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natanielruiz

2017-09-27 c13dba86b2dbe581353b72602d7fa6e40991964c

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

@@ -2,7 +2,7 @@
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
@@ -23,7 +23,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
@@ -36,7 +36,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
@@ -100,28 +100,21 @@
    "        face_h = row[8]\n",
    "\n",
    "        #Error correction\n",
    "        k = 0.15\n",
    "        x_min = face_x - image_w * k\n",
    "        x_max = face_x + image_w * (k+1)\n",
    "        y_min = face_y - image_h * k\n",
    "        y_max = face_y + image_h * (k+1)\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",
    "        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",
    "        #Crop the face from the image\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 = 260\n",
    "        to_size = 240\n",
    "        image_cropped = cv2.resize(image_cropped, (to_size,to_size), interpolation = cv2.INTER_AREA)\n",
    "        #Uncomment the line below if you want to use adaptive histogram normalisation\n",
    "        #clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(5,5))\n",
@@ -151,6 +144,25 @@
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test\n"
     ]
    }
   ],
   "source": [
    "print 'test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true