results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "import sys\n",
    "\n",
    "from skimage.io import imread\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import os\n",
    "#os.environ['KERAS_BACKEND'] = 'theano'\n",
    "#os.environ['THEANO_FLAGS'] = 'mode=FAST_RUN, device=gpu0, floatX=float32, optimizer=fast_compile'\n",
    "\n",
    "# Fix error with TF and Keras\n",
    "import tensorflow as tf\n",
    "tf.python.control_flow_ops = tf\n",
    "\n",
    "from keras import models\n",
    "from keras.optimizers import SGD\n",
    "from keras import backend as K\n",
    "\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = 'Intcatch_Dataset_Water_Resized/'\n",
    "img_w = 240\n",
    "img_h = 160\n",
    "n_labels = 2 #6\n",
    "#n_of_images= 191*2\n",
    "data_shape = img_w*img_h\n",
    "\n",
    "def normalized(bgr):\n",
    "    #return rgb/255.0\n",
    "    #norm=np.zeros((rgb.shape[0], rgb.shape[1], 3),np.float32)\n",
    "\n",
    "    #b=rgb[:,:,0]\n",
    "    #g=rgb[:,:,1]\n",
    "    #r=rgb[:,:,2]\n",
    "\n",
    "    #norm[:,:,0]=cv2.equalizeHist(b)\n",
    "    #norm[:,:,1]=cv2.equalizeHist(g)\n",
    "    #norm[:,:,2]=cv2.equalizeHist(r)\n",
    "\n",
    "    #return norm\n",
    "\n",
    "    b, g, r = cv2.split(bgr)\n",
    "    red = cv2.equalizeHist(r)\n",
    "    green = cv2.equalizeHist(g)\n",
    "    blue = cv2.equalizeHist(b)\n",
    "    return cv2.merge((blue, green, red))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from disk\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from keras.models import model_from_json\n",
    "\n",
    "# load json and create model\n",
    "json_file = open('modelli/modello_halfConv_240x160.json', 'r')\n",
    "model = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(model)\n",
    "# load weights into new model\n",
    "model.load_weights(\"weights/best2_halfConv_240x160-90Epochs.hdf5\")\n",
    "print(\"Loaded model from disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "NoLabel = [0,0,0]\n",
    "\n",
    "Water = [255,100,100] \n",
    "\n",
    "Sky = [153,17,105] \n",
    "\n",
    "Mountain = [0,255,0] \n",
    "\n",
    "Boat = [200,8,21] \n",
    "\n",
    "Other = [255,255,255] \n",
    "\n",
    "label_colours = np.array([NoLabel, Water, Sky, Mountain, Boat,\n",
    "                          Other])\n",
    "\n",
    "def visualize(temp, plot=False):\n",
    "    r = temp.copy()\n",
    "    g = temp.copy()\n",
    "    b = temp.copy()\n",
    "    for l in range(0,5):\n",
    "        r[temp==l]=label_colours[l,0]\n",
    "        g[temp==l]=label_colours[l,1]\n",
    "        b[temp==l]=label_colours[l,2]\n",
    "\n",
    "    rgb = np.zeros((temp.shape[0], temp.shape[1], 3), dtype=np.uint8)\n",
    "    rgb[:,:,0] = (b)#[:,:,0]\n",
    "    rgb[:,:,1] = (g)#[:,:,1]\n",
    "    rgb[:,:,2] = (r)#[:,:,2]\n",
    "    if plot:\n",
    "        plt.imshow(rgb)\n",
    "    else:\n",
    "        return rgb\n",
    "        \n",
    "        \n",
    "import math\n",
    "\n",
    "line=[]\n",
    "\n",
    "def grayscale(img):\n",
    "    \"\"\"Applies the Grayscale transform\n",
    "    This will return an image with only one color channel\n",
    "    but NOTE: to see the returned image as grayscale\n",
    "    (assuming your grayscaled image is called 'gray')\n",
    "    you should call plt.imshow(gray, cmap='gray')\"\"\"\n",
    "    return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "    # Or use BGR2GRAY if you read an image with cv2.imread()\n",
    "    # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    \n",
    "def canny(img, low_threshold, high_threshold):\n",
    "    \"\"\"Applies the Canny transform\"\"\"\n",
    "    return cv2.Canny(img, low_threshold, high_threshold)\n",
    "\n",
    "def median_blur(img, kernel_size):\n",
    "    \"\"\"Applies a Gaussian Noise kernel\"\"\"\n",
    "    return cv2.medianBlur(img, kernel_size)\n",
    "\n",
    "def region_of_interest(img, vertices):\n",
    "    \"\"\"\n",
    "    Applies an image mask.\n",
    "    \n",
    "    Only keeps the region of the image defined by the polygon\n",
    "    formed from `vertices`. The rest of the image is set to black.\n",
    "    \"\"\"\n",
    "    #defining a blank mask to start with\n",
    "    mask = np.zeros_like(img)   \n",
    "    \n",
    "    #defining a 3 channel or 1 channel color to fill the mask with depending on the input image\n",
    "    if len(img.shape) > 2:\n",
    "        channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image\n",
    "        ignore_mask_color = (255,) * channel_count\n",
    "    else:\n",
    "        ignore_mask_color = 255\n",
    "        \n",
    "    #filling pixels inside the polygon defined by \"vertices\" with the fill color    \n",
    "    cv2.fillPoly(mask, vertices, ignore_mask_color)\n",
    "    \n",
    "    #returning the image only where mask pixels are nonzero\n",
    "    masked_image = cv2.bitwise_and(img, mask)\n",
    "    return masked_image\n",
    "\n",
    "def get_fitline(img, f_lines):\n",
    "\n",
    "    rows,cols = img.shape[:2]\n",
    "    output = cv2.fitLine(f_lines,cv2.DIST_L2,0, 0.01, 0.01)\n",
    "    vx, vy, x, y = output[0], output[1], output[2], output[3]\n",
    "    x1, y1 = int(((img.shape[0]-1)-y)/vy*vx + x) , img.shape[0]-1\n",
    "    x2, y2 = int(((img.shape[0]/2+100)-y)/vy*vx + x) , int(img.shape[0]/2+100)\n",
    "    result = [x1,y1,x2,y2]\n",
    "\n",
    "    return result\n",
    "\n",
    "\n",
    "def draw_lines(img, lines, color=[255, 255, 255], thickness=2):\n",
    "    for line in lines:\n",
    "        for x1,y1,x2,y2 in line:\n",
    "            cv2.line(img, (x1, y1), (x2, y2), color, thickness)\n",
    "\n",
    "def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):\n",
    "    \"\"\"\n",
    "    `img` should be the output of a Canny transform.\n",
    "        \n",
    "    Returns an image with hough lines drawn.\n",
    "    \"\"\"\n",
    "    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)\n",
    "    #line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\n",
    "    #draw_lines(line_img, lines)\n",
    "\n",
    "    \n",
    "    return lines\n",
    "\n",
    "# Python 3 has support for cool math symbols.\n",
    "\n",
    "def weighted_img(img, initial_img, α=0.8, β=1., λ=0.):\n",
    "    \"\"\"\n",
    "    `img` is the output of the hough_lines(), An image with lines drawn on it.\n",
