hand_sign.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/shashank/miniconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import keras\n",
    "import imutils\n",
    "from keras.models import load_model\n",
    "from keras.models import model_from_json\n",
    "from keras.preprocessing.image import img_to_array\n",
    "%matplotlib inline\n",
    "from textgenrnn import textgenrnn\n",
    "from multiprocessing import Process,Queue\n",
    "from multiprocessing import Pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/shashank/miniconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/shashank/miniconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4074: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/shashank/miniconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model=load_model(\"./models/hands_sign_binary_final.hdf5\")\n",
    "txt=textgenrnn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def edgedetect( channel):\n",
    "    sobelX=cv2.Sobel(channel,cv2.CV_16S,1,0)\n",
    "    sobelY=cv2.Sobel(channel,cv2.CV_16S,0,1)\n",
    "    sobel=np.hypot(sobelX,sobelY)\n",
    "    sobel[sobel>255]=255\n",
    "    return sobel\n",
    "\n",
    "\n",
    "def findSignificantContours (img, edgeImg):\n",
    "    contours, heirarchy = cv2.findContours(edgeImg, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    level1 = []\n",
    "    for i, tupl in enumerate(heirarchy[0]):\n",
    "        # Each array is in format (Next, Prev, First child, Parent)\n",
    "        # Filter the ones without parent\n",
    "        if tupl[3] == -1:\n",
    "            tupl = np.insert(tupl, 0, [i])\n",
    "            level1.append(tupl)\n",
    "    significant = []\n",
    "    tooSmall = edgeImg.size * 5 / 100 # If contour isn't covering 5% of total area of image then it probably is too small\n",
    "    for tupl in level1:\n",
    "        contour = contours[tupl[0]]\n",
    "        area = cv2.contourArea(contour)\n",
    "        if area > tooSmall:\n",
    "            significant.append([contour, area])\n",
    "            # Draw the contour on the original image\n",
    "            #cv2.drawContours(img, [contour], 0, (0,255,0),2, cv2.LINE_AA, maxLevel=1)\n",
    "            #print(\"______________________________________\")\n",
    "    significant.sort(key=lambda x: x[1])\n",
    "    #print ([x[1] for x in significant]);\n",
    "    return [x[0] for x in significant]\n",
    "\n",
    "cap_region_x_begin=0.5  # start point/total width\n",
    "cap_region_y_end=0.8  # start point/total width\n",
    "threshold = 60  #  BINARY threshold\n",
    "blurValue = 41  # GaussianBlur parameter\n",
    "bgSubThreshold = 100\n",
    "learningRate = 0\n",
    "var_list=[]\n",
    "# variables\n",
    "isBgCaptured = 0   # bool, whether the background captured\n",
    "triggerSwitch = False  # if true, keyborad simulator works\n",
    "\n",
    "def printThreshold(thr):\n",
    "    print(\"! Changed threshold to \"+str(thr))\n",
    "\n",
    "\n",
    "def removeBG(frame):\n",
    "    fgmask = bgModel.apply(frame,learningRate=learningRate)\n",
    "    # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))\n",
    "    # res = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel)\n",
    "    kernel = np.ones((3, 3), np.uint8)\n",
    "    fgmask = cv2.erode(fgmask, kernel, iterations=1)\n",
    "    res = cv2.bitwise_and(frame, frame, mask=fgmask)\n",
    "    return res\n",
    "\n",
    "def detect(gray,frame,thresh):\n",
    "    cv2.rectangle(frame,(520,100),(790,500),(255,0,0),0)\n",
    "    #img_roi=frame[70:500,520:890]\n",
