Run.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import pandas\n",
    "import os\n",
    "import subprocess\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1 / (1 + math.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# SELECTED SHIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "haar=cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/datafinal/cascade.xml')\n",
    "haar20 = cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/ayushcascades/cascade20.xml')\n",
    "haar30 = cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/ayushcascades/cascade30.xml')\n",
    "haar40 = cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/ayushcascades/cascade40.xml')\n",
    "haar50 = cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/ayushcascades/cascade50.xml')\n",
    "haar60 = cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/ayushcascades/cascade60.xml')\n",
    "haar80 = cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/ayushcascades/cascade80.xml')\n",
    "haarnew = cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/ayushcascades/cascadenew.xml')\n",
    "haar1000 = cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/ayushcascades/cascade_1000500.xml')\n",
    "haarring = cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/ayushcascades/ringcascade.xml')\n",
    "haarringnew = cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/ayushcascades/ringcascadenew.xml')\n",
    "\n",
    "haarbigdata = cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/ayushcascades/cascade_bigData.xml')\n",
    "allmight2=cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/ayushcascades/allMightyLBP.xml')\n",
    "allmight=cv2.CascadeClassifier('/home/rishabh/Downloads/new street/Street-Sign-Detection/ayushcascades/allMightyHaar.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "\n",
    "listhaars= [allmight2,haarbigdata, haar,haar80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def detect(cascade, image):\n",
    "    img=image.copy()\n",
    "    signs= list(cascade.detectMultiScale(img))\n",
    "    if len(signs)==0:\n",
    "        img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)\n",
    "        img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\n",
    "        img = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\n",
    "        signs = cascade.detectMultiScale(img)\n",
    "    if len(signs)==0:\n",
    "        kernel_sharpen_3 = np.array([[-1,-1,-1,-1,-1],\n",
    "                             [-1,2,2,2,-1],\n",
    "                             [-1,2,8,2,-1],\n",
    "                             [-1,2,2,2,-1],\n",
    "                             [-1,-1,-1,-1,-1]]) / 8.0\n",
    "        img = cv2.filter2D(img, -1, kernel_sharpen_3)\n",
    "        signs = cascade.detectMultiScale(img)\n",
    "    img= image.copy()    \n",
    "    if len(signs)==0:\n",
    "        cv2.convertScaleAbs(img, img, 1.8, 0)\n",
    "        signs = cascade.detectMultiScale(img)\n",
    "        if len(signs)==0:\n",
    "            img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)\n",
    "            img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\n",
    "            img = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\n",
    "            signs = cascade.detectMultiScale(img)\n",
    "        if len(signs)==0:\n",
    "            kernel_sharpen_3 = np.array([[-1,-1,-1,-1,-1],\n",
    "                                 [-1,2,2,2,-1],\n",
    "                                 [-1,2,8,2,-1],\n",
    "                                 [-1,2,2,2,-1],\n",
    "                                 [-1,-1,-1,-1,-1]]) / 8.0\n",
    "            img = cv2.filter2D(img, -1, kernel_sharpen_3)\n",
    "            signs = cascade.detectMultiScale(img)\n",
    "   \n",
    "    if len(signs)==0:\n",
    "        cv2.convertScaleAbs(img, img, 2, 0)\n",
    "        signs = cascade.detectMultiScale(img)\n",
    "        if len(signs)==0:\n",
    "            img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)\n",
    "            img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\n",