    "    Should be a blank image (all black) with lines drawn on it.\n",
    "    \n",
    "    `initial_img` should be the image before any processing.\n",
    "    \n",
    "    The result image is computed as follows:\n",
    "    \n",
    "    initial_img * α + img * β + λ\n",
    "    NOTE: initial_img and img must be the same shape!\n",
    "    \"\"\"\n",
    "    return cv2.addWeighted(initial_img, α, img, β, λ)\n",
    "\n",
    "def Collect_points(lines):\n",
    "\n",
    "    # reshape [:4] to [:2]\n",
    "    #interp = lines.reshape(lines.shape[0]*2,2)\n",
    "    # interpolation & collecting points for RANSAC\n",
    "    points=[]\n",
    "    for line in lines:\n",
    "        #print(line[0],line[1],line[2],line[3])\n",
    "        #if(int(line[1])<400):\n",
    "        new_point = np.array([[int(line[0]),int(line[1])]])\n",
    "        points.append(new_point)\n",
    "        #interp = np.concatenate((interp,new_point),axis = 0)\n",
    "        #if(int(line[3])<400):\n",
    "        new_point = np.array([[int(line[2]),int(line[3])]])\n",
    "        points.append(new_point)\n",
    "        #interp = np.concatenate((interp,new_point),axis = 0)\n",
    "    return points\n",
    "\n",
    "def printPoints(points):\n",
    "    for point in points:\n",
    "        print(point)\n",
    "\n",
    "def draw_circle(img,points, color=[255, 255, 255]):\n",
    "    for point in points:\n",
    "        cv2.circle(img,(point[0],point[1]), 2, color, -1)\n",
    "        \n",
    "def smoothing(lines, pre_frame=10):\n",
    "    # collect frames & print average line\n",
    "    lines = np.squeeze(lines)\n",
    "    avg_line = np.array([0.0,0.0,0.0,0.0])\n",
    "    \n",
    "    for ii,line in enumerate(reversed(lines)):\n",
    "        if ii == pre_frame:\n",
    "            break\n",
    "        avg_line += line\n",
    "    avg_line = avg_line / pre_frame\n",
    "\n",
    "    return avg_line\n",
    "\n",
    "def horizon_line_pipeline(image):\n",
    "    #image=median_blur(image,101)  #ATTENTION\n",
    "    gray = grayscale(image)\n",
    "    gray=median_blur(gray, 21)  #ATTENTION\n",
    "    low_threshold = 0\n",
    "    high_threshold = 0\n",
    "    cannyimage=canny(gray, low_threshold, high_threshold)\n",
    "    rho = 2# distance resolution in pixels of the Hough grid\n",
    "    theta = np.pi/180 # angular resolution in radians of the Hough grid\n",
    "    threshold = 1     # minimum number of votes (intersections in Hough grid cell)\n",
    "    min_line_length = 2 #minimum number f pixels making up a line\n",
    "    max_line_gap = 5    # maximum gap in pixels between connectable line segments\n",
    "    lines=hough_lines(cannyimage, rho, theta, threshold, min_line_length, max_line_gap)\n",
    "    line_arr = np.squeeze(lines)\n",
    "    #slope_degree = (np.arctan2(line_arr[:,1] - line_arr[:,3], line_arr[:,0] - line_arr[:,2]) * 180) / np.pi\n",
    "    # ignore vertical slope lines\n",
    "    #line_arr = line_arr[np.abs(slope_degree)>95]\n",
    "    points = Collect_points(line_arr)\n",
    "    if(len(points)<2):\n",
    "        points= line_arr.reshape(lines.shape[0]*2,2)\n",
    "        \n",
    "    #printPoints(points)\n",
    "    #line_img = np.zeros((cannyimage.shape[0], cannyimage.shape[1], 3), dtype=np.uint8)\n",
    "    #draw_circle(line_img,points)\n",
    "    if(len(points)>2):\n",
    "        fit_line = cv2.fitLine(np.float32(points), cv2.DIST_HUBER, 1, 0.001, 0.001)\n",
    "        line.append(fit_line)\n",
    "    \n",
    "        #if len(line) > 10:\n",
    "        #    fit_line = smoothing(line, 1)\n",
    "        \n",
    "    \n",
    "        \n",
    "    \n",
    "    return fit_line\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.0\n",
      "1 1.3979332447052002\n",
      "2 1.4146347045898438\n",
      "3 1.4340879917144775\n",
      "4 1.4502074718475342\n",
      "5 1.4663233757019043\n",
      "6 1.4824059009552002\n",
      "7 1.506241798400879\n"
     ]
    },
    {
     "data": {
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4PZ8OaokvvSaGGcXQ6VQ3Ny/T1YlKZDpJr90grdeLsZZLwhRBSgxihqFQjBBC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\nxaUMCEdlkvZ0B7ll1RpOjCnRpFiUyXSYQiVetKC0nPqSCYY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kuL70+k6e9p+gSoWtI0YG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\nDrNnju1gjr1kewhg7MCGe1+Cle8D5FiMxRFYSOykvZ1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Uat/sPc9HH+ddXJjTzqzUhZRN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\nPuH2/wHRNvXLVZ/1WAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f050b7a6e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=16, resizing from (1080, 1920) to (1088, 1920) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to None (risking incompatibility). You may also see a FFMPEG warning concerning speedloss due to data not being aligned.\n"
     ]
    },
    {
     "data": {
      "image/png": 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y7t4JhzxgEg4+LJgok9RvyifgRAUe2w2HjsDhURmF/RHctxOKm5QLeOAQ1Bag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99j7VEnGfWfxekzZf6P3/Cs7n+OzL2Dyteuo1euEQRDGs1/xaWXPpavP7TJs5/9c1z8vJ/ia+9+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\nWzjsYuv+WRLJBGibKbMKGSXebLuRgpbTZGK6BkEU4YaH1vu9y4i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IGoBq8R34hDoefv1m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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f050b752e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 2.3270981311798096\n",
      "9 2.3667168617248535\n",
      "10 2.386296272277832\n",
      "11 2.415983200073242\n",
      "12 2.4313576221466064\n",
      "13 2.4480667114257812\n",
      "14 2.467912435531616\n"
     ]
    },
    {
     "data": {
      "image/png": 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BxkbpoO1QXcDTynDCBd9uOabWvx3+t41SCt+U1Vpgj+6P4mD2per6XUGbxdF6Ae17O8Az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\nMPIb48a4MW6MH/PxI8FaKZot9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J3bqWKLF11YzGO6LlqjIKhYR48afPD1ymN79KlzfPh9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\n56GUfmRFmGWO2+/xte26vRZxj8nMW+hkfLxe04HlknJ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DKzTdFjJsYZi8tHHWGyunwkrm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f050b6c2160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "OSError",
