives += np.count_nonzero(delta == 1)\n",
    "            false_negatives += np.count_nonzero(delta == -1)\n",
    "            true_total += np.count_nonzero(delta == 0)\n",
    "            if np.all((guess_out[0]>0.7) == (true_out>0.7)):\n",
    "                success += 1\n",
    "            smodel.learn(state,[true_out],loss='ce',method='adam',scale=0.001)\n",
    "            if trials >= cycles:\n",
    "                break\n",
    "        else:\n",
    "            continue\n",
    "        break\n",
    "    print(success,'/',trials)\n",
    "    print('FN:',false_negatives,'FP:',false_positives,'Correct:',true_total)\n",
    "    return success,trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    while True:\n",
    "        print('character model segment')\n",
    "        train_char_model()\n",
    "        print('split model segment')\n",
    "        train_split_model()\n",
    "        checkpoint()\n",
    "except KeyboardInterrupt:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def guess_characters(img,threshold=0.95,chunking=24,stride=5,make_plots=False):\n",
    "    imgs = list(chunk(img,chunks(img.shape[1],chunking=chunking,stride=stride)))\n",
    "    i = 0\n",
    "    detected = []\n",
    "    weights = []\n",
    "    last_boundidx = np.asarray([],dtype=np.int32)\n",
    "    for i,img in enumerate(imgs):\n",
    "        snn_in = np.swapaxes(img,0,1)/255\n",
    "        snn_out = smodel.guess([snn_in])[0]\n",
    "        bound = snn_out > threshold\n",
    "        sweight = snn_out[bound]\n",
    "\n",
    "        boundidx = np.nonzero(bound)[0]\n",
    "        if len(last_boundidx) > 0:\n",
    "            mask = boundidx >= last_boundidx[-1]\n",
    "            boundidx = boundidx[mask]\n",
    "        \n",
    "        if len(boundidx) > 1:\n",
    "            last_boundidx = boundidx\n",
    "            glyphs = [img[:,boundidx[j]-1:boundidx[j+1]+1,:] for j in range(len(boundidx)-1)]\n",
    "            glyphs = [g[:,:min(16,g.shape[1]),:] for g in glyphs]\n",
    "            glyphs = [np.pad(g,pad_width=((0,0),(0,16-g.shape[1]),(0,0)),mode='constant', constant_values=0) for g in glyphs]\n",
    "\n",
    "            inputs_list = [[np.swapaxes(g,0,1)/255] for g in glyphs]\n",
    "            cnn_out = np.asarray([cmodel.guess(inputs)[0] for inputs in inputs_list])\n",
    "            chars = np.argmax(cnn_out,axis=1)\n",
    "            cweights = np.max(cnn_out,axis=1)\n",
    "\n",