    "            img = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\n",
    "            signs = cascade.detectMultiScale(img)\n",
    "        if len(signs)==0:\n",
    "            kernel_sharpen_3 = np.array([[-1,-1,-1,-1,-1],\n",
    "                                 [-1,2,2,2,-1],\n",
    "                                 [-1,2,8,2,-1],\n",
    "                                 [-1,2,2,2,-1],\n",
    "                                 [-1,-1,-1,-1,-1]]) / 8.0\n",
    "            img = cv2.filter2D(img, -1, kernel_sharpen_3)\n",
    "            signs = cascade.detectMultiScale(img)\n",
    "    return np.array(signs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import skimage.data\n",
    "import skimage.transform\n",
    "save_model_path_swaggy=\"/home/rishabh/Downloads/new street/swaggycnn/\"\n",
    "save_model_path_60097=\"/home/rishabh/Downloads/new street/60097Epocs/\"\n",
    "save_model_path_100099=\"/home/rishabh/Downloads/new street/100099Epocs_best/\"\n",
    "save_model_path_sexy=\"/home/rishabh/Downloads/new street/sexyOverfit/\"\n",
    "def test_model(images, cnn):\n",
    "    \"\"\"\n",
    "    Test the saved model against the test dataset\n",
    "    \"\"\"\n",
    "    image32 = [skimage.transform.resize(image, (32, 32)) for image in images] \n",
    "    image32=np.array(image32)\n",
    "    image32=image32/255.0\n",
    "    #test_features, test_labels = pickle.load(open('preprocess_training.p', mode='rb'))\n",
    "    loaded_graph = tf.Graph()\n",
    "\n",
    "    with tf.Session(graph=loaded_graph) as sess:\n",
    "        # Load model\n",
    "        loader = tf.train.import_meta_graph(save_model_path_swaggy + '.meta')\n",
    "        if cnn == 'swaggycnn':\n",
    "            loader = tf.train.import_meta_graph(save_model_path_swaggy + '.meta')\n",
    "            loader.restore(sess, save_model_path_swaggy)\n",
    "        if cnn == '60097':\n",
    "            loader = tf.train.import_meta_graph(save_model_path_60097 + '.meta')\n",
    "            loader.restore(sess, save_model_path_60097)\n",
    "        if cnn == '100099':\n",
    "            loader = tf.train.import_meta_graph(save_model_path_100099 + '.meta')\n",
    "            loader.restore(sess, save_model_path_100099)\n",
    "        if cnn == 'sexy':\n",
    "            loader = tf.train.import_meta_graph(save_model_path_sexy + '.meta')\n",
    "            loader.restore(sess, save_model_path_sexy)\n",
    "\n",
    "        # Get Tensors from loaded model\n",
    "        #print(tf.all_variables())\n",
    "        loaded_x = loaded_graph.get_tensor_by_name('x:0')\n",
    "        loaded_y = loaded_graph.get_tensor_by_name('y:0')\n",
    "        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
    "        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n",
    "        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n",
    "        loaded_prediction = loaded_graph.get_tensor_by_name('prediction:0')\n",
    "        logit = sess.run([loaded_logits], feed_dict={loaded_x: image32, loaded_keep_prob: 1.})\n",
    "        prediction= sess.run([loaded_prediction], feed_dict={loaded_x: image32, loaded_keep_prob: 1.})\n",
    "#         accuracy = sess.run([loaded_acc], feed_dict={loaded_x: image32,loaded_y:labels_true, loaded_keep_prob: 1.})\n",
    "    return prediction, logit[0][0]\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def detect2(cascade, image):\n",
    "    \n",
    "    img=image.copy()\n",
    "    signs= list(cascade.detectMultiScale(img))\n",
    "    if len(signs)==0:\n",
    "        img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)\n",
    "        img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\n",
    "        img = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\n",
    "        signs = cascade.detectMultiScale(img)\n",
    "    if len(signs)==0:\n",
    "        kernel_sharpen_3 = np.array([[-1,-1,-1,-1,-1],\n",
    "                             [-1,2,2,2,-1],\n",
    "                             [-1,2,8,2,-1],\n",
    "                             [-1,2,2,2,-1],\n",