    "    #img_roi=frame[100:500,520:790]\n",
    "    lab={'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4, 'f': 5, 'g': 6, 'h': 7, 'i': 8, 'k': 9, 'l': 10, 'm': 11, 'n': 12, 'o': 13, 'p': 14,\n",
    "     'q': 15, 'r': 16, 's': 17, 't': 18, 'u': 19, 'v': 20, 'w': 21, 'x': 22, 'y': 23}\n",
    "    global var_list\n",
    "    \"\"\"\n",
    "    # step 1 blurring\n",
    "    blurred=cv2.GaussianBlur(img_roi,(5,5),0)\n",
    "    # Step 2 Sobel Detection:- As we are dealing with 3 colors we need it for all colors\n",
    "    edgeImg = np.max(np.array([edgedetect(blurred[:,:, 0]), edgedetect(blurred[:,:, 1]), edgedetect(blurred[:,:, 2]) ]), axis=0)\n",
    "    #edgeImg = np.max(np.array([edgedetect(blurred[:,:, 0])]), axis=0)\n",
    "    # Step 3 Removal of noise\n",
    "    mean=np.mean(edgeImg)\n",
    "    edgeImg[edgeImg<=mean]=0\n",
    "    cv2.imshow(\"edge\",edgeImg)\n",
    "    \n",
    "    #Step 4 Contour Detection and areal detection\n",
    "    edgeImg_8u = np.asarray(edgeImg, np.uint8)\n",
    "    cv2.imshow(\"edge\",edgeImg_8u)\n",
    "    \n",
    "    # Find contours\n",
    "    significant = findSignificantContours(img_roi, edgeImg_8u)\n",
    "    \n",
    "    # Mask\n",
    "    mask = edgeImg.copy()\n",
    "    mask[mask > 0] = 0\n",
    "    \n",
    "    cv2.fillPoly(mask, significant, 200)\n",
    "    \n",
    "    # Invert mask\n",
    "    mask = np.logical_not(mask)\n",
    "    \n",
    "    #print(mask)\n",
    "    #Finally remove the background\n",
    "    img_roi[mask] = 0\n",
    "    \"\"\"\n",
    "    #img_roi=fgbg.apply(img_roi)\n",
    "    #cv2.imshow(\"imag\",img_roi)\n",
    "    #print(img_roi.shape)\n",
    "    #image=cv2.resize(img_roi,(28,28))\n",
    "    \n",
    "    image=cv2.resize(thresh,(28,28))\n",
    "    image=image.astype(\"float\")/255.0\n",
    "    image=img_to_array(image)\n",
    "    image=np.expand_dims(image,axis=0)\n",
    "    pred=model.predict(image)\n",
    "    prob=np.max(pred)\n",
    "    labels=np.argmax(pred)\n",
    "    for i,j in lab.items():\n",
    "        if j==labels:\n",
    "            labels=i\n",
    "    \n",
    "    if prob>0.8:\n",
    "        cv2.putText(frame,labels,(500,50),cv2.FONT_HERSHEY_COMPLEX,2,(255,0,0),2,cv2.LINE_AA,False)\n",
    "        #a=txt.generate(n=3,return_as_list=True,max_gen_length=5,prefix=labels)\n",
    "        #print(a)\n",
    "        #b=int(input(\"Which One Do You Like To Choose?\"))\n",
    "        #b=b-1\n",
    "        #cv2.putText(frame,a[b],(500,50),cv2.FONT_HERSHEY_COMPLEX,2,(255,0,0),2,cv2.LINE_AA,False)\n",
    "        var_list.append(labels)\n",
    "        word_gen(var_list)\n",
    "        print(\"labels:\",labels,\"probability:\",prob)\n",
    "        \n",
    "    #cv2.imshow(\"roi\",img_roi)\n",
    "    return frame\n",
    "\n",
    "def word_gen(var_list):\n",
    "    if len(var_list)>0:\n",
    "        a=txt.generate(n=1,return_as_list=True,max_gen_length=4,prefix=var_list[-1])\n",
    "        cv2.putText(frame,a[-1],(500,50),cv2.FONT_HERSHEY_COMPLEX,2,(255,0,0),2,cv2.LINE_AA,False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I show you how to do segmentation for “simple” images like these. The algorithm that I have used is as follows:\n",
    "\n",
    "1. Run an edge detection algorithm on the image (like Sobel, Scharr or Prewitt)\n",
    "2. Reduce noise on the resulting edge image (using a simple trick I found from Octave forge/Matlab)\n",
    "3. Run contour detection over the edges, return the contour in hierarchical order and pick the contours in the first level heirarchy.\n",