     "evalue": "[Errno 32] Broken pipe\n\nFFMPEG COMMAND:\nffmpeg -y -f rawvideo -vcodec rawvideo -s 1920x1080 -pix_fmt rgb24 -r 9.00 -i - -an -vcodec libx264 -pix_fmt yuv420p -crf 25 -r 9 -vf scale=1920:1088 -v warning /home/lorenzo/UNIVR/Intcatch/Intcatch_Deep_Pixelwise_Segmentation/video_output/TMP.mp4\n\nFFMPEG STDERR OUTPUT:\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mBrokenPipeError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m/home/lorenzo/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/imageio/plugins/ffmpeg.py\u001b[0m in \u001b[0;36m_append_data\u001b[0;34m(self, im, meta)\u001b[0m\n\u001b[1;32m    620\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 621\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_proc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtostring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    622\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mIOError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mBrokenPipeError\u001b[0m: [Errno 32] Broken pipe",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-0afd149ba671>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0mimageOUT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbitwise_or\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_original\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpred_original_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimageOUT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfit_line\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mfit_line\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfit_line\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mfit_line\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfit_line\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mfit_line\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfit_line\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mfit_line\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m255\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m255\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimageOUT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimageOUT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/lorenzo/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/imageio/core/format.py\u001b[0m in \u001b[0;36mappend_data\u001b[0;34m(self, im, meta)\u001b[0m\n\u001b[1;32m    466\u001b[0m             \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    467\u001b[0m             \u001b[0;31m# Call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 468\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_append_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_meta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    469\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mset_meta_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/lorenzo/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/imageio/plugins/ffmpeg.py\u001b[0m in \u001b[0;36m_append_data\u001b[0;34m(self, im, meta)\u001b[0m\n\u001b[1;32m    624\u001b[0m                 \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'{0:}\\n\\nFFMPEG COMMAND:\\n{1:}\\n\\nFFMPEG STDERR '\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m                       \u001b[0;34m'OUTPUT:\\n'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cmd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 626\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    628\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mset_meta_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno 32] Broken pipe\n\nFFMPEG COMMAND:\nffmpeg -y -f rawvideo -vcodec rawvideo -s 1920x1080 -pix_fmt rgb24 -r 9.00 -i - -an -vcodec libx264 -pix_fmt yuv420p -crf 25 -r 9 -vf scale=1920:1088 -v warning /home/lorenzo/UNIVR/Intcatch/Intcatch_Deep_Pixelwise_Segmentation/video_output/TMP.mp4\n\nFFMPEG STDERR OUTPUT:\n"
     ]
    }
   ],
   "source": [
    "import imageio\n",
    "from time import time, sleep\n",
    "\n",
    "\n",
    "filename = \"video_input/prophr3.mp4\"\n",
    "reader = imageio.get_reader(filename,  'ffmpeg')\n",
    "fps = reader.get_meta_data()['fps']\n",
    "writer = imageio.get_writer('video_output/TMP.mp4', fps=9)\n",
    "now=time()\n",
    "start_time=now\n",
    "\n",
    "image=reader.get_data(0)\n",
    "image = np.array(image)\n",
    "image = image[:, :, ::-1].copy()\n",
    "image_original=cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "height,width,depth=image.shape\n",
    "image_original_hist=normalized(image_original)\n",
    "image = cv2.resize(image,(img_w,img_h))\n",
    "image = normalized(image)\n",
    "image = image[np.newaxis, ...]\n",
    "output = model.predict(image)\n",
    "pred = visualize(np.argmax(output[0],axis=1).reshape((img_h,img_w)), False)\n",
    "pred_original_size = cv2.resize(pred,(width,height))\n",
    "pred_original_size=horizon_line_pipeline(pred_original_size)\n",
    "\n",
    "prediction_framerate=7\n",
    "for i in range(len(reader)):\n",
    "    elapsed_time = now - start_time\n",
    "    print(i, elapsed_time)\n",
    "    image=reader.get_data(int(i))\n",
    "    image = np.array(image)\n",
    "    image = image[:, :, ::-1].copy()\n",
    "    image_original=cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    if(i==prediction_framerate):\n",
    "        height,width,depth=image.shape\n",
    "        image = cv2.resize(image,(img_w,img_h))\n",
    "        image = normalized(image)\n",
    "        plt.imshow(image)\n",
    "        plt.show()\n",
    "        image = image[np.newaxis, ...]\n",
    "        output = model.predict(image)\n",
    "        pred = visualize(np.argmax(output[0],axis=1).reshape((img_h,img_w)), False)\n",
    "        pred_original_size = cv2.resize(pred,(width,height))\n",
    "        fit_line = horizon_line_pipeline(pred_original_size)\n",
    "        prediction_framerate+=7\n",
    "        imageOUT = cv2.bitwise_or(image_original,pred_original_size)\n",
    "        cv2.line(imageOUT, (int(fit_line[2]-fit_line[0]*width), int(fit_line[3]-fit_line[1]*width)), (int(fit_line[2]+fit_line[0]*width), int(fit_line[3]+fit_line[1]*width)), (255, 0, 255), 12)\n",
    "        writer.append_data(imageOUT)\n",
    "        plt.imshow(imageOUT)\n",
    "        plt.show()\n",
    "    now=time()\n",