    "                             [-1,-1,-1,-1,-1]]) / 8.0\n",
    "        img = cv2.filter2D(img, -1, kernel_sharpen_3)\n",
    "        signs = cascade.detectMultiScale(img)\n",
    "        \n",
    "    img= image.copy()    \n",
    "    cv2.convertScaleAbs(img, img, 3, 0)\n",
    "    sign5 = cascade.detectMultiScale(img)\n",
    "    if len(sign5)==0:\n",
    "        img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)\n",
    "        img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\n",
    "        img = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\n",
    "        signs = cascade.detectMultiScale(img)\n",
    "    if len(sign5)==0:\n",
    "        kernel_sharpen_3 = np.array([[-1,-1,-1,-1,-1],\n",
    "                             [-1,2,2,2,-1],\n",
    "                             [-1,2,8,2,-1],\n",
    "                             [-1,2,2,2,-1],\n",
    "                             [-1,-1,-1,-1,-1]]) / 8.0\n",
    "        img = cv2.filter2D(img, -1, kernel_sharpen_3)\n",
    "        sign5 = cascade.detectMultiScale(img)\n",
    "        \n",
    "        \n",
    "    img=image.copy()\n",
    "    cv2.convertScaleAbs(img, img, 2, 0)\n",
    "    sign2 = cascade.detectMultiScale(img)\n",
    "    if len(sign2)==0:\n",
    "        img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)\n",
    "        img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\n",
    "        img = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\n",
    "        sign2 = cascade.detectMultiScale(img)\n",
    "    if len(sign2)==0:\n",
    "        kernel_sharpen_3 = np.array([[-1,-1,-1,-1,-1],\n",
    "                             [-1,2,2,2,-1],\n",
    "                             [-1,2,8,2,-1],\n",
    "                             [-1,2,2,2,-1],\n",
    "                             [-1,-1,-1,-1,-1]]) / 8.0\n",
    "        img = cv2.filter2D(img, -1, kernel_sharpen_3)\n",
    "        sign2 = cascade.detectMultiScale(img)\n",
    "\n",
    "        \n",
    "    img=image.copy()\n",
    "    cv2.convertScaleAbs(img, img, 3.5, 0)\n",
    "    sign3 = cascade.detectMultiScale(img)\n",
    "    if len(sign3)==0:\n",
    "        img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)\n",
    "        img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\n",
    "        img = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\n",
    "        sign3 = cascade.detectMultiScale(img)\n",
    "    if len(sign3)==0:\n",
    "        kernel_sharpen_3 = np.array([[-1,-1,-1,-1,-1],\n",
    "                             [-1,2,2,2,-1],\n",
    "                             [-1,2,8,2,-1],\n",
    "                             [-1,2,2,2,-1],\n",
    "                             [-1,-1,-1,-1,-1]]) / 8.0\n",
    "        img = cv2.filter2D(img, -1, kernel_sharpen_3)\n",
    "        sign3 = cascade.detectMultiScale(img)\n",
    "\n",
    "        \n",
    "    img=image.copy()\n",
    "    cv2.convertScaleAbs(img, img, 1.8, 0)\n",
    "    sign4 = cascade.detectMultiScale(img)\n",
    "    if len(sign4)==0:\n",
    "        img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)\n",
    "        img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\n",
    "        img = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\n",
    "        sign4 = cascade.detectMultiScale(img)\n",
    "    if len(sign4)==0:\n",
    "        kernel_sharpen_3 = np.array([[-1,-1,-1,-1,-1],\n",
    "                             [-1,2,2,2,-1],\n",
    "                             [-1,2,8,2,-1],\n",
    "                             [-1,2,2,2,-1],\n",
    "                             [-1,-1,-1,-1,-1]]) / 8.0\n",
    "        img = cv2.filter2D(img, -1, kernel_sharpen_3)\n",
    "        sign4 = cascade.detectMultiScale(img)\n",
    "    \n",
    "\n",
    "    \n",
    "    signs=list(signs)\n",
    "    signs = signs+ list(sign2)+list(sign3)+list(sign4)+list(sign5)\n",
    "    return np.array(signs)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def majorvote(signs):\n",
    "    count = 0\n",
    "    maxcount = 0\n",
    "    selectedshit = []\n",
    "    i = 0\n",
