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