    "            chars = letters[chars]\n",
    "            detected.extend(chars)\n",
    "            weights.extend(cweights)\n",
    "        else:\n",
    "            chars,weights = [],[]\n",
    "        last_boundidx = last_boundidx - stride\n",
    "        \n",
    "        if make_plots:\n",
    "            if i%5==0:\n",
    "                if i>0:\n",
    "                    plt.show()\n",
    "                    plt.close()\n",
    "                plt.figure(figsize=(15,3))\n",
    "            plt.subplot(1,5,i%5+1)\n",
    "            plt.title(' '.join(['\\'%s\\'(%s)'%(c,p) for c,p in zip(chars,['%.02f'%w for w in cweights])]))\n",
    "            xval = np.arange(-1,len(bound))-0.5\n",
    "            yval = np.concatenate([[16],16-16*bound])\n",
    "            plt.fill(xval,yval,'r',alpha=0.5)\n",
    "            plt.imshow(img)\n",
    "            plt.axis('off')\n",
    "    if make_plots:\n",
    "        plt.show()\n",
    "        plt.close()\n",
    "    out = ''.join([c for c in detected])\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def test(txt,verbose=False):\n",
    "    print('txt:',txt)\n",
    "    img = render_text(txt,origin=(3,-1),size=(200,16),font=default_font)\n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    out = guess_characters(img,make_plots=verbose)\n",
    "    print('out:',out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "txt: Hello i am dog~ woof woof!\n"
     ]
    },
    {
     "data": {
      "image/png": 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999DY2EgqlUKSJAYHB3nvvfeU8zc1NZGXl0dbWxs+n09xtXg8Hv7mb/6GUCiEJEkcPXqU+vp6vvWtb9HX18f169c5ceLETR0coOihtLSU9evXYzQaMRgM7NixA4/Hw5tvvsmTTz6ptJ+ioiLWr19PX18fkUiEyspK8vLykCRphi7SI/7z589TXl7Orl27FKM1X12MjIwwOjo6r7aRSCS4du0aGRkZPPLII2RnZ6PVatm8eTN6vZ7XX39dcXeZzWZ0Oh0bN24kEolw9epVCgoKKCwsRKfTzbhHADo6Ovj4448pKChQ4kdarRaHw4EkSZw9e1ZxL42Pj+P1emloaGD16tXs3r0bIcSsnU4aWZYJh8PKCFwNYi4+y8qANzQ00N/fj8vlmrGYR5IkampqlBEkTE75LRYLNpsNq9XKAw88gN1uV/6n0WjYtm0biUQCvV6PwWCYlwwajQabzYZGo8Hv99/UmaQ7iGAwqAQcF0pNTQ1FRUWsXbtWMTQFBQVkZ2djsVhmTb9KJpP4/f4ZMxVZlkkmk0QiEYLB4IwRTyqVIpVKodFokCRp1nqndevz+UgkErPWOxAIIMsyer0em81GPB5XYgzT90/LZzAYsFqtWK1W4vE4wWCQcDh807Hj8Th+v59EIkEymaSnp4dAIKBcA41Gw4kTJ7h27RoXLlzA6/Xi9XoZHx+fNdgZiUTo6+sjlUqRk5ODyWRCq9VSVVXFmTNnaGtrUxZtlZaWUlZWRmlpKaOjo/j9fsVtk15BO5suQqGQ0gbnqwuLxYJWO//bLpVK0d/frwQfrVYrJpOJiooK/H4/H3zwAevWrcNqtZKfn48kSZSVlRGLxXA6nRgMBgwGA36/X+l8b9STz+fDaDRis9mUFaHpa3Gret/unkqlUkrs58ZjqSwOt21JQohjwEOAW5bl9VNlduAloAxwAl+RZdmzUGEaGho4efIkFy5cmBHw0+l0PPjgg1RVVXHvvfem5cJkMmGxWDCbzRw4cIBdu3bNrNxUQC89fZ4PkiTNuHlvHDWkRxXhcBir1Trv486FRqNh3bp1bN26VVlSbbValZu8p6dnTgMeDAZvMl6pVGpGoG263KlUSuncZpPDZDIhyzJ+v3/W0VJ6NAWTwVSr1UosFpt1X1mWCQQCSJKEyWTCbDYrHctsC1USiYRSn0QiQXd3N+FweMY+p06dmk2FsxKNRnG5XCSTSbKzs5XOtrKyklOnTtHR0UFvby86nU4x4CUlJTQ1NTExMaF0nIFAYNapfygUIhKJKO1vvrpIxy/mSzKZZGhoCL/fr3ToZrOZ8vJynE4nHR0dOJ1OcnNzFbdKaWkpPT099Pf3K26VuR7lEI1GCQaDimst3YnPp946ne6WsqdSKfx+v+r//gyZz1DgZ8DzwIvTyr4H/FaW5R9MvYnne8CfLb54c5OOyI+NjTEwMMCLL77IW2+9NWOfdJZCV1cXwWBwXsdNJBJ4PJN9kcPhuKmRCiHIzMwkMzMTt9t9y4yO2+FwOKirq+PAgQOsXbuWv//7v2d0dBSfz0d1dTWrV69m//79s/52tsyLhZD2qQPKNP1GzGazcoNHIhG8Xi9msxmTyXRTJ5P2WcOku2d8fJxUKkVWVpZyjOmkfeq3MwrzJT2qHxwcpLOzk02bNmEymUgmk3i9XgYHB2lsbCQvL4+tW7dSW1vLmjVr+Pd//3d6e3uVBWXpRSg3kpWVhc1mw+12Mzo6isfjmbcu5spGmot4PI7H46GlpYVVq1axd+9etFotwWCQoaEhmpublThRfn4+GzZs4Nq1azQ2NpKRkUFubi4Oh4OMjIybjm2xWMjJyWFkZISxsTElJuFwOGatd3Z2NlarlZGRkdveU6Ojo3z/+9+fl+tS5dNxWwMuy/JJIUTZDcWHgXumPv8f4AOWwICnp9jRaBS3260s1U2PgrRaLZIkEYvF5m3s0il7sVgMnU6nGOu0bzadd26z2WhpaVmQATcYDOTm5mK327FYLLS3t+NyuRgfHycjIwOr1XrH/IbpaX4ikVDcHhaLhUAggE6nw2g0KmXpIHP6xjQYDEr+fSgUQq/XYzabycnJYWJiArfbjcfjQZIktFotJpOJrKwsgsEgiUQCo9GIxWIhMzNz0Qw4/K6Td7vdFBcXYzQa8Xq9yixgcHAQvV7Phg0bcDgcWK1WfD4fo6OjistHr9crOd1erxetVqvU12q10tzczNjY2CfSxSc14LIsE4lEGBoaIisri8rKSsLhMIFAgGg0yujoKIODg2zatImCggIyMzOVnHqbzUYoFFI6yOzsbHw+H7IsYzAYlJhId3e3ku4Yj8eVoOv0ehuNRsWAt7S0zEh3vJF0J5Z22xiNRjWQ+RnwaVdi5suyPAgw9ffmFR6fMalUSpk+arVa1q9fz6FDh7DZbMo+2dnZSq7tfEkvmBgcHCSRSLB7924OHDiAVqslOzub6upqduzYQV1dHWfOnKG9vf1T10Gr1WKxWIhGo4yPjzM6OkooFMJsNrN161buv//+O/bYgEgkQltbGx6PB6PRyF133cX27duRJAmHw0F9fT3r1q2jsrISjUbD2NgYzc3NeL1ejEYjDz74IJs2bUIIQXFxMXV1dezevRuz2cz7779PY2MjnZ2dyv8PHjyoBIrXrVvHxo0bWb9+/awB24Xgcrm4cuUKe/fuZcuWLXz00Uf09/eTSqW4cuUKTqeT7du3K+3E7XbjdDppbm5maGiIZDLJnj172L9/v5KZkm4DNTU1SprdJ9HFfAOY0/F4PFy6dIny8nIOHTqkZPEAdHd38/HHH1NbW8u6deuUgH86Q8npdBKPx6mrq+Phhx/GZrNhMpmoqqpiy5Yt7Ny5k+bmZk6fPs21a9cYHh4mlUpx9913s2/fPiRJIicnh+rqanbu3MnatWs5deoUnZ2dc8qbTi384Q9/yB/90R9RWVm5Yh6BsZL4zIOYn+ULHRKJBENDQ7z22msIIaivryc/P18ZccfjcSKRCCMjI/Me9ciyTCKRoK2tjddffx2bzcaOHTsoKipCp9ORkZHB9evXaWlpwe12z9s1Mxt+v5+Ojg7q6+vJy8vj8ccfJxgMEo1GMZvNdHV1zeiQPkvS+mpra+P48ePKMyzq6uqAyRFV2r2TSqUUPaVXGFZXV1NQUMDWrVsxmUyYTCZOnz7Nxx9/zNjYGOFwGI1Gw9tvv01GRga7d++msrKSYDCIJEkUFhbicrkW5UmQ0/F4PPT19WGz2YhGo3R1dTE+Pj4jq0in082YzaVHic3Nzbz++utkZ2fzuc99jpKSEvR6PUajke7ubmV0n565zFcXsVhs3kH1NKFQCKfTybZt28jNzaW3t1fJXAoGg4yPj6PRaEgkErhcLmXhz/R7RKvVUl9fr6Rjms1mUqkU7733Hn19fUxMTChZL2+88QY5OTns2rVLydQxGo10