    "    if(i>=5018): break\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 160 x 160"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  -  87\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'visualize' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-e0351f9afd5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m     \u001b[0mpred_output_image\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvisualize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_h\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mimg_w\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m     \u001b[0mpred_output_image\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_output_image\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0mpred_output_image\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_output_image\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCOLOR_BGR2RGB\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'visualize' is not defined"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "dataset_annotation=glob.glob(\"/home/lorenzo/Intcatch/definitivo/Test/*annotation.png\", recursive=True)\n",
    "\n",
    "Path_to_save_image='Results/Full_image/Big_Conv_80x80/Image_Results/'\n",
    "Path_to_save_results='Results/Full_image/Big_Conv_80x80/'\n",
    "\n",
    "label=[]\n",
    "pred=[]\n",
    "index=0\n",
    "for annotation in dataset_annotation:\n",
    "    index+=1\n",
    "    #if(index==2): break\n",
    "    print(index,\" - \",len(dataset_annotation))\n",
    "    pathRoot=annotation[:annotation.rfind('/')] + '/'\n",
    "    name_file=(annotation[annotation.rfind('/')+1 : annotation.rfind('-annotation')])\n",
    "    annotation_image=cv2.imread(annotation)\n",
    "    name_of_image=Path_to_save_image+ name_file +'-Mask.png'\n",
    "    cv2.imwrite(name_of_image,annotation_image)\n",
    "    \n",
    "    \n",
    "    annotation_image = cv2.cvtColor(annotation_image, cv2.COLOR_BGR2RGB)\n",
    "    annotation_image = cv2.resize(annotation_image,(img_h,img_w))\n",
    "    image=cv2.imread(pathRoot+name_file+\".png\")\n",
    "    image_original=image\n",
    "    name_of_image=Path_to_save_image + name_file +'.png'\n",
    "    cv2.imwrite(name_of_image,image)\n",
    "    image = np.array(image)\n",
    "    height,width,depth=image.shape\n",
    "    image = cv2.resize(image,(img_w,img_h))\n",
    "    image = normalized(image)\n",
    "    image = image[np.newaxis, ...]\n",
    "    output = model.predict(image)\n",
    "    \n",
    "    pred_output_image = visualize(np.argmax(output[0],axis=1).reshape((img_h,img_w)), False)\n",
    "    pred_output_image = cv2.resize(pred_output_image,(width,height))\n",
    "    pred_output_image = cv2.cvtColor(pred_output_image, cv2.COLOR_BGR2RGB)\n",
    "    name_of_image=Path_to_save_image+ name_file +'-Mask_Predicted.png'\n",
    "    cv2.imwrite(name_of_image, pred_output_image)\n",
    "    \n",
    "    imageOUT = cv2.bitwise_or(image_original,pred_output_image)\n",
    "    fit_line = horizon_line_pipeline(pred_output_image)\n",
    "    cv2.line(imageOUT, (int(fit_line[2]-fit_line[0]*width), int(fit_line[3]-fit_line[1]*width)), (int(fit_line[2]+fit_line[0]*width), int(fit_line[3]+fit_line[1]*width)), (255, 0, 255), 10)\n",
    "    \n",
    "    \n",
    "    name_of_image=Path_to_save_image+ name_file +'-Horizon.png'\n",
    "    cv2.imwrite(name_of_image,imageOUT)\n",
    "    \n",
    "    result_softmax=output[0].reshape(img_h,img_w,2)\n",
    "    height_softmax,width_softmax=result_softmax.shape[:2]\n",
    "    softmax_image=result_softmax[:,:,1]\n",
    "    \n",
    "    if(False):\n",
    "        height,width,depth=annotation_image.shape\n",
    "        fit_line = horizon_line_pipeline(annotation_image)\n",
    "        line_annotation_image = np.zeros([height,width], dtype = \"uint8\")\n",
    "        cv2.line(line_annotation_image, (int(fit_line[2]-fit_line[0]*width), int(fit_line[3]-fit_line[1]*width)), (int(fit_line[2]+fit_line[0]*width), int(fit_line[3]+fit_line[1]*width)), (255, 255, 255), 1)\n",
    "        for i in range (height):\n",
    "            if(line_annotation_image[i,0]==255):\n",
    "                y1=i\n",
    "                break\n",
    "        \n",
    "        for i in range (height):\n",
    "            if(line_annotation_image[i,width-1]==255):\n",
    "                y2=i\n",
    "                break\n",
    "            \n",
    "        avg_y= int((y1+y2)/2)\n",
    "        \n",
    "        annotation_image = annotation_image[avg_y-10:avg_y+10, 0:width]\n",
    "        softmax_image = softmax_image[avg_y-10:avg_y+10, 0:width]\n",
    "        \n",
    "        \n",
    "\n",
    "\n",
    "    \n",
    "            \n",
    "    \n",
    "    \n",
    "    \n",
    "    #prediction_image = visualize(np.argmax(output[0],axis=1).reshape((img_h,img_w)), False)\n",
    "    #prediction_image = cv2.resize(softmax_image,(width,height))\n",
    "    \n",
    "    \n",
    "    height,width=annotation_image.shape[:2]\n",
    "    annotation = np.zeros([height,width])\n",
    "    for i in range(height):\n",
    "        for j in range(width):\n",
    "            if(np.array_equal(annotation_image[i,j],(100,100,255))):\n",
    "                annotation[i,j]=1\n",
    "                \n",
    "    #image_sub=np.subtract(annotation,softmax_image)\n",
    "    Threshold=0.5\n",
    "    results = np.zeros([height,width,3],dtype=np.uint8)\n",
    "    softmax_image[np.where(softmax_image<Threshold)]=0\n",
    "    softmax_image[np.where(softmax_image>=Threshold)]=1\n",
    "    #results[np.where((annotation==0)&(softmax_image==0))]=()\n",
    "    #print(np.where((annotation==0)&(softmax_image==0)))\n",
    "    #plt.imshow(softmax_image)\n",
    "    #plt.show()\n",
    "    \n",
    "    for i in range(height):\n",
    "        for j in range(width):\n",
    "            if(annotation[i,j]==0 and softmax_image[i,j]==0):\n",
    "                results[i,j,:]=np.array([0,0,0])\n",
    "            if(annotation[i,j]==1 and softmax_image[i,j]==0):\n",
    "                results[i,j,:]=np.array([0,0,255])\n",
    "            if(annotation[i,j]==0 and softmax_image[i,j]==1):\n",
    "                results[i,j,:]=np.array([0,255,0])\n",
    "            if(annotation[i,j]==1 and softmax_image[i,j]==1):\n",
    "                results[i,j,:]=np.array([255,255,255])\n",
    "                \n",
    "    \n",
    "    results_output = cv2.resize(results,(1280,720))\n",
    "    name_of_image= Path_to_save_image + name_file +'-Score.png'\n",
    "    cv2.imwrite(name_of_image,cv2.cvtColor(results_output, cv2.COLOR_BGR2RGB))\n",