    "4. Now either pick the largest contour (in this case) or ignore all contours whose area is less than 5% of the total image area.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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      "!!!Background Captured!!!\n",
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      "!!!Reset BackGround!!!\n",
      "!!!Background Captured!!!\n",
      "!!!Reset BackGround!!!\n",
      "!!!Reset BackGround!!!\n",
      "!!!Background Captured!!!\n",
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      "labels: h probability: 0.9723256\n",
      "labels: h probability: 0.9971674\n",
      "labels: h probability: 0.8651642\n",
      "labels: h probability: 0.9928833\n",
      "labels: h probability: 0.9997917\n",
      "labels: h probability: 0.92674446\n",
      "labels: h probability: 0.9022165\n",
      "labels: h probability: 0.92809826\n",
      "labels: b probability: 0.9827145\n",
      "labels: b probability: 0.97990316\n",
      "labels: b probability: 0.9892315\n",
      "labels: h probability: 0.95295763\n",
      "labels: b probability: 0.996326\n",
      "labels: h probability: 0.997982\n",
      "labels: h probability: 0.9528398\n",
      "labels: h probability: 0.94491667\n",
      "labels: h probability: 0.9816172\n",
      "labels: h probability: 0.99207205\n",
      "labels: h probability: 0.93642503\n",
      "labels: h probability: 0.8869821\n",
      "labels: h probability: 0.929965\n",
      "labels: p probability: 0.80973744\n",
      "labels: p probability: 0.96525705\n",
      "labels: p probability: 0.98257345\n",
      "labels: h probability: 0.9988236\n",
      "labels: b probability: 0.99426264\n",
      "labels: b probability: 0.9905906\n",
      "labels: h probability: 0.82730484\n",
      "labels: h probability: 0.8223272\n",
      "labels: h probability: 0.89391565\n",
      "labels: h probability: 0.8223633\n",
      "labels: h probability: 0.8493594\n",
      "labels: b probability: 0.88845176\n",
      "labels: b probability: 0.86345404\n",
      "!!!Reset BackGround!!!\n",
      "!!!Reset BackGround!!!\n",
      "!!!Background Captured!!!\n",
      "labels: b probability: 0.9774736\n",
      "labels: h probability: 0.8552025\n",
      "labels: h probability: 0.9649984\n",
      "labels: b probability: 0.98851854\n",
      "labels: m probability: 0.95381594\n",
      "labels: m probability: 0.97887164\n",
      "labels: m probability: 0.91237885\n",
      "labels: m probability: 0.9694484\n",
      "labels: m probability: 0.8069585\n",
      "labels: h probability: 0.80374485\n",
      "labels: h probability: 0.9406589\n",
      "labels: h probability: 0.86147946\n",
      "labels: h probability: 0.9532831\n",
      "labels: h probability: 0.96830934\n",
      "labels: h probability: 0.9280765\n",
      "labels: h probability: 0.859621\n",
      "labels: h probability: 0.9121515\n",
      "labels: h probability: 0.98275226\n",
      "labels: h probability: 0.9817909\n",
      "labels: h probability: 0.9862244\n",
      "labels: h probability: 0.98288757\n",
      "labels: h probability: 0.98288757\n",
      "labels: h probability: 0.98300064\n",
      "labels: h probability: 0.98281\n",
      "labels: h probability: 0.9856512\n",
      "labels: h probability: 0.99117905\n",
      "labels: h probability: 0.9747438\n",
      "labels: h probability: 0.9788867\n",
      "labels: h probability: 0.93571645\n",
      "labels: h probability: 0.92090154\n",
      "labels: h probability: 0.9706232\n",
      "labels: h probability: 0.97975665\n",
