Batch testing and hopenet training complete

natanielruiz

2017-09-13 c495a0f6b13b794bab9f6e3423d5038ce645d816

 practice/.ipynb_checkpoints/load_AFLW-Copy1-checkpoint.ipynb

@@ -2,7 +2,7 @@
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
@@ -23,7 +23,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
@@ -31,16 +31,28 @@
   "source": [
    "#Change this paths according to your directories\n",
    "images_path = \"/Data/nruiz9/data/facial_landmarks/AFLW/aflw/data/flickr/\"\n",
    "storing_path = \"/Data/nruiz9/data/facial_landmarks/AFLW/aflw_cropped/\""
    "storing_path = \"/Data/nruiz9/data/facial_landmarks/AFLW/aflw_cropped_loose/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-1f2606c2a679>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_path\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;34m==\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;31m#image = cv2.imread(input_path, 0) #load in grayscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m         \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;31m#Image dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#Image counter\n",
    "counter = 1\n",
@@ -92,17 +104,19 @@
    "        face_h = row[8]\n",
    "\n",
    "        #Error correction\n",
    "        if(face_x < 0): face_x = 0\n",
    "        if(face_y < 0): face_y = 0\n",
    "        if(face_w > image_w): \n",
    "            face_w = image_w\n",
    "            face_h = image_w\n",
    "        if(face_h > image_h): \n",
    "            face_h = image_h\n",
    "            face_w = image_h\n",
    "\n",
    "        k = 0.35\n",
    "        x_min = face_x - face_w * k * 0.6\n",
    "        x_max = face_x + face_w + face_w * k * 0.6\n",
    "        y_min = face_y - face_h * k * 2\n",
    "        y_max = face_y + face_h + face_h * k * 0.6\n",
    "        \n",
    "        x_min = int(max(0, x_min))\n",
    "        x_max = int(min(image_w, x_max))\n",
    "        y_min = int(max(0, y_min))\n",
    "        y_max = int(min(image_h, y_max))\n",
    "        \n",
    "        #Crop the face from the image\n",
    "        image_cropped = np.copy(image[face_y:face_y+face_h, face_x:face_x+face_w])\n",
    "        image_cropped = np.copy(image[y_min:y_max, x_min:x_max])\n",
    "        #Uncomment the lines below if you want to rescale the image to a particular size\n",
    "        to_size = 240\n",
    "        image_cropped = cv2.resize(image_cropped, (to_size,to_size), interpolation = cv2.INTER_AREA)\n",
@@ -136,6 +150,17 @@
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print 'test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],