    "\n",
    "    \n",
    "    \n",
    "            \n",
    "            \n",
    "                \n",
    "                \n",
    "    label=np.concatenate((label, annotation.flatten()), axis=0)\n",
    "    pred=np.concatenate((pred, np.round(softmax_image.flatten(),1)), axis=0)\n",
    "    \n",
    "\n",
    "\n",
    "#print(label.shape)\n",
    "#print(pred.shape)\n",
    "\n",
    "#tmp=np.subtract(label,pred)\n",
    "\n",
    "#plt.hist(tmp, facecolor='green')\n",
    "\n",
    "#pred=np.round(pred)\n",
    "Threshold=0.5\n",
    "pred[np.where(pred<Threshold)]=0\n",
    "pred[np.where(pred>=Threshold)]=1\n",
    "True_neg=len(np.where((label==0)&(pred==0))[0])\n",
    "False_neg=len(np.where((label==1)&(pred==0))[0])\n",
    "True_pos=len(np.where((label==1)&(pred==1))[0])\n",
    "False_pos=len(np.where((label==0)&(pred==1))[0])\n",
    "precision=True_pos/(True_pos+False_pos)\n",
    "recall=True_pos/(True_pos+False_neg)\n",
    "specificity=1-(True_neg/(True_neg+False_pos))\n",
    "accuracy=(True_pos+True_neg)/(True_pos+True_neg+False_pos+False_neg)\n",
    "f1score=2*(precision*recall)/(precision+recall)\n",
    "\n",
    "print(\"recall = \",recall)\n",
    "print(\"precision = \",precision)\n",
    "print(\"specificity = \",specificity)\n",
    "print(\"accuracy = \",accuracy)\n",
    "print(\"f1score =\",f1score)\n",
    "\n",
    "\n",
    "with open(os.path.abspath(Path_to_save_results + \"Results.txt\"), \"a\") as myfile:\n",
    "        myfile.write(\"recall = \" + str(recall)+\"\\n\")\n",
    "        myfile.write(\"precision = \" + str(precision)+\"\\n\")\n",
    "        myfile.write(\"specificity = \" + str(specificity)+\"\\n\")\n",
    "        myfile.write(\"accuracy = \" + str(accuracy)+\"\\n\")\n",
    "        myfile.write(\"f1score = \" + str(f1score)+\"\\n\")\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cropped Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "recall =  0.9590510533715402\n",
      "precision =  0.9210199485696912\n",
      "specificity =  0.06627574098579003\n",
      "accuracy =  0.9450263409961686\n",
      "f1score = 0.9396508439733651\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "dataset_annotation=glob.glob(\"/home/lorenzo/Intcatch/definitivo/Test/*annotation.png\", recursive=True)\n",
    "\n",
    "Path_to_save_image='Results/Cropped_image/Big_Conv_160x160/Image_Results/'\n",
    "Path_to_save_results='Results/Cropped_image/Big_Conv_160x160/'\n",
    "Flag_Save=True\n",
    "\n",
    "label=[]\n",
    "pred=[]\n",
    "index=0\n",
    "for annotation in dataset_annotation:\n",
    "    index+=1\n",
    "    #if(index==2): break\n",
    "    print(index,\" - \",len(dataset_annotation))\n",
    "    pathRoot=annotation[:annotation.rfind('/')] + '/'\n",
    "    name_file=(annotation[annotation.rfind('/')+1 : annotation.rfind('-annotation')])\n",
    "    annotation_image=cv2.imread(annotation)\n",
    "    annotation_image_output=annotation_image\n",
    "    \n",
    "    \n",
    "    annotation_image = cv2.cvtColor(annotation_image, cv2.COLOR_BGR2RGB)\n",
    "    annotation_image = cv2.resize(annotation_image,(img_h,img_w))\n",
    "    image=cv2.imread(pathRoot+name_file+\".png\")\n",
    "    image_output=image\n",
    "    image = np.array(image)\n",
    "    height,width,depth=image.shape\n",
    "    image = cv2.resize(image,(img_w,img_h))\n",
    "    image = normalized(image)\n",
    "    image = image[np.newaxis, ...]\n",
    "    output = model.predict(image)\n",
    "    \n",
    "    pred_output_image = visualize(np.argmax(output[0],axis=1).reshape((img_h,img_w)), False)\n",
    "    \n",
    "    \n",
    "    #fit_line = horizon_line_pipeline(pred_output_image)\n",
    "    #cv2.line(pred_output_image, (int(fit_line[2]-fit_line[0]*width), int(fit_line[3]-fit_line[1]*width)), (int(fit_line[2]+fit_line[0]*width), int(fit_line[3]+fit_line[1]*width)), (255, 255, 255), 10)\n",
    "    #imageOUT = cv2.bitwise_or(image_original,pred_output_image)\n",
    "    \n",
    "   \n",
    "    \n",
    "    result_softmax=output[0].reshape(img_h,img_w,2)\n",
    "    height_softmax,width_softmax=result_softmax.shape[:2]\n",
    "    softmax_image=result_softmax[:,:,1]\n",
    "    \n",
    "    if(True):\n",
    "        height,width,depth=annotation_image.shape\n",
    "        fit_line = horizon_line_pipeline(annotation_image)\n",
    "        line_annotation_image = np.zeros([height,width], dtype = \"uint8\")\n",
    "        cv2.line(line_annotation_image, (int(fit_line[2]-fit_line[0]*width), int(fit_line[3]-fit_line[1]*width)), (int(fit_line[2]+fit_line[0]*width), int(fit_line[3]+fit_line[1]*width)), (255, 255, 255), 1)\n",
    "        for i in range (height):\n",
    "            if(line_annotation_image[i,0]==255):\n",
    "                y1=i\n",
    "                break\n",
    "        \n",
    "        for i in range (height):\n",
    "            if(line_annotation_image[i,width-1]==255):\n",
    "                y2=i\n",
    "                break\n",
    "            \n",
    "        avg_y= int((y1+y2)/2)\n",
    "        \n",
    "        annotation_image = annotation_image[avg_y-15:avg_y+15, 0:width]\n",
    "        softmax_image = softmax_image[avg_y-15:avg_y+15, 0:width]\n",
    "        \n",
    "        \n",
    "\n",
    "\n",
    "    \n",
    "            \n",
    "    \n",
    "    \n",
    "    \n",
    "    #prediction_image = visualize(np.argmax(output[0],axis=1).reshape((img_h,img_w)), False)\n",
    "    #prediction_image = cv2.resize(softmax_image,(width,height))\n",
    "    \n",
    "    height,width=annotation_image.shape[:2]\n",
    "    annotation = np.zeros([height,width])\n",
    "    for i in range(height):\n",
    "        for j in range(width):\n",
    "            if(np.array_equal(annotation_image[i,j],(100,100,255))):\n",
    "                annotation[i,j]=1\n",
    "                \n",
    "    #image_sub=np.subtract(annotation,softmax_image)\n",
    "    Threshold=0.5\n",
    "    results = np.zeros([height,width,3],dtype=np.uint8)\n",
    "    softmax_image[np.where(softmax_image<Threshold)]=0\n",
    "    softmax_image[np.where(softmax_image>=Threshold)]=1\n",
    "    #results[np.where((annotation==0)&(softmax_image==0))]=()\n",
    "    #print(np.where((annotation==0)&(softmax_image==0)))\n",
    "    #plt.imshow(softmax_image)\n",
    "    #plt.show()\n",
    "    \n",
    "    for i in range(height):\n",
