From 53a7efea63c4c90fd9b811076abf92efeae911fe Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期六, 12 八月 2017 10:35:21 +0800
Subject: [PATCH] Deleted some

---
 /dev/null |  133 --------------------------------------------
 1 files changed, 0 insertions(+), 133 deletions(-)

diff --git a/practice/.txt b/practice/.txt
deleted file mode 100644
index 4f37ac5..0000000
--- a/practice/.txt
+++ /dev/null
@@ -1 +0,0 @@
-/Data/nruiz9/data/facial_landmarks/AFLW/aflw/data/flickr/3/image00486.jpg -1.22287023067 0.0348203107715 -0.043205473572 353 112 383 383
diff --git a/practice/Untitled.ipynb b/practice/Untitled.ipynb
deleted file mode 100644
index ae2a33d..0000000
--- a/practice/Untitled.ipynb
+++ /dev/null
@@ -1,35 +0,0 @@
-{
- "cells": [
-  {
-   "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
-}
diff --git a/practice/aflw.sqlite b/practice/aflw.sqlite
deleted file mode 100644
index e69de29..0000000
--- a/practice/aflw.sqlite
+++ /dev/null
diff --git a/practice/aflw_example.py b/practice/aflw_example.py
deleted file mode 100644
index f81f333..0000000
--- a/practice/aflw_example.py
+++ /dev/null
@@ -1,133 +0,0 @@
-#!/usr/bin/env python
-
-##
-# Massimiliano Patacchiola, Plymouth University 2016
-# website: http://mpatacchiola.github.io/
-# email: massimiliano.patacchiola@plymouth.ac.uk
-# Python code for information retrieval from the Annotated Facial Landmarks in the Wild (AFLW) dataset.
-# In this example the faces are isolated and saved in a specified output folder.
-# Some information (roll, pitch, yaw) are returned, they can be used to filter the images.
-# This code requires OpenCV and Numpy. You can easily bypass the OpenCV calls if you want to use
-# a different library. In order to use the code you have to unzip the images and store them in
-# the directory "flickr" mantaining the original folders name (0, 2, 3).
-#
-# The following are the database properties available (last updated version 2012-11-28):
-#
-# databases: db_id, path, description
-# faceellipse: face_id, x, y, ra, rb, theta, annot_type_id, upsidedown
-# faceimages: image_id, db_id, file_id, filepath, bw, widht, height
-# facemetadata: face_id, sex, occluded, glasses, bw, annot_type_id
-# facepose: face_id, roll, pitch, yaw, annot_type_id
-# facerect: face_id, x, y, w, h, annot_type_id
-# faces: face_id, file_id, db_id
-# featurecoords: face_id, feature_id, x, y
-# featurecoordtype: feature_id, descr, code, x, y, z
-
-import sqlite3
-import cv2
-import os.path
-import numpy as np
-
-#Change this paths according to your directories
-images_path = "./flickr/"
-storing_path = "./output/"
-
-def main():
-
-    #Image counter
-    counter = 1
-
-    #Open the sqlite database
-    conn = sqlite3.connect('aflw.sqlite')
-    c = conn.cursor()
-
-    #Creating the query string for retriving: roll, pitch, yaw and faces position
-    #Change it according to what you want to retrieve
-    select_string = "faceimages.filepath, faces.face_id, facepose.roll, facepose.pitch, facepose.yaw, facerect.x, facerect.y, facerect.w, facerect.h"
-    from_string = "faceimages, faces, facepose, facerect"
-    where_string = "faces.face_id = facepose.face_id and faces.file_id = faceimages.file_id and faces.face_id = facerect.face_id"
-    query_string = "SELECT " + select_string + " FROM " + from_string + " WHERE " + where_string
-
-    #It iterates through the rows returned from the query
-    for row in c.execute(query_string):
-
-        #Using our specific query_string, the "row" variable will contain:
-        # row[0] = image path
-        # row[1] = face id
-        # row[2] = roll
-        # row[3] = pitch
-        # row[4] = yaw
-        # row[5] = face coord x
-        # row[6] = face coord y
-        # row[7] = face width
-        # row[8] = face heigh
-
-        #Creating the full path names for input and output
-        input_path = images_path + str(row[0])
-        output_path = storing_path + str(row[0])
-
-        #If the file exist then open it       
-        if(os.path.isfile(input_path)  == True):
-            #image = cv2.imread(input_path, 0) #load in grayscale
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #load the colour version
-
-            #Image dimensions
-            image_h, image_w = image.shape
-            #Roll, pitch and yaw
-            roll   = row[2]
-            pitch  = row[3]
-            yaw    = row[4]
-            #Face rectangle coords
-            face_x = row[5]
-            face_y = row[6]
-            face_w = row[7]
-            face_h = row[8]
-
-            #Error correction
-            if(face_x < 0): face_x = 0
-            if(face_y < 0): face_y = 0
-            if(face_w > image_w): 
-                face_w = image_w
-                face_h = image_w
-            if(face_h > image_h): 
-                face_h = image_h
-                face_w = image_h
-
-            #Crop the face from the image
-            image_cropped = np.copy(image[face_y:face_y+face_h, face_x:face_x+face_w])
-            #Uncomment the lines below if you want to rescale the image to a particular size
-            #to_size = 64
-            #image_rescaled = cv2.resize(image_cropped, (to_size,to_size), interpolation = cv2.INTER_AREA)
-            #Uncomment the line below if you want to use adaptive histogram normalisation
-            #clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(5,5))
-            #image_normalised = clahe.apply(image_rescaled)
-            #Save the image
-            #change "image_cropped" with the last uncommented variable name above
-            cv2.imwrite(output_path, image_cropped)
-
-            #Printing the information
-            print "Counter: " + str(counter)
-            print "iPath:    " + input_path
-            print "oPath:    " + output_path
-            print "Roll:    " + str(roll)
-            print "Pitch:   " + str(pitch)
-            print "Yaw:     " + str(yaw)
-            print "x:       " + str(face_x)
-            print "y:       " + str(face_y)
-            print "w:       " + str(face_w)
-            print "h:       " + str(face_h)
-            print ""
-
-            #Increasing the counter
-            counter = counter + 1 
-
-        #if the file does not exits it return an exception
-        else:
-            raise ValueError('Error: I cannot find the file specified: ' + str(input_path))
-
-    #Once finished the iteration it closes the database
-    c.close()
-
-if __name__ == "__main__":
-    main()
-

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