dHTg8XgYGRm55bXSaDTo9Xry8/Ox2+0YDIZZYy8qC+PTGvBhIcQqWZYHhRCrAPdcO87nhQ7pANut/Lrp/6X3S5f19/fz05/+lEcffZSDBw8qmRzJZJKOjg7a29s5efKkskBitnOly9KkUimampoYHh7mmWeeYcOGDcq0dWJigmPHjtHY2Mjw8PAtn3V843FvxOv1cvnyZfbt28e6dev49re/TSqVYmxsjDfeeIOmpibq6upu0sl0Hcylx1vJMpeOE4kEjY2N+Hw+nnnmGaqrq8nNzeX69euKeyedoZJeEXn8+HHa2tp49tlnKSoqorCwkPHxcQYGBvinf/on2tvblTz5VCrFSy+9xJ49e3jyySfJyclBkiQuXryoXK+FuKRmY3R0lO7ubiUw3dLSosgzMTGB1+tFCIHH48HpdCoB1lQqRWNjI4ODg3znO99RZh/BYJCJiQl+8pOf0NTUhNvtVtxcn0QXN16T2xEMBpURr91up7u7m76+yTcaBgIBJd4TDofp6OhQXDrpe+TYsWN8+ctf5v777+erX/2q0iH/x3/8By+99BJOp1NZ7Xvx4kVcLhff/e53qa2tpaKigmAwiMfj4cc//jFXrlzB7XYrWU2zta306s+cnBxsNptqwD8j5vVW+ikf+FvTslB+CIxNC2LaZVn+n/M4zqwns9vt1NTUMDAwgMfjwe/3z2jY6WBSeuGOy+ViZGQEmGwo6YU7drsdk8mkNJRQKEQwGKS7u1u5MSsqKigrK1MMVSKRoKysjIqKCi5fvozX6yWRSCi+ztLSUiwWCxkZGaRSKeLxOAMDA/j9fiYmJm7pW9+4cSM2m41Lly4RjUZv8menM0JKSkoUn2p6JJwOEqVX66VvXq1Wy/bt24lEIly5cmXGw48yMzPZsGED/f39DA8Pz0jXKykpoaCggL6+PoLB4JyG0mAwkJGRQWlpqZJXPD0tMb0AJBgM4vP5lP3Ly8uVFMj0Q4x6e3sJhUKKrzSd+pmdna3kJgsh8Pl8bNq0iccff5y3336bpqYmTpw4sSiPVTUajZhMJmpra0kmk7S1tREKhZQZmcViYePGjcpDl6Znv+h0OvR6vdIG0kHQeDxOf3+/0gZu1N18dJEOOiYSCbxer7KIay50Oh1ms5mysjKysrJobm5W0llhMnuqvr5eCW4ODAwog5bp90g6pRImg6Ojo6OMj48zMTGhtM90vcvKypTAbPo5/S6Xi0AgMKPelZWVGI1GxsfHlXYhSRIZGRls3LhRWayUjnmofCouyrK87abS6c+SmG0D/i8wCMSZfI3aU0AO8FugY+qv/XbHmTqWrG7/eTchhGw0GmW9Xi9PdeYyIBsMBvnQoUPymTNn5GeeeUbeu3evbDAYllxedVO3ZbRdmM2mzicL5Wtz/OvA7X6rojKd9CpBn8+H0+kkFoshSRLr169n48aNVFdXEwgE6OjoWNFvL1JRuVMsq5WYKr/fpJ+gZzQa2blzJ8lkEo1GQ35+Pg6Hgw8//FBx8air9lRUbo9qwFXuGHq9nm3btlFbW8uWLVuUANjY2BiXL1/m2LFjXL16ddEDmSoqv6/MK4i5aCebI4ip8p8DnU5HbW2tsqAkTSwWY2JiApfLhcfjuWkJvYqKyuxBTNWAq6ioqCx/ZjXgd9qFMgoEp/4ud3JZ/nKuBBlBlXOxUeVcXFaCnKWzFd7RETiAEOLCrPmMy4yVIOdKkBFUORcbVc7FZaXIORvq0igVFRWVFYpqwFVUVFRWKEthwH+8BOf8NKwEOVeCjKDKudioci4uK0XOm7jjPnAVFRUVlcVBdaGoqKiorFDumAEXQnxeCNEmhOiceoLhskAIUSKEeF8I0SKEuCaE+PZU+V8KIfqFEI1T24PLQFanEOLKlDwXpsrsQoh3hRAdU3+zb3ecz1jG6mk6axRC+IQQf7oc9CmEOCaEcAshrk4rm1N/Qohnp9prmxDigSWW84dCiFYhRJMQ4jUhRNZUeZkQIjxNr/+8hDLOeY2XmS5fmiajUwjROFW+JLpcEPN5iuBCN0ACuoAKQA9cBuruxLnnIdsqYMvUZyvQDtQBfwn8j6WW7wZZnUDuDWV/B3xv6vP3gL9dajlvuO5DTOawLrk+gbuBLcDV2+lvqg1cBgxA+VT7lZZQzvsB7dTnv50mZ9n0/ZZYl7Ne4+Wmyxv+/w/A/1pKXS5ku1Mj8O1ApyzL3bIsx4BfMvlezSVHluVBWZYvTX32Ay1A0dJK9Yk4zOR7SZn6+8WlE+UmDgBdsixfX2pBAGRZPgmM31A8l/4OA7+UZTkqy3IP0MlkO14SOWVZfkeW5fTDtM8CxXdClrmYQ5dzsax0mUZMvrjzK0w+MntFcqcMeBHQN+27i2VoJKdeXLEZODdV9K2pKeuxpXZNTCED7wghLk69qg6WwftJb8FjzLw5lps+YW79Lec2+w3g+LTv5UKIj4UQJ4QQe5dKqClmu8bLVZd7gWFZljumlS0nXd6WO2XAxSxlyyr9RQhhAf4f8KeyLPuAF4BKoJ7JF1r8w9JJp7BbluUtwCHgvwkh7l5qgeZCCKEHHgF+NVW0HPV5K5ZlmxVCPAckgH+bKhoEVsuyvBn478AvhBB35kWqNzPXNV6WugS+xswBxnLS5by4UwbcBZRM+14MDNyhc98WIYSOSeP9b7Isvwogy/KwLMtJWZZTwP/mDk35boUsywNTf93Aa0zKNCwm30uKuM37Se8wh4BLsiwPw/LU5xRz6W/ZtVkhxBPAQ8B/kaectlNuibGpzxeZ9C9XLYV8t7jGy1GXWuBLwEvpsuWky/lypwz4eWCtEKJ8amT2GPDrO3TuWzLlB/sp0CLL8o+mla+attsR4OqNv72TCCHMQghr+jOTQa2rTOrxiandngDeWBoJb2LG6Ga56XMac+nv18BjQgiDEKIcWAs0LIF8wGQWF/BnwCOyLIemlTuEENLU5wom5exeIhnnusbLSpdTHARaZVl2pQuWky7nzZ2KlgIPMpnh0QU8t9TR22ly7WFyOtcENE5tDwI/B65Mlf8aWLXEclYwGcm/DFxL65BP+X7Sz1hWEzAGZE4rW3J98gnf7wo8N9Ve24BDSyxnJ5N+5HQb/eepfR+dag+XgUvAw0so45zXeDnpcqr8Z8B/vWHfJdHlQjZ1JaaKiorKCkVdiamioqKyQlENuIqKisoKRTXgKioqKisU1YCrqKiorFBUA66ioqKyQlENuIqKisoKRTXgKioqKisU1YCrqKiorFD+PzU9HsBFQbNYAAAAAElFTkSuQmCC\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 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out: Hello i am dog~ woof woof! \n"
     ]
    }
   ],
   "source": [
    "test('Hello i am dog~ woof woof!',verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "txt: Olly is a noob\n"
     ]
    },
    {
     "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 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out: Olly is a noob \n"
     ]
    }
   ],
   "source": [
    "test('Olly is a noob',verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "txt: Greetings, Humans from Earth!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out: Greetings, Humans from Earth! \n"
     ]
    }
   ],
   "source": [
    "test('Greetings, Humans from Earth!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "txt: 867-5309, but what area code???\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out: 867-5309, but what area code??? \n"
     ]
    }
   ],
   "source": [
    "test('867-5309, but what area code???')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "txt: GE price is 413259134637825637\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out: GE price is 41325934637825637\n"
     ]
    }
   ],
   "source": [
    "test('GE price is 413259134637825637')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "txt: lost? https://lmgtfy.com/sendhelp\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out: lost? https://lmgtfy.com/sendhelp \n"
     ]
    }
   ],
   "source": [
    "test('lost? https://lmgtfy.com/sendhelp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8",
   "language": "python",
   "name": "python3.8"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}