Final Submission

.gitattributes

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+saved_model/model_0.883333_140.ckpt.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+saved_model/model_0.916667_138.ckpt.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+saved_model/model_0.916667_138.ckpt.meta filter=lfs diff=lfs merge=lfs -text
+saved_model/model_0.983333_50.ckpt.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+saved_model/model_0.883333_140.ckpt.index filter=lfs diff=lfs merge=lfs -text
+saved_model/model_0.883333_140.ckpt.meta filter=lfs diff=lfs merge=lfs -text
+saved_model/model_0.916667_138.ckpt.index filter=lfs diff=lfs merge=lfs -text
+saved_model/model_0.942857_1181.ckpt.index filter=lfs diff=lfs merge=lfs -text
+saved_model/model_0.942857_1181.ckpt.meta filter=lfs diff=lfs merge=lfs -text
+saved_model/model_0.942857_1181.ckpt.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -93,6 +93,7 @@ ENV/
 # Rope project settings
 .ropeproject
 
+trail/
 face_detection_dataset/
 saved_model/
 *.tar.gz

aflw_example.py

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+#!/usr/bin/env python
+
+##
+# Massimiliano Patacchiola, Plymouth University 2016
+# website: http://mpatacchiola.github.io/
+# email: [email protected]
+# 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
+import os
+
+os.mkdir('positive_ann')
+#Change this paths according to your directories
+images_path = "./flickr/"
+storing_path = "./positive_ann/"
+
+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
+    s = ''
+    #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 + row[0]
+        output_path = storing_path + str(counter) + ".png"
+        out = open('output.txt', 'w')
+        #If the file exist then open it
+        if(os.path.isfile(input_path)  == True):
+            image = cv2.imread(input_path) #load in grayscale
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #load the colour version
+            #Image dimensions
+            image_h, image_w = image.shape[0], image.shape[1]
+            #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 = 32
+            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_normalised)
+
+            #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 ""
+            '''
+            s = s + str(counter) + "," + input_path + ","+str(face_x) + ","+str(face_y)+","+str(face_w)+","+str(face_h)+"\n"
+            #Increasing the counter
+            counter = counter + 1
+            if counter > 5000:
+                break
+            print(input_path)
+
+        #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()
+    out.write(s)
+
+if __name__ == "__main__":
+    main()
+out.close()