    "    k = 0\n",
    "    while i < len(signs):\n",
    "        x = signs[i][0]\n",
    "        y = signs[i][1]\n",
    "        k = i+1\n",
    "        count = 1\n",
    "        while k < len(signs):\n",
    "            xdash = signs[k][0]\n",
    "            ydash = signs[k][1]\n",
    "            if abs(x-xdash) < 30 and abs(y-ydash) < 30:\n",
    "                count += 1\n",
    "            k+=1\n",
    "        if count > maxcount:\n",
    "            maxcount = count\n",
    "            selectedshit = signs[i]\n",
    "        i+=1\n",
    "    return selectedshit\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def check(image):\n",
    "    a, b = test_model(image, 'swaggycnn')\n",
    "    if a[0]==0:\n",
    "        return True\n",
    "    a, b = test_model(image, 'sexy')\n",
    "    if a[0]==0:\n",
    "        return True\n",
    "    a, b = test_model(image, '60097')\n",
    "    if a[0]==0:\n",
    "        return True\n",
    "    a, b = test_model(image, '100099')\n",
    "    if a[0]==0:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "directory = './Test/'\n",
    "test = pd.read_csv('testmapping.csv')\n",
    "finalshit = []\n",
    "pp = 1\n",
    "for filename in os.listdir(directory):\n",
    "    img = cv2.imread(os.path.join(directory, filename))\n",
    "    newfilename = list(test[test['filename'] == filename]['iname'])[0]\n",
    "    print(newfilename)\n",
    "    height, width = img.shape[:2]\n",
    "    img = img[30:height-30 :]\n",
    "    signs=[]\n",
    "    signleft=[]\n",
    "    k = 0\n",
    "    for cascade in listhaars:\n",
    "        temp=img.copy()\n",
    "        sign=list(detect(cascade,temp))\n",
    "        k += 1\n",
    "        signs += sign\n",
    "#         for i in sign:\n",
    "\n",
    "#             if i[0] not in signleft:\n",
    "#                 signs.append(i)\n",
    "#                 signleft.append(i[0])\n",
    "#                 print(\"cascade\"+str(k) ),   \n",
    "    j = 0\n",
    "#     signs += list(runHoughCircles(img))\n",
    "#     print(runHoughCircles(img))\n",
    "    r=[]\n",
    "    \n",
    "#     if len(signs)!=0:\n",
    "#         maxacc=-10000000000\n",
    "#         for selectedshit in signs:\n",
    "#             a,b=test_model([img[selectedshit[1]:selectedshit[1]+selectedshit[3], selectedshit[0]:selectedshit[0]+selectedshit[2]]])\n",
    "#             print(a)\n",
    "#             if a[0]==0:                    \n",
    "#                 if sigmoid(b[0])>maxacc:\n",
    "#                     maxacc=sigmoid(b[0])\n",
    "#                     r=selectedshit\n",
    "\n",
    "    r = majorvote(signs)\n",
    "    selectedshit = r\n",
    "    \n",
    "    # do cnn on one selected\n",
    "    \n",
    "    if len(r)!=0:\n",
    "        boob = check([img[r[1]:r[1]+r[3], r[0]:r[0]+r[2]]])\n",
    "        print (boob)\n",
    "        if boob==False:\n",
    "            selectedshit = []\n",
    "\n",
    "\n",
    "        \n",
    "            \n",
    "\n",
    "#     print selectedshit\n",
    "    \n",
    "    ###############################################################3\n",
    "    if len(selectedshit)>0:\n",
    "        selectedshit[1] += 30 # to counter for old shit done\n",
    "        # This will increase accuracy if we get too big a box to handle. Which wont really be the case\n",
    "        # Can take this lite\n",
    "        finalshit.append([filename+'_Top', selectedshit[1]])\n",
    "        finalshit.append([filename+'_Left', selectedshit[0]])\n",
    "        finalshit.append([filename+'_Width', selectedshit[2]])\n",
    "        finalshit.append([filename+'_Height', selectedshit[3]])\n",
    "        print(\"entered \" + str(filename))\n",
    "        selectedshit[1] -= 30\n",
    "        crop = img[selectedshit[1]:selectedshit[1]+selectedshit[3], selectedshit[0]:selectedshit[0]+selectedshit[2]]\n",
    "        cv2.imwrite('onlyhaar2/'+str(newfilename)+'_'+str(j)+'.jpg',crop)\n",
    "        j+=1\n",
    "        continue\n",
    "    ########################################################3\n",