      "labels: h probability: 0.97328025\n",
      "labels: h probability: 0.9805017\n",
      "labels: h probability: 0.9986638\n"
     ]
    }
   ],
   "source": [
    "cap=cv2.VideoCapture(-1)\n",
    "#fgbg=cv2.createBackgroundSubtractorMOG2()\n",
    "cap.set(10,200)\n",
    "cv2.namedWindow('trackbar')\n",
    "cv2.createTrackbar('trh1', 'trackbar', threshold, 100, printThreshold)\n",
    "count=0\n",
    "\n",
    "while True:\n",
    "    ret,frame=cap.read()\n",
    "    threshold = cv2.getTrackbarPos('trh1', 'trackbar')\n",
    "    frame = cv2.bilateralFilter(frame, 5, 50, 100)  # smoothing filter\n",
    "    frame=cv2.resize(frame,(940,640),interpolation=cv2.INTER_LINEAR)\n",
    "    frame=cv2.flip(frame,1)\n",
    "    frame1=frame[100:500,520:790]\n",
    "    if isBgCaptured == 1:  # this part wont run until background captured\n",
    "        img = removeBG(frame1)\n",
    "        #cv2.imshow('mask', img)\n",
    "        # convert the image into binary image\n",
    "        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "        blur = cv2.GaussianBlur(gray, (blurValue, blurValue), 0)\n",
    "        #cv2.imshow('blur', blur)\n",
    "        ret, thresh = cv2.threshold(blur, threshold, 255, cv2.THRESH_BINARY)\n",
    "        cv2.imshow('roi', thresh)\n",
    "        #gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n",
    "        canvas=detect(gray,frame,thresh)\n",
    "        cv2.imshow(\"image\",canvas)\n",
    "        \n",
    "    k = cv2.waitKey(10)\n",
    "    if k == ord('q'):  # press ESC to exit\n",
    "        cap.release()\n",
    "        cv2.destroyAllWindows()\n",
    "        break\n",
    "    elif k == ord('b'):  # press 'b' to capture the background\n",
    "        bgModel = cv2.createBackgroundSubtractorMOG2(0, bgSubThreshold)\n",
    "        isBgCaptured = 1\n",
    "        print( '!!!Background Captured!!!')\n",
    "    elif k == ord('r'):  # press 'r' to reset the background\n",
    "        bgModel = None\n",
    "        triggerSwitch = False\n",
    "        isBgCaptured = 0\n",
    "        print ('!!!Reset BackGround!!!')\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def check(k,dirname):\n",
    "    count=0\n",
    "    image=cv2.imread(k)\n",
    "    image=image.astype(\"float\")/255.0\n",
    "    image=img_to_array(image)\n",
    "    image=np.expand_dims(image,axis=0)\n",
    "    pred=model.predict(image)\n",
    "    prob=np.max(pred)\n",
    "    labels=np.argmax(pred)\n",
    "    lab={'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4, 'f': 5, 'g': 6, 'h': 7, 'i': 8, 'k': 9, 'l': 10, 'm': 11, 'n': 12, 'o': 13, 'p': 14,\n",
    "     'q': 15, 'r': 16, 's': 17, 't': 18, 'u': 19, 'v': 20, 'w': 21, 'x': 22, 'y': 23}\n",
    "    for i,j in lab.items():\n",
    "        if j==labels:\n",
    "            labels=i\n",
    "    #labels=chr(65+labels)\n",
    "    if labels!=dirname:\n",
    "        count+=1\n",
    "    #print(prob,labels)\n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "import os\n",
    "count=0\n",
    "for i in os.listdir(\"./handsign_images1/cool_test/\"): \n",
    "    for j in os.listdir(\"./handsign_images1/cool_test/\"+i+\"/\"):\n",
    "        a=check(\"./handsign_images1/cool_test/\"+i+\"/\"+j,i)\n",
    "        print(count)\n",
    "        if a==1:\n",
    "            count+=1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "cap=cv2.VideoCapture(0)\n",
    "fgbg=cv2.createBackgroundSubtractorMOG2()\n",
    "\n",
    "while True:\n",
    "    ret,frame=cap.read()\n",
    "    frame=cv2.flip(frame,1)\n",
    "    fgmask=fgbg.apply(frame)\n",
