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| {
| "cells": [
| {
| "cell_type": "code",
| "execution_count": 30,
| "metadata": {
| "collapsed": true
| },
| "outputs": [],
| "source": [
| "%matplotlib inline\n",
| "import numpy as np\n",
| "import torch\n",
| "from torch.utils.serialization import load_lua\n",
| "import os\n",
| "import scipy.io as sio\n",
| "import cv2\n",
| "import math\n",
| "from matplotlib import pyplot as plt\n",
| "\n",
| "import sqlite3"
| ]
| },
| {
| "cell_type": "code",
| "execution_count": 31,
| "metadata": {
| "collapsed": true
| },
| "outputs": [],
| "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_loose_test/\""
| ]
| },
| {
| "cell_type": "code",
| "execution_count": 32,
| "metadata": {
| "collapsed": false
| },
| "outputs": [
| {
| "name": "stdout",
| "output_type": "stream",
| "text": [
| "(573, 1)\n",
| "(232, 1)\n",
| "(165, 1)\n",
| "3\n",
| "(970, 1)\n"
| ]
| }
| ],
| "source": [
| "#Load KEPLER split file\n",
| "test_set = sio.loadmat('/Data/nruiz9/data/facial_landmarks/AFLW/testset.mat')\n",
| "print test_set['test'][0][0][0].shape\n",
| "print test_set['test'][0][0][1].shape\n",
| "print test_set['test'][0][0][2].shape\n",
| "print len(test_set['test'][0][0])\n",
| "\n",
| "id_test_set = np.concatenate([test_set['test'][0][0][0], test_set['test'][0][0][1], test_set['test'][0][0][2]])\n",
| "print id_test_set.shape\n",
| "\n",
| "# I will just use every single one for now. If results are not good I'll sample equally."
| ]
| },
| {
| "cell_type": "code",
| "execution_count": 38,
| "metadata": {
| "collapsed": false
| },
| "outputs": [
| {
| "name": "stdout",
| "output_type": "stream",
| "text": [
| "970\n",
| "Done\n"
| ]
| }
| ],
| "source": [
| "#Image counter\n",
| "counter = 0\n",
| "\n",
| "#Open the sqlite database\n",
| "conn = sqlite3.connect('/Data/nruiz9/data/facial_landmarks/AFLW/aflw/data/aflw.sqlite')\n",
| "c = conn.cursor()\n",
| "\n",
| "#Creating the query string for retriving: roll, pitch, yaw and faces position\n",
| "#Change it according to what you want to retrieve\n",
| "select_string = \"faceimages.filepath, faces.face_id, facepose.roll, facepose.pitch, facepose.yaw, facerect.x, facerect.y, facerect.w, facerect.h\"\n",
| "from_string = \"faceimages, faces, facepose, facerect\"\n",
| "where_string = \"faces.face_id = facepose.face_id and faces.file_id = faceimages.file_id and faces.face_id = facerect.face_id\"\n",
| "query_string = \"SELECT \" + select_string + \" FROM \" + from_string + \" WHERE \" + where_string\n",
| "\n",
| "test_file_txt = '/Data/nruiz9/data/facial_landmarks/AFLW/KEPLER_test_split.txt'\n",
| "out_txt = open(test_file_txt, 'w')\n",
| "#It iterates through the rows returned from the query\n",
| "for row in c.execute(query_string):\n",
| "\n",
| " #Using our specific query_string, the \"row\" variable will contain:\n",
| " # row[0] = image path\n",
| " # row[1] = face id\n",
| " # row[2] = rollgma\n",
| " # row[3] = pitch\n",
| " # row[4] = yaw\n",
| " # row[5] = face coord x\n",
| " # row[6] = face coord y\n",
| " # row[7] = face width\n",
| " # row[8] = face heigh\n",
| " if row[1] in id_test_set:\n",
| " #Creating the full path names for input and output\n",
| " input_path = images_path + str(row[0])\n",
| " output_path = storing_path + str(row[0])\n",
| "\n",
| " #If the file exist then open it \n",
| " if(os.path.isfile(input_path) == True):\n",
| "\n",
| " out_txt.write(str(row[0]) + '.jpg\\n')\n",
| " #Increasing the counter\n",
| " counter = counter + 1 \n",
| "\n",
| " #if the file does not exits it return an exception\n",
| " else:\n",
| " raise ValueError('Error: I cannot find the file specified: ' + str(input_path))\n",
| "\n",
| "#Once finished the iteration it closes the database\n",
| "print counter\n",
| "out_txt.close()\n",
| "c.close()\n",
| "print 'Done'"
| ]
| },
| {
| "cell_type": "code",
| "execution_count": 4,
| "metadata": {
| "collapsed": false
| },
| "outputs": [
| {
| "name": "stdout",
| "output_type": "stream",
| "text": [
| "test\n"
| ]
| }
| ],
| "source": [
| "print 'test'"
| ]
| },
| {
| "cell_type": "code",
| "execution_count": null,
| "metadata": {
| "collapsed": true
| },
| "outputs": [],
| "source": []
| }
| ],
| "metadata": {
| "anaconda-cloud": {},
| "kernelspec": {
| "display_name": "Python [conda root]",
| "language": "python",
| "name": "conda-root-py"
| },
| "language_info": {
| "codemirror_mode": {
| "name": "ipython",
| "version": 2
| },
| "file_extension": ".py",
| "mimetype": "text/x-python",
| "name": "python",
| "nbconvert_exporter": "python",
| "pygments_lexer": "ipython2",
| "version": "2.7.12"
| }
| },
| "nbformat": 4,
| "nbformat_minor": 1
| }
|
|