    "    print(\"1st pass failed\")\n",
    "    j=0\n",
    "    signs=[]\n",
    "    k=0\n",
    "    for cascade in listhaars:\n",
    "        temp=img.copy()\n",
    "        sign=list(detect2(cascade,temp))\n",
    "        k += 1            \n",
    "        signs+=sign\n",
    "#         print(\"cascade\"+str(k) )\n",
    "#         print(sign)\n",
    "\n",
    "    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
    "    h, s, v = cv2.split(hsv)\n",
    "    s += 100\n",
    "    img2 = cv2.merge((h, s, v))\n",
    "    img2 = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\n",
    "    \n",
    "    for cascade in listhaars:\n",
    "        temp=img.copy()\n",
    "        sign=list(detect2(cascade,temp))\n",
    "        k += 1            \n",
    "        signs+=sign\n",
    "   \n",
    "    \n",
    "    r = majorvote(signs)\n",
    "    selectedshit = r\n",
    "    \n",
    "    # do cnn on one selected\n",
    "    if len(r)!=0:\n",
    "        boob = check([img[r[1]:r[1]+r[3], r[0]:r[0]+r[2]]])\n",
    "        print (boob)\n",
    "        if boob==False:\n",
    "            selectedSigns = []\n",
    "            if len(signs)!=0: \n",
    "                #maxacc=-10000000\n",
    "                for ss in signs:\n",
    "                    a,b=test_model([img[ss[1]:ss[1]+ss[3], ss[0]:ss[0]+ss[2]]], 'swaggycnn')\n",
    "                    print(a)\n",
    "                    if a[0]==0:\n",
    "                        selectedSigns.append(ss)\n",
    "            #                     if sigmoid(b[0])>maxacc:\n",
    "            #                         maxacc=sigmoid(b[0])\n",
    "            #                         r=selectedshit\n",
    "\n",
    "            r = majorvote(selectedSigns)    \n",
    "            selectedshit=r   \n",
    "\n",
    "\n",
    "\n",
    "    #if len(selectedshit) == 0:\n",
    "    #    selectedshit = majorvote(signs)\n",
    "            \n",
    "    #########################################################33\n",
    "    if len(selectedshit)>0:\n",
    "        selectedshit[1] += 30 # to counter for old shit done\n",
    "        # This will increase accuracy if we get too big a box to handle. Which wont really be the case\n",
    "        # Can take this lite\n",
    "        finalshit.append([filename+'_Top', selectedshit[1]])\n",
    "        finalshit.append([filename+'_Left', selectedshit[0]])\n",
    "        finalshit.append([filename+'_Width', selectedshit[2]])\n",
    "        finalshit.append([filename+'_Height', selectedshit[3]])\n",
    "        print(\"entered \" + str(filename))\n",
    "        selectedshit[1] -= 30\n",
    "        crop = img[selectedshit[1]:selectedshit[1]+selectedshit[3], selectedshit[0]:selectedshit[0]+selectedshit[2]]\n",
    "        cv2.imwrite('onlyhaar2/'+str(newfilename)+'_'+str(j)+'.jpg',crop)\n",
    "        j+=1\n",
    "       \n",
    "    else:\n",
    "        \n",
    "        finalshit.append([filename+'_Top', height/2])\n",
    "        finalshit.append([filename+'_Left', width/2])\n",
    "        finalshit.append([filename+'_Width', 50])\n",
    "        finalshit.append([filename+'_Height', 50])\n",
    "            #print \"couldnt find \", filename\n",
    "        pp+=1\n",
    "        continue\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-6b7a291142ff>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msubmit3\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'Img_Label'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfinalshit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Val'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfinalshit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "submit3 = pd.DataFrame({'Img_Label':np.array(finalshit)[:, 0], 'Val':np.array(finalshit)[:, 1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "submit3.to_csv('./submitswaggy2.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#submit3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#finalshit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#df = pd.read_csv('submitswaggy2.csv')\n",