    "    cv2.imshow(\"frame\",frame)\n",
    "    cv2.imshow(\"mask\",fgmask)\n",
    "    if cv2.waitKey(1)&0xff==ord('q'):\n",
    "        cv2.destroyAllWindows()\n",
    "        break\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(None, 28, 28, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.input_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/shashank/miniconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Controls:\n",
      "\ts: stop.\tx: backspace.\to: write your own.\n",
      "\n",
      "Options:\n",
      "\t1:  \n",
      "\t2: n\n",
      "\t3: l\n",
      "\t4: t\n",
      "\t5: b\n",
      "\n",
      "Progress: a\n",
      "\n",
      "Your choice?\n",
      "> 2\n",
      "Controls:\n",
      "\ts: stop.\tx: backspace.\to: write your own.\n",
      "\n",
      "Options:\n",
      "\t1: y\n",
      "\t2: d\n",
      "\t3:  \n",
      "\t4: n\n",
      "\t5: i\n",
      "\n",
      "Progress: an\n",
      "\n",
      "Your choice?\n",
      "> s\n",
      "Controls:\n",
      "\ts: stop.\tx: backspace.\to: write your own.\n",
      "\n",
      "Options:\n",
      "\t1: A\n",
      "\t2: O\n",
      "\t3: E\n",
      "\t4: I\n",
      "\t5: o\n",
      "\n",
      "Progress: an<s>\n",
      "\n",
      "Your choice?\n",
      "> end\n",
      "That's not an option!\n",
      "Controls:\n",
      "\ts: stop.\tx: backspace.\to: write your own.\n",
      "\n",
      "Options:\n",
      "\t1: A\n",
      "\t2: O\n",
      "\t3: E\n",
      "\t4: I\n",
      "\t5: o\n",
      "\n",
      "Progress: an<s>\n",
      "\n",
      "Your choice?\n",
      "> end\n",
      "That's not an option!\n",
      "Controls:\n",
      "\ts: stop.\tx: backspace.\to: write your own.\n",
      "\n",
      "Options:\n",
      "\t1: A\n",
      "\t2: O\n",
      "\t3: E\n",
      "\t4: I\n",
      "\t5: o\n",
      "\n",
      "Progress: an<s>\n",
      "\n",
      "Your choice?\n",
      "> .\n",
      "That's not an option!\n",
      "Controls:\n",
      "\ts: stop.\tx: backspace.\to: write your own.\n",
      "\n",
      "Options:\n",
      "\t1: A\n",
      "\t2: O\n",
      "\t3: E\n",
      "\t4: I\n",
      "\t5: o\n",
      "\n",
      "Progress: an<s>\n",
      "\n",
      "Your choice?\n",
      "> >\n",
      "That's not an option!\n",
      "Controls:\n",
      "\ts: stop.\tx: backspace.\to: write your own.\n",
      "\n",
      "Options:\n",
      "\t1: A\n",
      "\t2: O\n",
      "\t3: E\n",
      "\t4: I\n",
      "\t5: o\n",
      "\n",
      "Progress: an<s>\n",
      "\n",
      "Your choice?\n",
      "> <\n",
      "That's not an option!\n",
      "Controls:\n",
      "\ts: stop.\tx: backspace.\to: write your own.\n",
      "\n",
      "Options:\n",
      "\t1: A\n",
      "\t2: O\n",
      "\t3: E\n",
      "\t4: I\n",
      "\t5: o\n",
      "\n",
      "Progress: an<s>\n",
      "\n",
      "Your choice?\n",
      "> end\n",
      "That's not an option!\n",
      "Controls:\n",
      "\ts: stop.\tx: backspace.\to: write your own.\n",
      "\n",
      "Options:\n",
      "\t1: A\n",
      "\t2: O\n",
      "\t3: E\n",
      "\t4: I\n",
      "\t5: o\n",
      "\n",
      "Progress: an<s>\n",
      "\n",
      "Your choice?\n"
     ]
    }
   ],
   "source": [
    "txt=textgenrnn()\n",
    "txt.generate(interactive=True,top_n=5,prefix=\"a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import  numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.expand_dims()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
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 },
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