    "        for j in range(width):\n",
    "            if(annotation[i,j]==0 and softmax_image[i,j]==0):\n",
    "                results[i,j,:]=np.array([0,0,0])\n",
    "            if(annotation[i,j]==1 and softmax_image[i,j]==0):\n",
    "                results[i,j,:]=np.array([0,0,255])\n",
    "            if(annotation[i,j]==0 and softmax_image[i,j]==1):\n",
    "                results[i,j,:]=np.array([0,255,0])\n",
    "            if(annotation[i,j]==1 and softmax_image[i,j]==1):\n",
    "                results[i,j,:]=np.array([255,255,255])\n",
    "                \n",
    "                \n",
    "    if(Flag_Save==True):\n",
    "        \n",
    "        name_of_image=Path_to_save_image+ name_file +'-Mask_Horizon.png'\n",
    "        output=cv2.resize(annotation_image_output,(1280,180))\n",
    "        cv2.imwrite(name_of_image,output)\n",
    "        \n",
    "        name_of_image=Path_to_save_image+ name_file +'-Mask_Predicted_Horizon.png'\n",
    "        output=cv2.resize(pred_output_image[avg_y-15:avg_y+15, 0:width],(1280,180))\n",
    "        cv2.imwrite(name_of_image,cv2.cvtColor(output, cv2.COLOR_BGR2RGB))\n",
    "\n",
    "        results_output = results\n",
    "        name_of_image= Path_to_save_image + name_file +'-Score_Horizon.png'\n",
    "        output=cv2.resize(results_output,(1280,180))\n",
    "        cv2.imwrite(name_of_image,cv2.cvtColor(output, cv2.COLOR_BGR2RGB))\n",
    "    \n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "            \n",
    "            \n",
    "                \n",
    "                \n",
    "    label=np.concatenate((label, annotation.flatten()), axis=0)\n",
    "    pred=np.concatenate((pred, np.round(softmax_image.flatten(),1)), axis=0)\n",
    "    \n",
    "\n",
    "\n",
    "#print(label.shape)\n",
    "#print(pred.shape)\n",
    "\n",
    "#tmp=np.subtract(label,pred)\n",
    "\n",
    "#plt.hist(tmp, facecolor='green')\n",
    "\n",
    "#pred=np.round(pred)\n",
    "Threshold=0.5\n",
    "pred[np.where(pred<Threshold)]=0\n",
    "pred[np.where(pred>=Threshold)]=1\n",
    "True_neg=len(np.where((label==0)&(pred==0))[0])\n",
    "False_neg=len(np.where((label==1)&(pred==0))[0])\n",
    "True_pos=len(np.where((label==1)&(pred==1))[0])\n",
    "False_pos=len(np.where((label==0)&(pred==1))[0])\n",
    "precision=True_pos/(True_pos+False_pos)\n",
    "recall=True_pos/(True_pos+False_neg)\n",
    "specificity=1-(True_neg/(True_neg+False_pos))\n",
    "accuracy=(True_pos+True_neg)/(True_pos+True_neg+False_pos+False_neg)\n",
    "f1score=2*(precision*recall)/(precision+recall)\n",
    "\n",
    "print(\"recall = \",recall)\n",
    "print(\"precision = \",precision)\n",
    "print(\"specificity = \",specificity)\n",
    "print(\"accuracy = \",accuracy)\n",
    "print(\"f1score =\",f1score)\n",
    "\n",
    "\n",
    "with open(os.path.abspath(Path_to_save_results + \"Results.txt\"), \"a\") as myfile:\n",
    "        myfile.write(\"recall = \" + str(recall)+\"\\n\")\n",
    "        myfile.write(\"precision = \" + str(precision)+\"\\n\")\n",
    "        myfile.write(\"specificity = \" + str(specificity)+\"\\n\")\n",
    "        myfile.write(\"accuracy = \" + str(accuracy)+\"\\n\")\n",
    "        myfile.write(\"f1score = \" + str(f1score)+\"\\n\")\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9600,)\n",
      "average done\n",
      "[ 0.80095193  0.75951696]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f783dbeae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import average_precision_score, f1_score\n",
    "\n",
    "average_precision = average_precision_score(label, pred)\n",
    "print(label.shape)\n",
    "\n",
    "print(\"average done\")\n",
    "\n",
    "precision, recall, treshold = precision_recall_curve(label, pred)\n",
    "#print(precision)\n",
    "#print(recall)\n",
    "#print(treshold)\n",
    "\n",
    "plt.step(recall, precision, color='b', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                 color='b')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('2-class Precision-Recall curve: AUC={0:0.2f}'.format(average_precision))\n",
    "prediction=np.round(pred)\n",
    "f1=f1_score(label, prediction, average=None)\n",
    "\n",
    "print(f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Segmentation on the Horizon line Acuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "dataset_annotation=glob.glob(\"/home/lorenzo/Intcatch/definitivo/Test/*annotation.png\", recursive=True)\n",
    "label=[]\n",
    "pred=[]\n",
    "index=0\n",
    "for annotation in dataset_annotation:\n",
    "    index+=1\n",
    "    print(index,\" - \",len(annotation))\n",
    "    pathRoot=annotation[:annotation.rfind('/')] + '/'\n",
    "    name_file=(annotation[annotation.rfind('/')+1 : annotation.rfind('-annotation')])\n",
    "    image=cv2.imread(pathRoot+name_file+\".png\")\n",
    "    image = np.array(image)\n",
    "    height,width,depth=image.shape\n",
    "    \n",
    "    \n",
    "    annotation_image=cv2.imread(annotation)\n",
    "    annotation_image = cv2.cvtColor(annotation_image, cv2.COLOR_BGR2RGB)\n",
    "    \n",
    "    fit_line = horizon_line_pipeline(annotation_image)\n",
    "    line_annotation_image = np.zeros([height,width], dtype = \"uint8\")\n",
    "    cv2.line(line_annotation_image, (int(fit_line[2]-fit_line[0]*width), int(fit_line[3]-fit_line[1]*width)), (int(fit_line[2]+fit_line[0]*width), int(fit_line[3]+fit_line[1]*width)), (255, 255, 255), 1)\n",
    "    \n",
    "    for i in range (height):\n",
    "        if(line_annotation_image[i,0]==255):\n",
    "            y1=i\n",
    "            break\n",
    "        \n",
    "    for i in range (height):\n",
    "        if(line_annotation_image[i,width-1]==255):\n",
    "            y2=i\n",
    "            break\n",
    "            \n",
    "    avg_y= int((y1+y2)/2)\n",
    "    \n",
    "    \n",
    "    image = cv2.resize(image,(img_w,img_h))\n",
    "    image = normalized(image)\n",
    "    image = image[np.newaxis, ...]\n",
    "    output = model.predict(image)\n",
    "    \n",
    "    \n",
    "    prediction_image = visualize(np.argmax(output[0],axis=1).reshape((img_h,img_w)), False)\n",
    "    prediction_image = cv2.resize(prediction_image,(width,height))\n",
    "    fit_line = horizon_line_pipeline(prediction_image)\n",
    "    line_prediction_image = np.zeros([height,width], dtype = \"uint8\")\n",