    "#df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "directory = './Test/'\n",
    "test = pd.read_csv('testmapping.csv')\n",
    "finalshit = []\n",
    "pp = 1\n",
    "for filename in os.listdir(directory):\n",
    "    img = cv2.imread(os.path.join(directory, filename))\n",
    "    newfilename = list(test[test['filename'] == filename]['iname'])[0]\n",
    "    print(newfilename)\n",
    "    height, width = img.shape[:2]\n",
    "    img = img[30:height-30 :]\n",
    "    signs=[]\n",
    "    signleft=[]\n",
    "    k = 0\n",
    "    \n",
    "    j=0\n",
    "    signs=[]\n",
    "    k=0\n",
    "    \n",
    "    for cascade in listhaars:\n",
    "        temp=img.copy()\n",
    "        sign=list(detect2(cascade,temp))\n",
    "        k += 1            \n",
    "        signs+=sign\n",
    "\n",
    "        \n",
    "    \n",
    "    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
    "    h, s, v = cv2.split(hsv)\n",
    "    s += 100\n",
    "    img2 = cv2.merge((h, s, v))\n",
    "    img2 = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)\n",
    "    for cascade in listhaars:\n",
    "        temp=img.copy()\n",
    "        sign=list(detect2(cascade,temp))\n",
    "        k += 1            \n",
    "        signs+=sign\n",
    "\n",
    "    \n",
    "    \n",
    "    r=[]\n",
    "    \n",
    "    if len(signs)!=0: \n",
    "        maxacc=-10000000\n",
    "        for selectedshit in signs:\n",
    "            try:\n",
    "                a,b=test_model([img[selectedshit[1]:selectedshit[1]+selectedshit[3], selectedshit[0]:selectedshit[0]+selectedshit[2]]])\n",
    "                print(a)\n",
    "                if a[0]==0:                    \n",
    "                    if sigmoid(b[0])>maxacc:\n",
    "                        maxacc=sigmoid(b[0])\n",
    "                        r=selectedshit\n",
    "            except:\n",
    "                pass\n",
    "    \n",
    "    selectedshit=r     \n",
    "            \n",
    "    #########################################################33\n",
    "    if len(selectedshit)>0:\n",
    "        selectedshit[1] += 30 # to counter for old shit done\n",
    "        # This will increase accuracy if we get too big a box to handle. Which wont really be the case\n",
    "        # Can take this lite\n",
    "        if selectedshit[2] > 150 and selectedshit[3] > 150: # too big to handle ;)\n",
    "            finalshit.append([filename+'_Top', selectedshit[1]+(selectedshit[3]/2)   ])\n",
    "            finalshit.append([filename+'_Left', selectedshit[0]+(selectedshit[2]/2)  ])\n",
    "            finalshit.append([filename+'_Width', 55])\n",
    "            finalshit.append([filename+'_Height', 55])\n",
    "        else:\n",
    "            finalshit.append([filename+'_Top', selectedshit[1]])\n",
    "            finalshit.append([filename+'_Left', selectedshit[0]])\n",
    "            finalshit.append([filename+'_Width', selectedshit[2]])\n",
    "            finalshit.append([filename+'_Height', selectedshit[3]])\n",
    "        print(\"entered\")\n",
    "        selectedshit[1] -= 30\n",
    "        crop = img[selectedshit[1]:selectedshit[1]+selectedshit[3], selectedshit[0]:selectedshit[0]+selectedshit[2]]\n",
    "        cv2.imwrite('all2/'+str(newfilename)+'_'+str(j)+'.jpg',crop)\n",
    "        j+=1\n",
    "    \n",
    "    \n",
    "    else:  \n",
    "        finalshit.append([filename+'_Top', height/2])\n",
    "        finalshit.append([filename+'_Left', width/2])\n",
    "        finalshit.append([filename+'_Width', 50])\n",
    "        finalshit.append([filename+'_Height', 50])\n",
    "            #print \"couldnt find \", filename\n",
    "        pp+=1\n",
    "        continue\n",
    "    \n",
    "    \n",
    "#     cv2.imshow('cropped', crop)\n",
    "#     cv2.waitKey(0)\n",
    "#     cv2.destroyAllWindows()\n",
    "    \n",
    "    #     cv2.imshow('cropped', crop)\n",
    "    #     cv2.waitKey(0)\n",
    "    #     cv2.destroyAllWindows()\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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