    "    cv2.line(line_prediction_image, (int(fit_line[2]-fit_line[0]*width), int(fit_line[3]-fit_line[1]*width)), (int(fit_line[2]+fit_line[0]*width), int(fit_line[3]+fit_line[1]*width)), (255, 255, 255), 1)\n",
    "    \n",
    "    annotation_image_crop = annotation_image[avg_y-100:avg_y+100, 0:width]\n",
    "    prediction_image_crop = prediction_image[avg_y-100:avg_y+100, 0:width]\n",
    "    \n",
    "    height,width=annotation_image_crop.shape[:2]\n",
    "    annotation = np.zeros([height,width], dtype = \"uint8\")\n",
    "    for i in range(height):\n",
    "        for j in range(width):\n",
    "            if(np.array_equal(annotation_image[i,j],(100,100,255))):\n",
    "                annotation[i,j]=1\n",
    "                            \n",
    "    height,width=prediction_image_crop.shape[:2]\n",
    "    prediction = np.zeros([height,width], dtype = \"uint8\")\n",
    "    for i in range(height):\n",
    "        for j in range(width):\n",
    "            if(np.array_equal(prediction_image[i,j],(100,100,255))):\n",
    "                prediction[i,j]=1\n",
    "                \n",
    "                \n",
    "                \n",
    "    label=np.concatenate((label, annotation.flatten()), axis=0)\n",
    "    pred=np.concatenate((pred, prediction.flatten()), axis=0)\n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import average_precision_score\n",
    "print(len(label))\n",
    "print(len(pred))\n",
    "average_precision = average_precision_score(label, pred)\n",
    "\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(label, pred)\n",
    "\n",
    "print(precision)\n",
    "print(recall)\n",
    "\n",
    "plt.step(recall, precision, color='b', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                 color='b')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('2-class Precision-Recall curve: AUC={0:0.2f}'.format(\n",
    "          average_precision))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# line Acuracy residual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  -  87\n",
      "2  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2614f367b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e29700b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2615000828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2614f1e898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4  -  87\n",
      "5  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f260c1224e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2614974240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6  -  87\n",
      "7  -  87\n",
      "8  -  87\n",
      "9  -  87\n",
      "10  -  87\n",
      "11  -  87\n",
      "12  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2615047588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e28c7d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e81651d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5fKhv7PqTfHeSj7d9v58kX+u1DNXybGv67fYz97Ek9ye5aWjfdNZUVVP7ApYBnwK+FbgO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\nA6YuUQULBEYRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d85e7b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14  -  87\n",
      "15  -  87\n",
      "16  -  87\n",
      "17  -  87\n",
      "18  -  87\n",
      "19  -  87\n",
      "20  -  87\n",
      "21  -  87\n",
      "22  -  87\n",
      "23  -  87\n",
      "24  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e1006320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3AbcA/wxsmWVNY9a9DnhNG38L8GlgC/DbwJ42vwf4rTbe0npbDWxqPa+YdR8v0NevAH8F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\n6pxBL0md+1/v1jJoL1qgtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d84c6eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25  -  87\n",
      "26  -  87\n",
      "27  -  87\n",
      "28  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d84bfe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d842fdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d84adac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d8314dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30  -  87\n",
      "31  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d8479208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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H5sauP8nPJ/lK2/ffkuRH3ctQLS/X039uf+e+nORjSe4Y2jednqpqar+AVcDXgJ8GbgH+\nAtg2zZpGrHsD8MY2/nHgq8A24D8B+9r8PuB323hb620tsKX1vGrafbxMX/8e+CPg42171vs5CPxm\nG98C3DGrPTG4A+xp4BVt+zDwrlnqB3gz8Ebg6aG5setncEuVe4EA/wf41RXW068Aq9v4d1dCT9M+\no98OLFTV16vqB8AhBvezX9Gq6kJVfbGNvws8w+Af4szeoz/JJuDXgA8MTc9yP69m8I/wgwBV9YOq\n+jYz3BOD9728Islq4JXAN5mhfqrqc8DfXDM9Vv3tBoq3V9Xna5CQ/2vomB+5l+upqj5dVVfb5ucZ\n3NgRptjTtIN+5u9dn+R1wBuAx5nte/T/HvBbwD8Mzc1yP1uAbwF/0C5HfaDds2kme6qq88B/Ab4B\nXAD+rqo+zYz2M2Tc+je28bXzK9W/YnCGDlPsadpBP9OSvAr4E+A9VfWd4X3tO/NMvKQpyduAS1X1\n5PXWzFI/zWoG/6X+/ap6A/A92sddvmiWemrXrncx+Ab2WuC2JO8cXjNL/bycWa//Wkney+DGjh+e\ndi3TDvqR7l2/EiVZwyDkP1xVH23TS7pH/xS9CXh7kr9kcPnsl5P8IbPbDwzOis5V1eNt+wiD4J/V\nnv45cLqqvlVVV4CPAv+U2e3nRePWf56XLoUMz68oSd4FvA349fYNDKbY07SD/gvA1iRbMvgEq90M\n7me/orWfiH8QeKaq3j+0aybv0V9V+6tqU1W9jsGfwWeq6p3MaD8AVXUROJvk9W1qB4NbZ89qT98A\n7k3yyvb3bweDnw3Naj8vGqv+dpnnO0nubb8P/3LomBUhyU4Gl0HfXlXfH9o1vZ6m9dPqoZ9Qv5XB\nq1a+Brx32vWMWPMvMvgv5peBp9qvtzK4lfNx4DngMWDd0DHvbT2eYoqvEhiht1/ipVfdzHQ/wN3A\nfPtz+lPgzlnuCfgd4FngaeB/M3j1xsz0w+CDiy4AVxj8j+vBm6kfmGu/B18D/jvtjZ8rqKcFBtfi\nX8yG/zHtnnxnrCR1btqXbiRJE2bQS1LnDHpJ6pxBL0mdM+glqXMGvSR1zqCXpM4Z9JLUuf8Lhw9A\nWAJycncAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d8395f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32  -  87\n",
      "33  -  87\n",
      "34  -  87\n",
      "35  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d8390ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d83f49b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2614a2da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d839d668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37  -  87\n",
      "38  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e80e70f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d8339b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e80e7978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d84deeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e28b2e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e103ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41  -  87\n",
      "42  -  87\n",
      "43  -  87\n",
      "44  -  87\n",
      "45  -  87\n",
      "46  -  87\n",
      "47  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d855dd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e28deeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d85195f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e2976f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2614f367b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f260c9fed30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2614a2e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e28d0080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51  -  87\n",
      "52  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2614b49390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e81b3b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53  -  87\n",
      "54  -  87\n",
      "55  -  87\n",
      "56  -  87\n",
      "57  -  87\n",
      "58  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e80e6ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d84cdeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e80471d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d8422f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d85d2630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GL0makKkHfZJd7b7180kOTLueUSTZkuRTSU4lOZnkPa1/pu/Rn2RVkr9M8om2PevzuTXJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d848cdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d831c748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d8412f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62  -  87\n",
      "63  -  87\n",
      "64  -  87\n",
      "65  -  87\n",
      "66  -  87\n",
      "67  -  87\n",
      "68  -  87\n",
      "69  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d831c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d84cd6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70  -  87\n",
      "71  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e80ce0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d84be358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e8079e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e298fb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73  -  87\n",
      "74  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e8068cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3AZ8CtkyzphHrXge8uo2/DfgssAX4DWBPm98DvKuNt7TeVgObWs8rpt3HC/T1y8CfAx9u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\nUuf+G67dL87po5tyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e28a0eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75  -  87\n",
      "76  -  87\n",
      "77  -  87\n",
      "78  -  87\n",
      "79  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e80686a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e28f5dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e29958d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e2932cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e10250b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e1029f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82  -  87\n",
      "83  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25d8440588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2614f5b160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84  -  87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2614fe3438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25e1012978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85  -  87\n",
      "86  -  87\n",
      "87  -  87\n",
      "Median error : 43.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2614aad208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "dataset_annotation=glob.glob(\"/home/lorenzo/Intcatch/definitivo/test_precision_recall/*annotation.png\", recursive=True)\n",
    "label=[]\n",
    "pred=[]\n",
    "avg_distance=[]\n",
    "max_distance=[]\n",
    "index=0\n",
    "for annotation in dataset_annotation:\n",
    "    index+=1\n",
    "    print(index,\" - \",len(dataset_annotation))\n",
    "    pathRoot=annotation[:annotation.rfind('/')] + '/'\n",
    "    name_file=(annotation[annotation.rfind('/')+1 : annotation.rfind('-annotation')])\n",
    "    image=cv2.imread(pathRoot+name_file+\".png\")\n",
    "    image = np.array(image)\n",
    "    height,width,depth=image.shape\n",
    "    \n",
    "    \n",
    "    annotation_image=cv2.imread(annotation)\n",
    "    annotation_image = cv2.cvtColor(annotation_image, cv2.COLOR_BGR2RGB)\n",
    "    fit_line = horizon_line_pipeline(annotation_image)\n",
    "    line_annotation_image = np.zeros([height,width], dtype = \"uint8\")\n",
    "    cv2.line(line_annotation_image, (int(fit_line[2]-fit_line[0]*width), int(fit_line[3]-fit_line[1]*width)), (int(fit_line[2]+fit_line[0]*width), int(fit_line[3]+fit_line[1]*width)), (255, 255, 255), 1)\n",
    "    \n",
    "    image = cv2.resize(image,(img_w,img_h))\n",
    "    image = normalized(image)\n",
    "    image = image[np.newaxis, ...]\n",
    "    output = model.predict(image)\n",
    "    prediction_image = visualize(np.argmax(output[0],axis=1).reshape((img_h,img_w)), False)\n",
    "    prediction_image = cv2.resize(prediction_image,(width,height))\n",
    "    fit_line = horizon_line_pipeline(prediction_image)\n",
    "    line_prediction_image = np.zeros([height,width], dtype = \"uint8\")\n",
    "    cv2.line(line_prediction_image, (int(fit_line[2]-fit_line[0]*width), int(fit_line[3]-fit_line[1]*width)), (int(fit_line[2]+fit_line[0]*width), int(fit_line[3]+fit_line[1]*width)), (255, 255, 255), 1)\n",
    "    distance=[]\n",
    "    for j in range (width):\n",
    "        for i in range (height):\n",
    "            if(line_annotation_image[i,j]==255):\n",
    "                y1=i\n",
    "            if(line_prediction_image[i,j]==255):\n",
    "                y2=i\n",
    "        distance.append(abs(y1-y2))\n",
    "    \n",
    "        \n",
    "    \n",
    "        \n",
    "    \n",
    "    \n",
    "    avg_distance.append(np.mean(distance)/width)\n",
    "    max_distance.append(max(distance))\n",
    "    \n",
    "    if(max(distance)>50):\n",
    "        plt.imshow(line_annotation_image)\n",
    "        plt.show()\n",
    "        plt.imshow(line_prediction_image)\n",
    "        plt.show()\n",
    "        \n",
    "print(\"Median error :\", np.median(max_distance))\n",
    "plt.hist(max_distance, facecolor='green')\n",
    "plt.show()\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
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